Modeling In H2O¶
Supervised¶
H2OANOVAGLMEstimator
¶
-
class
h2o.estimators.anovaglm.
H2OANOVAGLMEstimator
(model_id=None, training_frame=None, seed=-1, response_column=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, offset_column=None, weights_column=None, family='auto', tweedie_variance_power=0.0, tweedie_link_power=1.0, theta=0.0, solver='irlsm', missing_values_handling='mean_imputation', plug_values=None, compute_p_values=True, standardize=True, non_negative=False, max_iterations=0, link='family_default', prior=0.0, alpha=None, lambda_=[0.0], lambda_search=False, stopping_rounds=0, stopping_metric='auto', early_stopping=False, stopping_tolerance=0.001, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_runtime_secs=0.0, save_transformed_framekeys=False, highest_interaction_term=0, nparallelism=4, type=0)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
ANOVA for Generalized Linear Model
H2O ANOVAGLM is used to calculate Type III SS which is used to evaluate the contributions of individual predictors and their interactions to a model. Predictors or interactions with negligible contributions to the model will have high p-values while those with more contributions will have low p-values.
-
property
Lambda
¶ DEPRECATED. Use
self.lambda_
instead
-
property
alpha
¶ Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise.
Type:
List[float]
.
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.
-
property
compute_p_values
¶ Request p-values computation, p-values work only with IRLSM solver and no regularization
Type:
bool
, defaults toTrue
.
-
property
early_stopping
¶ Stop early when there is no more relative improvement on train or validation (if provided).
Type:
bool
, defaults toFalse
.
-
property
family
¶ Family. Use binomial for classification with logistic regression, others are for regression problems.
Type:
Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"]
, defaults to"auto"
.
-
property
highest_interaction_term
¶ Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on… Default to 2.
Type:
int
, defaults to0
.
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
lambda_
¶ Regularization strength
Type:
List[float]
, defaults to[0.0]
.
-
property
lambda_search
¶ Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Type:
bool
, defaults toFalse
.
-
property
link
¶ Link function.
Type:
Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]
, defaults to"family_default"
.
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.
-
property
max_iterations
¶ Maximum number of iterations
Type:
int
, defaults to0
.
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.
-
property
missing_values_handling
¶ Handling of missing values. Either MeanImputation, Skip or PlugValues.
Type:
Literal["mean_imputation", "skip", "plug_values"]
, defaults to"mean_imputation"
.
-
property
non_negative
¶ Restrict coefficients (not intercept) to be non-negative
Type:
bool
, defaults toFalse
.
-
property
nparallelism
¶ Number of models to build in parallel. Default to 4. Adjust according to your system.
Type:
int
, defaults to4
.
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.
-
property
plug_values
¶ Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues)
Type:
Union[None, str, H2OFrame]
.
-
property
prior
¶ Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.
Type:
float
, defaults to0.0
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
result
()[source]¶ Get result frame that contains information about the model building process like for modelselection and anovaglm. :return: the H2OFrame that contains information about the model building process like for modelselection and anovaglm.
-
property
save_transformed_framekeys
¶ true to save the keys of transformed predictors and interaction column.
Type:
bool
, defaults toFalse
.
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.
-
property
solver
¶ AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.
Type:
Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"]
, defaults to"irlsm"
.
-
property
standardize
¶ Standardize numeric columns to have zero mean and unit variance
Type:
bool
, defaults toTrue
.
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.001
.
-
property
theta
¶ Theta
Type:
float
, defaults to0.0
.
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
tweedie_link_power
¶ Tweedie link power
Type:
float
, defaults to1.0
.
-
property
tweedie_variance_power
¶ Tweedie variance power
Type:
float
, defaults to0.0
.
-
property
type
¶ Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3
Type:
int
, defaults to0
.
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.
-
property
H2OCoxProportionalHazardsEstimator
¶
-
class
h2o.estimators.coxph.
H2OCoxProportionalHazardsEstimator
(model_id=None, training_frame=None, start_column=None, stop_column=None, response_column=None, ignored_columns=None, weights_column=None, offset_column=None, stratify_by=None, ties='efron', init=0.0, lre_min=9.0, max_iterations=20, interactions=None, interaction_pairs=None, interactions_only=None, use_all_factor_levels=False, export_checkpoints_dir=None, single_node_mode=False)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Cox Proportional Hazards
Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset.
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> checkpoints_dir = tempfile.mkdtemp() >>> coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... export_checkpoints_dir=checkpoints_dir) >>> coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> len(listdir(checkpoints_dir))
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
init
¶ Coefficient starting value.
Type:
float
, defaults to0.0
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... init=2.9) >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> heart_coxph.scoring_history()
-
property
interaction_pairs
¶ A list of pairwise (first order) column interactions.
Type:
List[tuple]
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> interaction_pairs = [("start","stop")] >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... interaction_pairs=interaction_pairs) >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> heart_coxph.scoring_history()
-
property
interactions
¶ A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
Type:
List[str]
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> interactions = ['start','stop'] >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... interactions=interactions) >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> heart_coxph.scoring_history()
-
property
interactions_only
¶ A list of columns that should only be used to create interactions but should not itself participate in model training.
Type:
List[str]
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> interactions = ['start','stop'] >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... interactions_only=interactions) >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> heart_coxph.scoring_history()
-
property
lre_min
¶ Minimum log-relative error.
Type:
float
, defaults to9.0
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... lre_min=5) >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> heart_coxph.scoring_history()
-
property
max_iterations
¶ Maximum number of iterations.
Type:
int
, defaults to20
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... max_iterations=50) >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> heart_coxph.scoring_history()
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... offset_column="transplant") >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> heart_coxph.scoring_history()
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
single_node_mode
¶ Run on a single node to reduce the effect of network overhead (for smaller datasets)
Type:
bool
, defaults toFalse
.
-
property
start_column
¶ Start Time Column.
Type:
str
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> train, valid = heart.split_frame(ratios=[.8]) >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop") >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> heart_coxph.scoring_history()
-
property
stop_column
¶ Stop Time Column.
Type:
str
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> train, valid = heart.split_frame(ratios=[.8]) >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop") >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> heart_coxph.scoring_history()
-
property
stratify_by
¶ List of columns to use for stratification.
Type:
List[str]
.
-
property
ties
¶ Method for Handling Ties.
Type:
Literal["efron", "breslow"]
, defaults to"efron"
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> train, valid = heart.split_frame(ratios=[.8]) >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... ties="breslow") >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> heart_coxph.scoring_history()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> train, valid = heart.split_frame(ratios=[.8]) >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop") >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> heart_coxph.scoring_history()
-
property
use_all_factor_levels
¶ (Internal. For development only!) Indicates whether to use all factor levels.
Type:
bool
, defaults toFalse
.- Examples
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv") >>> predictor = "age" >>> response = "event" >>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start", ... stop_column="stop", ... use_all_factor_levels=True) >>> heart_coxph.train(x=predictor, ... y=response, ... training_frame=heart) >>> heart_coxph.scoring_history()
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.
-
property
H2ODeepLearningEstimator
¶
-
class
h2o.estimators.deeplearning.
H2ODeepLearningEstimator
(model_id=None, training_frame=None, validation_frame=None, nfolds=0, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, fold_assignment='auto', fold_column=None, response_column=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, weights_column=None, offset_column=None, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_confusion_matrix_size=20, checkpoint=None, pretrained_autoencoder=None, overwrite_with_best_model=True, use_all_factor_levels=True, standardize=True, activation='rectifier', hidden=[200, 200], epochs=10.0, train_samples_per_iteration=-2, target_ratio_comm_to_comp=0.05, seed=-1, adaptive_rate=True, rho=0.99, epsilon=1e-08, rate=0.005, rate_annealing=1e-06, rate_decay=1.0, momentum_start=0.0, momentum_ramp=1000000.0, momentum_stable=0.0, nesterov_accelerated_gradient=True, input_dropout_ratio=0.0, hidden_dropout_ratios=None, l1=0.0, l2=0.0, max_w2=3.4028235e+38, initial_weight_distribution='uniform_adaptive', initial_weight_scale=1.0, initial_weights=None, initial_biases=None, loss='automatic', distribution='auto', quantile_alpha=0.5, tweedie_power=1.5, huber_alpha=0.9, score_interval=5.0, score_training_samples=10000, score_validation_samples=0, score_duty_cycle=0.1, classification_stop=0.0, regression_stop=1e-06, stopping_rounds=5, stopping_metric='auto', stopping_tolerance=0.0, max_runtime_secs=0.0, score_validation_sampling='uniform', diagnostics=True, fast_mode=True, force_load_balance=True, variable_importances=True, replicate_training_data=True, single_node_mode=False, shuffle_training_data=False, missing_values_handling='mean_imputation', quiet_mode=False, autoencoder=False, sparse=False, col_major=False, average_activation=0.0, sparsity_beta=0.0, max_categorical_features=2147483647, reproducible=False, export_weights_and_biases=False, mini_batch_size=1, categorical_encoding='auto', elastic_averaging=False, elastic_averaging_moving_rate=0.9, elastic_averaging_regularization=0.001, export_checkpoints_dir=None, auc_type='auto')[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Deep Learning
Build a Deep Neural Network model using CPUs Builds a feed-forward multilayer artificial neural network on an H2OFrame
- Examples
>>> from h2o.estimators.deeplearning import H2ODeepLearningEstimator >>> rows = [[1,2,3,4,0], [2,1,2,4,1], [2,1,4,2,1], ... [0,1,2,34,1], [2,3,4,1,0]] * 50 >>> fr = h2o.H2OFrame(rows) >>> fr[4] = fr[4].asfactor() >>> model = H2ODeepLearningEstimator() >>> model.train(x=range(4), y=4, training_frame=fr) >>> model.logloss()
-
property
activation
¶ Activation function.
Type:
Literal["tanh", "tanh_with_dropout", "rectifier", "rectifier_with_dropout", "maxout", "maxout_with_dropout"]
, defaults to"rectifier"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> cars_dl = H2ODeepLearningEstimator(activation="tanh") >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
adaptive_rate
¶ Adaptive learning rate.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> cars_dl = H2ODeepLearningEstimator(adaptive_rate=True) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
autoencoder
¶ Auto-Encoder.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> cars_dl = H2ODeepLearningEstimator(autoencoder=True) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
average_activation
¶ Average activation for sparse auto-encoder. #Experimental
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> cars_dl = H2ODeepLearningEstimator(average_activation=1.5, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cov_dl = H2ODeepLearningEstimator(balance_classes=True, ... seed=1234) >>> cov_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_dl.mse()
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> encoding = "one_hot_internal" >>> airlines_dl = H2ODeepLearningEstimator(categorical_encoding=encoding, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.mse()
-
property
checkpoint
¶ Model checkpoint to resume training with.
Type:
Union[None, str, H2OEstimator]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(activation="tanh", ... autoencoder=True, ... seed=1234, ... model_id="cars_dl") >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse() >>> cars_cont = H2ODeepLearningEstimator(checkpoint=cars_dl, ... seed=1234) >>> cars_cont.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_cont.mse()
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.] >>> cars_dl = H2ODeepLearningEstimator(balance_classes=True, ... class_sampling_factors=sample_factors, ... seed=1234) >>> cov_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_dl.mse()
-
property
classification_stop
¶ Stopping criterion for classification error fraction on training data (-1 to disable).
Type:
float
, defaults to0.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(classification_stop=1.5, ... seed=1234) >>> cov_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_dl.mse()
-
property
col_major
¶ #DEPRECATED Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow down backpropagation.
Type:
bool
, defaults toFalse
.
-
property
diagnostics
¶ Enable diagnostics for hidden layers.
Type:
bool
, defaults toTrue
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(diagnostics=True, ... seed=1234) >>> cov_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_dl.mse()
-
property
distribution
¶ Distribution function
Type:
Literal["auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(distribution="poisson", ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
elastic_averaging
¶ Elastic averaging between compute nodes can improve distributed model convergence. #Experimental
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(elastic_averaging=True, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
elastic_averaging_moving_rate
¶ Elastic averaging moving rate (only if elastic averaging is enabled).
Type:
float
, defaults to0.9
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(elastic_averaging_moving_rate=.8, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
elastic_averaging_regularization
¶ Elastic averaging regularization strength (only if elastic averaging is enabled).
Type:
float
, defaults to0.001
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(elastic_averaging_regularization=.008, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
epochs
¶ How many times the dataset should be iterated (streamed), can be fractional.
Type:
float
, defaults to10.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(epochs=15, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
epsilon
¶ Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress).
Type:
float
, defaults to1e-08
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(epsilon=1e-6, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> checkpoints_dir = tempfile.mkdtemp() >>> cars_dl = H2ODeepLearningEstimator(export_checkpoints_dir=checkpoints_dir, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> len(listdir(checkpoints_dir))
-
property
export_weights_and_biases
¶ Whether to export Neural Network weights and biases to H2O Frames.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(export_weights_and_biases=True, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
fast_mode
¶ Enable fast mode (minor approximation in back-propagation).
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(fast_mode=False, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(fold_assignment="Random", ... nfolds=5, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234) >>> fold_numbers.set_names(["fold_numbers"]) >>> cars = cars.cbind(fold_numbers) >>> print(cars['fold_numbers']) >>> cars_dl = H2ODeepLearningEstimator(seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars, ... fold_column="fold_numbers") >>> cars_dl.mse()
-
property
force_load_balance
¶ Force extra load balancing to increase training speed for small datasets (to keep all cores busy).
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(force_load_balance=False, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
Hidden layer sizes (e.g. [100, 100]).
Type:
List[int]
, defaults to[200, 200]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(hidden=[100,100], ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.mse()
Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.
Type:
List[float]
.- Examples
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz") >>> valid = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz") >>> features = list(range(0,784)) >>> target = 784 >>> train[target] = train[target].asfactor() >>> valid[target] = valid[target].asfactor() >>> model = H2ODeepLearningEstimator(epochs=20, ... hidden=[200,200], ... hidden_dropout_ratios=[0.5,0.5], ... seed=1234, ... activation='tanhwithdropout') >>> model.train(x=features, ... y=target, ... training_frame=train, ... validation_frame=valid) >>> model.mse()
-
property
huber_alpha
¶ Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
Type:
float
, defaults to0.9
.- Examples
>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> predictors = insurance.columns[0:4] >>> response = 'Claims' >>> insurance['Group'] = insurance['Group'].asfactor() >>> insurance['Age'] = insurance['Age'].asfactor() >>> train, valid = insurance.split_frame(ratios=[.8], seed=1234) >>> insurance_dl = H2ODeepLearningEstimator(distribution="huber", ... huber_alpha=0.9, ... seed=1234) >>> insurance_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> insurance_dl.mse()
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(seed=1234, ... ignore_const_cols=True) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
initial_biases
¶ A list of H2OFrame ids to initialize the bias vectors of this model with.
Type:
List[Union[None, str, H2OFrame]]
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv") >>> dl1 = H2ODeepLearningEstimator(hidden=[10,10], ... export_weights_and_biases=True) >>> dl1.train(x=list(range(4)), y=4, training_frame=iris) >>> p1 = dl1.model_performance(iris).logloss() >>> ll1 = dl1.predict(iris) >>> print(p1) >>> w1 = dl1.weights(0) >>> w2 = dl1.weights(1) >>> w3 = dl1.weights(2) >>> b1 = dl1.biases(0) >>> b2 = dl1.biases(1) >>> b3 = dl1.biases(2) >>> dl2 = H2ODeepLearningEstimator(hidden=[10,10], ... initial_weights=[w1, w2, w3], ... initial_biases=[b1, b2, b3], ... epochs=0) >>> dl2.train(x=list(range(4)), y=4, training_frame=iris) >>> dl2.initial_biases
-
property
initial_weight_distribution
¶ Initial weight distribution.
Type:
Literal["uniform_adaptive", "uniform", "normal"]
, defaults to"uniform_adaptive"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(initial_weight_distribution="Uniform", ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
initial_weight_scale
¶ Uniform: -value…value, Normal: stddev.
Type:
float
, defaults to1.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(initial_weight_scale=1.5, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
initial_weights
¶ A list of H2OFrame ids to initialize the weight matrices of this model with.
Type:
List[Union[None, str, H2OFrame]]
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv") >>> dl1 = H2ODeepLearningEstimator(hidden=[10,10], ... export_weights_and_biases=True) >>> dl1.train(x=list(range(4)), y=4, training_frame=iris) >>> p1 = dl1.model_performance(iris).logloss() >>> ll1 = dl1.predict(iris) >>> print(p1) >>> w1 = dl1.weights(0) >>> w2 = dl1.weights(1) >>> w3 = dl1.weights(2) >>> b1 = dl1.biases(0) >>> b2 = dl1.biases(1) >>> b3 = dl1.biases(2) >>> dl2 = H2ODeepLearningEstimator(hidden=[10,10], ... initial_weights=[w1, w2, w3], ... initial_biases=[b1, b2, b3], ... epochs=0) >>> dl2.train(x=list(range(4)), y=4, training_frame=iris) >>> dl2.initial_weights
-
property
input_dropout_ratio
¶ Input layer dropout ratio (can improve generalization, try 0.1 or 0.2).
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(input_dropout_ratio=0.2, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(keep_cross_validation_fold_assignment=True, ... nfolds=5, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> print(cars_dl.cross_validation_fold_assignment())
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(keep_cross_validation_models=True, ... nfolds=5, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> print(cars_dl.cross_validation_models())
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(keep_cross_validation_predictions=True, ... nfolds=5, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> print(cars_dl.cross_validation_predictions())
-
property
l1
¶ L1 regularization (can add stability and improve generalization, causes many weights to become 0).
Type:
float
, defaults to0.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> hh_imbalanced = H2ODeepLearningEstimator(l1=1e-5, ... activation="Rectifier", ... loss="CrossEntropy", ... hidden=[200,200], ... epochs=1, ... balance_classes=False, ... reproducible=True, ... seed=1234) >>> hh_imbalanced.train(x=list(range(54)),y=54, training_frame=covtype) >>> hh_imbalanced.mse()
-
property
l2
¶ L2 regularization (can add stability and improve generalization, causes many weights to be small.
Type:
float
, defaults to0.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> hh_imbalanced = H2ODeepLearningEstimator(l2=1e-5, ... activation="Rectifier", ... loss="CrossEntropy", ... hidden=[200,200], ... epochs=1, ... balance_classes=False, ... reproducible=True, ... seed=1234) >>> hh_imbalanced.train(x=list(range(54)),y=54, training_frame=covtype) >>> hh_imbalanced.mse()
-
property
loss
¶ Loss function.
Type:
Literal["automatic", "cross_entropy", "quadratic", "huber", "absolute", "quantile"]
, defaults to"automatic"
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> hh_imbalanced = H2ODeepLearningEstimator(l1=1e-5, ... activation="Rectifier", ... loss="CrossEntropy", ... hidden=[200,200], ... epochs=1, ... balance_classes=False, ... reproducible=True, ... seed=1234) >>> hh_imbalanced.train(x=list(range(54)),y=54, training_frame=covtype) >>> hh_imbalanced.mse()
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> max = .85 >>> cov_dl = H2ODeepLearningEstimator(balance_classes=True, ... max_after_balance_size=max, ... seed=1234) >>> cov_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_dl.logloss()
-
property
max_categorical_features
¶ Max. number of categorical features, enforced via hashing. #Experimental
Type:
int
, defaults to2147483647
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cov_dl = H2ODeepLearningEstimator(balance_classes=True, ... max_categorical_features=2147483647, ... seed=1234) >>> cov_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_dl.logloss()
-
property
max_confusion_matrix_size
¶ [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.
Type:
int
, defaults to20
.
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(max_runtime_secs=10, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
max_w2
¶ Constraint for squared sum of incoming weights per unit (e.g. for Rectifier).
Type:
float
, defaults to3.4028235e+38
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cov_dl = H2ODeepLearningEstimator(activation="RectifierWithDropout", ... hidden=[10,10], ... epochs=10, ... input_dropout_ratio=0.2, ... l1=1e-5, ... max_w2=10.5, ... stopping_rounds=0) >>> cov_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_dl.mse()
-
property
mini_batch_size
¶ Mini-batch size (smaller leads to better fit, larger can speed up and generalize better).
Type:
int
, defaults to1
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cov_dl = H2ODeepLearningEstimator(activation="RectifierWithDropout", ... hidden=[10,10], ... epochs=10, ... input_dropout_ratio=0.2, ... l1=1e-5, ... max_w2=10.5, ... stopping_rounds=0) ... mini_batch_size=35 >>> cov_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_dl.mse()
-
property
missing_values_handling
¶ Handling of missing values. Either MeanImputation or Skip.
Type:
Literal["mean_imputation", "skip"]
, defaults to"mean_imputation"
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> boston.insert_missing_values() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_dl = H2ODeepLearningEstimator(missing_values_handling="skip") >>> boston_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_dl.mse()
-
property
momentum_ramp
¶ Number of training samples for which momentum increases.
Type:
float
, defaults to1000000.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Year","Month","DayofMonth","DayOfWeek","CRSDepTime", ... "CRSArrTime","UniqueCarrier","FlightNum"] >>> response_col = "IsDepDelayed" >>> airlines_dl = H2ODeepLearningEstimator(hidden=[200,200], ... activation="Rectifier", ... input_dropout_ratio=0.0, ... momentum_start=0.9, ... momentum_stable=0.99, ... momentum_ramp=1e7, ... epochs=100, ... stopping_rounds=4, ... train_samples_per_iteration=30000, ... mini_batch_size=32, ... score_duty_cycle=0.25, ... score_interval=1) >>> airlines_dl.train(x=predictors, ... y=response_col, ... training_frame=airlines) >>> airlines_dl.mse()
-
property
momentum_stable
¶ Final momentum after the ramp is over (try 0.99).
Type:
float
, defaults to0.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Year","Month","DayofMonth","DayOfWeek","CRSDepTime", ... "CRSArrTime","UniqueCarrier","FlightNum"] >>> response_col = "IsDepDelayed" >>> airlines_dl = H2ODeepLearningEstimator(hidden=[200,200], ... activation="Rectifier", ... input_dropout_ratio=0.0, ... momentum_start=0.9, ... momentum_stable=0.99, ... momentum_ramp=1e7, ... epochs=100, ... stopping_rounds=4, ... train_samples_per_iteration=30000, ... mini_batch_size=32, ... score_duty_cycle=0.25, ... score_interval=1) >>> airlines_dl.train(x=predictors, ... y=response_col, ... training_frame=airlines) >>> airlines_dl.mse()
-
property
momentum_start
¶ Initial momentum at the beginning of training (try 0.5).
Type:
float
, defaults to0.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Year","Month","DayofMonth","DayOfWeek","CRSDepTime", ... "CRSArrTime","UniqueCarrier","FlightNum"] >>> response_col = "IsDepDelayed" >>> airlines_dl = H2ODeepLearningEstimator(hidden=[200,200], ... activation="Rectifier", ... input_dropout_ratio=0.0, ... momentum_start=0.9, ... momentum_stable=0.99, ... momentum_ramp=1e7, ... epochs=100, ... stopping_rounds=4, ... train_samples_per_iteration=30000, ... mini_batch_size=32, ... score_duty_cycle=0.25, ... score_interval=1) >>> airlines_dl.train(x=predictors, ... y=response_col, ... training_frame=airlines) >>> airlines_dl.mse()
-
property
nesterov_accelerated_gradient
¶ Use Nesterov accelerated gradient (recommended).
Type:
bool
, defaults toTrue
.- Examples
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz") >>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz") >>> predictors = list(range(0,784)) >>> resp = 784 >>> train[resp] = train[resp].asfactor() >>> test[resp] = test[resp].asfactor() >>> nclasses = train[resp].nlevels()[0] >>> model = H2ODeepLearningEstimator(activation="RectifierWithDropout", ... adaptive_rate=False, ... rate=0.01, ... rate_decay=0.9, ... rate_annealing=1e-6, ... momentum_start=0.95, ... momentum_ramp=1e5, ... momentum_stable=0.99, ... nesterov_accelerated_gradient=False, ... input_dropout_ratio=0.2, ... train_samples_per_iteration=20000, ... classification_stop=-1, ... l1=1e-5) >>> model.train (x=predictors, ... y=resp, ... training_frame=train, ... validation_frame=test) >>> model.model_performance()
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(nfolds=5, seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> boston["offset"] = boston["medv"].log() >>> train, valid = boston.split_frame(ratios=[.8], seed=1234) >>> boston_dl = H2ODeepLearningEstimator(offset_column="offset", ... seed=1234) >>> boston_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_dl.mse()
-
property
overwrite_with_best_model
¶ If enabled, override the final model with the best model found during training.
Type:
bool
, defaults toTrue
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> boston["offset"] = boston["medv"].log() >>> train, valid = boston.split_frame(ratios=[.8], seed=1234) >>> boston_dl = H2ODeepLearningEstimator(overwrite_with_best_model=True, ... seed=1234) >>> boston_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_dl.mse()
-
property
pretrained_autoencoder
¶ Pretrained autoencoder model to initialize this model with.
Type:
Union[None, str, H2OEstimator]
.- Examples
>>> from h2o.estimators.deeplearning import H2OAutoEncoderEstimator >>> resp = 784 >>> nfeatures = 20 >>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz") >>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz") >>> train[resp] = train[resp].asfactor() >>> test[resp] = test[resp].asfactor() >>> sid = train[0].runif(0) >>> train_unsupervised = train[sid>=0.5] >>> train_unsupervised.pop(resp) >>> train_supervised = train[sid<0.5] >>> ae_model = H2OAutoEncoderEstimator(activation="Tanh", ... hidden=[nfeatures], ... model_id="ae_model", ... epochs=1, ... ignore_const_cols=False, ... reproducible=True, ... seed=1234) >>> ae_model.train(list(range(resp)), training_frame=train_unsupervised) >>> ae_model.mse() >>> pretrained_model = H2ODeepLearningEstimator(activation="Tanh", ... hidden=[nfeatures], ... epochs=1, ... reproducible = True, ... seed=1234, ... ignore_const_cols=False, ... pretrained_autoencoder="ae_model") >>> pretrained_model.train(list(range(resp)), resp, ... training_frame=train_supervised, ... validation_frame=test) >>> pretrained_model.mse()
-
property
quantile_alpha
¶ Desired quantile for Quantile regression, must be between 0 and 1.
Type:
float
, defaults to0.5
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8], seed=1234) >>> boston_dl = H2ODeepLearningEstimator(distribution="quantile", ... quantile_alpha=.8, ... seed=1234) >>> boston_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_dl.mse()
-
property
quiet_mode
¶ Enable quiet mode for less output to standard output.
Type:
bool
, defaults toFalse
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> titanic_dl = H2ODeepLearningEstimator(quiet_mode=True, ... seed=1234) >>> titanic_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_dl.mse()
-
property
rate
¶ Learning rate (higher => less stable, lower => slower convergence).
Type:
float
, defaults to0.005
.- Examples
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz") >>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz") >>> predictors = list(range(0,784)) >>> resp = 784 >>> train[resp] = train[resp].asfactor() >>> test[resp] = test[resp].asfactor() >>> nclasses = train[resp].nlevels()[0] >>> model = H2ODeepLearningEstimator(activation="RectifierWithDropout", ... adaptive_rate=False, ... rate=0.01, ... rate_decay=0.9, ... rate_annealing=1e-6, ... momentum_start=0.95, ... momentum_ramp=1e5, ... momentum_stable=0.99, ... nesterov_accelerated_gradient=False, ... input_dropout_ratio=0.2, ... train_samples_per_iteration=20000, ... classification_stop=-1, ... l1=1e-5) >>> model.train (x=predictors,y=resp, training_frame=train, validation_frame=test) >>> model.model_performance(valid=True)
-
property
rate_annealing
¶ Learning rate annealing: rate / (1 + rate_annealing * samples).
Type:
float
, defaults to1e-06
.- Examples
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz") >>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz") >>> predictors = list(range(0,784)) >>> resp = 784 >>> train[resp] = train[resp].asfactor() >>> test[resp] = test[resp].asfactor() >>> nclasses = train[resp].nlevels()[0] >>> model = H2ODeepLearningEstimator(activation="RectifierWithDropout", ... adaptive_rate=False, ... rate=0.01, ... rate_decay=0.9, ... rate_annealing=1e-6, ... momentum_start=0.95, ... momentum_ramp=1e5, ... momentum_stable=0.99, ... nesterov_accelerated_gradient=False, ... input_dropout_ratio=0.2, ... train_samples_per_iteration=20000, ... classification_stop=-1, ... l1=1e-5) >>> model.train (x=predictors, ... y=resp, ... training_frame=train, ... validation_frame=test) >>> model.mse()
-
property
rate_decay
¶ Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1).
Type:
float
, defaults to1.0
.- Examples
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz") >>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz") >>> predictors = list(range(0,784)) >>> resp = 784 >>> train[resp] = train[resp].asfactor() >>> test[resp] = test[resp].asfactor() >>> nclasses = train[resp].nlevels()[0] >>> model = H2ODeepLearningEstimator(activation="RectifierWithDropout", ... adaptive_rate=False, ... rate=0.01, ... rate_decay=0.9, ... rate_annealing=1e-6, ... momentum_start=0.95, ... momentum_ramp=1e5, ... momentum_stable=0.99, ... nesterov_accelerated_gradient=False, ... input_dropout_ratio=0.2, ... train_samples_per_iteration=20000, ... classification_stop=-1, ... l1=1e-5) >>> model.train (x=predictors, ... y=resp, ... training_frame=train, ... validation_frame=test) >>> model.model_performance()
-
property
regression_stop
¶ Stopping criterion for regression error (MSE) on training data (-1 to disable).
Type:
float
, defaults to1e-06
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(regression_stop=1e-6, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
replicate_training_data
¶ Replicate the entire training dataset onto every node for faster training on small datasets.
Type:
bool
, defaults toTrue
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> airlines_dl = H2ODeepLearningEstimator(replicate_training_data=False) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=airlines) >>> airlines_dl.auc()
-
property
reproducible
¶ Force reproducibility on small data (will be slow - only uses 1 thread).
Type:
bool
, defaults toFalse
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(reproducible=True) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
rho
¶ Adaptive learning rate time decay factor (similarity to prior updates).
Type:
float
, defaults to0.99
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(rho=0.9, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
score_duty_cycle
¶ Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring).
Type:
float
, defaults to0.1
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(score_duty_cycle=0.2, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(score_each_iteration=True, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
score_interval
¶ Shortest time interval (in seconds) between model scoring.
Type:
float
, defaults to5.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(score_interval=3, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
score_training_samples
¶ Number of training set samples for scoring (0 for all).
Type:
int
, defaults to10000
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(score_training_samples=10000, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
score_validation_samples
¶ Number of validation set samples for scoring (0 for all).
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(score_validation_samples=3, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
score_validation_sampling
¶ Method used to sample validation dataset for scoring.
Type:
Literal["uniform", "stratified"]
, defaults to"uniform"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(score_validation_sampling="uniform", ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
seed
¶ Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded.
Type:
int
, defaults to-1
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
shuffle_training_data
¶ Enable shuffling of training data (recommended if training data is replicated and train_samples_per_iteration is close to #nodes x #rows, of if using balance_classes).
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(shuffle_training_data=True, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
single_node_mode
¶ Run on a single node for fine-tuning of model parameters.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(single_node_mode=True, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
sparse
¶ Sparse data handling (more efficient for data with lots of 0 values).
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(sparse=True, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
sparsity_beta
¶ Sparsity regularization. #Experimental
Type:
float
, defaults to0.0
.- Examples
>>> from h2o.estimators import H2OAutoEncoderEstimator >>> resp = 784 >>> nfeatures = 20 >>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz") >>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz") >>> train[resp] = train[resp].asfactor() >>> test[resp] = test[resp].asfactor() >>> sid = train[0].runif(0) >>> train_unsupervised = train[sid>=0.5] >>> train_unsupervised.pop(resp) >>> ae_model = H2OAutoEncoderEstimator(activation="Tanh", ... hidden=[nfeatures], ... epochs=1, ... ignore_const_cols=False, ... reproducible=True, ... sparsity_beta=0.5, ... seed=1234) >>> ae_model.train(list(range(resp)), ... training_frame=train_unsupervised) >>> ae_model.mse()
-
property
standardize
¶ If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_dl = H2ODeepLearningEstimator(standardize=True, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_dl.auc()
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to5
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
target_ratio_comm_to_comp
¶ Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning).
Type:
float
, defaults to0.05
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(target_ratio_comm_to_comp=0.05, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
train_samples_per_iteration
¶ Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2: automatic.
Type:
int
, defaults to-2
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(train_samples_per_iteration=-1, ... epochs=1, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator() >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
tweedie_power
¶ Tweedie power for Tweedie regression, must be between 1 and 2.
Type:
float
, defaults to1.5
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(tweedie_power=1.5, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.auc()
-
property
use_all_factor_levels
¶ Use all factor levels of categorical variables. Otherwise, the first factor level is omitted (without loss of accuracy). Useful for variable importances and auto-enabled for autoencoder.
Type:
bool
, defaults toTrue
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(use_all_factor_levels=True, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.mse()
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(standardize=True, ... seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
-
property
variable_importances
¶ Compute variable importances for input features (Gedeon method) - can be slow for large networks.
Type:
bool
, defaults toTrue
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_dl = H2ODeepLearningEstimator(variable_importances=True, ... seed=1234) >>> airlines_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_dl.mse()
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_dl = H2ODeepLearningEstimator(seed=1234) >>> cars_dl.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_dl.auc()
H2OGeneralizedAdditiveEstimator
¶
-
class
h2o.estimators.gam.
H2OGeneralizedAdditiveEstimator
(model_id=None, training_frame=None, validation_frame=None, nfolds=0, seed=-1, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, fold_assignment='auto', fold_column=None, response_column=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, offset_column=None, weights_column=None, family='auto', tweedie_variance_power=0.0, tweedie_link_power=0.0, theta=0.0, solver='auto', alpha=None, lambda_=None, lambda_search=False, early_stopping=True, nlambdas=-1, standardize=False, missing_values_handling='mean_imputation', plug_values=None, compute_p_values=False, remove_collinear_columns=False, intercept=True, non_negative=False, max_iterations=-1, objective_epsilon=-1.0, beta_epsilon=0.0001, gradient_epsilon=-1.0, link='family_default', startval=None, prior=-1.0, cold_start=False, lambda_min_ratio=-1.0, beta_constraints=None, max_active_predictors=-1, interactions=None, interaction_pairs=None, obj_reg=-1.0, export_checkpoints_dir=None, stopping_rounds=0, stopping_metric='auto', stopping_tolerance=0.001, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_confusion_matrix_size=20, max_runtime_secs=0.0, custom_metric_func=None, num_knots=None, spline_orders=None, knot_ids=None, gam_columns=None, standardize_tp_gam_cols=False, scale_tp_penalty_mat=False, bs=None, scale=None, keep_gam_cols=False, auc_type='auto')[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Generalized Additive Model
Fits a generalized additive model, specified by a response variable, a set of predictors, and a description of the error distribution.
A subclass of
ModelBase
is returned. The specific subclass depends on the machine learning task at hand (if it’s binomial classification, then an H2OBinomialModel is returned, if it’s regression then a H2ORegressionModel is returned). The default print-out of the models is shown, but further GAM-specific information can be queried out of the object. Upon completion of the GAM, the resulting object has coefficients, normalized coefficients, residual/null deviance, aic, and a host of model metrics including MSE, AUC (for logistic regression), degrees of freedom, and confusion matrices.-
property
Lambda
¶ [Deprecated] Use
lambda_
instead
-
property
alpha
¶ Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise.
Type:
List[float]
.
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.
-
property
beta_constraints
¶ Beta constraints
Type:
Union[None, str, H2OFrame]
.
-
property
beta_epsilon
¶ Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver
Type:
float
, defaults to0.0001
.
-
property
bs
¶ Basis function type for each gam predictors, 0 for cr, 1 for thin plate regression with knots, 2 for monotone splines. If specified, must be the same size as gam_columns
Type:
List[int]
.
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.
-
property
cold_start
¶ Only applicable to multiple alpha/lambda values when calling GLM from GAM. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch.
Type:
bool
, defaults toFalse
.
-
property
compute_p_values
¶ Request p-values computation, p-values work only with IRLSM solver and no regularization
Type:
bool
, defaults toFalse
.
-
property
custom_metric_func
¶ Reference to custom evaluation function, format: language:keyName=funcName
Type:
str
.
-
property
early_stopping
¶ Stop early when there is no more relative improvement on train or validation (if provided)
Type:
bool
, defaults toTrue
.
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.
-
property
family
¶ Family. Use binomial for classification with logistic regression, others are for regression problems.
Type:
Literal["auto", "gaussian", "binomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial", "fractionalbinomial"]
, defaults to"auto"
.
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.
-
property
gam_columns
¶ Arrays of predictor column names for gam for smoothers using single or multiple predictors like {{‘c1’},{‘c2’,’c3’},{‘c4’},…}
Type:
List[List[str]]
.
-
property
gradient_epsilon
¶ Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively.
Type:
float
, defaults to-1.0
.
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
interaction_pairs
¶ A list of pairwise (first order) column interactions.
Type:
List[tuple]
.
-
property
interactions
¶ A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
Type:
List[str]
.
-
property
intercept
¶ Include constant term in the model
Type:
bool
, defaults toTrue
.
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.
-
property
keep_gam_cols
¶ Save keys of model matrix
Type:
bool
, defaults toFalse
.
-
property
knot_ids
¶ Array storing frame keys of knots. One for each gam column set specified in gam_columns
Type:
List[str]
.
-
property
lambda_
¶ Regularization strength
Type:
List[float]
.
-
property
lambda_min_ratio
¶ Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01.
Type:
float
, defaults to-1.0
.
-
property
lambda_search
¶ Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Type:
bool
, defaults toFalse
.
-
property
link
¶ Link function.
Type:
Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]
, defaults to"family_default"
.
-
property
max_active_predictors
¶ Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000.
Type:
int
, defaults to-1
.
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.
-
property
max_confusion_matrix_size
¶ [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
Type:
int
, defaults to20
.
-
property
max_iterations
¶ Maximum number of iterations
Type:
int
, defaults to-1
.
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.
-
property
missing_values_handling
¶ Handling of missing values. Either MeanImputation, Skip or PlugValues.
Type:
Literal["mean_imputation", "skip", "plug_values"]
, defaults to"mean_imputation"
.
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.
-
property
nlambdas
¶ Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100.
Type:
int
, defaults to-1
.
-
property
non_negative
¶ Restrict coefficients (not intercept) to be non-negative
Type:
bool
, defaults toFalse
.
-
property
num_knots
¶ Number of knots for gam predictors
Type:
List[int]
.
-
property
obj_reg
¶ Likelihood divider in objective value computation, default is 1/nobs
Type:
float
, defaults to-1.0
.
-
property
objective_epsilon
¶ Converge if objective value changes less than this. Default indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001.
Type:
float
, defaults to-1.0
.
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.
-
property
plug_values
¶ Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues)
Type:
Union[None, str, H2OFrame]
.
-
property
prior
¶ Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.
Type:
float
, defaults to-1.0
.
-
property
remove_collinear_columns
¶ In case of linearly dependent columns, remove some of the dependent columns
Type:
bool
, defaults toFalse
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
scale
¶ Smoothing parameter for gam predictors. If specified, must be of the same length as gam_columns
Type:
List[float]
.
-
property
scale_tp_penalty_mat
¶ Scale penalty matrix for tp (thin plate) smoothers as in R
Type:
bool
, defaults toFalse
.
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.
-
scoring_history
()[source]¶ Retrieve Model Score History.
- Returns
The score history as an H2OTwoDimTable or a Pandas DataFrame.
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.
-
property
solver
¶ AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.
Type:
Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"]
, defaults to"auto"
.
-
property
spline_orders
¶ Order of I-splines used for gam predictors. If specified, must be the same size as gam_columns.Values for bs=0 or 1 will be ignored.
Type:
List[int]
.
-
property
standardize
¶ Standardize numeric columns to have zero mean and unit variance
Type:
bool
, defaults toFalse
.
-
property
standardize_tp_gam_cols
¶ standardize tp (thin plate) predictor columns
Type:
bool
, defaults toFalse
.
-
property
startval
¶ double array to initialize coefficients for GAM.
Type:
List[float]
.
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.001
.
-
property
theta
¶ Theta
Type:
float
, defaults to0.0
.
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
tweedie_link_power
¶ Tweedie link power
Type:
float
, defaults to0.0
.
-
property
tweedie_variance_power
¶ Tweedie variance power
Type:
float
, defaults to0.0
.
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.
-
property
H2OGradientBoostingEstimator
¶
-
class
h2o.estimators.gbm.
H2OGradientBoostingEstimator
(model_id=None, training_frame=None, validation_frame=None, nfolds=0, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, score_each_iteration=False, score_tree_interval=0, fold_assignment='auto', fold_column=None, response_column=None, ignored_columns=None, ignore_const_cols=True, offset_column=None, weights_column=None, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_confusion_matrix_size=20, ntrees=50, max_depth=5, min_rows=10.0, nbins=20, nbins_top_level=1024, nbins_cats=1024, r2_stopping=None, stopping_rounds=0, stopping_metric='auto', stopping_tolerance=0.001, max_runtime_secs=0.0, seed=-1, build_tree_one_node=False, learn_rate=0.1, learn_rate_annealing=1.0, distribution='auto', quantile_alpha=0.5, tweedie_power=1.5, huber_alpha=0.9, checkpoint=None, sample_rate=1.0, sample_rate_per_class=None, col_sample_rate=1.0, col_sample_rate_change_per_level=1.0, col_sample_rate_per_tree=1.0, min_split_improvement=1e-05, histogram_type='auto', max_abs_leafnode_pred=None, pred_noise_bandwidth=0.0, categorical_encoding='auto', calibrate_model=False, calibration_frame=None, custom_metric_func=None, custom_distribution_func=None, export_checkpoints_dir=None, monotone_constraints=None, check_constant_response=True, gainslift_bins=-1, auc_type='auto', interaction_constraints=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Gradient Boosting Machine
Builds gradient boosted trees on a parsed data set, for regression or classification. The default distribution function will guess the model type based on the response column type. Otherwise, the response column must be an enum for “bernoulli” or “multinomial”, and numeric for all other distributions.
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cov_gbm = H2OGradientBoostingEstimator(balance_classes=True, ... seed=1234) >>> cov_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_gbm.logloss(valid=True)
-
property
build_tree_one_node
¶ Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(build_tree_one_node=True, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc(valid=True)
-
property
calibrate_model
¶ Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.
Type:
bool
, defaults toFalse
.- Examples
>>> ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv") >>> ecology['Angaus'] = ecology['Angaus'].asfactor() >>> response = 'Angaus' >>> train, calib = ecology.split_frame(seed = 12354) >>> predictors = ecology.columns[3:13] >>> w = h2o.create_frame(binary_fraction=1, ... binary_ones_fraction=0.5, ... missing_fraction=0, ... rows=744, cols=1) >>> w.set_names(["weight"]) >>> train = train.cbind(w) >>> ecology_gbm = H2OGradientBoostingEstimator(ntrees=10, ... max_depth=5, ... min_rows=10, ... learn_rate=0.1, ... distribution="multinomial", ... weights_column="weight", ... calibrate_model=True, ... calibration_frame=calib) >>> ecology_gbm.train(x=predictors, ... y="Angaus", ... training_frame=train) >>> ecology_gbm.auc()
-
property
calibration_frame
¶ Calibration frame for Platt Scaling
Type:
Union[None, str, H2OFrame]
.- Examples
>>> ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv") >>> ecology['Angaus'] = ecology['Angaus'].asfactor() >>> response = 'Angaus' >>> predictors = ecology.columns[3:13] >>> train, calib = ecology.split_frame(seed=12354) >>> w = h2o.create_frame(binary_fraction=1, ... binary_ones_fraction=0.5, ... missing_fraction=0, ... rows=744,cols=1) >>> w.set_names(["weight"]) >>> train = train.cbind(w) >>> ecology_gbm = H2OGradientBoostingEstimator(ntrees=10, ... max_depth=5, ... min_rows=10, ... learn_rate=0.1, ... distribution="multinomial", ... calibrate_model=True, ... calibration_frame=calib) >>> ecology_gbm.train(x=predictors, ... y="Angaus", ... training_frame=train, ... weights_column="weight") >>> ecology_gbm.auc()
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(categorical_encoding="labelencoder", ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
check_constant_response
¶ Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not.
Type:
bool
, defaults toTrue
.- Examples
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv") >>> train["constantCol"] = 1 >>> my_gbm = H2OGradientBoostingEstimator(check_constant_response=False) >>> my_gbm.train(x=list(range(1,5)), ... y="constantCol", ... training_frame=train)
-
property
checkpoint
¶ Model checkpoint to resume training with.
Type:
Union[None, str, H2OEstimator]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(ntrees=1, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(cars_gbm.auc(valid=True)) >>> print("Number of trees built for cars_gbm model:", cars_gbm.ntrees) >>> cars_gbm_continued = H2OGradientBoostingEstimator(checkpoint=cars_gbm.model_id, ... ntrees=50, ... seed=1234) >>> cars_gbm_continued.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm_continued.auc(valid=True) >>> print("Number of trees built for cars_gbm model:",cars_gbm_continued.ntrees)
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.] >>> cov_gbm = H2OGradientBoostingEstimator(balance_classes=True, ... class_sampling_factors=sample_factors, ... seed=1234) >>> cov_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_gbm.logloss(valid=True)
-
property
col_sample_rate
¶ Column sample rate (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(col_sample_rate=.7, ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
col_sample_rate_change_per_level
¶ Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(col_sample_rate_change_per_level=.9, ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
col_sample_rate_per_tree
¶ Column sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(col_sample_rate_per_tree=.7, ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
custom_distribution_func
¶ Reference to custom distribution, format: language:keyName=funcName
Type:
str
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(ntrees=3, ... max_depth=5, ... distribution="bernoulli", ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame valid) >>> from h2o.utils.distributions import CustomDistributionBernoulli >>> custom_distribution_bernoulli = h2o.upload_custom_distribution(CustomDistributionBernoulli, ... func_name="custom_bernoulli", ... func_file="custom_bernoulli.py") >>> airlines_gbm_custom = H2OGradientBoostingEstimator(ntrees=3, ... max_depth=5, ... distribution="custom", ... custom_distribution_func=custom_distribution_bernoulli, ... seed=1235) >>> airlines_gbm_custom.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc()
-
property
custom_metric_func
¶ Reference to custom evaluation function, format: language:keyName=funcName
Type:
str
.
-
property
distribution
¶ Distribution function
Type:
Literal["auto", "bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(distribution="poisson", ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.mse(valid=True)
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex") >>> predictors = ["DayofMonth", "DayOfWeek"] >>> response = "IsDepDelayed" >>> hyper_parameters = {'ntrees': [5,10]} >>> search_crit = {'strategy': "RandomDiscrete", ... 'max_models': 5, ... 'seed': 1234, ... 'stopping_rounds': 3, ... 'stopping_metric': "AUTO", ... 'stopping_tolerance': 1e-2} >>> checkpoints_dir = tempfile.mkdtemp() >>> air_grid = H2OGridSearch(H2OGradientBoostingEstimator, ... hyper_params=hyper_parameters, ... search_criteria=search_crit) >>> air_grid.train(x=predictors, ... y=response, ... training_frame=airlines, ... distribution="bernoulli", ... learn_rate=0.1, ... max_depth=3, ... export_checkpoints_dir=checkpoints_dir) >>> len(listdir(checkpoints_dir))
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> assignment_type = "Random" >>> cars_gbm = H2OGradientBoostingEstimator(fold_assignment=assignment_type, ... nfolds=5, ... seed=1234) >>> cars_gbm.train(x=predictors, y=response, training_frame=cars) >>> cars_gbm.auc(xval=True)
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> fold_numbers = cars.kfold_column(n_folds=5, ... seed=1234) >>> fold_numbers.set_names(["fold_numbers"]) >>> cars = cars.cbind(fold_numbers) >>> cars_gbm = H2OGradientBoostingEstimator(seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=cars, ... fold_column="fold_numbers") >>> cars_gbm.auc(xval=True)
-
property
gainslift_bins
¶ Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv") >>> model = H2OGradientBoostingEstimator(ntrees=1, gainslift_bins=20) >>> model.train(x=["Origin", "Distance"], ... y="IsDepDelayed", ... training_frame=airlines) >>> model.gains_lift()
-
property
histogram_type
¶ What type of histogram to use for finding optimal split points
Type:
Literal["auto", "uniform_adaptive", "random", "quantiles_global", "round_robin", "uniform_robust"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(histogram_type="UniformAdaptive", ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
huber_alpha
¶ Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
Type:
float
, defaults to0.9
.- Examples
>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> predictors = insurance.columns[0:4] >>> response = 'Claims' >>> insurance['Group'] = insurance['Group'].asfactor() >>> insurance['Age'] = insurance['Age'].asfactor() >>> train, valid = insurance.split_frame(ratios=[.8], seed=1234) >>> insurance_gbm = H2OGradientBoostingEstimator(distribution="huber", ... huber_alpha=0.9, ... seed=1234) >>> insurance_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> insurance_gbm.mse(valid=True)
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(seed=1234, ... ignore_const_cols=True) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc(valid=True)
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
interaction_constraints
¶ A set of allowed column interactions.
Type:
List[List[str]]
.
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> folds = 5 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_fold_assignment=True, ... nfolds=5, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc()
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> folds = 5 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_models=True, ... nfolds=5, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc()
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> folds = 5 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_predictions=True, ... nfolds=5, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc()
-
property
learn_rate
¶ Learning rate (from 0.0 to 1.0)
Type:
float
, defaults to0.1
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> titanic_gbm = H2OGradientBoostingEstimator(ntrees=10000, ... learn_rate=0.01, ... stopping_rounds=5, ... stopping_metric="AUC", ... stopping_tolerance=1e-4, ... seed=1234) >>> titanic_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_gbm.auc(valid=True)
-
property
learn_rate_annealing
¶ Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999)
Type:
float
, defaults to1.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> titanic_gbm = H2OGradientBoostingEstimator(ntrees=10000, ... learn_rate=0.05, ... learn_rate_annealing=.9, ... stopping_rounds=5, ... stopping_metric="AUC", ... stopping_tolerance=1e-4, ... seed=1234) >>> titanic_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_gbm.auc(valid=True)
-
property
max_abs_leafnode_pred
¶ Maximum absolute value of a leaf node prediction
Type:
float
, defaults to∞
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cov_gbm = H2OGradientBoostingEstimator(max_abs_leafnode_pred=2, ... seed=1234) >>> cov_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_gbm.logloss(valid=True)
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> max = .85 >>> cov_gbm = H2OGradientBoostingEstimator(balance_classes=True, ... max_after_balance_size=max, ... seed=1234) >>> cov_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_gbm.logloss(valid=True)
-
property
max_confusion_matrix_size
¶ [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
Type:
int
, defaults to20
.
-
property
max_depth
¶ Maximum tree depth (0 for unlimited).
Type:
int
, defaults to5
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(ntrees=100, ... max_depth=2, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc(valid=True)
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(max_runtime_secs=10, ... ntrees=10000, ... max_depth=10, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc(valid=True)
-
property
min_rows
¶ Fewest allowed (weighted) observations in a leaf.
Type:
float
, defaults to10.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(min_rows=16, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc(valid=True)
-
property
min_split_improvement
¶ Minimum relative improvement in squared error reduction for a split to happen
Type:
float
, defaults to1e-05
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(min_split_improvement=1e-3, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc(valid=True)
-
property
monotone_constraints
¶ A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.
Type:
dict
.- Examples
>>> prostate_hex = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate_hex["CAPSULE"] = prostate_hex["CAPSULE"].asfactor() >>> response = "CAPSULE" >>> seed = 42 >>> monotone_constraints = {"AGE":1} >>> gbm_model = H2OGradientBoostingEstimator(seed=seed, ... monotone_constraints=monotone_constraints) >>> gbm_model.train(y=response, ... ignored_columns=["ID"], ... training_frame=prostate_hex) >>> gbm_model.scoring_history()
-
property
nbins
¶ For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
Type:
int
, defaults to20
.- Examples
>>> eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv") >>> eeg['eyeDetection'] = eeg['eyeDetection'].asfactor() >>> predictors = eeg.columns[:-1] >>> response = 'eyeDetection' >>> train, valid = eeg.split_frame(ratios=[.8], seed=1234) >>> bin_num = [16, 32, 64, 128, 256, 512] >>> label = ["16", "32", "64", "128", "256", "512"] >>> for key, num in enumerate(bin_num): ... eeg_gbm = H2OGradientBoostingEstimator(nbins=num, seed=1234) ... eeg_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) ... print(label[key], 'training score', eeg_gbm.auc(train=True)) ... print(label[key], 'validation score', eeg_gbm.auc(valid=True))
-
property
nbins_cats
¶ For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.
Type:
int
, defaults to1024
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> bin_num = [8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] >>> label = ["8", "16", "32", "64", "128", "256", "512", "1024", "2048", "4096"] >>> for key, num in enumerate(bin_num): ... airlines_gbm = H2OGradientBoostingEstimator(nbins_cats=num, seed=1234) ... airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) ... print(label[key], 'training score', airlines_gbm.auc(train=True)) ... print(label[key], 'validation score', airlines_gbm.auc(valid=True))
-
property
nbins_top_level
¶ For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level
Type:
int
, defaults to1024
.- Examples
>>> eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv") >>> eeg['eyeDetection'] = eeg['eyeDetection'].asfactor() >>> predictors = eeg.columns[:-1] >>> response = 'eyeDetection' >>> train, valid = eeg.split_frame(ratios=[.8], seed=1234) >>> bin_num = [32, 64, 128, 256, 512, 1024, 2048, 4096] >>> label = ["32", "64", "128", "256", "512", "1024", "2048", "4096"] >>> for key, num in enumerate(bin_num): ... eeg_gbm = H2OGradientBoostingEstimator(nbins_top_level=num, seed=1234) ... eeg_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) ... print(label[key], 'training score', eeg_gbm.auc(train=True)) ... print(label[key], 'validation score', eeg_gbm.auc(valid=True))
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> folds = 5 >>> cars_gbm = H2OGradientBoostingEstimator(nfolds=folds, ... seed=1234 >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_gbm.auc()
-
property
ntrees
¶ Number of trees.
Type:
int
, defaults to50
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> tree_num = [20, 50, 80, 110, 140, 170, 200] >>> label = ["20", "50", "80", "110", "140", "170", "200"] >>> for key, num in enumerate(tree_num): ... titanic_gbm = H2OGradientBoostingEstimator(ntrees=num, ... seed=1234) ... titanic_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) ... print(label[key], 'training score', titanic_gbm.auc(train=True)) ... print(label[key], 'validation score', titanic_gbm.auc(valid=True))
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> boston["offset"] = boston["medv"].log() >>> train, valid = boston.split_frame(ratios=[.8], seed=1234) >>> boston_gbm = H2OGradientBoostingEstimator(offset_column="offset", ... seed=1234) >>> boston_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_gbm.mse(valid=True)
-
property
pred_noise_bandwidth
¶ Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions
Type:
float
, defaults to0.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> titanic_gbm = H2OGradientBoostingEstimator(pred_noise_bandwidth=0.1, ... seed=1234) >>> titanic_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_gbm.auc(valid = True)
-
property
quantile_alpha
¶ Desired quantile for Quantile regression, must be between 0 and 1.
Type:
float
, defaults to0.5
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8], seed=1234) >>> boston_gbm = H2OGradientBoostingEstimator(distribution="quantile", ... quantile_alpha=.8, ... seed=1234) >>> boston_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_gbm.mse(valid=True)
-
property
r2_stopping
¶ r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this
Type:
float
, defaults to∞
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
sample_rate
¶ Row sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(sample_rate=.7, ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
sample_rate_per_class
¶ A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree
Type:
List[float]
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> rate_per_class_list = [1, .4, 1, 1, 1, 1, 1] >>> cov_gbm = H2OGradientBoostingEstimator(sample_rate_per_class=rate_per_class_list, ... seed=1234) >>> cov_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cov_gbm.logloss(valid=True)
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(score_each_iteration=True, ... ntrees=55, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.scoring_history()
-
property
score_tree_interval
¶ Score the model after every so many trees. Disabled if set to 0.
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(score_tree_interval=True, ... ntrees=55, ... seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.scoring_history()
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> gbm_w_seed_1 = H2OGradientBoostingEstimator(col_sample_rate=.7, ... seed=1234) >>> gbm_w_seed_1.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('auc for the 1st model built with a seed:', gbm_w_seed_1.auc(valid=True))
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.001
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_gbm = H2OGradientBoostingEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_gbm.auc(valid=True)
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc(valid=True)
-
property
tweedie_power
¶ Tweedie power for Tweedie regression, must be between 1 and 2.
Type:
float
, defaults to1.5
.- Examples
>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> predictors = insurance.columns[0:4] >>> response = 'Claims' >>> insurance['Group'] = insurance['Group'].asfactor() >>> insurance['Age'] = insurance['Age'].asfactor() >>> train, valid = insurance.split_frame(ratios=[.8], seed=1234) >>> insurance_gbm = H2OGradientBoostingEstimator(distribution="tweedie", ... tweedie_power=1.2, ... seed=1234) >>> insurance_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> insurance_gbm.mse(valid=True)
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_gbm.auc(valid=True)
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_gbm = H2OGradientBoostingEstimator(seed=1234) >>> cars_gbm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid, ... weights_column="weight") >>> cars_gbm.auc(valid=True)
-
property
H2OGeneralizedLinearEstimator
¶
-
class
h2o.estimators.glm.
H2OGeneralizedLinearEstimator
(model_id=None, training_frame=None, validation_frame=None, nfolds=0, checkpoint=None, export_checkpoints_dir=None, seed=-1, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, fold_assignment='auto', fold_column=None, response_column=None, ignored_columns=None, random_columns=None, ignore_const_cols=True, score_each_iteration=False, score_iteration_interval=-1, offset_column=None, weights_column=None, family='auto', rand_family=None, tweedie_variance_power=0.0, tweedie_link_power=1.0, theta=1e-10, solver='auto', alpha=None, lambda_=None, lambda_search=False, early_stopping=True, nlambdas=-1, standardize=True, missing_values_handling='mean_imputation', plug_values=None, compute_p_values=False, remove_collinear_columns=False, intercept=True, non_negative=False, max_iterations=-1, objective_epsilon=-1.0, beta_epsilon=0.0001, gradient_epsilon=-1.0, link='family_default', rand_link=None, startval=None, calc_like=False, HGLM=False, prior=-1.0, cold_start=False, lambda_min_ratio=-1.0, beta_constraints=None, max_active_predictors=-1, interactions=None, interaction_pairs=None, obj_reg=-1.0, stopping_rounds=0, stopping_metric='auto', stopping_tolerance=0.001, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_confusion_matrix_size=20, max_runtime_secs=0.0, custom_metric_func=None, generate_scoring_history=False, auc_type='auto')[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Generalized Linear Modeling
Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.
A subclass of
ModelBase
is returned. The specific subclass depends on the machine learning task at hand (if it’s binomial classification, then an H2OBinomialModel is returned, if it’s regression then a H2ORegressionModel is returned). The default print-out of the models is shown, but further GLM-specific information can be queried out of the object. Upon completion of the GLM, the resulting object has coefficients, normalized coefficients, residual/null deviance, aic, and a host of model metrics including MSE, AUC (for logistic regression), degrees of freedom, and confusion matrices.-
property
HGLM
¶ If set to true, will return HGLM model. Otherwise, normal GLM model will be returned
Type:
bool
, defaults toFalse
.
-
property
Lambda
¶ [Deprecated] Use
lambda_
instead
-
property
alpha
¶ Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise.
Type:
List[float]
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(alpha=.25) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_glm.mse(valid=True))
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse()
-
property
beta_constraints
¶ Beta constraints
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> n = len(predictors) >>> constraints = h2o.H2OFrame({'names':predictors, ... 'lower_bounds': [-1000]*n, ... 'upper_bounds': [1000]*n, ... 'beta_given': [1]*n, ... 'rho': [0.2]*n}) >>> cars_glm = H2OGeneralizedLinearEstimator(standardize=True, ... beta_constraints=constraints) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse()
-
property
beta_epsilon
¶ Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver
Type:
float
, defaults to0.0001
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(beta_epsilon=1e-3) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse()
-
property
calc_like
¶ if true, will return likelihood function value for HGLM.
Type:
bool
, defaults toFalse
.
-
property
checkpoint
¶ Model checkpoint to resume training with.
Type:
Union[None, str, H2OEstimator]
.
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.] >>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True, ... class_sampling_factors=sample_factors, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse()
-
property
cold_start
¶ Only applicable to multiple alpha/lambda values. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch.
Type:
bool
, defaults toFalse
.
-
property
compute_p_values
¶ Request p-values computation, p-values work only with IRLSM solver and no regularization
Type:
bool
, defaults toFalse
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8]) >>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial', ... lambda_=0, ... remove_collinear_columns=True, ... compute_p_values=True) >>> airlines_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_glm.mse()
-
property
custom_metric_func
¶ Reference to custom evaluation function, format: language:keyName=funcName
Type:
str
.
-
property
early_stopping
¶ Stop early when there is no more relative improvement on train or validation (if provided)
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial', ... early_stopping=True) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(valid=True)
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> checkpoints = tempfile.mkdtemp() >>> cars_glm = H2OGeneralizedLinearEstimator(export_checkpoints_dir=checkpoints, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse() >>> len(listdir(checkpoints_dir))
-
property
family
¶ Family. Use binomial for classification with logistic regression, others are for regression problems.
Type:
Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(valid = True)
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> assignment_type = "Random" >>> cars_gml = H2OGeneralizedLinearEstimator(fold_assignment=assignment_type, ... nfolds=5, ... family='binomial', ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_glm.auc(train=True)
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234) >>> fold_numbers.set_names(["fold_numbers"]) >>> cars = cars.cbind(fold_numbers) >>> print(cars['fold_numbers']) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=cars, ... fold_column="fold_numbers") >>> cars_glm.auc(xval=True)
-
property
generate_scoring_history
¶ If set to true, will generate scoring history for GLM. This may significantly slow down the algo.
Type:
bool
, defaults toFalse
.
-
static
getAlphaBest
(model)[source]¶ Extract best alpha value found from glm model.
- Parameters
model – source lambda search model
- Examples
>>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> bestAlpha = H2OGeneralizedLinearEstimator.getAlphaBest(m) >>> print("Best alpha found is {0}".format(bestAlpha))
-
static
getGLMRegularizationPath
(model)[source]¶ Extract full regularization path explored during lambda search from glm model.
- Parameters
model – source lambda search model
- Examples
>>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(m) >>> m2 = H2OGeneralizedLinearEstimator.makeGLMModel(model=m, ... coefs=r['coefficients'][10]) >>> dev1 = r['explained_deviance_train'][10] >>> p = m2.model_performance(d) >>> dev2 = 1-p.residual_deviance()/p.null_deviance() >>> print(dev1, " =?= ", dev2)
-
static
getLambdaBest
(model)[source]¶ Extract best lambda value found from glm model.
- Parameters
model – source lambda search model
- Examples
>>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> bestLambda = H2OGeneralizedLinearEstimator.getLambdaBest(m) >>> print("Best lambda found is {0}".format(bestLambda))
-
static
getLambdaMax
(model)[source]¶ Extract the maximum lambda value used during lambda search.
- Parameters
model – source lambda search model
- Examples
>>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> maxLambda = H2OGeneralizedLinearEstimator.getLambdaMax(m) >>> print("Maximum lambda found is {0}".format(maxLambda))
-
static
getLambdaMin
(model)[source]¶ Extract the minimum lambda value calculated during lambda search from glm model. Note that due to early stop, this minimum lambda value may not be used in the actual lambda search.
- Parameters
model – source lambda search model
- Examples
>>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family = 'binomial', ... lambda_search = True, ... solver = 'COORDINATE_DESCENT') >>> m.train(training_frame = d, ... x = [2,3,4,5,6,7,8], ... y = 1) >>> minLambda = H2OGeneralizedLinearEstimator.getLambdaMin(m) >>> print("Minimum lambda found is {0}".format(minLambda))
-
property
gradient_epsilon
¶ Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively.
Type:
float
, defaults to-1.0
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(gradient_epsilon=1e-3) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse()
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... ignore_const_cols=True, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(valid=True)
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
interaction_pairs
¶ A list of pairwise (first order) column interactions.
Type:
List[tuple]
.- Examples
>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> XY = [df.names[i-1] for i in [1,2,3,4,6,8,9,13,17,18,19,31]] >>> interactions = [XY[i-1] for i in [5,7,9]] >>> m = H2OGeneralizedLinearEstimator(lambda_search=True, ... family="binomial", ... interactions=interactions) >>> m.train(x=XY[:len(XY)], y=XY[-1],training_frame=df) >>> m._model_json['output']['coefficients_table'] >>> coef_m = m._model_json['output']['coefficients_table'] >>> interaction_pairs = [("CRSDepTime", "UniqueCarrier"), ... ("CRSDepTime", "Origin"), ... ("UniqueCarrier", "Origin")] >>> mexp = H2OGeneralizedLinearEstimator(lambda_search=True, ... family="binomial", ... interaction_pairs=interaction_pairs) >>> mexp.train(x=XY[:len(XY)], y=XY[-1],training_frame=df) >>> mexp._model_json['output']['coefficients_table']
-
property
interactions
¶ A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
Type:
List[str]
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> interactions_list = ['crim', 'dis'] >>> boston_glm = H2OGeneralizedLinearEstimator(interactions=interactions_list) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse()
-
property
intercept
¶ Include constant term in the model
Type:
bool
, defaults toTrue
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris['class'] = iris['class'].asfactor() >>> predictors = iris.columns[:-1] >>> response = 'class' >>> train, valid = iris.split_frame(ratios=[.8]) >>> iris_glm = H2OGeneralizedLinearEstimator(family='multinomial', ... intercept=True) >>> iris_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> iris_glm.logloss(valid=True)
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_fold_assignment=True, ... nfolds=5, ... seed=1234, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_glm.cross_validation_fold_assignment()
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_models=True, ... nfolds=5, ... seed=1234, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_glm_cv_models = cars_glm.cross_validation_models() >>> print(cars_glm.cross_validation_models())
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_predictions=True, ... nfolds=5, ... seed=1234, ... family="binomial") >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_glm.cross_validation_predictions()
-
property
lambda_
¶ Regularization strength
Type:
List[float]
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8]) >>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial', ... lambda_=.0001) >>> airlines_glm.train(x=predictors, ... y=response ... trainig_frame=train, ... validation_frame=valid) >>> print(airlines_glm.auc(valid=True))
-
property
lambda_min_ratio
¶ Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01.
Type:
float
, defaults to-1.0
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(lambda_min_ratio=.0001) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse()
-
property
lambda_search
¶ Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Type:
bool
, defaults toFalse
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(lambda_search=True) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_glm.mse(valid=True))
-
property
link
¶ Link function.
Type:
Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]
, defaults to"family_default"
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris['class'] = iris['class'].asfactor() >>> predictors = iris.columns[:-1] >>> response = 'class' >>> train, valid = iris.split_frame(ratios=[.8]) >>> iris_glm = H2OGeneralizedLinearEstimator(family='multinomial', ... link='family_default') >>> iris_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> iris_glm.logloss()
-
static
makeGLMModel
(model, coefs, threshold=0.5)[source]¶ Create a custom GLM model using the given coefficients.
Needs to be passed source model trained on the dataset to extract the dataset information from.
- Parameters
model – source model, used for extracting dataset information
coefs – dictionary containing model coefficients
threshold – (optional, only for binomial) decision threshold used for classification
- Examples
>>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> m = H2OGeneralizedLinearEstimator(family='binomial', ... lambda_search=True, ... solver='COORDINATE_DESCENT') >>> m.train(training_frame=d, ... x=[2,3,4,5,6,7,8], ... y=1) >>> r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(m) >>> m2 = H2OGeneralizedLinearEstimator.makeGLMModel(model=m, ... coefs=r['coefficients'][10]) >>> dev1 = r['explained_deviance_train'][10] >>> p = m2.model_performance(d) >>> dev2 = 1-p.residual_deviance()/p.null_deviance() >>> print(dev1, " =?= ", dev2)
-
property
max_active_predictors
¶ Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000.
Type:
int
, defaults to-1
.- Examples
>>> higgs= h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/testng/higgs_train_5k.csv") >>> predictors = higgs.names >>> predictors.remove('response') >>> response = "response" >>> train, valid = higgs.split_frame(ratios=[.8]) >>> higgs_glm = H2OGeneralizedLinearEstimator(family='binomial', ... max_active_predictors=200) >>> higgs_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> higgs_glm.auc()
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","year"] >>> response = "acceleration" >>> train, valid = cars.split_frame(ratios=[.8]) >>> max = .85 >>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True, ... max_after_balance_size=max, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse()
-
property
max_confusion_matrix_size
¶ [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
Type:
int
, defaults to20
.
-
property
max_iterations
¶ Maximum number of iterations
Type:
int
, defaults to-1
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial', ... max_iterations=50) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse()
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm = H2OGeneralizedLinearEstimator(max_runtime_secs=10, ... seed=1234) >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.mse()
-
property
missing_values_handling
¶ Handling of missing values. Either MeanImputation, Skip or PlugValues.
Type:
Literal["mean_imputation", "skip", "plug_values"]
, defaults to"mean_imputation"
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> boston.insert_missing_values() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(missing_values_handling="skip") >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse()
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> folds = 5 >>> cars_glm = H2OGeneralizedLinearEstimator(nfolds=folds, ... seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_glm.auc(xval=True)
-
property
nlambdas
¶ Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100.
Type:
int
, defaults to-1
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(lambda_search=True, ... nlambdas=50) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_glm.mse(valid=True))
-
property
non_negative
¶ Restrict coefficients (not intercept) to be non-negative
Type:
bool
, defaults toFalse
.- Examples
>>> airlines = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8]) >>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial', ... non_negative=True) >>> airlines_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_glm.auc()
-
property
obj_reg
¶ Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs
Type:
float
, defaults to-1.0
.- Examples
>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/glm_ordinal_logit/ordinal_multinomial_training_set.csv") >>> df["C11"] = df["C11"].asfactor() >>> ordinal_fit = H2OGeneralizedLinearEstimator(family="ordinal", ... alpha=1.0, ... lambda_=0.000000001, ... obj_reg=0.00001, ... max_iterations=1000, ... beta_epsilon=1e-8, ... objective_epsilon=1e-10) >>> ordinal_fit.train(x=list(range(0,10)), ... y="C11", ... training_frame=df) >>> ordinal_fit.mse()
-
property
objective_epsilon
¶ Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001.
Type:
float
, defaults to-1.0
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(objective_epsilon=1e-3) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse()
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> boston["offset"] = boston["medv"].log() >>> train, valid = boston.split_frame(ratios=[.8], seed=1234) >>> boston_glm = H2OGeneralizedLinearEstimator(offset_column="offset", ... seed=1234) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse(valid=True)
-
property
plug_values
¶ Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues)
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars = cars.drop(0) >>> means = cars.mean() >>> means = H2OFrame._expr(ExprNode("mean", cars, True, 0)) >>> glm_means = H2OGeneralizedLinearEstimator(seed=42) >>> glm_means.train(training_frame=cars, y="cylinders") >>> glm_plugs1 = H2OGeneralizedLinearEstimator(seed=42, ... missing_values_handling="PlugValues", ... plug_values=means) >>> glm_plugs1.train(training_frame=cars, y="cylinders") >>> glm_means.coef() == glm_plugs1.coef() >>> not_means = 0.1 + (means * 0.5) >>> glm_plugs2 = H2OGeneralizedLinearEstimator(seed=42, ... missing_values_handling="PlugValues", ... plug_values=not_means) >>> glm_plugs2.train(training_frame=cars, y="cylinders") >>> glm_means.coef() != glm_plugs2.coef()
-
property
prior
¶ Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.
Type:
float
, defaults to-1.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_glm1 = H2OGeneralizedLinearEstimator(family='binomial', prior=0.5) >>> cars_glm1.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm1.mse()
-
property
rand_family
¶ Random Component Family array. One for each random component. Only support gaussian for now.
Type:
List[Literal["[gaussian]"]]
.
-
property
rand_link
¶ Link function array for random component in HGLM.
Type:
List[Literal["[identity]", "[family_default]"]]
.
-
property
random_columns
¶ random columns indices for HGLM.
Type:
List[int]
.
-
property
remove_collinear_columns
¶ In case of linearly dependent columns, remove some of the dependent columns
Type:
bool
, defaults toFalse
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8]) >>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial', ... lambda_=0, ... remove_collinear_columns=True) >>> airlines_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_glm.auc()
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(score_each_iteration=True, ... seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.scoring_history()
-
property
score_iteration_interval
¶ Perform scoring for every score_iteration_interval iterations
Type:
int
, defaults to-1
.
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid = airlines.split_frame(ratios=[.8], seed=1234) >>> glm_w_seed = H2OGeneralizedLinearEstimator(family='binomial', ... seed=1234) >>> glm_w_seed.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(glm_w_seed_1.auc(valid=True))
-
property
solver
¶ AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.
Type:
Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"]
, defaults to"auto"
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(solver='irlsm') >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_glm.mse(valid=True))
-
property
standardize
¶ Standardize numeric columns to have zero mean and unit variance
Type:
bool
, defaults toTrue
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_glm = H2OGeneralizedLinearEstimator(standardize=True) >>> boston_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_glm.mse()
-
property
startval
¶ double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.
Type:
List[float]
.
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.001
.
-
property
theta
¶ Theta
Type:
float
, defaults to1e-10
.- Examples
>>> h2o_df = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/glm_test/Motor_insurance_sweden.txt") >>> predictors = ["Payment", "Insured", "Kilometres", "Zone", "Bonus", "Make"] >>> response = "Claims" >>> negativebinomial_fit = H2OGeneralizedLinearEstimator(family="negativebinomial", ... link="identity", ... theta=0.5) >>> negativebinomial_fit.train(x=predictors, ... y=response, ... training_frame=h2o_df) >>> negativebinomial_fit.scoring_history()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(train=True)
-
property
tweedie_link_power
¶ Tweedie link power
Type:
float
, defaults to1.0
.- Examples
>>> auto = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/auto.csv") >>> predictors = auto.names >>> predictors.remove('y') >>> response = "y" >>> train, valid = auto.split_frame(ratios=[.8]) >>> auto_glm = H2OGeneralizedLinearEstimator(family='tweedie', ... tweedie_link_power=1) >>> auto_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(auto_glm.mse(valid=True))
-
property
tweedie_variance_power
¶ Tweedie variance power
Type:
float
, defaults to0.0
.- Examples
>>> auto = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/auto.csv") >>> predictors = auto.names >>> predictors.remove('y') >>> response = "y" >>> train, valid = auto.split_frame(ratios=[.8]) >>> auto_glm = H2OGeneralizedLinearEstimator(family='tweedie', ... tweedie_variance_power=1) >>> auto_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(auto_glm.mse(valid=True))
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_glm.auc(valid=True)
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234, ... family='binomial') >>> cars_glm.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid, ... weights_column="weight") >>> cars_glm.auc(valid=True)
-
property
H2OInfogram
¶
-
class
h2o.estimators.infogram.
H2OInfogram
(model_id=None, training_frame=None, validation_frame=None, seed=-1, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, nfolds=0, fold_assignment='auto', fold_column=None, response_column=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, offset_column=None, weights_column=None, standardize=False, distribution='auto', plug_values=None, max_iterations=0, stopping_rounds=0, stopping_metric='auto', stopping_tolerance=0.001, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_runtime_secs=0.0, custom_metric_func=None, auc_type='auto', algorithm='auto', algorithm_params=None, protected_columns=None, total_information_threshold=-1.0, net_information_threshold=-1.0, relevance_index_threshold=-1.0, safety_index_threshold=-1.0, data_fraction=1.0, top_n_features=50)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Information Diagram
The infogram is a graphical information-theoretic interpretability tool which allows the user to quickly spot the core, decision-making variables that uniquely and safely drive the response, in supervised classification problems. The infogram can significantly cut down the number of predictors needed to build a model by identifying only the most valuable, admissible features. When protected variables such as race or gender are present in the data, the admissibility of a variable is determined by a safety and relevancy index, and thus serves as a diagnostic tool for fairness. The safety of each feature can be quantified and variables that are unsafe will be considered inadmissible. Models built using only admissible features will naturally be more interpretable, given the reduced feature set. Admissible models are also less susceptible to overfitting and train faster, while providing similar accuracy as models built using all available features.
-
property
algorithm
¶ Type of machine learning algorithm used to build the infogram. Options include ‘AUTO’ (gbm), ‘deeplearning’ (Deep Learning with default parameters), ‘drf’ (Random Forest with default parameters), ‘gbm’ (GBM with default parameters), ‘glm’ (GLM with default parameters), or ‘xgboost’ (if available, XGBoost with default parameters).
Type:
Literal["auto", "deeplearning", "drf", "gbm", "glm", "xgboost"]
, defaults to"auto"
.
-
property
algorithm_params
¶ Customized parameters for the machine learning algorithm specified in the algorithm parameter.
Type:
dict
.
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.
-
property
custom_metric_func
¶ Reference to custom evaluation function, format: language:keyName=funcName
Type:
str
.
-
property
data_fraction
¶ The fraction of training frame to use to build the infogram model. Defaults to 1.0, and any value greater than 0 and less than or equal to 1.0 is acceptable.
Type:
float
, defaults to1.0
.
-
property
distribution
¶ Distribution function
Type:
Literal["auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"]
, defaults to"auto"
.
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.
-
get_admissible_relevance
()[source]¶ - Returns
a list of relevance (variable importance) for admissible attributes
-
get_admissible_score_frame
(valid=False, xval=False)[source]¶ Retreive admissible score frame which includes relevance and CMI information in an H2OFrame for training dataset by default :param valid: return infogram info on validation dataset if True :param xval: return infogram info on cross-validation hold outs if True :return: H2OFrame
-
get_all_predictor_cmi
()[source]¶ Get normalized CMI of all predictors. :return: two tuples, first one is predictor names and second one is cmi
-
get_all_predictor_cmi_raw
()[source]¶ Get raw CMI of all predictors. :return: two tuples, first one is predictor names and second one is cmi
-
get_all_predictor_relevance
()[source]¶ Get relevance of all predictors :return: two tuples, first one is predictor names and second one is relevance
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.
-
property
max_iterations
¶ Maximum number of iterations.
Type:
int
, defaults to0
.
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.
-
property
net_information_threshold
¶ A number between 0 and 1 representing a threshold for net information, defaulting to 0.1. For a specific feature, if the net information is higher than this threshold, and the corresponding total information is also higher than the total_information_threshold, that feature will be considered admissible. The net information is the y-axis of the Core Infogram. Default is -1 which gets set to 0.1.
Type:
float
, defaults to-1.0
.
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.
-
plot
(train=True, valid=False, xval=False, figsize=(10, 10), title='Infogram', legend_on=False, server=False)[source]¶ Plot the infogram. By default, it will plot the infogram calculated from training dataset. Note that the frame rel_cmi_frame contains the following columns: - 0: predictor names - 1: admissible - 2: admissible index - 3: relevance-index or total information - 4: safety-index or net information, normalized from 0 to 1 - 5: safety-index or net information not normalized
- Parameters
train – True if infogram is generated from training dataset
valid – True if infogram is generated from validation dataset
xval – True if infogram is generated from cross-validation holdout dataset
figsize – size of infogram plot
title – string to denote title of the plot
legend_on – legend text is included if True
server – True will not generate plot, False will produce plot
- Returns
infogram plot if server=True or None if server=False
-
property
plug_values
¶ Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues).
Type:
Union[None, str, H2OFrame]
.
-
property
protected_columns
¶ Columns that contain features that are sensitive and need to be protected (legally, or otherwise), if applicable. These features (e.g. race, gender, etc) should not drive the prediction of the response.
Type:
List[str]
.
-
property
relevance_index_threshold
¶ A number between 0 and 1 representing a threshold for the relevance index, defaulting to 0.1. This is only used when
protected_columns
is set by the user. For a specific feature, if the relevance index value is higher than this threshold, and the corresponding safety index is also higher than the safety_index_threshold``, that feature will be considered admissible. The relevance index is the x-axis of the Fair Infogram. Default is -1 which gets set to 0.1.Type:
float
, defaults to-1.0
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
safety_index_threshold
¶ A number between 0 and 1 representing a threshold for the safety index, defaulting to 0.1. This is only used when protected_columns is set by the user. For a specific feature, if the safety index value is higher than this threshold, and the corresponding relevance index is also higher than the relevance_index_threshold, that feature will be considered admissible. The safety index is the y-axis of the Fair Infogram. Default is -1 which gets set to 0.1.
Type:
float
, defaults to-1.0
.
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.
-
property
seed
¶ Seed for pseudo random number generator (if applicable).
Type:
int
, defaults to-1
.
-
property
standardize
¶ Standardize numeric columns to have zero mean and unit variance.
Type:
bool
, defaults toFalse
.
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.001
.
-
property
top_n_features
¶ An integer specifying the number of columns to evaluate in the infogram. The columns are ranked by variable importance, and the top N are evaluated. Defaults to 50.
Type:
int
, defaults to50
.
-
property
total_information_threshold
¶ A number between 0 and 1 representing a threshold for total information, defaulting to 0.1. For a specific feature, if the total information is higher than this threshold, and the corresponding net information is also higher than the threshold
net_information_threshold
, that feature will be considered admissible. The total information is the x-axis of the Core Infogram. Default is -1 which gets set to 0.1.Type:
float
, defaults to-1.0
.
-
train
(x=None, y=None, training_frame=None, verbose=False, **kwargs)[source]¶ Train the H2O model.
- Parameters
x – A list of column names or indices indicating the predictor columns.
y – An index or a column name indicating the response column.
training_frame (H2OFrame) – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
offset_column – The name or index of the column in training_frame that holds the offsets.
fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
weights_column – The name or index of the column in training_frame that holds the per-row weights.
validation_frame – H2OFrame with validation data to be scored on while training.
max_runtime_secs (float) – Maximum allowed runtime in seconds for model training. Use 0 to disable.
verbose (bool) – Print scoring history to stdout. Defaults to False.
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.
-
property
H2OModelSelectionEstimator
¶
-
class
h2o.estimators.model_selection.
H2OModelSelectionEstimator
(model_id=None, training_frame=None, validation_frame=None, nfolds=0, seed=-1, fold_assignment='auto', fold_column=None, response_column=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, score_iteration_interval=0, offset_column=None, weights_column=None, family='auto', link='family_default', tweedie_variance_power=0.0, tweedie_link_power=0.0, theta=0.0, solver='irlsm', alpha=None, lambda_=None, lambda_search=False, early_stopping=False, nlambdas=0, standardize=True, missing_values_handling='mean_imputation', plug_values=None, compute_p_values=False, remove_collinear_columns=False, intercept=False, non_negative=False, max_iterations=0, objective_epsilon=-1.0, beta_epsilon=0.0001, gradient_epsilon=0.0, startval=None, prior=0.0, cold_start=False, lambda_min_ratio=0.0, beta_constraints=None, max_active_predictors=-1, obj_reg=0.0, stopping_rounds=0, stopping_metric='auto', stopping_tolerance=0.001, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_confusion_matrix_size=20, max_runtime_secs=0.0, custom_metric_func=None, nparallelism=0, max_predictor_number=1, min_predictor_number=1, mode='maxr', p_values_threshold=0.0)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Model Selection
H2O ModelSelection is used to build the best model with one predictor, two predictors, … up to max_predictor_number specified in the algorithm parameters when mode=allsubsets. The best model is the one with the highest R2 value. When mode=maxr, the model returned is no longer guaranteed to have the best R2 value.
-
property
alpha
¶ Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise.
Type:
List[float]
.
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.
-
property
beta_constraints
¶ Beta constraints
Type:
Union[None, str, H2OFrame]
.
-
property
beta_epsilon
¶ Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver
Type:
float
, defaults to0.0001
.
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.
-
coef
(predictor_size=None)[source]¶ Get the coefficients for all models built with different number of predictors.
- Parameters
predictor_size – predictor subset size, will only return model coefficients of that subset size.
- Returns
list of Python Dicts of coefficients for all models built with different predictor numbers
-
coef_norm
(predictor_size=None)[source]¶ Get the normalized coefficients for all models built with different number of predictors.
- Parameters
predictor_size – predictor subset size, will only return model coefficients of that subset size.
- Returns
list of Python Dicts of coefficients for all models built with different predictor numbers
-
property
cold_start
¶ Only applicable to multiple alpha/lambda values. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch.
Type:
bool
, defaults toFalse
.
-
property
compute_p_values
¶ Request p-values computation, p-values work only with IRLSM solver and no regularization
Type:
bool
, defaults toFalse
.
-
property
custom_metric_func
¶ Reference to custom evaluation function, format: language:keyName=funcName
Type:
str
.
-
property
early_stopping
¶ Stop early when there is no more relative improvement on train or validation (if provided)
Type:
bool
, defaults toFalse
.
-
property
family
¶ Family. For MaxR, only gaussian. For backward, ordinal and multinomial families are not supported
Type:
Literal["auto", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"]
, defaults to"auto"
.
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.
-
get_best_R2_values
()[source]¶ Get list of best R2 values of models with 1 predictor, 2 predictors, …, max_predictor_number of predictors
- Returns
a list of best r2 values
-
get_best_model_predictors
()[source]¶ Get list of best models with 1 predictor, 2 predictors, …, max_predictor_number of predictors that have the highest r2 values
- Returns
a list of best r2 values
-
property
gradient_epsilon
¶ Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively.
Type:
float
, defaults to0.0
.
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
intercept
¶ Include constant term in the model
Type:
bool
, defaults toFalse
.
-
property
lambda_
¶ Regularization strength
Type:
List[float]
.
-
property
lambda_min_ratio
¶ Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01.
Type:
float
, defaults to0.0
.
-
property
lambda_search
¶ Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Type:
bool
, defaults toFalse
.
-
property
link
¶ Link function.
Type:
Literal["family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"]
, defaults to"family_default"
.
-
property
max_active_predictors
¶ Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000.
Type:
int
, defaults to-1
.
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.
-
property
max_confusion_matrix_size
¶ [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
Type:
int
, defaults to20
.
-
property
max_iterations
¶ Maximum number of iterations
Type:
int
, defaults to0
.
-
property
max_predictor_number
¶ Maximum number of predictors to be considered when building GLM models. Defaults to 1.
Type:
int
, defaults to1
.
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.
-
property
min_predictor_number
¶ For mode = ‘backward’ only. Minimum number of predictors to be considered when building GLM models starting with all predictors to be included. Defaults to 1.
Type:
int
, defaults to1
.
-
property
missing_values_handling
¶ Handling of missing values. Either MeanImputation, Skip or PlugValues.
Type:
Literal["mean_imputation", "skip", "plug_values"]
, defaults to"mean_imputation"
.
-
property
mode
¶ Mode: Used to choose model selection algorithms to use. Options include ‘allsubsets’ for all subsets, ‘maxr’ for MaxR, ‘backward’ for backward selection
Type:
Literal["allsubsets", "maxr", "backward"]
, defaults to"maxr"
.
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.
-
property
nlambdas
¶ Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100.
Type:
int
, defaults to0
.
-
property
non_negative
¶ Restrict coefficients (not intercept) to be non-negative
Type:
bool
, defaults toFalse
.
-
property
nparallelism
¶ number of models to build in parallel. Defaults to 0.0 which is adaptive to the system capability
Type:
int
, defaults to0
.
-
property
obj_reg
¶ Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs
Type:
float
, defaults to0.0
.
-
property
objective_epsilon
¶ Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001.
Type:
float
, defaults to-1.0
.
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.
-
property
p_values_threshold
¶ For mode=’backward’ only. If specified, will stop the model building process when all coefficientsp-values drop below this threshold
Type:
float
, defaults to0.0
.
-
property
plug_values
¶ Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues)
Type:
Union[None, str, H2OFrame]
.
-
property
prior
¶ Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.
Type:
float
, defaults to0.0
.
-
property
remove_collinear_columns
¶ In case of linearly dependent columns, remove some of the dependent columns
Type:
bool
, defaults toFalse
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
result
()[source]¶ Get result frame that contains information about the model building process like for modelselection and anovaglm. :return: the H2OFrame that contains information about the model building process like for modelselection and anovaglm.
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.
-
property
score_iteration_interval
¶ Perform scoring for every score_iteration_interval iterations
Type:
int
, defaults to0
.
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.
-
property
solver
¶ AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.
Type:
Literal["auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr"]
, defaults to"irlsm"
.
-
property
standardize
¶ Standardize numeric columns to have zero mean and unit variance
Type:
bool
, defaults toTrue
.
-
property
startval
¶ double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.
Type:
List[float]
.
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.001
.
-
property
theta
¶ Theta
Type:
float
, defaults to0.0
.
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
tweedie_link_power
¶ Tweedie link power
Type:
float
, defaults to0.0
.
-
property
tweedie_variance_power
¶ Tweedie variance power
Type:
float
, defaults to0.0
.
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.
-
property
H2ONaiveBayesEstimator
¶
-
class
h2o.estimators.naive_bayes.
H2ONaiveBayesEstimator
(model_id=None, nfolds=0, seed=-1, fold_assignment='auto', fold_column=None, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, training_frame=None, validation_frame=None, response_column=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_confusion_matrix_size=20, laplace=0.0, min_sdev=0.001, eps_sdev=0.0, min_prob=0.001, eps_prob=0.0, compute_metrics=True, max_runtime_secs=0.0, export_checkpoints_dir=None, gainslift_bins=-1, auc_type='auto')[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Naive Bayes
The naive Bayes classifier assumes independence between predictor variables conditional on the response, and a Gaussian distribution of numeric predictors with mean and standard deviation computed from the training dataset. When building a naive Bayes classifier, every row in the training dataset that contains at least one NA will be skipped completely. If the test dataset has missing values, then those predictors are omitted in the probability calculation during prediction.
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris_nb = H2ONaiveBayesEstimator(balance_classes=False, ... nfolds=3, ... seed=1234) >>> iris_nb.train(x=list(range(4)), ... y=4, ... training_frame=iris) >>> iris_nb.mse()
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.] >>> cov_nb = H2ONaiveBayesEstimator(class_sampling_factors=sample_factors, ... seed=1234) >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> cov_nb.train(x=predictors, y=response, training_frame=covtype) >>> cov_nb.logloss()
-
property
compute_metrics
¶ Compute metrics on training data
Type:
bool
, defaults toTrue
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor() >>> prostate['RACE'] = prostate['RACE'].asfactor() >>> prostate['DCAPS'] = prostate['DCAPS'].asfactor() >>> prostate['DPROS'] = prostate['DPROS'].asfactor() >>> response_col = 'CAPSULE' >>> prostate_nb = H2ONaiveBayesEstimator(laplace=0, ... compute_metrics=False) >>> prostate_nb.train(x=list(range(3,9)), ... y=response_col, ... training_frame=prostate) >>> prostate_nb.show()
-
property
eps_prob
¶ Cutoff below which probability is replaced with min_prob
Type:
float
, defaults to0.0
.- Examples
>>> import random >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> problem = random.sample(["binomial","multinomial"],1) >>> predictors = ["displacement","power","weight","acceleration","year"] >>> if problem == "binomial": ... response_col = "economy_20mpg" ... else: ... response_col = "cylinders" >>> cars[response_col] = cars[response_col].asfactor() >>> cars_nb = H2ONaiveBayesEstimator(min_prob=0.1, ... eps_prob=0.5, ... seed=1234) >>> cars_nb.train(x=predictors, y=response_col, training_frame=cars) >>> cars_nb.mse()
-
property
eps_sdev
¶ Cutoff below which standard deviation is replaced with min_sdev
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> problem = random.sample(["binomial","multinomial"],1) >>> predictors = ["displacement","power","weight","acceleration","year"] >>> if problem == "binomial": ... response_col = "economy_20mpg" ... else: ... response_col = "cylinders" >>> cars[response_col] = cars[response_col].asfactor() >>> cars_nb = H2ONaiveBayesEstimator(min_sdev=0.1, ... eps_sdev=0.5, ... seed=1234) >>> cars_nb.train(x=predictors, y=response_col, training_frame=cars) >>> cars_nb.mse()
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex") >>> predictors = ["DayofMonth", "DayOfWeek"] >>> response = "IsDepDelayed" >>> checkpoints_dir = tempfile.mkdtemp() >>> air_nb = H2ONaiveBayesEstimator(export_checkpoints_dir=checkpoints_dir) >>> air_nb.train(x=predictors, y=response, training_frame=airlines) >>> len(listdir(checkpoints_dir))
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> cars_nb = H2ONaiveBayesEstimator(fold_assignment="Random", ... nfolds=5, ... seed=1234) >>> response = "economy_20mpg" >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> cars_nb.train(x=predictors, y=response, training_frame=cars) >>> cars_nb.auc()
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234) >>> fold_numbers.set_names(["fold_numbers"]) >>> cars = cars.cbind(fold_numbers) >>> cars_nb = H2ONaiveBayesEstimator(seed=1234) >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=cars, ... fold_column="fold_numbers") >>> cars_nb.auc()
-
property
gainslift_bins
¶ Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv") >>> model = H2ONaiveBayesEstimator(gainslift_bins=20) >>> model.train(x=["Origin", "Distance"], ... y="IsDepDelayed", ... training_frame=airlines) >>> model.gains_lift()
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_nb = H2ONaiveBayesEstimator(seed=1234, ... ignore_const_cols=True) >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_nb.auc()
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_nb = H2ONaiveBayesEstimator(keep_cross_validation_fold_assignment=True, ... nfolds=5, ... seed=1234) >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_nb.cross_validation_fold_assignment()
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_nb = H2ONaiveBayesEstimator(keep_cross_validation_models=True, ... nfolds=5, ... seed=1234) >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_nb.cross_validation_models()
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_nb = H2ONaiveBayesEstimator(keep_cross_validation_predictions=True, ... nfolds=5, ... seed=1234) >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_nb.cross_validation_predictions()
-
property
laplace
¶ Laplace smoothing parameter
Type:
float
, defaults to0.0
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor() >>> prostate['RACE'] = prostate['RACE'].asfactor() >>> prostate['DCAPS'] = prostate['DCAPS'].asfactor() >>> prostate['DPROS'] = prostate['DPROS'].asfactor() >>> prostate_nb = H2ONaiveBayesEstimator(laplace=1) >>> prostate_nb.train(x=list(range(3,9)), ... y=response_col, ... training_frame=prostate) >>> prostate_nb.mse()
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> max = .85 >>> cov_nb = H2ONaiveBayesEstimator(max_after_balance_size=max, ... seed=1234) >>> cov_nb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_nb.logloss()
-
property
max_confusion_matrix_size
¶ [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
Type:
int
, defaults to20
.
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_nb = H2ONaiveBayesEstimator(max_runtime_secs=10, ... seed=1234) >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_nb.auc()
-
property
min_prob
¶ Min. probability to use for observations with not enough data
Type:
float
, defaults to0.001
.- Examples
>>> import random >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> problem = random.sample(["binomial","multinomial"],1) >>> predictors = ["displacement","power","weight","acceleration","year"] >>> if problem == "binomial": ... response_col = "economy_20mpg" ... else: ... response_col = "cylinders" >>> cars[response_col] = cars[response_col].asfactor() >>> cars_nb = H2ONaiveBayesEstimator(min_prob=0.1, ... eps_prob=0.5, ... seed=1234) >>> cars_nb.train(x=predictors, ... y=response_col, ... training_frame=cars) >>> cars_nb.show()
-
property
min_sdev
¶ Min. standard deviation to use for observations with not enough data
Type:
float
, defaults to0.001
.- Examples
>>> import random >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> problem = random.sample(["binomial","multinomial"],1) >>> predictors = ["displacement","power","weight","acceleration","year"] >>> if problem == "binomial": ... response_col = "economy_20mpg" ... else: ... response_col = "cylinders" >>> cars[response_col] = cars[response_col].asfactor() >>> cars_nb = H2ONaiveBayesEstimator(min_sdev=0.1, ... eps_sdev=0.5, ... seed=1234) >>> cars_nb.train(x=predictors, ... y=response_col, ... training_frame=cars) >>> cars_nb.show()
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars_nb = H2ONaiveBayesEstimator(nfolds=5, ... seed=1234) >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_nb.auc()
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_nb = H2ONaiveBayesEstimator(score_each_iteration=True, ... seed=1234) >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_nb.auc()
-
property
seed
¶ Seed for pseudo random number generator (only used for cross-validation and fold_assignment=”Random” or “AUTO”)
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> nb_w_seed = H2ONaiveBayesEstimator(seed=1234) >>> nb_w_seed.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> nb_wo_seed = H2ONaiveBayesEstimator() >>> nb_wo_seed.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> nb_w_seed.auc() >>> nb_wo_seed.auc()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_nb = H2ONaiveBayesEstimator() >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_nb.auc()
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_nb = H2ONaiveBayesEstimator() >>> cars_nb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_nb.auc()
-
property
H2OSupportVectorMachineEstimator
¶
-
class
h2o.estimators.psvm.
H2OSupportVectorMachineEstimator
(model_id=None, training_frame=None, validation_frame=None, response_column=None, ignored_columns=None, ignore_const_cols=True, hyper_param=1.0, kernel_type='gaussian', gamma=-1.0, rank_ratio=-1.0, positive_weight=1.0, negative_weight=1.0, disable_training_metrics=True, sv_threshold=0.0001, fact_threshold=1e-05, feasible_threshold=0.001, surrogate_gap_threshold=0.001, mu_factor=10.0, max_iterations=200, seed=-1)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
PSVM
-
property
disable_training_metrics
¶ Disable calculating training metrics (expensive on large datasets)
Type:
bool
, defaults toTrue
.- Examples
>>> from h2o.estimators import H2OSupportVectorMachineEstimator >>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.01, ... rank_ratio=0.1, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
fact_threshold
¶ Convergence threshold of the Incomplete Cholesky Factorization (ICF)
Type:
float
, defaults to1e-05
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(disable_training_metrics=False, ... fact_threshold=1e-7) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
feasible_threshold
¶ Convergence threshold for primal-dual residuals in the IPM iteration
Type:
float
, defaults to0.001
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(disable_training_metrics=False, ... fact_threshold=1e-7) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
gamma
¶ Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features)
Type:
float
, defaults to-1.0
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.01, ... rank_ratio=0.1, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
hyper_param
¶ Penalty parameter C of the error term
Type:
float
, defaults to1.0
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.01, ... rank_ratio=0.1, ... hyper_param=0.01, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.01, ... rank_ratio=0.1, ... ignore_const_cols=False, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
kernel_type
¶ Type of used kernel
Type:
Literal["gaussian"]
, defaults to"gaussian"
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.1, ... rank_ratio=0.1, ... hyper_param=0.01, ... kernel_type="gaussian", ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
max_iterations
¶ Maximum number of iteration of the algorithm
Type:
int
, defaults to200
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.1, ... rank_ratio=0.1, ... hyper_param=0.01, ... max_iterations=20, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
mu_factor
¶ Increasing factor mu
Type:
float
, defaults to10.0
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.1, ... mu_factor=100.5, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
negative_weight
¶ Weight of positive (-1) class of observations
Type:
float
, defaults to1.0
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.1, ... rank_ratio=0.1, ... negative_weight=10, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
positive_weight
¶ Weight of positive (+1) class of observations
Type:
float
, defaults to1.0
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.1, ... rank_ratio=0.1, ... positive_weight=0.1, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
rank_ratio
¶ Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use sqrt(#rows)).
Type:
float
, defaults to-1.0
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.01, ... rank_ratio=0.1, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.1, ... rank_ratio=0.1, ... seed=1234, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.model_performance
-
property
surrogate_gap_threshold
¶ Feasibility criterion of the surrogate duality gap (eta)
Type:
float
, defaults to0.001
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.01, ... rank_ratio=0.1, ... surrogate_gap_threshold=0.1, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
sv_threshold
¶ Threshold for accepting a candidate observation into the set of support vectors
Type:
float
, defaults to0.0001
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> svm = H2OSupportVectorMachineEstimator(gamma=0.01, ... rank_ratio=0.1, ... sv_threshold=0.01, ... disable_training_metrics=False) >>> svm.train(y="C1", training_frame=splice) >>> svm.mse()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> train, valid = splice.split_frame(ratios=[0.8]) >>> svm = H2OSupportVectorMachineEstimator(disable_training_metrics=False) >>> svm.train(y="C1", training_frame=train) >>> svm.mse()
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") >>> train, valid = splice.split_frame(ratios=[0.8]) >>> svm = H2OSupportVectorMachineEstimator(disable_training_metrics=False) >>> svm.train(y="C1", training_frame=train, validation_frame=valid) >>> svm.mse()
-
property
H2ORandomForestEstimator
¶
-
class
h2o.estimators.random_forest.
H2ORandomForestEstimator
(model_id=None, training_frame=None, validation_frame=None, nfolds=0, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, score_each_iteration=False, score_tree_interval=0, fold_assignment='auto', fold_column=None, response_column=None, ignored_columns=None, ignore_const_cols=True, weights_column=None, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_confusion_matrix_size=20, ntrees=50, max_depth=20, min_rows=1.0, nbins=20, nbins_top_level=1024, nbins_cats=1024, r2_stopping=None, stopping_rounds=0, stopping_metric='auto', stopping_tolerance=0.001, max_runtime_secs=0.0, seed=-1, build_tree_one_node=False, mtries=-1, sample_rate=0.632, sample_rate_per_class=None, binomial_double_trees=False, checkpoint=None, col_sample_rate_change_per_level=1.0, col_sample_rate_per_tree=1.0, min_split_improvement=1e-05, histogram_type='auto', categorical_encoding='auto', calibrate_model=False, calibration_frame=None, distribution='auto', custom_metric_func=None, export_checkpoints_dir=None, check_constant_response=True, gainslift_bins=-1, auc_type='auto')[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Distributed Random Forest
Builds a Distributed Random Forest (DRF) on a parsed dataset, for regression or classification.
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
balance_classes
¶ Balance training data class counts via over/under-sampling (for imbalanced data).
Type:
bool
, defaults toFalse
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cov_drf = H2ORandomForestEstimator(balance_classes=True, ... seed=1234) >>> cov_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('logloss', cov_drf.logloss(valid=True))
-
property
binomial_double_trees
¶ For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(binomial_double_trees=False, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('without binomial_double_trees:', ... cars_drf.auc(valid=True)) >>> cars_drf_2 = H2ORandomForestEstimator(binomial_double_trees=True, ... seed=1234) >>> cars_drf_2.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('with binomial_double_trees:', cars_drf_2.auc(valid=True))
-
property
build_tree_one_node
¶ Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(build_tree_one_node=True, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_drf.auc(valid=True)
-
property
calibrate_model
¶ Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.
Type:
bool
, defaults toFalse
.- Examples
>>> ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv") >>> ecology['Angaus'] = ecology['Angaus'].asfactor() >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> response = 'Angaus' >>> predictors = ecology.columns[3:13] >>> train, calib = ecology.split_frame(seed=12354) >>> w = h2o.create_frame(binary_fraction=1, ... binary_ones_fraction=0.5, ... missing_fraction=0, ... rows=744, cols=1) >>> w.set_names(["weight"]) >>> train = train.cbind(w) >>> ecology_drf = H2ORandomForestEstimator(ntrees=10, ... max_depth=5, ... min_rows=10, ... distribution="multinomial", ... weights_column="weight", ... calibrate_model=True, ... calibration_frame=calib) >>> ecology_drf.train(x=predictors, ... y="Angaus", ... training_frame=train) >>> predicted = ecology_drf.predict(calib)
-
property
calibration_frame
¶ Calibration frame for Platt Scaling
Type:
Union[None, str, H2OFrame]
.- Examples
>>> ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv") >>> ecology['Angaus'] = ecology['Angaus'].asfactor() >>> response = 'Angaus' >>> predictors = ecology.columns[3:13] >>> train, calib = ecology.split_frame(seed = 12354) >>> w = h2o.create_frame(binary_fraction=1, ... binary_ones_fraction=0.5, ... missing_fraction=0, ... rows=744, cols=1) >>> w.set_names(["weight"]) >>> train = train.cbind(w) >>> ecology_drf = H2ORandomForestEstimator(ntrees=10, ... max_depth=5, ... min_rows=10, ... distribution="multinomial", ... calibrate_model=True, ... calibration_frame=calib) >>> ecology_drf.train(x=predictors, ... y="Angaus, ... training_frame=train, ... weights_column="weight") >>> predicted = ecology_drf.predict(train)
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> encoding = "one_hot_explicit" >>> airlines_drf = H2ORandomForestEstimator(categorical_encoding=encoding, ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_drf.auc(valid=True)
-
property
check_constant_response
¶ Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not.
Type:
bool
, defaults toTrue
.- Examples
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv") >>> train["constantCol"] = 1 >>> my_drf = H2ORandomForestEstimator(check_constant_response=False) >>> my_drf.train(x=list(range(1,5)), ... y="constantCol", ... training_frame=train)
-
property
checkpoint
¶ Model checkpoint to resume training with.
Type:
Union[None, str, H2OEstimator]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_drf = H2ORandomForestEstimator(ntrees=1, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(cars_drf.auc(valid=True))
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Type:
List[float]
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> print(covtype[54].table()) >>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.] >>> cov_drf = H2ORandomForestEstimator(balance_classes=True, ... class_sampling_factors=sample_factors, ... seed=1234) >>> cov_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('logloss', cov_drf.logloss(valid=True))
-
property
col_sample_rate_change_per_level
¶ Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_drf = H2ORandomForestEstimator(col_sample_rate_change_per_level=.9, ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_drf.auc(valid=True))
-
property
col_sample_rate_per_tree
¶ Column sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_drf = H2ORandomForestEstimator(col_sample_rate_per_tree=.7, ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_drf.auc(valid=True))
-
property
custom_metric_func
¶ Reference to custom evaluation function, format: language:keyName=funcName
Type:
str
.
-
property
distribution
¶ Distribution function
Type:
Literal["auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(distribution="poisson", ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_drf.mse(valid=True)
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> from h2o.grid.grid_search import H2OGridSearch >>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex") >>> predictors = ["DayofMonth", "DayOfWeek"] >>> response = "IsDepDelayed" >>> hyper_parameters = {'ntrees': [5,10]} >>> search_crit = {'strategy': "RandomDiscrete", ... 'max_models': 5, ... 'seed': 1234, ... 'stopping_rounds': 3, ... 'stopping_metric': "AUTO", ... 'stopping_tolerance': 1e-2} >>> checkpoints_dir = tempfile.mkdtemp() >>> air_grid = H2OGridSearch(H2ORandomForestEstimator, ... hyper_params=hyper_parameters, ... search_criteria=search_crit) >>> air_grid.train(x=predictors, ... y=response, ... training_frame=airlines, ... distribution="bernoulli", ... max_depth=3, ... export_checkpoints_dir=checkpoints_dir) >>> num_files = len(listdir(checkpoints_dir)) >>> num_files
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> assignment_type = "Random" >>> cars_drf = H2ORandomForestEstimator(fold_assignment=assignment_type, ... nfolds=5, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_drf.auc(xval=True)
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234) >>> fold_numbers.set_names(["fold_numbers"]) >>> cars = cars.cbind(fold_numbers) >>> print(cars['fold_numbers']) >>> cars_drf = H2ORandomForestEstimator(seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=cars, ... fold_column="fold_numbers") >>> cars_drf.auc(xval=True)
-
property
gainslift_bins
¶ Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv") >>> model = H2ORandomForestEstimator(ntrees=1, gainslift_bins=20) >>> model.train(x=["Origin", "Distance"], ... y="IsDepDelayed", ... training_frame=airlines) >>> model.gains_lift()
-
property
histogram_type
¶ What type of histogram to use for finding optimal split points
Type:
Literal["auto", "uniform_adaptive", "random", "quantiles_global", "round_robin", "uniform_robust"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_drf = H2ORandomForestEstimator(histogram_type="UniformAdaptive", ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_drf.auc(valid=True))
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(seed=1234, ... ignore_const_cols=True) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_drf.auc(valid=True)
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(keep_cross_validation_fold_assignment=True, ... nfolds=5, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_drf.cross_validation_fold_assignment()
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(keep_cross_validation_models=True, ... nfolds=5, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_drf.auc()
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(keep_cross_validation_predictions=True, ... nfolds=5, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train) >>> cars_drf.cross_validation_predictions()
-
property
max_after_balance_size
¶ Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
Type:
float
, defaults to5.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> print(covtype[54].table()) >>> max = .85 >>> cov_drf = H2ORandomForestEstimator(balance_classes=True, ... max_after_balance_size=max, ... seed=1234) >>> cov_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('logloss', cov_drf.logloss(valid=True))
-
property
max_confusion_matrix_size
¶ [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
Type:
int
, defaults to20
.
-
property
max_depth
¶ Maximum tree depth (0 for unlimited).
Type:
int
, defaults to20
.- Examples
>>> df = h2o.import_file(path = "http://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> response = "survived" >>> df[response] = df[response].asfactor() >>> predictors = df.columns >>> del predictors[1:3] >>> train, valid, test = df.split_frame(ratios=[0.6,0.2], ... seed=1234, ... destination_frames= ... ['train.hex','valid.hex','test.hex']) >>> drf = H2ORandomForestEstimator() >>> drf.train(x=predictors, ... y=response, ... training_frame=train) >>> perf = drf.model_performance(valid) >>> print perf.auc()
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(max_runtime_secs=10, ... ntrees=10000, ... max_depth=10, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_drf.auc(valid = True)
-
property
min_rows
¶ Fewest allowed (weighted) observations in a leaf.
Type:
float
, defaults to1.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(min_rows=16, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(cars_drf.auc(valid=True))
-
property
min_split_improvement
¶ Minimum relative improvement in squared error reduction for a split to happen
Type:
float
, defaults to1e-05
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(min_split_improvement=1e-3, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(cars_drf.auc(valid=True))
-
property
mtries
¶ Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors
Type:
int
, defaults to-1
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], seed=1234) >>> cov_drf = H2ORandomForestEstimator(mtries=30, seed=1234) >>> cov_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('logloss', cov_drf.logloss(valid=True))
-
property
nbins
¶ For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
Type:
int
, defaults to20
.- Examples
>>> eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv") >>> eeg['eyeDetection'] = eeg['eyeDetection'].asfactor() >>> predictors = eeg.columns[:-1] >>> response = 'eyeDetection' >>> train, valid = eeg.split_frame(ratios=[.8], seed=1234) >>> bin_num = [16, 32, 64, 128, 256, 512] >>> label = ["16", "32", "64", "128", "256", "512"] >>> for key, num in enumerate(bin_num): # Insert integer for 'num' and 'key' >>> eeg_drf = H2ORandomForestEstimator(nbins=num, seed=1234) >>> eeg_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(label[key], 'training score', ... eeg_drf.auc(train=True)) >>> print(label[key], 'validation score', ... eeg_drf.auc(train=True))
-
property
nbins_cats
¶ For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.
Type:
int
, defaults to1024
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> bin_num = [8, 16, 32, 64, 128, 256, ... 512, 1024, 2048, 4096] >>> label = ["8", "16", "32", "64", "128", ... "256", "512", "1024", "2048", "4096"] >>> for key, num in enumerate(bin_num): # Insert integer for 'num' and 'key' >>> airlines_drf = H2ORandomForestEstimator(nbins_cats=num, ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(label[key], 'training score', ... airlines_gbm.auc(train=True)) >>> print(label[key], 'validation score', ... airlines_gbm.auc(valid=True))
-
property
nbins_top_level
¶ For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level
Type:
int
, defaults to1024
.- Examples
>>> eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv") >>> eeg['eyeDetection'] = eeg['eyeDetection'].asfactor() >>> predictors = eeg.columns[:-1] >>> response = 'eyeDetection' >>> train, valid = eeg.split_frame(ratios=[.8], ... seed=1234) >>> bin_num = [32, 64, 128, 256, 512, ... 1024, 2048, 4096] >>> label = ["32", "64", "128", "256", ... "512", "1024", "2048", "4096"] >>> for key, num in enumerate(bin_num): # Insert integer for 'num' and 'key' >>> eeg_drf = H2ORandomForestEstimator(nbins_top_level=32, ... seed=1234) >>> eeg_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(label[key], 'training score', ... eeg_gbm.auc(train=True)) >>> print(label[key], 'validation score', ... eeg_gbm.auc(valid=True))
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> folds = 5 >>> cars_drf = H2ORandomForestEstimator(nfolds=folds, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=cars) >>> cars_drf.auc(xval=True)
-
property
ntrees
¶ Number of trees.
Type:
int
, defaults to50
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> tree_num = [20, 50, 80, 110, ... 140, 170, 200] >>> label = ["20", "50", "80", "110", ... "140", "170", "200"] >>> for key, num in enumerate(tree_num): # Input an integer for 'num' and 'key' >>> titanic_drf = H2ORandomForestEstimator(ntrees=num, ... seed=1234) >>> titanic_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(label[key], 'training score', ... titanic_drf.auc(train=True)) >>> print(label[key], 'validation score', ... titanic_drf.auc(valid=True))
-
property
offset_column
¶ [Deprecated] The property was removed and will be ignored.
-
property
r2_stopping
¶ r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this
Type:
float
, defaults to∞
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
sample_rate
¶ Row sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to0.632
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_drf = H2ORandomForestEstimator(sample_rate=.7, ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_drf.auc(valid=True))
-
property
sample_rate_per_class
¶ A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree
Type:
List[float]
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], ... seed=1234) >>> print(train[response].table()) >>> rate_per_class_list = [1, .4, 1, 1, 1, 1, 1] >>> cov_drf = H2ORandomForestEstimator(sample_rate_per_class=rate_per_class_list, ... seed=1234) >>> cov_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('logloss', cov_drf.logloss(valid=True))
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(score_each_iteration=True, ... ntrees=55, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame = valid) >>> cars_drf.scoring_history()
-
property
score_tree_interval
¶ Score the model after every so many trees. Disabled if set to 0.
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_drf = H2ORandomForestEstimator(score_tree_interval=5, ... seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_drf.scoring_history()
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> drf_w_seed_1 = H2ORandomForestEstimator(seed=1234) >>> drf_w_seed_1.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('auc for the 1st model build with a seed:', ... drf_w_seed_1.auc(valid=True))
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_drf = H2ORandomForestEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_drf.auc(valid=True)
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_drf = H2ORandomForestEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_drf.auc(valid=True)
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.001
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_drf = H2ORandomForestEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_drf.auc(valid=True)
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_drf = H2ORandomForestEstimator(seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_drf.auc(valid=True)
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_drf = H2ORandomForestEstimator(seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_drf.auc(valid=True)
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","acceleration","year"] >>> response = "economy_20mpg" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_drf = H2ORandomForestEstimator(seed=1234) >>> cars_drf.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid, ... weights_column="weight") >>> cars_drf.auc(valid=True)
-
property
H2ORuleFitEstimator
¶
-
class
h2o.estimators.rulefit.
H2ORuleFitEstimator
(model_id=None, training_frame=None, validation_frame=None, seed=-1, response_column=None, ignored_columns=None, algorithm='auto', min_rule_length=3, max_rule_length=3, max_num_rules=-1, model_type='rules_and_linear', weights_column=None, distribution='auto', rule_generation_ntrees=50, auc_type='auto', remove_duplicates=True, lambda_=None, max_categorical_levels=10)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
RuleFit
Builds a RuleFit on a parsed dataset, for regression or classification.
-
property
Lambda
¶ [Deprecated] Use
lambda_
instead
-
property
algorithm
¶ The algorithm to use to generate rules.
Type:
Literal["auto", "drf", "gbm"]
, defaults to"auto"
.
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
distribution
¶ Distribution function
Type:
Literal["auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"]
, defaults to"auto"
.
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
lambda_
¶ Lambda for LASSO regressor.
Type:
List[float]
.
-
property
max_categorical_levels
¶ For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited.
Type:
int
, defaults to10
.
-
property
max_num_rules
¶ The maximum number of rules to return. defaults to -1 which means the number of rules is selected by diminishing returns in model deviance.
Type:
int
, defaults to-1
.
-
property
max_rule_length
¶ Maximum length of rules. Defaults to 3.
Type:
int
, defaults to3
.
-
property
min_rule_length
¶ Minimum length of rules. Defaults to 3.
Type:
int
, defaults to3
.
-
property
model_type
¶ Specifies type of base learners in the ensemble.
Type:
Literal["rules_and_linear", "rules", "linear"]
, defaults to"rules_and_linear"
.
-
predict_rules
(frame, rule_ids)[source]¶ Evaluates validity of the given rules on the given data.
- Parameters
frame – H2OFrame on which rule validity is to be evaluated
rule_ids – string array of rule ids to be evaluated against the frame
- Returns
H2OFrame with a column per each input ruleId, representing a flag whether given rule is applied to the observation or not.
-
property
remove_duplicates
¶ Whether to remove rules which are identical to an earlier rule. Defaults to true.
Type:
bool
, defaults toTrue
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
rule_generation_ntrees
¶ Specifies the number of trees to build in the tree model. Defaults to 50.
Type:
int
, defaults to50
.
-
property
seed
¶ Seed for pseudo random number generator (if applicable).
Type:
int
, defaults to-1
.
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.
-
property
H2OStackedEnsembleEstimator
¶
-
class
h2o.estimators.stackedensemble.
H2OStackedEnsembleEstimator
(model_id=None, training_frame=None, response_column=None, validation_frame=None, blending_frame=None, base_models=[], metalearner_algorithm='auto', metalearner_nfolds=0, metalearner_fold_assignment=None, metalearner_fold_column=None, metalearner_params=None, metalearner_transform='none', max_runtime_secs=0.0, weights_column=None, offset_column=None, seed=-1, score_training_samples=10000, keep_levelone_frame=False, export_checkpoints_dir=None, auc_type='auto')[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Stacked Ensemble
Builds a stacked ensemble (aka “super learner”) machine learning method that uses two or more H2O learning algorithms to improve predictive performance. It is a loss-based supervised learning method that finds the optimal combination of a collection of prediction algorithms.This method supports regression and binary classification.
- Examples
>>> import h2o >>> h2o.init() >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> col_types = ["numeric", "numeric", "numeric", "enum", ... "enum", "numeric", "numeric", "numeric", "numeric"] >>> data = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv", col_types=col_types) >>> train, test = data.split_frame(ratios=[.8], seed=1) >>> x = ["CAPSULE","GLEASON","RACE","DPROS","DCAPS","PSA","VOL"] >>> y = "AGE" >>> nfolds = 5 >>> gbm = H2OGradientBoostingEstimator(nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True) >>> gbm.train(x=x, y=y, training_frame=train) >>> rf = H2ORandomForestEstimator(nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True) >>> rf.train(x=x, y=y, training_frame=train) >>> stack = H2OStackedEnsembleEstimator(model_id="ensemble", ... training_frame=train, ... validation_frame=test, ... base_models=[gbm.model_id, rf.model_id]) >>> stack.train(x=x, y=y, training_frame=train, validation_frame=test) >>> stack.model_performance()
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
property
base_models
¶ List of models or grids (or their ids) to ensemble/stack together. Grids are expanded to individual models. If not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be identical across models.
Type:
List[str]
, defaults to[]
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> col_types = ["numeric", "numeric", "numeric", "enum", ... "enum", "numeric", "numeric", "numeric", "numeric"] >>> data = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv", col_types=col_types) >>> train, test = data.split_frame(ratios=[.8], seed=1) >>> x = ["CAPSULE","GLEASON","RACE","DPROS","DCAPS","PSA","VOL"] >>> y = "AGE" >>> nfolds = 5 >>> gbm = H2OGradientBoostingEstimator(nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True) >>> gbm.train(x=x, y=y, training_frame=train) >>> rf = H2ORandomForestEstimator(nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True) >>> rf.train(x=x, y=y, training_frame=train) >>> stack = H2OStackedEnsembleEstimator(model_id="ensemble", ... training_frame=train, ... validation_frame=test, ... base_models=[gbm.model_id, rf.model_id]) >>> stack.train(x=x, y=y, training_frame=train, validation_frame=test) >>> stack.model_performance()
-
property
blending_frame
¶ Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided)
Type:
Union[None, str, H2OFrame]
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc()
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> import tempfile >>> from os import listdir >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> checkpoints_dir = tempfile.mkdtemp() >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... export_checkpoints_dir=checkpoints_dir) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> len(listdir(checkpoints_dir))
-
property
keep_levelone_frame
¶ Keep level one frame used for metalearner training.
Type:
bool
, defaults toFalse
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... keep_levelone_frame=True) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc()
-
levelone_frame_id
()[source]¶ Fetch the levelone_frame_id for an H2OStackedEnsembleEstimator.
- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... keep_levelone_frame=True) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.levelone_frame_id()
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.
-
metalearner
()[source]¶ Print the metalearner of an H2OStackedEnsembleEstimator.
- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... keep_levelone_frame=True) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.metalearner()
-
property
metalearner_algorithm
¶ Type of algorithm to use as the metalearner. Options include ‘AUTO’ (GLM with non negative weights; if validation_frame is present, a lambda search is performed), ‘deeplearning’ (Deep Learning with default parameters), ‘drf’ (Random Forest with default parameters), ‘gbm’ (GBM with default parameters), ‘glm’ (GLM with default parameters), ‘naivebayes’ (NaiveBayes with default parameters), or ‘xgboost’ (if available, XGBoost with default parameters).
Type:
Literal["auto", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost"]
, defaults to"auto"
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_algorithm="gbm") >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc()
-
property
metalearner_fold_assignment
¶ Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is currently set to Random). The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_fold_assignment="Random") >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc()
-
property
metalearner_fold_column
¶ Column with cross-validation fold index assignment per observation for cross-validation of the metalearner.
Type:
str
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_test_5k.csv") >>> fold_column = "fold_id" >>> train[fold_column] = train.kfold_column(n_folds=3, seed=1) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> x.remove(fold_column) >>> train[y] = train[y].asfactor() >>> test[y] = test[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... metalearner_fold_column=fold_column, ... metalearner_params=dict(keep_cross_validation_models=True)) >>> stack.train(x=x, y=y, training_frame=train) >>> stack.model_performance().auc()
-
property
metalearner_nfolds
¶ Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2).
Type:
int
, defaults to0
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_nfolds=3) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc()
-
property
metalearner_params
¶ Parameters for metalearner algorithm
Type:
dict
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> gbm_params = {"ntrees" : 100, "max_depth" : 6} >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... metalearner_algorithm="gbm", ... metalearner_params=gbm_params) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc()
-
property
metalearner_transform
¶ Transformation used for the level one frame.
Type:
Literal["none", "logit"]
, defaults to"none"
.
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
score_training_samples
¶ Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples, enter 0.
Type:
int
, defaults to10000
.
-
property
seed
¶ Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number)
Type:
int
, defaults to-1
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_fold_assignment="Random") >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc()
-
train
(x=None, y=None, training_frame=None, blending_frame=None, verbose=False, **kwargs)[source]¶ Train the H2O model.
- Parameters
x – A list of column names or indices indicating the predictor columns.
y – An index or a column name indicating the response column.
training_frame (H2OFrame) – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
offset_column – The name or index of the column in training_frame that holds the offsets.
fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
weights_column – The name or index of the column in training_frame that holds the per-row weights.
validation_frame – H2OFrame with validation data to be scored on while training.
max_runtime_secs (float) – Maximum allowed runtime in seconds for model training. Use 0 to disable.
verbose (bool) – Print scoring history to stdout. Defaults to False.
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, valid = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_fold_assignment="Random") >>> stack_blend.train(x=x, y=y, training_frame=train, validation_frame=valid) >>> stack_blend.model_performance(blend).auc()
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, valid = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_fold_assignment="Random") >>> stack_blend.train(x=x, y=y, training_frame=train, validation_frame=valid) >>> stack_blend.model_performance(blend).auc()
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.
H2OTargetEncoderEstimator
¶
-
class
h2o.estimators.targetencoder.
H2OTargetEncoderEstimator
(model_id=None, training_frame=None, fold_column=None, response_column=None, ignored_columns=None, columns_to_encode=None, keep_original_categorical_columns=True, blending=False, inflection_point=10.0, smoothing=20.0, data_leakage_handling='none', noise=0.01, seed=-1)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
TargetEncoder
-
property
blending
¶ If true, enables blending of posterior probabilities (computed for a given categorical value) with prior probabilities (computed on the entire set). This allows to mitigate the effect of categorical values with small cardinality. The blending effect can be tuned using the inflection_point and smoothing parameters.
Type:
bool
, defaults toFalse
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = ["home.dest", "cabin", "embarked"] >>> response = "survived" >>> titanic["survived"] = titanic["survived"].asfactor() >>> fold_col = "kfold_column" >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234) >>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35, ... smoothing=25, ... blending=True) >>> titanic_te.train(x=predictors, ... y=response, ... training_frame=titanic) >>> titanic_te
-
property
columns_to_encode
¶ List of categorical columns or groups of categorical columns to encode. When groups of columns are specified, each group is encoded as a single column (interactions are created internally).
Type:
List[List[str]]
.
-
property
data_leakage_handling
¶ Data leakage handling strategy used to generate the encoding. Supported options are: 1) “none” (default) - no holdout, using the entire training frame. 2) “leave_one_out” - current row’s response value is subtracted from the per-level frequencies pre-calculated on the entire training frame. 3) “k_fold” - encodings for a fold are generated based on out-of-fold data.
Type:
Literal["leave_one_out", "k_fold", "none"]
, defaults to"none"
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = ["home.dest", "cabin", "embarked"] >>> response = "survived" >>> titanic["survived"] = titanic["survived"].asfactor() >>> fold_col = "kfold_column" >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234) >>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35, ... smoothing=25, ... data_leakage_handling="k_fold", ... blending=True) >>> titanic_te.train(x=predictors, ... y=response, ... training_frame=titanic) >>> titanic_te
-
property
f
¶ [Deprecated] Use
smoothing
instead
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = ["home.dest", "cabin", "embarked"] >>> response = "survived" >>> titanic["survived"] = titanic["survived"].asfactor() >>> fold_col = "kfold_column" >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234) >>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35, ... smoothing=25, ... blending=True) >>> titanic_te.train(x=predictors, ... y=response, ... training_frame=titanic) >>> titanic_te
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
inflection_point
¶ Inflection point of the sigmoid used to blend probabilities (see blending parameter). For a given categorical value, if it appears less that inflection_point in a data sample, then the influence of the posterior probability will be smaller than the prior.
Type:
float
, defaults to10.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = ["home.dest", "cabin", "embarked"] >>> response = "survived" >>> titanic["survived"] = titanic["survived"].asfactor() >>> fold_col = "kfold_column" >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234) >>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35, ... smoothing=25, ... blending=True) >>> titanic_te.train(x=predictors, ... y=response, ... training_frame=titanic) >>> titanic_te
-
property
k
¶ [Deprecated] Use
inflection_point
instead
-
property
keep_original_categorical_columns
¶ If true, the original non-encoded categorical features will remain in the result frame.
Type:
bool
, defaults toTrue
.
-
property
noise
¶ The amount of noise to add to the encoded column. Use 0 to disable noise, and -1 (=AUTO) to let the algorithm determine a reasonable amount of noise.
Type:
float
, defaults to0.01
.
-
property
noise_level
¶ [Deprecated] Use
noise
instead
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
seed
¶ Seed used to generate the noise. By default, the seed is chosen randomly.
Type:
int
, defaults to-1
.
-
property
smoothing
¶ Smoothing factor corresponds to the inverse of the slope at the inflection point on the sigmoid used to blend probabilities (see blending parameter). If smoothing tends towards 0, then the sigmoid used for blending turns into a Heaviside step function.
Type:
float
, defaults to20.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = ["home.dest", "cabin", "embarked"] >>> response = "survived" >>> titanic["survived"] = titanic["survived"].asfactor() >>> fold_col = "kfold_column" >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234) >>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35, ... smoothing=25, ... blending=True) >>> titanic_te.train(x=predictors, ... y=response, ... training_frame=titanic) >>> titanic_te
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = ["home.dest", "cabin", "embarked"] >>> response = "survived" >>> titanic["survived"] = titanic["survived"].asfactor() >>> fold_col = "kfold_column" >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234) >>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35, ... smoothing=25, ... blending=True) >>> titanic_te.train(x=predictors, ... y=response, ... training_frame=titanic) >>> titanic_te
-
transform
(frame, blending=None, inflection_point=None, smoothing=None, noise=None, as_training=False, **kwargs)[source]¶ Apply transformation to te_columns based on the encoding maps generated during train() method call.
- Parameters
frame (H2OFrame) – the frame on which to apply the target encoding transformations.
blending (boolean) – If provided, this overrides the blending parameter on the model.
inflection_point (float) – If provided, this overrides the inflection_point parameter on the model.
smoothing (float) – If provided, this overrides the smoothing parameter on the model.
noise (float) – If provided, this overrides the amount of random noise added to the target encoding defined on the model, this helps prevent overfitting.
as_training (boolean) – Must be set to True when encoding the training frame. Defaults to False.
- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = ["home.dest", "cabin", "embarked"] >>> response = "survived" >>> titanic[response] = titanic[response].asfactor() >>> fold_col = "kfold_column" >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234) >>> titanic_te = H2OTargetEncoderEstimator(data_leakage_handling="leave_one_out", ... inflection_point=35, ... smoothing=25, ... blending=True, ... seed=1234) >>> titanic_te.train(x=predictors, ... y=response, ... training_frame=titanic) >>> transformed = titanic_te.transform(frame=titanic)
-
property
H2OUpliftRandomForestEstimator
¶
-
class
h2o.estimators.uplift_random_forest.
H2OUpliftRandomForestEstimator
(model_id=None, training_frame=None, validation_frame=None, score_each_iteration=False, score_tree_interval=0, response_column=None, ignored_columns=None, ignore_const_cols=True, ntrees=50, max_depth=20, min_rows=1.0, nbins=20, nbins_top_level=1024, nbins_cats=1024, max_runtime_secs=0.0, seed=-1, mtries=-2, sample_rate=0.632, sample_rate_per_class=None, col_sample_rate_change_per_level=1.0, col_sample_rate_per_tree=1.0, histogram_type='auto', categorical_encoding='auto', distribution='auto', check_constant_response=True, treatment_column='treatment', uplift_metric='auto', auuc_type='auto', auuc_nbins=-1)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Uplift Distributed Random Forest
-
property
auuc_nbins
¶ Number of bins to calculate Area Under Uplift Curve.
Type:
int
, defaults to-1
.
-
property
auuc_type
¶ Metric used to calculate Area Under Uplift Curve.
Type:
Literal["auto", "qini", "lift", "gain"]
, defaults to"auto"
.
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.
-
property
check_constant_response
¶ Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not.
Type:
bool
, defaults toTrue
.
-
property
col_sample_rate_change_per_level
¶ Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
Type:
float
, defaults to1.0
.
-
property
col_sample_rate_per_tree
¶ Column sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.
-
property
distribution
¶ Distribution function
Type:
Literal["auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"]
, defaults to"auto"
.
-
property
histogram_type
¶ What type of histogram to use for finding optimal split points
Type:
Literal["auto", "uniform_adaptive", "random", "quantiles_global", "round_robin", "uniform_robust"]
, defaults to"auto"
.
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
max_depth
¶ Maximum tree depth (0 for unlimited).
Type:
int
, defaults to20
.
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.
-
property
min_rows
¶ Fewest allowed (weighted) observations in a leaf.
Type:
float
, defaults to1.0
.
-
property
mtries
¶ Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors
Type:
int
, defaults to-2
.
-
property
nbins
¶ For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
Type:
int
, defaults to20
.
-
property
nbins_cats
¶ For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.
Type:
int
, defaults to1024
.
-
property
nbins_top_level
¶ For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level
Type:
int
, defaults to1024
.
-
property
ntrees
¶ Number of trees.
Type:
int
, defaults to50
.
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
sample_rate
¶ Row sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to0.632
.
-
property
sample_rate_per_class
¶ A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree
Type:
List[float]
.
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.
-
property
score_tree_interval
¶ Score the model after every so many trees. Disabled if set to 0.
Type:
int
, defaults to0
.
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
treatment_column
¶ Define the column which will be used for computing uplift gain to select best split for a tree. The column has to divide the dataset into treatment (value 1) and control (value 0) groups.
Type:
str
, defaults to"treatment"
.
-
property
uplift_metric
¶ Divergence metric used to find best split when building an uplift tree.
Type:
Literal["auto", "kl", "euclidean", "chi_squared"]
, defaults to"auto"
.
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
H2OXGBoostEstimator
¶
-
class
h2o.estimators.xgboost.
H2OXGBoostEstimator
(model_id=None, training_frame=None, validation_frame=None, nfolds=0, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, score_each_iteration=False, fold_assignment='auto', fold_column=None, response_column=None, ignored_columns=None, ignore_const_cols=True, offset_column=None, weights_column=None, stopping_rounds=0, stopping_metric='auto', stopping_tolerance=0.001, max_runtime_secs=0.0, seed=-1, distribution='auto', tweedie_power=1.5, categorical_encoding='auto', quiet_mode=True, checkpoint=None, export_checkpoints_dir=None, ntrees=50, max_depth=6, min_rows=1.0, min_child_weight=1.0, learn_rate=0.3, eta=0.3, sample_rate=1.0, subsample=1.0, col_sample_rate=1.0, colsample_bylevel=1.0, col_sample_rate_per_tree=1.0, colsample_bytree=1.0, colsample_bynode=1.0, max_abs_leafnode_pred=0.0, max_delta_step=0.0, monotone_constraints=None, interaction_constraints=None, score_tree_interval=0, min_split_improvement=0.0, gamma=0.0, nthread=-1, save_matrix_directory=None, build_tree_one_node=False, calibrate_model=False, calibration_frame=None, max_bins=256, max_leaves=0, sample_type='uniform', normalize_type='tree', rate_drop=0.0, one_drop=False, skip_drop=0.0, tree_method='auto', grow_policy='depthwise', booster='gbtree', reg_lambda=1.0, reg_alpha=0.0, dmatrix_type='auto', backend='auto', gpu_id=None, gainslift_bins=-1, auc_type='auto', scale_pos_weight=1.0)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
XGBoost
Builds an eXtreme Gradient Boosting model using the native XGBoost backend.
-
property
auc_type
¶ Set default multinomial AUC type.
Type:
Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
, defaults to"auto"
.
-
static
available
()[source]¶ Ask the H2O server whether a XGBoost model can be built (depends on availability of native backends). :return: True if a XGBoost model can be built, or False otherwise.
- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_xgb = H2OXGBoostEstimator(seed=1234) >>> boston_xgb.available()
-
property
backend
¶ Backend. By default (auto), a GPU is used if available.
Type:
Literal["auto", "gpu", "cpu"]
, defaults to"auto"
.- Examples
>>> pros = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> pros["CAPSULE"] = pros["CAPSULE"].asfactor() >>> pros_xgb = H2OXGBoostEstimator(tree_method="exact", ... seed=123, ... backend="cpu") >>> pros_xgb.train(y="CAPSULE", ... ignored_columns=["ID"], ... training_frame=pros) >>> pros_xgb.auc()
-
property
booster
¶ Booster type
Type:
Literal["gbtree", "gblinear", "dart"]
, defaults to"gbtree"
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(booster='dart', ... normalize_type="tree", ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
build_tree_one_node
¶ Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
Type:
bool
, defaults toFalse
.
-
property
calibrate_model
¶ Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.
Type:
bool
, defaults toFalse
.
-
property
calibration_frame
¶ Calibration frame for Platt Scaling
Type:
Union[None, str, H2OFrame]
.
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> encoding = "one_hot_explicit" >>> airlines_xgb = H2OXGBoostEstimator(categorical_encoding=encoding, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_xgb.auc(valid=True)
-
property
checkpoint
¶ Model checkpoint to resume training with.
Type:
Union[None, str, H2OEstimator]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() >>> predictors = ["displacement","power","weight","year","economy_20mpg"] >>> response = "acceleration" >>> from h2o.estimators import H2OXGBoostEstimator >>> cars_xgb = H2OXGBoostEstimator(seed=1234) >>> train, valid = cars.split_frame(ratios=[.8]) >>> cars_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_xgb.mse() >>> cars_xgb_continued = H2OXGBoostEstimator(checkpoint=cars_xgb.model_id, ... ntrees=51, ... seed=1234) >>> cars_xgb_continued.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_xgb_continued.mse()
-
property
col_sample_rate
¶ (same as colsample_bylevel) Column sample rate (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(col_sample_rate=.7, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
col_sample_rate_per_tree
¶ (same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(col_sample_rate_per_tree=.7, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
colsample_bylevel
¶ (same as col_sample_rate) Column sample rate (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(col_sample_rate=.7, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
colsample_bynode
¶ Column sample rate per tree node (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(colsample_bynode=.5, ... seed=1234) >>> airlines_xgb.train(x=predictors, y=response, ... training_frame=train, validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
colsample_bytree
¶ (same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(col_sample_rate_per_tree=.7, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
convert_H2OXGBoostParams_2_XGBoostParams
()[source]¶ In order to use convert_H2OXGBoostParams_2_XGBoostParams and convert_H2OFrame_2_DMatrix, you must import the following toolboxes: xgboost, pandas, numpy and scipy.sparse.
Given an H2OXGBoost model, this method will generate the corresponding parameters that should be used by native XGBoost in order to give exactly the same result, assuming that the same dataset (derived from h2oFrame) is used to train the native XGBoost model.
Follow the steps below to compare H2OXGBoost and native XGBoost:
Train the H2OXGBoost model with H2OFrame trainFile and generate a prediction:
h2oModelD = H2OXGBoostEstimator(**h2oParamsD) # parameters specified as a dict()
h2oModelD.train(x=myX, y=y, training_frame=trainFile) # train with H2OFrame trainFile
h2oPredict = h2oPredictD = h2oModelD.predict(trainFile)
Derive the DMatrix from H2OFrame:
nativeDMatrix = trainFile.convert_H2OFrame_2_DMatrix(myX, y, h2oModelD)
Derive the parameters for native XGBoost:
nativeParams = h2oModelD.convert_H2OXGBoostParams_2_XGBoostParams()
Train your native XGBoost model and generate a prediction:
nativeModel = xgb.train(params=nativeParams[0], dtrain=nativeDMatrix, num_boost_round=nativeParams[1])
nativePredict = nativeModel.predict(data=nativeDMatrix, ntree_limit=nativeParams[1]
Compare the predictions h2oPredict from H2OXGBoost, nativePredict from native XGBoost.
- Returns
nativeParams, num_boost_round
-
property
distribution
¶ Distribution function
Type:
Literal["auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"]
, defaults to"auto"
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> response = "cylinders" >>> train, valid = cars.split_frame(ratios=[.8], ... seed=1234) >>> cars_xgb = H2OXGBoostEstimator(distribution="poisson", ... seed=1234) >>> cars_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> cars_xgb.mse(valid=True)
-
property
dmatrix_type
¶ Type of DMatrix. For sparse, NAs and 0 are treated equally.
Type:
Literal["auto", "dense", "sparse"]
, defaults to"auto"
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_xgb = H2OXGBoostEstimator(dmatrix_type="auto", ... seed=1234) >>> boston_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_xgb.mse()
-
property
eta
¶ (same as learn_rate) Learning rate (from 0.0 to 1.0)
Type:
float
, defaults to0.3
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(ntrees=10000, ... learn_rate=0.01, ... stopping_rounds=5, ... stopping_metric="AUC", ... stopping_tolerance=1e-4, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from h2o.grid.grid_search import H2OGridSearch >>> from os import listdir >>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex") >>> predictors = ["DayofMonth", "DayOfWeek"] >>> response = "IsDepDelayed" >>> hyper_parameters = {'ntrees': [5,10]} >>> search_crit = {'strategy': "RandomDiscrete", ... 'max_models': 5, ... 'seed': 1234, ... 'stopping_rounds': 3, ... 'stopping_metric': "AUTO", ... 'stopping_tolerance': 1e-2} >>> checkpoints_dir = tempfile.mkdtemp() >>> air_grid = H2OGridSearch(H2OXGBoostEstimator, ... hyper_params=hyper_parameters, ... search_criteria=search_crit) >>> air_grid.train(x=predictors, ... y=response, ... training_frame=airlines, ... distribution="bernoulli", ... learn_rate=0.1, ... max_depth=3, ... export_checkpoints_dir=checkpoints_dir) >>> len(listdir(checkpoints_dir))
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> assignment_type = "Random" >>> titanic_xgb = H2OXGBoostEstimator(fold_assignment=assignment_type, ... nfolds=5, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=titanic) >>> titanic_xgb.auc(xval=True)
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> fold_numbers = titanic.kfold_column(n_folds=5, ... seed=1234) >>> fold_numbers.set_names(["fold_numbers"]) >>> titanic = titanic.cbind(fold_numbers) >>> print(titanic['fold_numbers']) >>> titanic_xgb = H2OXGBoostEstimator(seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=titanic, ... fold_column="fold_numbers") >>> titanic_xgb.auc(xval=True)
-
property
gainslift_bins
¶ Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv") >>> model = H2OXGBoostEstimator(ntrees=1, gainslift_bins=20) >>> model.train(x=["Origin", "Distance"], ... y="IsDepDelayed", ... training_frame=airlines) >>> model.gains_lift()
-
property
gamma
¶ (same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen
Type:
float
, defaults to0.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(min_split_improvement=1e-3, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
gpu_id
¶ Which GPU(s) to use.
Type:
List[int]
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_xgb = H2OXGBoostEstimator(gpu_id=0, ... seed=1234) >>> boston_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> boston_xgb.mse()
-
property
grow_policy
¶ Grow policy - depthwise is standard GBM, lossguide is LightGBM
Type:
Literal["depthwise", "lossguide"]
, defaults to"depthwise"
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> titanic["const_1"] = 6 >>> titanic["const_2"] = 7 >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(seed=1234, ... grow_policy="depthwise") >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_xgb.auc(valid=True)
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> titanic["const_1"] = 6 >>> titanic["const_2"] = 7 >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(seed=1234, ... ignore_const_cols=True) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_xgb.auc(valid=True)
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
interaction_constraints
¶ A set of allowed column interactions.
Type:
List[List[str]]
.
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(keep_cross_validation_fold_assignment=True, ... nfolds=5, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train) >>> titanic_xgb.cross_validation_fold_assignment()
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(keep_cross_validation_models=True, ... nfolds=5 , ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train) >>> titanic_xgb.cross_validation_models()
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(keep_cross_validation_predictions=True, ... nfolds=5, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train) >>> titanic_xgb.cross_validation_predictions()
-
property
learn_rate
¶ (same as eta) Learning rate (from 0.0 to 1.0)
Type:
float
, defaults to0.3
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(ntrees=10000, ... learn_rate=0.01, ... stopping_rounds=5, ... stopping_metric="AUC", ... stopping_tolerance=1e-4, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
max_abs_leafnode_pred
¶ (same as max_delta_step) Maximum absolute value of a leaf node prediction
Type:
float
, defaults to0.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], ... seed=1234) >>> cov_xgb = H2OXGBoostEstimator(max_abs_leafnode_pred=float(2), ... seed=1234) >>> cov_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(cov_xgb.logloss(valid=True))
-
property
max_bins
¶ For tree_method=hist only: maximum number of bins
Type:
int
, defaults to256
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], ... seed=1234) >>> cov_xgb = H2OXGBoostEstimator(max_bins=200, ... seed=1234) >>> cov_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(cov_xgb.logloss(valid=True))
-
property
max_delta_step
¶ (same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction
Type:
float
, defaults to0.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], ... seed=1234) >>> cov_xgb = H2OXGBoostEstimator(max_delta_step=float(2), ... seed=1234) >>> cov_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(cov_xgb.logloss(valid=True))
-
property
max_depth
¶ Maximum tree depth (0 for unlimited).
Type:
int
, defaults to6
.- Examples
>>> df = h2o.import_file(path = "http://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> response = "survived" >>> df[response] = df[response].asfactor() >>> predictors = df.columns >>> del predictors[1:3] >>> train, valid, test = df.split_frame(ratios=[0.6,0.2], ... seed=1234, ... destination_frames= ... ['train.hex', ... 'valid.hex', ... 'test.hex']) >>> xgb = H2OXGBoostEstimator() >>> xgb.train(x=predictors, ... y=response, ... training_frame=train) >>> perf = xgb.model_performance(valid) >>> print perf.auc()
-
property
max_leaves
¶ For tree_method=hist only: maximum number of leaves
Type:
int
, defaults to0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(max_leaves=0, seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> covtype[54] = covtype[54].asfactor() >>> predictors = covtype.columns[0:54] >>> response = 'C55' >>> train, valid = covtype.split_frame(ratios=[.8], ... seed=1234) >>> cov_xgb = H2OXGBoostEstimator(max_runtime_secs=10, ... ntrees=10000, ... max_depth=10, ... seed=1234) >>> cov_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(cov_xgb.logloss(valid=True))
-
property
min_child_weight
¶ (same as min_rows) Fewest allowed (weighted) observations in a leaf.
Type:
float
, defaults to1.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(min_child_weight=16, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
min_rows
¶ (same as min_child_weight) Fewest allowed (weighted) observations in a leaf.
Type:
float
, defaults to1.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(min_rows=16, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
min_split_improvement
¶ (same as gamma) Minimum relative improvement in squared error reduction for a split to happen
Type:
float
, defaults to0.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(min_split_improvement=0.55, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
monotone_constraints
¶ A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.
Type:
dict
.- Examples
>>> prostate_hex = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate_hex["CAPSULE"] = prostate_hex["CAPSULE"].asfactor() >>> response = "CAPSULE" >>> seed=42 >>> monotone_constraints={"AGE":1} >>> xgb_model = H2OXGBoostEstimator(seed=seed, ... monotone_constraints=monotone_constraints) >>> xgb_model.train(y=response, ... ignored_columns=["ID"], ... training_frame=prostate_hex) >>> xgb_model.scoring_history()
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> folds = 5 >>> titanic_xgb = H2OXGBoostEstimator(nfolds=folds, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=titanic) >>> titanic_xgb.auc(xval=True)
-
property
normalize_type
¶ For booster=dart only: normalize_type
Type:
Literal["tree", "forest"]
, defaults to"tree"
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(booster='dart', ... normalize_type="tree", ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
nthread
¶ Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads parameter). Defaults to maximum available
Type:
int
, defaults to-1
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> thread = 4 >>> titanic_xgb = H2OXGBoostEstimator(nthread=thread, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=titanic) >>> print(titanic_xgb.auc(train=True))
-
property
ntrees
¶ (same as n_estimators) Number of trees.
Type:
int
, defaults to50
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> tree_num = [20, 50, 80, 110, 140, 170, 200] >>> label = ["20", "50", "80", "110", ... "140", "170", "200"] >>> for key, num in enumerate(tree_num): # Input integer for 'num' and 'key' >>> titanic_xgb = H2OXGBoostEstimator(ntrees=num, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(label[key], 'training score', ... titanic_xgb.auc(train=True)) >>> print(label[key], 'validation score', ... titanic_xgb.auc(valid=True))
-
property
offset_column
¶ Offset column. This will be added to the combination of columns before applying the link function.
Type:
str
.
-
property
one_drop
¶ For booster=dart only: one_drop
Type:
bool
, defaults toFalse
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(booster='dart', ... one_drop=True, ... seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
quiet_mode
¶ Enable quiet mode
Type:
bool
, defaults toTrue
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(seed=1234, quiet_mode=True) >>> titanic_xgb.train(x=predictors ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_xgb.mse(valid=True)
-
property
rate_drop
¶ For booster=dart only: rate_drop (0..1)
Type:
float
, defaults to0.0
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(rate_drop=0.1, seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(titanic_xgb.auc(valid=True))
-
property
reg_alpha
¶ L1 regularization
Type:
float
, defaults to0.0
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> response = "medv" >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_xgb = H2OXGBoostEstimator(reg_alpha=.25) >>> boston_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(boston_xgb.mse(valid=True))
-
property
reg_lambda
¶ L2 regularization
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8]) >>> airlines_xgb = H2OXGBoostEstimator(reg_lambda=.0001, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
sample_rate
¶ (same as subsample) Row sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(sample_rate=.7, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
sample_type
¶ For booster=dart only: sample_type
Type:
Literal["uniform", "weighted"]
, defaults to"uniform"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(sample_type="weighted", ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
save_matrix_directory
¶ Directory where to save matrices passed to XGBoost library. Useful for debugging.
Type:
str
.
-
property
scale_pos_weight
¶ Controls the effect of observations with positive labels in relation to the observations with negative labels on gradient calculation. Useful for imbalanced problems.
Type:
float
, defaults to1.0
.
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(score_each_iteration=True, ... ntrees=55, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_xgb.scoring_history()
-
property
score_tree_interval
¶ Score the model after every so many trees. Disabled if set to 0.
Type:
int
, defaults to0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(score_tree_interval=5, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_xgb.scoring_history()
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> xgb_w_seed_1 = H2OXGBoostEstimator(col_sample_rate=.7, ... seed=1234) >>> xgb_w_seed_1.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> xgb_w_seed_2 = H2OXGBoostEstimator(col_sample_rate = .7, ... seed = 1234) >>> xgb_w_seed_2.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print('auc for the 1st model built with a seed:', ... xgb_w_seed_1.auc(valid=True)) >>> print('auc for the 2nd model built with a seed:', ... xgb_w_seed_2.auc(valid=True))
-
property
skip_drop
¶ For booster=dart only: skip_drop (0..1)
Type:
float
, defaults to0.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(skip_drop=0.5, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train) >>> airlines_xgb.auc(train=True)
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_xgb.auc(valid=True)
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_xgb.auc(valid=True)
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.001
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(stopping_metric="auc", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> airlines_xgb.auc(valid=True)
-
property
subsample
¶ (same as sample_rate) Row sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> airlines_xgb = H2OXGBoostEstimator(sample_rate=.7, ... seed=1234) >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_xgb.auc(valid=True)
-
property
tree_method
¶ Tree method
Type:
Literal["auto", "exact", "approx", "hist"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"] = airlines["Year"].asfactor() >>> airlines["Month"] = airlines["Month"].asfactor() >>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() >>> airlines["Cancelled"] = airlines["Cancelled"].asfactor() >>> airlines['FlightNum'] = airlines['FlightNum'].asfactor() >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> response = "IsDepDelayed" >>> train, valid= airlines.split_frame(ratios=[.8], ... seed=1234) >>> >>> airlines_xgb = H2OXGBoostEstimator(seed=1234, ... tree_method="approx") >>> airlines_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(airlines_xgb.auc(valid=True))
-
property
tweedie_power
¶ Tweedie power for Tweedie regression, must be between 1 and 2.
Type:
float
, defaults to1.5
.- Examples
>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> predictors = insurance.columns[0:4] >>> response = 'Claims' >>> insurance['Group'] = insurance['Group'].asfactor() >>> insurance['Age'] = insurance['Age'].asfactor() >>> train, valid = insurance.split_frame(ratios=[.8], ... seed=1234) >>> insurance_xgb = H2OXGBoostEstimator(distribution="tweedie", ... tweedie_power=1.2, ... seed=1234) >>> insurance_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(insurance_xgb.mse(valid=True))
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance['Group'] = insurance['Group'].asfactor() >>> insurance['Age'] = insurance['Age'].asfactor() >>> predictors = insurance.columns[0:4] >>> response = 'Claims' >>> train, valid = insurance.split_frame(ratios=[.8], ... seed=1234) >>> insurance_xgb = H2OXGBoostEstimator(seed=1234) >>> insurance_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> print(insurance_xgb.mse(valid=True))
-
property
weights_column
¶ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Type:
str
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic['survived'] = titanic['survived'].asfactor() >>> predictors = titanic.columns >>> del predictors[1:3] >>> response = 'survived' >>> train, valid = titanic.split_frame(ratios=[.8], ... seed=1234) >>> titanic_xgb = H2OXGBoostEstimator(seed=1234) >>> titanic_xgb.train(x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_xgb.auc(valid=True)
-
property
Unsupervised¶
H2OAggregatorEstimator
¶
-
class
h2o.estimators.aggregator.
H2OAggregatorEstimator
(model_id=None, training_frame=None, response_column=None, ignored_columns=None, ignore_const_cols=True, target_num_exemplars=5000, rel_tol_num_exemplars=0.5, transform='normalize', categorical_encoding='auto', save_mapping_frame=False, num_iteration_without_new_exemplar=500, export_checkpoints_dir=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Aggregator
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.- Examples
>>> df = h2o.create_frame(rows=10000, ... cols=10, ... categorical_fraction=0.6, ... integer_fraction=0, ... binary_fraction=0, ... real_range=100, ... integer_range=100, ... missing_fraction=0, ... factors=100, ... seed=1234) >>> params = {"target_num_exemplars": 1000, ... "rel_tol_num_exemplars": 0.5, ... "categorical_encoding": "eigen"} >>> agg = H2OAggregatorEstimator(**params) >>> agg.train(training_frame=df) >>> new_df = agg.aggregated_frame >>> new_df
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> checkpoints_dir = tempfile.mkdtemp() >>> model = H2OAggregatorEstimator(target_num_exemplars=500, ... rel_tol_num_exemplars=0.3, ... export_checkpoints_dir=checkpoints_dir) >>> model.train(training_frame=df) >>> new_df = model.aggregated_frame >>> new_df >>> len(listdir(checkpoints_dir))
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> params = {"ignore_const_cols": False, ... "target_num_exemplars": 500, ... "rel_tol_num_exemplars": 0.3, ... "transform": "standardize", ... "categorical_encoding": "eigen"} >>> model = H2OAggregatorEstimator(**params) >>> model.train(training_frame=df) >>> new_df = model.aggregated_frame >>> new_df
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
num_iteration_without_new_exemplar
¶ The number of iterations to run before aggregator exits if the number of exemplars collected didn’t change
Type:
int
, defaults to500
.- Examples
>>> df = h2o.create_frame(rows=10000, ... cols=10, ... categorical_fraction=0.6, ... integer_fraction=0, ... binary_fraction=0, ... real_range=100, ... integer_range=100, ... missing_fraction=0, ... factors=100, ... seed=1234) >>> params = {"target_num_exemplars": 1000, ... "rel_tol_num_exemplars": 0.5, ... "categorical_encoding": "eigen", ... "num_iteration_without_new_exemplar": 400} >>> agg = H2OAggregatorEstimator(**params) >>> agg.train(training_frame=df) >>> new_df = agg.aggregated_frame >>> new_df
-
property
rel_tol_num_exemplars
¶ Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents)
Type:
float
, defaults to0.5
.- Examples
>>> df = h2o.create_frame(rows=10000, ... cols=10, ... categorical_fraction=0.6, ... integer_fraction=0, ... binary_fraction=0, ... real_range=100, ... integer_range=100, ... missing_fraction=0, ... factors=100, ... seed=1234) >>> params = {"target_num_exemplars": 1000, ... "rel_tol_num_exemplars": 0.5, ... "categorical_encoding": "eigen", ... "num_iteration_without_new_exemplar": 400} >>> agg = H2OAggregatorEstimator(**params) >>> agg.train(training_frame=df) >>> new_df = agg.aggregated_frame >>> new_df
-
property
response_column
¶ Response variable column.
Type:
str
.
-
property
save_mapping_frame
¶ Whether to export the mapping of the aggregated frame
Type:
bool
, defaults toFalse
.- Examples
>>> df = h2o.create_frame(rows=10000, ... cols=10, ... categorical_fraction=0.6, ... integer_fraction=0, ... binary_fraction=0, ... real_range=100, ... integer_range=100, ... missing_fraction=0, ... factors=100, ... seed=1234) >>> params = {"target_num_exemplars": 1000, ... "rel_tol_num_exemplars": 0.5, ... "categorical_encoding": "eigen", ... "save_mapping_frame": True} >>> agg = H2OAggregatorEstimator(**params) >>> agg.train(training_frame=df) >>> mapping_frame = agg.mapping_frame >>> mapping_frame
-
property
target_num_exemplars
¶ Targeted number of exemplars
Type:
int
, defaults to5000
.- Examples
>>> df = h2o.create_frame(rows=10000, ... cols=10, ... categorical_fraction=0.6, ... integer_fraction=0, ... binary_fraction=0, ... real_range=100, ... integer_range=100, ... missing_fraction=0, ... factors=100, ... seed=1234) >>> params = {"target_num_exemplars": 1000, ... "rel_tol_num_exemplars": 0.5, ... "categorical_encoding": "eigen", ... "num_iteration_without_new_exemplar": 400} >>> agg = H2OAggregatorEstimator(**params) >>> agg.train(training_frame=df) >>> new_df = agg.aggregated_frame >>> new_df
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> df = h2o.create_frame(rows=10000, ... cols=10, ... categorical_fraction=0.6, ... integer_fraction=0, ... binary_fraction=0, ... real_range=100, ... integer_range=100, ... missing_fraction=0, ... factors=100, ... seed=1234) >>> params = {"target_num_exemplars": 1000, ... "rel_tol_num_exemplars": 0.5, ... "categorical_encoding": "eigen", ... "num_iteration_without_new_exemplar": 400} >>> agg = H2OAggregatorEstimator(**params) >>> agg.train(training_frame=df) >>> new_df = agg.aggregated_frame >>> new_df
-
property
transform
¶ Transformation of training data
Type:
Literal["none", "standardize", "normalize", "demean", "descale"]
, defaults to"normalize"
.- Examples
>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> params = {"ignore_const_cols": False, ... "target_num_exemplars": 500, ... "rel_tol_num_exemplars": 0.3, ... "transform": "standardize", ... "categorical_encoding": "eigen"} >>> model = H2OAggregatorEstimator(**params) >>> model.train(training_frame=df) >>> new_df = model.aggregated_frame
-
property
H2OAutoEncoderEstimator
¶
-
class
h2o.estimators.deeplearning.
H2OAutoEncoderEstimator
(**kwargs)[source]¶ Bases:
h2o.estimators.deeplearning.H2ODeepLearningEstimator
- Examples
>>> import h2o as ml >>> from h2o.estimators.deeplearning import H2OAutoEncoderEstimator >>> ml.init() >>> rows = [[1,2,3,4,0]*50, [2,1,2,4,1]*50, [2,1,4,2,1]*50, [0,1,2,34,1]*50, [2,3,4,1,0]*50] >>> fr = ml.H2OFrame(rows) >>> fr[4] = fr[4].asfactor() >>> model = H2OAutoEncoderEstimator() >>> model.train(x=range(4), training_frame=fr)
H2OExtendedIsolationForestEstimator
¶
-
class
h2o.estimators.extended_isolation_forest.
H2OExtendedIsolationForestEstimator
(model_id=None, training_frame=None, ignored_columns=None, ignore_const_cols=True, categorical_encoding='auto', ntrees=100, sample_size=256, extension_level=0, seed=-1)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Extended Isolation Forest
Builds an Extended Isolation Forest model. Extended Isolation Forest generalizes its predecessor algorithm, Isolation Forest. The original Isolation Forest algorithm suffers from bias due to tree branching. Extension of the algorithm mitigates the bias by adjusting the branching, and the original algorithm becomes just a special case. Extended Isolation Forest’s attribute “extension_level” allows leveraging the generalization. The minimum value is 0 and means the Isolation Forest’s behavior. Maximum value is (numCols - 1) and stands for full extension. The rest of the algorithm is analogical to the Isolation Forest algorithm. Each iteration builds a tree that partitions the sample observations’ space until it isolates observation. The length of the path from root to a leaf node of the resulting tree is used to calculate the anomaly score. Anomalies are easier to isolate, and their average tree path is expected to be shorter than paths of regular observations. Anomaly score is a number between 0 and 1. A number closer to 0 is a normal point, and a number closer to 1 is a more anomalous point.
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> encoding = "one_hot_explicit" >>> airlines_eif = H2OExtendedIsolationForestEstimator(categorical_encoding = encoding, ... seed = 1234) >>> airlines_eif.train(x = predictors, ... training_frame = airlines) >>> airlines_eif.model_performance()
-
property
extension_level
¶ Maximum is N - 1 (N = numCols). Minimum is 0. Extended Isolation Forest with extension_Level = 0 behaves like Isolation Forest.
Type:
int
, defaults to0
.- Examples
>>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/single_blob.csv") >>> eif_model = H2OExtendedIsolationForestEstimator(extension_level = 1, ... ntrees=7) >>> eif_model.train(training_frame = train) >>> print(eif_model)
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year","const_1","const_2"] >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios = [.8], seed = 1234) >>> cars_eif = H2OExtendedIsolationForestEstimator(seed = 1234, ... ignore_const_cols = True) >>> cars_eif.train(x = predictors, ... training_frame = cars) >>> cars_eif.model_performance()
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
ntrees
¶ Number of Extended Isolation Forest trees.
Type:
int
, defaults to100
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = titanic.columns >>> tree_num = [20, 50, 80, 110, 140, 170, 200] >>> label = ["20", "50", "80", "110", "140", "170", "200"] >>> for key, num in enumerate(tree_num): ... titanic_eif = H2OExtendedIsolationForestEstimator(ntrees = num, ... seed = 1234, ... extension_level = titanic.dim[1] - 1) ... titanic_eif.train(x = predictors, ... training_frame = titanic)
-
property
sample_size
¶ Number of randomly sampled observations used to train each Extended Isolation Forest tree.
Type:
int
, defaults to256
.- Examples
>>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_train.csv") >>> eif_model = H2OExtendedIsolationForestEstimator(sample_size = 5, ... ntrees=7) >>> eif_model.train(training_frame = train) >>> print(eif_model)
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> eif_w_seed = H2OExtendedIsolationForestEstimator(seed = 1234) >>> eif_w_seed.train(x = predictors, ... training_frame = airlines) >>> eif_wo_seed = H2OExtendedIsolationForestEstimator() >>> eif_wo_seed.train(x = predictors, ... training_frame = airlines) >>> print(eif_w_seed) >>> print(eif_wo_seed)
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_eif = H2OExtendedIsolationForestEstimator(seed = 1234, ... sample_size = 256, ... extension_level = cars.dim[1] - 1) >>> cars_eif.train(x = predictors, ... training_frame = cars) >>> print(cars_eif)
-
property
H2OGenericEstimator
¶
-
class
h2o.estimators.generic.
H2OGenericEstimator
(model_id=None, model_key=None, path=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Import MOJO Model
-
static
from_file
(file=<class 'str'>, model_id=None)[source]¶ Creates new Generic model by loading existing embedded model into library, e.g. from H2O MOJO. The imported model must be supported by H2O.
- Parameters
file – A string containing path to the file to create the model from
model_id – Model ID
- Returns
H2OGenericEstimator instance representing the generic model
- Examples
>>> from h2o.estimators import H2OIsolationForestEstimator, H2OGenericEstimator >>> import tempfile >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv") >>> ifr = H2OIsolationForestEstimator(ntrees=1) >>> ifr.train(x=["Origin","Dest"], y="Distance", training_frame=airlines) >>> original_model_filename = tempfile.mkdtemp() >>> original_model_filename = ifr.download_mojo(original_model_filename) >>> model = H2OGenericEstimator.from_file(original_model_filename) >>> model.model_performance()
-
property
model_key
¶ Key to the self-contained model archive already uploaded to H2O.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> from h2o.estimators import H2OGenericEstimator, H2OXGBoostEstimator >>> import tempfile >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv") >>> y = "IsDepDelayed" >>> x = ["fYear","fMonth","Origin","Dest","Distance"] >>> xgb = H2OXGBoostEstimator(ntrees=1, nfolds=3) >>> xgb.train(x=x, y=y, training_frame=airlines) >>> original_model_filename = tempfile.mkdtemp() >>> original_model_filename = xgb.download_mojo(original_model_filename) >>> key = h2o.lazy_import(original_model_filename) >>> fr = h2o.get_frame(key[0]) >>> model = H2OGenericEstimator(model_key=fr) >>> model.train() >>> model.auc()
-
property
path
¶ Path to file with self-contained model archive.
Type:
str
.- Examples
>>> from h2o.estimators import H2OIsolationForestEstimator, H2OGenericEstimator >>> import tempfile >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv") >>> ifr = H2OIsolationForestEstimator(ntrees=1) >>> ifr.train(x=["Origin","Dest"], y="Distance", training_frame=airlines) >>> generic_mojo_filename = tempfile.mkdtemp("zip","genericMojo") >>> generic_mojo_filename = model.download_mojo(path=generic_mojo_filename) >>> model = H2OGenericEstimator.from_file(generic_mojo_filename) >>> model.model_performance()
-
static
H2OGeneralizedLowRankEstimator
¶
-
class
h2o.estimators.glrm.
H2OGeneralizedLowRankEstimator
(model_id=None, training_frame=None, validation_frame=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, representation_name=None, loading_name=None, transform='none', k=1, loss='quadratic', loss_by_col=None, loss_by_col_idx=None, multi_loss='categorical', period=1, regularization_x='none', regularization_y='none', gamma_x=0.0, gamma_y=0.0, max_iterations=1000, max_updates=2000, init_step_size=1.0, min_step_size=0.0001, seed=-1, init='plus_plus', svd_method='randomized', user_y=None, user_x=None, expand_user_y=True, impute_original=False, recover_svd=False, max_runtime_secs=0.0, export_checkpoints_dir=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Generalized Low Rank Modeling
Builds a generalized low rank model of a H2O dataset.
-
property
expand_user_y
¶ Expand categorical columns in user-specified initial Y
Type:
bool
, defaults toTrue
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> rank = 3 >>> gx = 0.5 >>> gy = 0.5 >>> trans = "standardize" >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=rank, ... loss="Quadratic", ... gamma_x=gx, ... gamma_y=gy, ... transform=trans, ... expand_user_y=False) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> iris_glrm.show()
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> checkpoints_dir = tempfile.mkdtemp() >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3, ... export_checkpoints_dir=checkpoints_dir, ... seed=1234) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> len(listdir(checkpoints_dir))
-
property
gamma_x
¶ Regularization weight on X matrix
Type:
float
, defaults to0.0
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> rank = 3 >>> gx = 0.5 >>> gy = 0.5 >>> trans = "standardize" >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=rank, ... loss="Quadratic", ... gamma_x=gx, ... gamma_y=gy, ... transform=trans) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> iris_glrm.show()
-
property
gamma_y
¶ Regularization weight on Y matrix
Type:
float
, defaults to0.0
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> rank = 3 >>> gx = 0.5 >>> gy = 0.5 >>> trans = "standardize" >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=rank, ... loss="Quadratic", ... gamma_x=gx, ... gamma_y=gy, ... transform=trans) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> iris_glrm.show()
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3, ... ignore_const_cols=False, ... seed=1234) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> iris_glrm.show()
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
impute_original
¶ Reconstruct original training data by reversing transform
Type:
bool
, defaults toFalse
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> rank = 3 >>> gx = 0.5 >>> gy = 0.5 >>> trans = "standardize" >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=rank, ... loss="Quadratic", ... gamma_x=gx, ... gamma_y=gy, ... transform=trans ... impute_original=True) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> iris_glrm.show()
-
property
init
¶ Initialization mode
Type:
Literal["random", "svd", "plus_plus", "user"]
, defaults to"plus_plus"
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3, ... init="svd", ... seed=1234) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> iris_glrm.show()
-
property
init_step_size
¶ Initial step size
Type:
float
, defaults to1.0
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3, ... init_step_size=2.5, ... seed=1234) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> iris_glrm.show()
-
property
k
¶ Rank of matrix approximation
Type:
int
, defaults to1
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3) >>> iris_glrm.train(x=iris.names, training_frame=iris) >>> iris_glrm.show()
-
property
loading_name
¶ [Deprecated] Use representation_name instead. Frame key to save resulting X.
Type:
str
.- Examples
>>> # loading_name will be deprecated. Use representation_name instead. >>> acs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/census/ACS_13_5YR_DP02_cleaned.zip") >>> acs_fill = acs.drop("ZCTA5") >>> acs_glrm = H2OGeneralizedLowRankEstimator(k=10, ... transform="standardize", ... loss="quadratic", ... regularization_x="quadratic", ... regularization_y="L1", ... gamma_x=0.25, ... gamma_y=0.5, ... max_iterations=1, ... loading_name="acs_full") >>> acs_glrm.train(x=acs_fill.names, training_frame=acs) >>> acs_glrm.loading_name >>> acs_glrm.show()
-
property
loss
¶ Numeric loss function
Type:
Literal["quadratic", "absolute", "huber", "poisson", "hinge", "logistic", "periodic"]
, defaults to"quadratic"
.- Examples
>>> acs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/census/ACS_13_5YR_DP02_cleaned.zip") >>> acs_fill = acs.drop("ZCTA5") >>> acs_glrm = H2OGeneralizedLowRankEstimator(k=10, ... transform="standardize", ... loss="absolute", ... regularization_x="quadratic", ... regularization_y="L1", ... gamma_x=0.25, ... gamma_y=0.5, ... max_iterations=700) >>> acs_glrm.train(x=acs_fill.names, training_frame=acs) >>> acs_glrm.show()
-
property
loss_by_col
¶ Loss function by column (override)
Type:
List[Literal["quadratic", "absolute", "huber", "poisson", "hinge", "logistic", "periodic", "categorical", "ordinal"]]
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... loss="quadratic", ... loss_by_col=["absolute","huber"], ... loss_by_col_idx=[0,3], ... regularization_x="quadratic", ... regularization_y="l1") >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
loss_by_col_idx
¶ Loss function by column index (override)
Type:
List[int]
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... loss="quadratic", ... loss_by_col=["absolute","huber"], ... loss_by_col_idx=[0,3], ... regularization_x="quadratic", ... regularization_y="l1") >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
max_iterations
¶ Maximum number of iterations
Type:
int
, defaults to1000
.- Examples
>>> acs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/census/ACS_13_5YR_DP02_cleaned.zip") >>> acs_fill = acs.drop("ZCTA5") >>> acs_glrm = H2OGeneralizedLowRankEstimator(k=10, ... transform="standardize", ... loss="quadratic", ... regularization_x="quadratic", ... regularization_y="L1", ... gamma_x=0.25, ... gamma_y=0.5, ... max_iterations=700) >>> acs_glrm.train(x=acs_fill.names, training_frame=acs) >>> acs_glrm.show()
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... max_runtime_secs=15, ... max_iterations=500, ... max_updates=900, ... min_step_size=0.005) >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
max_updates
¶ Maximum number of updates, defaults to 2*max_iterations
Type:
int
, defaults to2000
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... max_runtime_secs=15, ... max_iterations=500, ... max_updates=900, ... min_step_size=0.005) >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
min_step_size
¶ Minimum step size
Type:
float
, defaults to0.0001
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... max_runtime_secs=15, ... max_iterations=500, ... max_updates=900, ... min_step_size=0.005) >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
multi_loss
¶ Categorical loss function
Type:
Literal["categorical", "ordinal"]
, defaults to"categorical"
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... loss="quadratic", ... loss_by_col=["absolute","huber"], ... loss_by_col_idx=[0,3], ... regularization_x="quadratic", ... regularization_y="l1" ... multi_loss="ordinal") >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
period
¶ Length of period (only used with periodic loss function)
Type:
int
, defaults to1
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... max_runtime_secs=15, ... max_iterations=500, ... max_updates=900, ... min_step_size=0.005, ... period=5) >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
recover_svd
¶ Recover singular values and eigenvectors of XY
Type:
bool
, defaults toFalse
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv") >>> prostate[0] = prostate[0].asnumeric() >>> prostate[4] = prostate[4].asnumeric() >>> loss_all = ["Hinge", "Quadratic", "Categorical", "Categorical", ... "Hinge", "Quadratic", "Quadratic", "Quadratic"] >>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5, ... loss_by_col=loss_all, ... recover_svd=True, ... transform="standardize", ... seed=12345) >>> pros_glrm.train(x=prostate.names, training_frame=prostate) >>> pros_glrm.show()
-
property
regularization_x
¶ Regularization function for X matrix
Type:
Literal["none", "quadratic", "l2", "l1", "non_negative", "one_sparse", "unit_one_sparse", "simplex"]
, defaults to"none"
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... loss="quadratic", ... loss_by_col=["absolute","huber"], ... loss_by_col_idx=[0,3], ... regularization_x="quadratic", ... regularization_y="l1") >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
regularization_y
¶ Regularization function for Y matrix
Type:
Literal["none", "quadratic", "l2", "l1", "non_negative", "one_sparse", "unit_one_sparse", "simplex"]
, defaults to"none"
.- Examples
>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv") >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3, ... loss="quadratic", ... loss_by_col=["absolute","huber"], ... loss_by_col_idx=[0,3], ... regularization_x="quadratic", ... regularization_y="l1") >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
representation_name
¶ Frame key to save resulting X
Type:
str
.- Examples
>>> acs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/census/ACS_13_5YR_DP02_cleaned.zip") >>> acs_fill = acs.drop("ZCTA5") >>> acs_glrm = H2OGeneralizedLowRankEstimator(k=10, ... transform="standardize", ... loss="quadratic", ... regularization_x="quadratic", ... regularization_y="L1", ... gamma_x=0.25, ... gamma_y=0.5, ... max_iterations=1, ... representation_name="acs_full") >>> acs_glrm.train(x=acs_fill.names, training_frame=acs) >>> acs_glrm.loading_name >>> acs_glrm.show()
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv") >>> prostate[0] = prostate[0].asnumeric() >>> prostate[4] = prostate[4].asnumeric() >>> loss_all = ["Hinge", "Quadratic", "Categorical", "Categorical", ... "Hinge", "Quadratic", "Quadratic", "Quadratic"] >>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5, ... loss_by_col=loss_all, ... score_each_iteration=True, ... transform="standardize", ... seed=12345) >>> pros_glrm.train(x=prostate.names, training_frame=prostate) >>> pros_glrm.show()
-
property
seed
¶ RNG seed for initialization
Type:
int
, defaults to-1
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv") >>> prostate[0] = prostate[0].asnumeric() >>> prostate[4] = prostate[4].asnumeric() >>> glrm_w_seed = H2OGeneralizedLowRankEstimator(k=5, seed=12345) >>> glrm_w_seed.train(x=prostate.names, training_frame=prostate) >>> glrm_wo_seed = H2OGeneralizedLowRankEstimator(k=5, >>> glrm_wo_seed.train(x=prostate.names, training_frame=prostate) >>> glrm_w_seed.show() >>> glrm_wo_seed.show()
-
property
svd_method
¶ Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable)
Type:
Literal["gram_s_v_d", "power", "randomized"]
, defaults to"randomized"
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv") >>> prostate[0] = prostate[0].asnumeric() >>> prostate[4] = prostate[4].asnumeric() >>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5, ... svd_method="power", ... seed=1234) >>> pros_glrm.train(x=prostate.names, training_frame=prostate) >>> pros_glrm.show()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv") >>> prostate[0] = prostate[0].asnumeric() >>> prostate[4] = prostate[4].asnumeric() >>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5, ... seed=1234) >>> pros_glrm.train(x=prostate.names, training_frame=prostate) >>> pros_glrm.show()
-
property
transform
¶ Transformation of training data
Type:
Literal["none", "standardize", "normalize", "demean", "descale"]
, defaults to"none"
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv") >>> prostate[0] = prostate[0].asnumeric() >>> prostate[4] = prostate[4].asnumeric() >>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5, ... score_each_iteration=True, ... transform="standardize", ... seed=12345) >>> pros_glrm.train(x=prostate.names, training_frame=prostate) >>> pros_glrm.show()
-
transform_frame
(fr)[source]¶ GLRM performs A=X*Y during training. When a new dataset is given, GLRM will perform Anew = Xnew*Y. When predict is called, Xnew*Y is returned. When transform_frame is called, Xnew is returned instead. :return: an H2OFrame that contains Xnew.
-
property
user_x
¶ User-specified initial X
Type:
Union[None, str, H2OFrame]
.- Examples
>>> arrestsH2O = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> initial_x = ([[5.412, 65.24, -7.54, -0.032, 2.212, 92.24, -17.54, 23.268, 0.312, ... 123.24, 14.46, 9.768, 1.012, 19.24, -15.54, -1.732, 5.412, 65.24, ... -7.54, -0.032, 2.212, 92.24, -17.54, 23.268, 0.312, 123.24, 14.46, ... 9.76, 1.012, 19.24, -15.54, -1.732, 5.412, 65.24, -7.54, -0.032, ... 2.212, 92.24, -17.54, 23.268, 0.312, 123.24, 14.46, 9.768, 1.012, ... 19.24, -15.54, -1.732, 5.412, 65.24]]*4) >>> initial_x_h2o = h2o.H2OFrame(list(zip(*initial_x))) >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=4, ... transform="demean", ... loss="quadratic", ... gamma_x=0.5, ... gamma_y=0.3, ... init="user", ... user_x=initial_x_h2o, ... recover_svd=True) >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
user_y
¶ User-specified initial Y
Type:
Union[None, str, H2OFrame]
.- Examples
>>> arrestsH2O = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> initial_y = [[5.412, 65.24, -7.54, -0.032], ... [2.212, 92.24, -17.54, 23.268], ... [0.312, 123.24, 14.46, 9.768], ... [1.012, 19.24, -15.54, -1.732]] >>> initial_y_h2o = h2o.H2OFrame(list(zip(*initial_y))) >>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=4, ... transform="demean", ... loss="quadratic", ... gamma_x=0.5, ... gamma_y=0.3, ... init="user", ... user_y=initial_y_h2o, ... recover_svd=True) >>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O) >>> arrests_glrm.show()
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> iris = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv") >>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3, ... loss="quadratic", ... gamma_x=0.5, ... gamma_y=0.5, ... transform="standardize") >>> iris_glrm.train(x=iris.names, ... training_frame=iris, ... validation_frame=iris) >>> iris_glrm.show()
-
property
H2OIsolationForestEstimator
¶
-
class
h2o.estimators.isolation_forest.
H2OIsolationForestEstimator
(model_id=None, training_frame=None, score_each_iteration=False, score_tree_interval=0, ignored_columns=None, ignore_const_cols=True, ntrees=50, max_depth=8, min_rows=1.0, max_runtime_secs=0.0, seed=-1, build_tree_one_node=False, mtries=-1, sample_size=256, sample_rate=-1.0, col_sample_rate_change_per_level=1.0, col_sample_rate_per_tree=1.0, categorical_encoding='auto', stopping_rounds=0, stopping_metric='auto', stopping_tolerance=0.01, export_checkpoints_dir=None, contamination=-1.0, validation_frame=None, validation_response_column=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Isolation Forest
Builds an Isolation Forest model. Isolation Forest algorithm samples the training frame and in each iteration builds a tree that partitions the space of the sample observations until it isolates each observation. Length of the path from root to a leaf node of the resulting tree is used to calculate the anomaly score. Anomalies are easier to isolate and their average tree path is expected to be shorter than paths of regular observations.
-
property
build_tree_one_node
¶ Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(build_tree_one_node=True, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance()
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> encoding = "one_hot_explicit" >>> airlines_if = H2OIsolationForestEstimator(categorical_encoding=encoding, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance()
-
property
col_sample_rate_change_per_level
¶ Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(col_sample_rate_change_per_level=.9, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance()
-
property
col_sample_rate_per_tree
¶ Column sample rate per tree (from 0.0 to 1.0)
Type:
float
, defaults to1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(col_sample_rate_per_tree=.7, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance()
-
property
contamination
¶ Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined).
Type:
float
, defaults to-1.0
.
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex") >>> predictors = ["DayofMonth", "DayOfWeek"] >>> checkpoints_dir = tempfile.mkdtemp() >>> air_if = H2OIsolationForestEstimator(max_depth=3, ... seed=1234, ... export_checkpoints_dir=checkpoints_dir) >>> air_if.train(x=predictors, ... training_frame=airlines) >>> len(listdir(checkpoints_dir))
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year","const_1","const_2"] >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_if = H2OIsolationForestEstimator(seed=1234, ... ignore_const_cols=True) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance()
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
max_depth
¶ Maximum tree depth (0 for unlimited).
Type:
int
, defaults to8
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(max_depth=2, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance()
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(max_runtime_secs=10, ... ntrees=10000, ... max_depth=10, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance()
-
property
min_rows
¶ Fewest allowed (weighted) observations in a leaf.
Type:
float
, defaults to1.0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(min_rows=16, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance()
-
property
mtries
¶ Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3.
Type:
int
, defaults to-1
.- Examples
>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> predictors = covtype.columns[0:54] >>> cov_if = H2OIsolationForestEstimator(mtries=30, seed=1234) >>> cov_if.train(x=predictors, ... training_frame=covtype) >>> cov_if.model_performance()
-
property
ntrees
¶ Number of trees.
Type:
int
, defaults to50
.- Examples
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = titanic.columns >>> tree_num = [20, 50, 80, 110, 140, 170, 200] >>> label = ["20", "50", "80", "110", "140", "170", "200"] >>> for key, num in enumerate(tree_num): ... titanic_if = H2OIsolationForestEstimator(ntrees=num, ... seed=1234) ... titanic_if.train(x=predictors, ... training_frame=titanic) ... print(label[key], 'training score', titanic_if.mse(train=True))
-
property
sample_rate
¶ Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead.
Type:
float
, defaults to-1.0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(sample_rate=.7, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance()
-
property
sample_size
¶ Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored.
Type:
int
, defaults to256
.- Examples
>>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_train.csv") >>> isofor_model = H2OIsolationForestEstimator(sample_size=5, ... ntrees=7) >>> isofor_model.train(training_frame=train) >>> isofor_model.model_performance()
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(score_each_iteration=True, ... ntrees=55, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance()
-
property
score_tree_interval
¶ Score the model after every so many trees. Disabled if set to 0.
Type:
int
, defaults to0
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(score_tree_interval=5, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance()
-
property
seed
¶ Seed for pseudo random number generator (if applicable)
Type:
int
, defaults to-1
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> isofor_w_seed = H2OIsolationForestEstimator(seed=1234) >>> isofor_w_seed.train(x=predictors, ... training_frame=airlines) >>> isofor_wo_seed = H2OIsolationForestEstimator() >>> isofor_wo_seed.train(x=predictors, ... training_frame=airlines) >>> isofor_w_seed.model_performance() >>> isofor_wo_seed.model_performance()
-
property
stopping_metric
¶ Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
Type:
Literal["auto", "anomaly_score", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "misclassification", "mean_per_class_error"]
, defaults to"auto"
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance()
-
property
stopping_rounds
¶ Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
Type:
int
, defaults to0
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance()
-
property
stopping_tolerance
¶ Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type:
float
, defaults to0.01
.- Examples
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance()
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.
-
property
validation_response_column
¶ (experimental) Name of the response column in the validation frame. Response column should be binary and indicate not anomaly/anomaly.
Type:
str
.
-
property
H2OKMeansEstimator
¶
-
class
h2o.estimators.kmeans.
H2OKMeansEstimator
(model_id=None, training_frame=None, validation_frame=None, nfolds=0, keep_cross_validation_models=True, keep_cross_validation_predictions=False, keep_cross_validation_fold_assignment=False, fold_assignment='auto', fold_column=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, k=1, estimate_k=False, user_points=None, max_iterations=10, standardize=True, seed=-1, init='furthest', max_runtime_secs=0.0, categorical_encoding='auto', export_checkpoints_dir=None, cluster_size_constraints=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
K-means
Performs k-means clustering on an H2O dataset.
-
property
categorical_encoding
¶ Encoding scheme for categorical features
Type:
Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
, defaults to"auto"
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"] >>> train, valid = prostate.split_frame(ratios=[.8], seed=1234) >>> encoding = "one_hot_explicit" >>> pros_km = H2OKMeansEstimator(categorical_encoding=encoding, ... seed=1234) >>> pros_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> pros_km.scoring_history()
-
property
cluster_size_constraints
¶ An array specifying the minimum number of points that should be in each cluster. The length of the constraints array has to be the same as the number of clusters.
Type:
List[int]
.- Examples
>>> iris_h2o = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv") >>> k=3 >>> start_points = h2o.H2OFrame( ... [[4.9, 3.0, 1.4, 0.2], ... [5.6, 2.5, 3.9, 1.1], ... [6.5, 3.0, 5.2, 2.0]]) >>> kmm = H2OKMeansEstimator(k=k, ... user_points=start_points, ... standardize=True, ... cluster_size_constraints=[2, 5, 8], ... score_each_iteration=True) >>> kmm.train(x=list(range(7)), training_frame=iris_h2o) >>> kmm.scoring_history()
-
property
estimate_k
¶ Whether to estimate the number of clusters (<=k) iteratively and deterministically.
Type:
bool
, defaults toFalse
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris['class'] = iris['class'].asfactor() >>> predictors = iris.columns[:-1] >>> train, valid = iris.split_frame(ratios=[.8], seed=1234) >>> iris_kmeans = H2OKMeansEstimator(k=10, ... estimate_k=True, ... standardize=False, ... seed=1234) >>> iris_kmeans.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> iris_kmeans.scoring_history()
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex") >>> predictors = ["DayofMonth", "DayOfWeek"] >>> checkpoints_dir = tempfile.mkdtemp() >>> air_km = H2OKMeansEstimator(export_checkpoints_dir=checkpoints_dir, ... seed=1234) >>> air_km.train(x=predictors, training_frame=airlines) >>> len(listdir(checkpoints_dir))
-
property
fold_assignment
¶ Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.
Type:
Literal["auto", "random", "modulo", "stratified"]
, defaults to"auto"
.- Examples
>>> ozone = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/ozone.csv") >>> predictors = ["radiation","temperature","wind"] >>> train, valid = ozone.split_frame(ratios=[.8], seed=1234) >>> ozone_km = H2OKMeansEstimator(fold_assignment="Random", ... nfolds=5, ... seed=1234) >>> ozone_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> ozone_km.scoring_history()
-
property
fold_column
¶ Column with cross-validation fold index assignment per observation.
Type:
str
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234) >>> fold_numbers.set_names(["fold_numbers"]) >>> cars = cars.cbind(fold_numbers) >>> print(cars['fold_numbers']) >>> cars_km = H2OKMeansEstimator(seed=1234) >>> cars_km.train(x=predictors, ... training_frame=cars, ... fold_column="fold_numbers") >>> cars_km.scoring_history()
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_km = H2OKMeansEstimator(ignore_const_cols=True, ... seed=1234) >>> cars_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> cars_km.scoring_history()
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
init
¶ Initialization mode
Type:
Literal["random", "plus_plus", "furthest", "user"]
, defaults to"furthest"
.- Examples
>>> seeds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt") >>> predictors = seeds.columns[0:7] >>> train, valid = seeds.split_frame(ratios=[.8], seed=1234) >>> seeds_km = H2OKMeansEstimator(k=3, ... init='Furthest', ... seed=1234) >>> seeds_km.train(x=predictors, ... training_frame=train, ... validation_frame= valid) >>> seeds_km.scoring_history()
-
property
k
¶ The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find up to k centroids.
Type:
int
, defaults to1
.- Examples
>>> seeds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt") >>> predictors = seeds.columns[0:7] >>> train, valid = seeds.split_frame(ratios=[.8], seed=1234) >>> seeds_km = H2OKMeansEstimator(k=3, seed=1234) >>> seeds_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> seeds_km.scoring_history()
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep the cross-validation fold assignment.
Type:
bool
, defaults toFalse
.- Examples
>>> ozone = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/ozone.csv") >>> predictors = ["radiation","temperature","wind"] >>> train, valid = ozone.split_frame(ratios=[.8], seed=1234) >>> ozone_km = H2OKMeansEstimator(keep_cross_validation_fold_assignment=True, ... nfolds=5, ... seed=1234) >>> ozone_km.train(x=predictors, ... training_frame=train) >>> ozone_km.scoring_history()
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validation models.
Type:
bool
, defaults toTrue
.- Examples
>>> ozone = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/ozone.csv") >>> predictors = ["radiation","temperature","wind"] >>> train, valid = ozone.split_frame(ratios=[.8], seed=1234) >>> ozone_km = H2OKMeansEstimator(keep_cross_validation_models=True, ... nfolds=5, ... seed=1234) >>> ozone_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> ozone_km.scoring_history()
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation models.
Type:
bool
, defaults toFalse
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", ... "PSA", "VOL", "GLEASON"] >>> train, valid = prostate.split_frame(ratios=[.8], seed=1234) >>> pros_km = H2OKMeansEstimator(keep_cross_validation_predictions=True, ... nfolds=5, ... seed=1234) >>> pros_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> pros_km.scoring_history()
-
property
max_iterations
¶ Maximum training iterations (if estimate_k is enabled, then this is for each inner Lloyds iteration)
Type:
int
, defaults to10
.- Examples
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK", ... "AGP1","AGMN","LIV","AGLP"] >>> train, valid = benign.split_frame(ratios=[.8], seed=1234) >>> benign_km = H2OKMeansEstimator(max_iterations=50) >>> benign_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> benign_km.scoring_history()
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK", ... "AGP1","AGMN","LIV","AGLP"] >>> train, valid = benign.split_frame(ratios=[.8], seed=1234) >>> benign_km = H2OKMeansEstimator(max_runtime_secs=10, ... seed=1234) >>> benign_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> benign_km.scoring_history()
-
property
nfolds
¶ Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type:
int
, defaults to0
.- Examples
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK", ... "AGP1","AGMN","LIV","AGLP"] >>> train, valid = benign.split_frame(ratios=[.8], seed=1234) >>> benign_km = H2OKMeansEstimator(nfolds=5, seed=1234) >>> benign_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> benign_km.scoring_history()
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK", ... "AGP1","AGMN","LIV","AGLP"] >>> train, valid = benign.split_frame(ratios=[.8], seed=1234) >>> benign_km = H2OKMeansEstimator(score_each_iteration=True, ... seed=1234) >>> benign_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> benign_km.scoring_history()
-
property
seed
¶ RNG Seed
Type:
int
, defaults to-1
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"] >>> train, valid = prostate.split_frame(ratios=[.8], seed=1234) >>> pros_w_seed = H2OKMeansEstimator(seed=1234) >>> pros_w_seed.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> pros_wo_seed = H2OKMeansEstimator() >>> pros_wo_seed.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> pros_w_seed.scoring_history() >>> pros_wo_seed.scoring_history()
-
property
standardize
¶ Standardize columns before computing distances
Type:
bool
, defaults toTrue
.- Examples
>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") >>> predictors = boston.columns[:-1] >>> boston['chas'] = boston['chas'].asfactor() >>> train, valid = boston.split_frame(ratios=[.8]) >>> boston_km = H2OKMeansEstimator(standardize=True) >>> boston_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> boston_km.scoring_history()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", ... "PSA", "VOL", "GLEASON"] >>> train, valid = prostate.split_frame(ratios=[.8], seed=1234) >>> pros_km = H2OKMeansEstimator(seed=1234) >>> pros_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> pros_km.scoring_history()
-
property
user_points
¶ This option allows you to specify a dataframe, where each row represents an initial cluster center. The user- specified points must have the same number of columns as the training observations. The number of rows must equal the number of clusters
Type:
Union[None, str, H2OFrame]
.- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> iris['class'] = iris['class'].asfactor() >>> predictors = iris.columns[:-1] >>> train, valid = iris.split_frame(ratios=[.8], seed=1234) >>> point1 = [4.9,3.0,1.4,0.2] >>> point2 = [5.6,2.5,3.9,1.1] >>> point3 = [6.5,3.0,5.2,2.0] >>> points = h2o.H2OFrame([point1, point2, point3]) >>> iris_km = H2OKMeansEstimator(k=3, ... user_points=points, ... seed=1234) >>> iris_km.train(x=predictors, ... training_frame=iris, ... validation_frame=valid) >>> iris_kmeans.tot_withinss(valid=True)
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", ... "PSA", "VOL", "GLEASON"] >>> train, valid = prostate.split_frame(ratios=[.8], seed=1234) >>> pros_km = H2OKMeansEstimator(seed=1234) >>> pros_km.train(x=predictors, ... training_frame=train, ... validation_frame=valid) >>> pros_km.scoring_history()
-
property
H2OPrincipalComponentAnalysisEstimator
¶
-
class
h2o.estimators.pca.
H2OPrincipalComponentAnalysisEstimator
(model_id=None, training_frame=None, validation_frame=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, transform='none', pca_method='gram_s_v_d', pca_impl=None, k=1, max_iterations=1000, use_all_factor_levels=False, compute_metrics=True, impute_missing=False, seed=-1, max_runtime_secs=0.0, export_checkpoints_dir=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Principal Components Analysis
-
property
compute_metrics
¶ Whether to compute metrics on the training data
Type:
bool
, defaults toTrue
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor() >>> prostate['RACE'] = prostate['RACE'].asfactor() >>> prostate['DCAPS'] = prostate['DCAPS'].asfactor() >>> prostate['DPROS'] = prostate['DPROS'].asfactor() >>> pros_pca = H2OPrincipalComponentAnalysisEstimator(compute_metrics=False) >>> pros_pca.train(x=prostate.names, training_frame=prostate) >>> pros_pca.show()
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor() >>> prostate['RACE'] = prostate['RACE'].asfactor() >>> prostate['DCAPS'] = prostate['DCAPS'].asfactor() >>> prostate['DPROS'] = prostate['DPROS'].asfactor() >>> checkpoints_dir = tempfile.mkdtemp() >>> pros_pca = H2OPrincipalComponentAnalysisEstimator(impute_missing=True, ... export_checkpoints_dir=checkpoints_dir) >>> pros_pca.train(x=prostate.names, training_frame=prostate) >>> len(listdir(checkpoints_dir))
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor() >>> prostate['RACE'] = prostate['RACE'].asfactor() >>> prostate['DCAPS'] = prostate['DCAPS'].asfactor() >>> prostate['DPROS'] = prostate['DPROS'].asfactor() >>> pros_pca = H2OPrincipalComponentAnalysisEstimator(ignore_const_cols=False) >>> pros_pca.train(x=prostate.names, training_frame=prostate) >>> pros_pca.show()
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
property
impute_missing
¶ Whether to impute missing entries with the column mean
Type:
bool
, defaults toFalse
.- Examples
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor() >>> prostate['RACE'] = prostate['RACE'].asfactor() >>> prostate['DCAPS'] = prostate['DCAPS'].asfactor() >>> prostate['DPROS'] = prostate['DPROS'].asfactor() >>> pros_pca = H2OPrincipalComponentAnalysisEstimator(impute_missing=True) >>> pros_pca.train(x=prostate.names, training_frame=prostate) >>> pros_pca.show()
-
init_for_pipeline
()[source]¶ Returns H2OPCA object which implements fit and transform method to be used in sklearn.Pipeline properly. All parameters defined in self.__params, should be input parameters in H2OPCA.__init__ method.
- Returns
H2OPCA object
- Examples
>>> from sklearn.pipeline import Pipeline >>> from h2o.transforms.preprocessing import H2OScaler >>> from h2o.estimators import H2ORandomForestEstimator >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") >>> pipe = Pipeline([("standardize", H2OScaler()), ... ("pca", H2OPrincipalComponentAnalysisEstimator(k=2).init_for_pipeline()), ... ("rf", H2ORandomForestEstimator(seed=42,ntrees=5))]) >>> pipe.fit(iris[:4], iris[4])
-
property
k
¶ Rank of matrix approximation
Type:
int
, defaults to1
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1, ... transform="standardize", ... pca_method="power", ... impute_missing=True, ... max_iterations=800) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
max_iterations
¶ Maximum training iterations
Type:
int
, defaults to1000
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1, ... transform="standardize", ... pca_method="power", ... impute_missing=True, ... max_iterations=800) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1, ... transform="standardize", ... pca_method="power", ... impute_missing=True, ... max_iterations=800 ... max_runtime_secs=15) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
pca_impl
¶ Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ - https://github.com/fommil/matrix-toolkits-java/
Type:
Literal["mtj_evd_densematrix", "mtj_evd_symmmatrix", "mtj_svd_densematrix", "jama"]
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3, ... pca_impl="jama", ... impute_missing=True, ... max_iterations=1200) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
pca_method
¶ Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental)
Type:
Literal["gram_s_v_d", "power", "randomized", "glrm"]
, defaults to"gram_s_v_d"
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1, ... transform="standardize", ... pca_method="power", ... impute_missing=True, ... max_iterations=800) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3, ... score_each_iteration=True, ... seed=1234, ... impute_missing=True) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
seed
¶ RNG seed for initialization
Type:
int
, defaults to-1
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3, ... seed=1234, ... impute_missing=True) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator() >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
transform
¶ Transformation of training data
Type:
Literal["none", "standardize", "normalize", "demean", "descale"]
, defaults to"none"
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1, ... transform="standardize", ... pca_method="power", ... impute_missing=True, ... max_iterations=800) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
use_all_factor_levels
¶ Whether first factor level is included in each categorical expansion
Type:
bool
, defaults toFalse
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3, ... use_all_factor_levels=True, ... seed=1234) >>> data_pca.train(x=data.names, training_frame=data) >>> data_pca.show()
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip") >>> train, valid = data.split_frame(ratios=[.8], seed=1234) >>> model_pca = H2OPrincipalComponentAnalysisEstimator(impute_missing=True) >>> model_pca.train(x=data.names, ... training_frame=train, ... validation_frame=valid) >>> model_pca.show()
-
property
Miscellaneous¶
automl
¶
H2OAutoML
¶
-
class
h2o.automl.
H2OAutoML
(nfolds=-1, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_runtime_secs=None, max_runtime_secs_per_model=None, max_models=None, distribution='AUTO', stopping_metric='AUTO', stopping_tolerance=None, stopping_rounds=3, seed=None, project_name=None, exclude_algos=None, include_algos=None, exploitation_ratio=-1, modeling_plan=None, preprocessing=None, monotone_constraints=None, keep_cross_validation_predictions=False, keep_cross_validation_models=False, keep_cross_validation_fold_assignment=False, sort_metric='AUTO', export_checkpoints_dir=None, verbosity='warn', **kwargs)[source]¶ Bases:
h2o.automl._base.H2OAutoMLBaseMixin
,h2o.base.Keyed
Automatic Machine Learning
The Automatic Machine Learning (AutoML) function automates the supervised machine learning model training process. It trains several models, cross-validated by default, by using the following available algorithms:
XGBoost
GBM (Gradient Boosting Machine)
GLM (Generalized Linear Model)
DRF (Distributed Random Forest)
XRT (eXtremely Randomized Trees)
DeepLearning (Fully Connected Deep Neural Network)
It also applies HPO on the following algorithms:
XGBoost
GBM
DeepLearning
In some cases, there will not be enough time to complete all the algorithms, so some may be missing from the leaderboard. Finally, AutoML also trains several Stacked Ensemble models at various stages during the run. Mainly two kinds of Stacked Ensemble models are trained:
one of all available models at time t
one of only the best models of each kind at time t.
Note that Stacked Ensemble models are trained only if there isn’t another stacked ensemble with the same base models.
- Examples
>>> import h2o >>> from h2o.automl import H2OAutoML >>> h2o.init() >>> # Import a sample binary outcome train/test set into H2O >>> train = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv") >>> test = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv") >>> # Identify the response and set of predictors >>> y = "response" >>> x = list(train.columns) #if x is defined as all columns except the response, then x is not required >>> x.remove(y) >>> # For binary classification, response should be a factor >>> train[y] = train[y].asfactor() >>> test[y] = test[y].asfactor() >>> # Run AutoML for 30 seconds >>> aml = H2OAutoML(max_runtime_secs = 30) >>> aml.train(x = x, y = y, training_frame = train) >>> # Print Leaderboard (ranked by xval metrics) >>> aml.leaderboard >>> # (Optional) Evaluate performance on a test set >>> perf = aml.leader.model_performance(test) >>> perf.auc()
-
property
balance_classes
¶ - Specify whether to oversample the minority classes to balance the class distribution. This option can increase
the data frame size. This option is only applicable for classification. If the oversampled size of the dataset exceeds the maximum size calculated using the
max_after_balance_size
parameter, then the majority classes will be undersampled to satisfy the size limit. Defaults toFalse
.
Type: bool
-
property
class_sampling_factors
¶ Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires
balance_classes
set toTrue
.
-
property
distribution
¶ - Distribution function used by algorithms that support it; other algorithms
use their defaults. Possible values: “AUTO”, “bernoulli”, “multinomial”, “gaussian”, “poisson”, “gamma”, “tweedie”, “laplace”, “quantile”, “huber”, “custom”, and for parameterized distributions dictionary form is used to specify the parameter, e.g.,
dict(type="tweedie", tweedie_power=1.5)
. Defaults toAUTO
.
Type: Union[str, dict]
-
property
event_log
¶ Retrieve the backend event log from an H2OAutoML object
- Returns
an H2OFrame with detailed events occurred during the AutoML training.
-
property
exclude_algos
¶ List the algorithms to skip during the model-building phase. The full list of options is:
"DRF"
(Random Forest and Extremely-Randomized Trees)"GLM"
"XGBoost"
"GBM"
"DeepLearning"
"StackedEnsemble"
Defaults to
None
, which means that all appropriate H2O algorithms will be used, if the search stopping criteria allow. Optional. Usage example:exclude_algos = ["GLM", "DeepLearning", "DRF"]
-
property
exploitation_ratio
¶ The budget ratio (between 0 and 1) dedicated to the exploitation (vs exploration) phase. By default, the exploitation phase is
0
(disabled) as this is still experimental; to activate it, it is recommended to try a ratio around 0.1. Note that the current exploitation phase only tries to fine-tune the best XGBoost and the best GBM found during exploration.
-
property
export_checkpoints_dir
¶ Path to a directory where every model will be stored in binary form.
-
property
include_algos
¶ List the algorithms to restrict to during the model-building phase. This can’t be used in combination with
exclude_algos
param. Defaults toNone
, which means that all appropriate H2O algorithms will be used, if the search stopping criteria allow. Optional. Usage example:include_algos = ["GLM", "DeepLearning", "DRF"]
-
property
keep_cross_validation_fold_assignment
¶ Whether to keep fold assignments in the models. Deleting them will save memory in the H2O cluster. Defaults to
False
.
-
property
keep_cross_validation_models
¶ Whether to keep the cross-validated models. Keeping cross-validation models may consume significantly more memory in the H2O cluster. Defaults to
False
.
-
property
keep_cross_validation_predictions
¶ Whether to keep the predictions of the cross-validation predictions. This needs to be set to
True
if running the same AutoML object for repeated runs because CV predictions are required to build additional Stacked Ensemble models in AutoML. Defaults toFalse
.
-
property
key
¶ - Returns
the unique key representing the object on the backend
-
property
leader
¶ Retrieve the top model from an H2OAutoML object
- Returns
an H2O model
- Examples
>>> # Set up an H2OAutoML object >>> aml = H2OAutoML(max_runtime_secs=30) >>> # Launch an AutoML run >>> aml.train(y=y, training_frame=train) >>> # Get the best model in the AutoML Leaderboard >>> aml.leader >>> >>> # Get AutoML object by `project_name` >>> get_aml = h2o.automl.get_automl(aml.project_name) >>> # Get the best model in the AutoML Leaderboard >>> get_aml.leader
-
property
leaderboard
¶ Retrieve the leaderboard from an H2OAutoML object
- Returns
an H2OFrame with model ids in the first column and evaluation metric in the second column sorted by the evaluation metric
- Examples
>>> # Set up an H2OAutoML object >>> aml = H2OAutoML(max_runtime_secs=30) >>> # Launch an AutoML run >>> aml.train(y=y, training_frame=train) >>> # Get the AutoML Leaderboard >>> aml.leaderboard >>> >>> # Get AutoML object by `project_name` >>> get_aml = h2o.automl.get_automl(aml.project_name) >>> # Get the AutoML Leaderboard >>> get_aml.leaderboard
-
property
max_after_balance_size
¶ - Maximum relative size of the training data after balancing class counts (can be less than 1.0).
Requires
balance_classes
. Defaults to5.0
.
Type: float
-
property
max_models
¶ - Specify the maximum number of models to build in an AutoML run, excluding the Stacked Ensemble models.
Defaults to
None
(disabled: no limitation). Always set this parameter to ensure AutoML reproducibility: all models are then trained until convergence and none is constrained by a time budget.
Type: int
-
property
max_runtime_secs
¶ - Specify the maximum time that the AutoML process will run for.
If both
max_runtime_secs
andmax_models
are specified, then the AutoML run will stop as soon as it hits either of these limits. If neithermax_runtime_secs
normax_models
are specified, thenmax_runtime_secs
dynamically defaults to 3600 seconds (1 hour). Otherwise, defaults to0
(no limit).
Type: int
-
property
max_runtime_secs_per_model
¶ - Controls the max time the AutoML run will dedicate to each individual model.
Defaults to
0
(disabled: no time limit). Note that models constrained by a time budget are not guaranteed reproducible.
Type: int
-
property
modeling_plan
¶ List of modeling steps to be used by the AutoML engine (they may not all get executed, depending on other constraints). Defaults to
None
(Expert usage only).
-
property
modeling_steps
¶ Expose the modeling steps effectively used by the AutoML run. This executed plan can be directly reinjected as the modeling_plan property of a new AutoML instance to improve reproducibility across AutoML versions.
- Returns
a list of dictionaries representing the effective modeling plan.
-
property
monotone_constraints
¶ A mapping that represents monotonic constraints. Use
+1
to enforce an increasing constraint and-1
to specify a decreasing constraint.
-
property
nfolds
¶ - Number of folds for k-fold cross-validation.
Use
0
to disable cross-validation; this will also disable Stacked Ensemble (thus decreasing the overall model performance). Defaults to-1
.
Type: int
-
predict
(test_data)[source]¶ Predict on a dataset.
- Parameters
test_data (H2OFrame) – Data on which to make predictions.
- Returns
A new H2OFrame of predictions.
- Examples
>>> # Set up an H2OAutoML object >>> aml = H2OAutoML(max_runtime_secs=30) >>> # Launch an H2OAutoML run >>> aml.train(y=y, training_frame=train) >>> # Predict with top model from AutoML Leaderboard on a H2OFrame called 'test' >>> aml.predict(test) >>> >>> # Get AutoML object by `project_name` >>> get_aml = h2o.automl.get_automl(aml.project_name) >>> # Predict with top model from AutoML Leaderboard on a H2OFrame called 'test' >>> get_aml.predict(test)
-
property
preprocessing
¶ List of preprocessing steps to run. Only
["target_encoding"]
is currently supported. Experimental.
-
property
project_name
¶ - Character string to identify an AutoML project.
Defaults to
None
, which means a project name will be auto-generated based on the training frame ID. More models can be trained on an existing AutoML project by specifying the same project name in multiple calls to the AutoML function (as long as the same training frame, or a sample, is used in subsequent runs).
Type: str
-
property
seed
¶ - Set a seed for reproducibility.
AutoML can only guarantee reproducibility if
max_models
or early stopping is used becausemax_runtime_secs
is resource limited, meaning that if the resources are not the same between runs, AutoML may be able to train more models on one run vs another. In addition, H2O Deep Learning models are not reproducible by default for performance reasons, soexclude_algos
must containDeepLearning
. Defaults toNone
.
Type: int
-
property
sort_metric
¶ Metric to sort the leaderboard by at the end of an AutoML run. For binomial classification, select from the following options:
"auc"
"aucpr"
"logloss"
"mean_per_class_error"
"rmse"
"mse"
For multinomial classification, select from the following options:
"mean_per_class_error"
"logloss"
"rmse"
"mse"
For regression, select from the following options:
"deviance"
"rmse"
"mse"
"mae"
"rmlse"
Defaults to
"AUTO"
(This translates to"auc"
for binomial classification,"mean_per_class_error"
for multinomial classification,"deviance"
for regression).
-
property
stopping_metric
¶ - Specifies the metric to use for early stopping.
The available options are:
"AUTO"
(This defaults to"logloss"
for classification,"deviance"
for regression)"deviance"
"logloss"
"mse"
"rmse"
"mae"
"rmsle"
"auc"
aucpr
"lift_top_group"
"misclassification"
"mean_per_class_error"
"r2"
Defaults to
"AUTO"
.
Type: str
-
property
stopping_rounds
¶ - Stop training new models in the AutoML run when the option selected for
stopping_metric
doesn’t improve for the specified number of models, based on a simple moving average. To disable this feature, set it to0
. Defaults to3
and must be an non-negative integer.
Type: int
-
property
stopping_tolerance
¶ - Specify the relative tolerance for the metric-based stopping criterion to stop a grid search and
the training of individual models within the AutoML run. Defaults to
0.001
if the dataset is at least 1 million rows; otherwise it defaults to a value determined by the size of the dataset and the non-NA-rate, in which case the value is computed as 1/sqrt(nrows * non-NA-rate).
Type: float
-
train
(x=None, y=None, training_frame=None, fold_column=None, weights_column=None, validation_frame=None, leaderboard_frame=None, blending_frame=None)[source]¶ Begins an AutoML task, a background task that automatically builds a number of models with various algorithms and tracks their performance in a leaderboard. At any point in the process you may use H2O’s performance or prediction functions on the resulting models.
- Parameters
x – A list of column names or indices indicating the predictor columns.
y – An index or a column name indicating the response column.
fold_column – The name or index of the column in training_frame that holds per-row fold assignments.
weights_column – The name or index of the column in training_frame that holds per-row weights.
training_frame – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold_column or weights_column).
validation_frame – H2OFrame with validation data. This argument is ignored unless the user sets nfolds = 0. If cross-validation is turned off, then a validation frame can be specified and used for early stopping of individual models and early stopping of the grid searches. By default and when nfolds > 1, cross-validation metrics will be used for early stopping and thus validation_frame will be ignored.
leaderboard_frame – H2OFrame with test data for scoring the leaderboard. This is optional and if this is set to None (the default), then cross-validation metrics will be used to generate the leaderboard rankings instead.
blending_frame – H2OFrame used to train the the metalearning algorithm in Stacked Ensembles (instead of relying on cross-validated predicted values). This is optional, but when provided, it is also recommended to disable cross validation by setting nfolds=0 and to provide a leaderboard frame for scoring purposes.
- Returns
An H2OAutoML object.
- Examples
>>> # Set up an H2OAutoML object >>> aml = H2OAutoML(max_runtime_secs=30) >>> # Launch an AutoML run >>> aml.train(y=y, training_frame=train)
-
property
training_info
¶ Expose the name/value columns of event_log as a simple dictionary, for example start_epoch, stop_epoch, … See
event_log()
to obtain a description of those key/value pairs.- Returns
a dictionary with event_log[‘name’] column as keys and event_log[‘value’] column as values.
H2OEstimator
¶
-
class
h2o.estimators.estimator_base.
H2OEstimator
[source]¶ Bases:
h2o.model.model_base.ModelBase
Base class for H2O Estimators.
H2O Estimators implement the following methods for model construction:
start()
- Top-level user-facing API for asynchronous model buildjoin()
- Top-level user-facing API for blocking on async model buildtrain()
- Top-level user-facing API for model building.fit()
- Used by scikit-learn.
Because H2OEstimator instances are instances of ModelBase, these objects can use the H2O model API.
-
fit
(X, y=None, **params)[source]¶ Fit an H2O model as part of a scikit-learn pipeline or grid search.
A warning will be issued if a caller other than sklearn attempts to use this method.
-
get_params
(deep=True)[source]¶ Obtain parameters for this estimator.
Used primarily for sklearn Pipelines and sklearn grid search.
- Parameters
deep – If True, return parameters of all sub-objects that are estimators.
- Returns
A dict of parameters
-
set_params
(**parms)[source]¶ Used by sklearn for updating parameters during grid search.
- Parameters
parms – A dictionary of parameters that will be set on this model.
- Returns
self, the current estimator object with the parameters all set as desired.
-
start
(x, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, **params)[source]¶ Train the model asynchronously (to block for results call
join()
).- Parameters
x – A list of column names or indices indicating the predictor columns.
y – An index or a column name indicating the response column.
training_frame (H2OFrame) – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
offset_column – The name or index of the column in training_frame that holds the offsets.
fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
weights_column – The name or index of the column in training_frame that holds the per-row weights.
validation_frame – H2OFrame with validation data to be scored on while training.
-
train
(x=None, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None, model_id=None, verbose=False)[source]¶ Train the H2O model.
- Parameters
x – A list of column names or indices indicating the predictor columns.
y – An index or a column name indicating the response column.
training_frame (H2OFrame) – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
offset_column – The name or index of the column in training_frame that holds the offsets.
fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
weights_column – The name or index of the column in training_frame that holds the per-row weights.
validation_frame – H2OFrame with validation data to be scored on while training.
max_runtime_secs (float) – Maximum allowed runtime in seconds for model training. Use 0 to disable.
verbose (bool) – Print scoring history to stdout. Defaults to False.
-
train_segments
(x=None, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None, segments=None, segment_models_id=None, parallelism=1, verbose=False)[source]¶ Trains H2O model for each segment (subpopulation) of the training dataset.
- Parameters
x – A list of column names or indices indicating the predictor columns.
y – An index or a column name indicating the response column.
training_frame (H2OFrame) – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
offset_column – The name or index of the column in training_frame that holds the offsets.
fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
weights_column – The name or index of the column in training_frame that holds the per-row weights.
validation_frame – H2OFrame with validation data to be scored on while training.
max_runtime_secs (float) – Maximum allowed runtime in seconds for each model training. Use 0 to disable. Please note that regardless of how this parameter is set, a model will be built for each input segment. This parameter only affects individual model training.
segments – A list of columns to segment-by. H2O will group the training (and validation) dataset by the segment-by columns and train a separate model for each segment (group of rows). As an alternative to providing a list of columns, users can also supply an explicit enumeration of segments to build the models for. This enumeration needs to be represented as H2OFrame.
segment_models_id – Identifier for the returned collection of Segment Models. If not specified it will be automatically generated.
parallelism – Level of parallelism of the bulk segment models building, it is the maximum number of models each H2O node will be building in parallel.
verbose (bool) – Enable to print additional information during model building. Defaults to False.
- Examples
>>> response = "survived" >>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> titanic[response] = titanic[response].asfactor() >>> predictors = ["survived","name","sex","age","sibsp","parch","ticket","fare","cabin"] >>> train, valid = titanic.split_frame(ratios=[.8], seed=1234) >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> titanic_gbm = H2OGradientBoostingEstimator(seed=1234) >>> titanic_models = titanic_gbm.train_segments(segments=["pclass"], ... x=predictors, ... y=response, ... training_frame=train, ... validation_frame=valid) >>> titanic_models.as_frame()
H2OSingularValueDecompositionEstimator
¶
-
class
h2o.estimators.svd.
H2OSingularValueDecompositionEstimator
(model_id=None, training_frame=None, validation_frame=None, ignored_columns=None, ignore_const_cols=True, score_each_iteration=False, transform='none', svd_method='gram_s_v_d', nv=1, max_iterations=1000, seed=-1, keep_u=True, u_name=None, use_all_factor_levels=True, max_runtime_secs=0.0, export_checkpoints_dir=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Singular Value Decomposition
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> checkpoints_dir = tempfile.mkdtemp() >>> fit_h2o = H2OSingularValueDecompositionEstimator(export_checkpoints_dir=checkpoints_dir, ... seed=-5) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> len(listdir(checkpoints_dir))
-
property
ignore_const_cols
¶ Ignore constant columns.
Type:
bool
, defaults toTrue
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(ignore_const_cols=False, ... nv=4) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
ignored_columns
¶ Names of columns to ignore for training.
Type:
List[str]
.
-
init_for_pipeline
()[source]¶ Returns H2OSVD object which implements fit and transform method to be used in sklearn.Pipeline properly. All parameters defined in self.__params, should be input parameters in H2OSVD.__init__ method.
- Returns
H2OSVD object
- Examples
>>> from h2o.transforms.preprocessing import H2OScaler >>> from h2o.estimators import H2ORandomForestEstimator >>> from h2o.estimators import H2OSingularValueDecompositionEstimator >>> from sklearn.pipeline import Pipeline >>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> pipe = Pipeline([("standardize", H2OScaler()), ... ("svd", H2OSingularValueDecompositionEstimator(nv=3).init_for_pipeline()), ... ("rf", H2ORandomForestEstimator(seed=42,ntrees=50))]) >>> pipe.fit(arrests[1:], arrests[0])
-
property
keep_u
¶ Save left singular vectors?
Type:
bool
, defaults toTrue
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(keep_u=False) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
max_iterations
¶ Maximum iterations
Type:
int
, defaults to1000
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, ... transform="standardize", ... max_iterations=2000) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, ... transform="standardize", ... max_runtime_secs=25) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
nv
¶ Number of right singular vectors
Type:
int
, defaults to1
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, ... transform="standardize", ... max_iterations=2000) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
score_each_iteration
¶ Whether to score during each iteration of model training.
Type:
bool
, defaults toFalse
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, ... score_each_iteration=True) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
seed
¶ RNG seed for k-means++ initialization
Type:
int
, defaults to-1
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, seed=-3) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
svd_method
¶ Method for computing SVD (Caution: Randomized is currently experimental and unstable)
Type:
Literal["gram_s_v_d", "power", "randomized"]
, defaults to"gram_s_v_d"
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(svd_method="power") >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator() >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
transform
¶ Transformation of training data
Type:
Literal["none", "standardize", "normalize", "demean", "descale"]
, defaults to"none"
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, ... transform="standardize", ... max_iterations=2000) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
u_name
¶ Frame key to save left singular vectors
Type:
str
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(u_name="fit_h2o") >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o.u_name >>> fit_h2o
-
property
use_all_factor_levels
¶ Whether first factor level is included in each categorical expansion
Type:
bool
, defaults toTrue
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> fit_h2o = H2OSingularValueDecompositionEstimator(use_all_factor_levels=False) >>> fit_h2o.train(x=list(range(4)), training_frame=arrests) >>> fit_h2o
-
property
validation_frame
¶ Id of the validation data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv") >>> train, valid = arrests.split_frame(ratios=[.8]) >>> fit_h2o = H2OSingularValueDecompositionEstimator() >>> fit_h2o.train(x=list(range(4)), ... training_frame=train, ... validation_frame=valid) >>> fit_h2o
-
property
H2OWord2vecEstimator
¶
-
class
h2o.estimators.word2vec.
H2OWord2vecEstimator
(model_id=None, training_frame=None, min_word_freq=5, word_model='skip_gram', norm_model='hsm', vec_size=100, window_size=5, sent_sample_rate=0.001, init_learning_rate=0.025, epochs=5, pre_trained=None, max_runtime_secs=0.0, export_checkpoints_dir=None)[source]¶ Bases:
h2o.estimators.estimator_base.H2OEstimator
Word2Vec
-
property
epochs
¶ Number of training iterations to run
Type:
int
, defaults to5
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(sent_sample_rate = 0.0, epochs = 10) >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("teacher", count = 5) >>> print(synonyms) >>> >>> w2v_model2 = H2OWord2vecEstimator(sent_sample_rate = 0.0, epochs = 1) >>> w2v_model2.train(training_frame=words) >>> synonyms2 = w2v_model2.find_synonyms("teacher", 3) >>> print(synonyms2)
-
property
export_checkpoints_dir
¶ Automatically export generated models to this directory.
Type:
str
.- Examples
>>> import tempfile >>> from os import listdir >>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> checkpoints_dir = tempfile.mkdtemp() >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=1, ... max_runtime_secs=10, ... export_checkpoints_dir=checkpoints_dir) >>> w2v_model.train(training_frame=words) >>> len(listdir(checkpoints_dir))
-
static
from_external
(external=<class 'h2o.frame.H2OFrame'>)[source]¶ Creates new H2OWord2vecEstimator based on an external model.
- Parameters
external – H2OFrame with an external model
- Returns
H2OWord2vecEstimator instance representing the external model
- Examples
>>> words = h2o.create_frame(rows=10, cols=1, ... string_fraction=1.0, ... missing_fraction=0.0) >>> embeddings = h2o.create_frame(rows=10, cols=100, ... real_fraction=1.0, ... missing_fraction=0.0) >>> word_embeddings = words.cbind(embeddings) >>> w2v_model = H2OWord2vecEstimator.from_external(external=word_embeddings)
-
property
init_learning_rate
¶ Set the starting learning rate
Type:
float
, defaults to0.025
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=3, init_learning_rate=0.05) >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("assistant", 3) >>> print(synonyms)
-
property
max_runtime_secs
¶ Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type:
float
, defaults to0.0
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=1, max_runtime_secs=10) >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("tutor", 3) >>> print(synonyms)
-
property
min_word_freq
¶ This will discard words that appear less than <int> times
Type:
int
, defaults to5
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=1, min_word_freq=4) >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("teacher", 3) >>> print(synonyms)
-
property
norm_model
¶ Use Hierarchical Softmax
Type:
Literal["hsm"]
, defaults to"hsm"
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=1, norm_model="hsm") >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("teacher", 3) >>> print(synonyms)
-
property
pre_trained
¶ Id of a data frame that contains a pre-trained (external) word2vec model
Type:
Union[None, str, H2OFrame]
.- Examples
>>> words = h2o.create_frame(rows=1000,cols=1, ... string_fraction=1.0, ... missing_fraction=0.0) >>> embeddings = h2o.create_frame(rows=1000,cols=100, ... real_fraction=1.0, ... missing_fraction=0.0) >>> word_embeddings = words.cbind(embeddings) >>> w2v_model = H2OWord2vecEstimator(pre_trained=word_embeddings) >>> w2v_model.train(training_frame=word_embeddings) >>> model_id = w2v_model.model_id >>> model = h2o.get_model(model_id)
-
property
sent_sample_rate
¶ - Set threshold for occurrence of words. Those that appear with higher frequency in the training data
will be randomly down-sampled; useful range is (0, 1e-5)
Type:
float
, defaults to0.001
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=1, sent_sample_rate=0.01) >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("teacher", 3) >>> print(synonyms)
-
property
training_frame
¶ Id of the training data frame.
Type:
Union[None, str, H2OFrame]
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator() >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("tutor", 3) >>> print(synonyms)
-
property
vec_size
¶ Set size of word vectors
Type:
int
, defaults to100
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=3, vec_size=50) >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("tutor", 3) >>> print(synonyms)
-
property
window_size
¶ Set max skip length between words
Type:
int
, defaults to5
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=3, window_size=2) >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("teacher", 3) >>> print(synonyms)
-
property
word_model
¶ The word model to use (SkipGram or CBOW)
Type:
Literal["skip_gram", "cbow"]
, defaults to"skip_gram"
.- Examples
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), ... col_names = ["category", "jobtitle"], ... col_types = ["string", "string"], ... header = 1) >>> words = job_titles.tokenize(" ") >>> w2v_model = H2OWord2vecEstimator(epochs=3, word_model="skip_gram") >>> w2v_model.train(training_frame=words) >>> synonyms = w2v_model.find_synonyms("assistant", 3) >>> print(synonyms)
-
property
H2OGridSearch
¶
-
class
h2o.grid.
H2OGridSearch
(model, hyper_params, grid_id=None, search_criteria=None, export_checkpoints_dir=None, recovery_dir=None, parallelism=1)[source]¶ Bases:
h2o.grid.grid_search.H2OGridSearch
Grid Search of a Hyper-Parameter Space for a Model
Examples
>>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_parameters) >>> training_data = h2o.import_file("smalldata/logreg/benign.csv") >>> gs.train(x=[3, 4-11], y=3, training_frame=training_data) >>> gs.show()
-
aic
(train=False, valid=False, xval=False)[source]¶ Get the AIC(s).
If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.
- Parameters
train (bool) – If train is True, then return the AIC value for the training data.
valid (bool) – If valid is True, then return the AIC value for the validation data.
xval (bool) – If xval is True, then return the AIC value for the validation data.
- Returns
The AIC.
- Examples
>>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> prostate[2] = prostate[2].asfactor() >>> prostate[4] = prostate[4].asfactor() >>> prostate[5] = prostate[5].asfactor() >>> prostate[8] = prostate[8].asfactor() >>> predictors = ["AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"] >>> response = "CAPSULE" >>> hyper_params = {'alpha': [0.01,0.5], ... 'lambda': [1e-5,1e-6]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=predictors, y=response, training_frame=prostate) >>> gs.aic()
-
auc
(train=False, valid=False, xval=False)[source]¶ Get the AUC(s).
If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.
- Parameters
train (bool) – If train is True, then return the AUC value for the training data.
valid (bool) – If valid is True, then return the AUC value for the validation data.
xval (bool) – If xval is True, then return the AUC value for the validation data.
- Returns
The AUC.
- Examples
>>> from h2o.estimators import H2OGradientBoostingEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> data = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv") >>> test = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv") >>> x = data.columns >>> y = "response" >>> x.remove(y) >>> data[y] = data[y].asfactor() >>> test[y] = test[y].asfactor() >>> ss = data.split_frame(seed = 1) >>> train = ss[0] >>> valid = ss[1] >>> gbm_params1 = {'learn_rate': [0.01, 0.1], ... 'max_depth': [3, 5, 9], ... 'sample_rate': [0.8, 1.0], ... 'col_sample_rate': [0.2, 0.5, 1.0]} >>> gbm_grid1 = H2OGridSearch(model=H2OGradientBoostingEstimator, ... grid_id='gbm_grid1', ... hyper_params=gbm_params1) >>> gbm_grid1.train(x=x, y=y, ... training_frame=train, ... validation_frame=valid, ... ntrees=100, ... seed=1) >>> gbm_pridperf1 = gbm_grid1.get_grid(sort_by='auc', decreasing=True) >>> best_gbm1 = gbm_gridperf1.models[0] >>> best_gbm_perf1 = best_gbm1.model_performance(test) >>> best_gbm_perf1.auc()
-
aucpr
(train=False, valid=False, xval=False)[source]¶ Get the aucPR (Area Under PRECISION RECALL Curve).
If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.
- Parameters
train (bool) – If train is True, then return the aucpr value for the training data.
valid (bool) – If valid is True, then return the aucpr value for the validation data.
xval (bool) – If xval is True, then return the aucpr value for the validation data.
- Returns
The AUCPR for the models in this grid.
-
biases
(vector_id=0)[source]¶ Return the frame for the respective bias vector.
- Parameters
vector_id – an integer, ranging from 0 to number of layers, that specifies the bias vector to return.
- Returns
an H2OFrame which represents the bias vector identified by vector_id
- Examples
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv") >>> hh = H2ODeepLearningEstimator(hidden=[], ... loss="CrossEntropy", ... export_weights_and_biases=True) >>> hh.train(x=list(range(4)), y=4, training_frame=iris) >>> hh.biases(0)
-
catoffsets
()[source]¶ Categorical offsets for one-hot encoding
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv") >>> hh = H2ODeepLearningEstimator(hidden=[], ... loss="CrossEntropy", ... export_weights_and_biases=True) >>> hh.train(x=list(range(4)), y=4, training_frame=iris) >>> hh.catoffsets()
-
coef
()[source]¶ Return the coefficients that can be applied to the non-standardized data.
Note: standardize = True by default. If set to False, then coef() returns the coefficients that are fit directly.
- Examples
>>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv") >>> hyper_parameters = {'alpha': [0.01,0.5], ... 'lambda': [1e-5,1e-6]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_parameters) >>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data) >>> gs.coef()
-
coef_norm
()[source]¶ Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.
- Examples
>>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv") >>> hyper_parameters = {'alpha': [0.01,0.5], ... 'lambda': [1e-5,1e-6]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_parameters) >>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data) >>> gs.coef_norm()
-
deepfeatures
(test_data, layer)[source]¶ Obtain a hidden layer’s details on a dataset.
- Parameters
test_data – Data to create a feature space on.
layer (int) – Index of the hidden layer.
- Returns
A dictionary of hidden layer details for each model.
- Examples
>>> from h2o.estimators import H2OAutoEncoderEstimator >>> resp = 784 >>> nfeatures = 20 >>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz") >>> train[resp] = train[resp].asfactor() >>> test = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz") >>> test[resp] = test[resp].asfactor() >>> sid = train[0].runif(0) >>> train_unsup = train[sid >= 0.5] >>> train_unsup.pop(resp) >>> train_sup = train[sid < 0.5] >>> ae_model = H2OAutoEncoderEstimator(activation="Tanh", ... hidden=[nfeatures], ... model_id="ae_model", ... epochs=1, ... ignore_const_cols=False, ... reproducible=True, ... seed=1234) >>> ae_model.train(list(range(resp)), training_frame=train_unsup) >>> ae_model.deepfeatures(train_sup[0:resp], 0)
-
property
failed_params
¶ Return a list of failed parameters. :examples:
>>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator >>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv") >>> hyper_parameters = {'alpha': [0.01,0.5], ... 'lambda': [1e-5,1e-6], ... 'beta_epsilon': [0.05]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_parameters) >>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data) >>> gs.failed_params
-
get_grid
(sort_by=None, decreasing=None)[source]¶ Retrieve an H2OGridSearch instance.
Optionally specify a metric by which to sort models and a sort order. Note that if neither cross-validation nor a validation frame is used in the grid search, then the training metrics will display in the “get grid” output. If a validation frame is passed to the grid, and
nfolds = 0
, then the validation metrics will display. However, ifnfolds
> 1, then cross-validation metrics will display even if a validation frame is provided.- Parameters
sort_by (str) – A metric by which to sort the models in the grid space. Choices are:
"logloss"
,"residual_deviance"
,"mse"
,"auc"
,"r2"
,"accuracy"
,"precision"
,"recall"
,"f1"
, etc.decreasing (bool) – Sort the models in decreasing order of metric if true, otherwise sort in increasing order (default).
- Returns
A new H2OGridSearch instance optionally sorted on the specified metric.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.get_grid(sort_by='F1', decreasing=True)
-
get_hyperparams
(id, display=True)[source]¶ Get the hyperparameters of a model explored by grid search.
- Parameters
id (str) – The model id of the model with hyperparameters of interest.
display (bool) – Flag to indicate whether to display the hyperparameter names.
- Returns
A list of the hyperparameters for the specified model.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> best_model_id = gs.get_grid(sort_by='F1', ... decreasing=True).model_ids[0] >>> gs.get_hyperparams(best_model_id)
-
get_hyperparams_dict
(id, display=True)[source]¶ Derived and returned the model parameters used to train the particular grid search model.
- Parameters
id (str) – The model id of the model with hyperparameters of interest.
display (bool) – Flag to indicate whether to display the hyperparameter names.
- Returns
A dict of model pararmeters derived from the hyper-parameters used to train this particular model.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> best_model_id = gs.get_grid(sort_by='F1', ... decreasing=True).model_ids[0] >>> gs.get_hyperparams_dict(best_model_id)
-
get_xval_models
(key=None)[source]¶ Return a Model object.
- Parameters
key (str) – If None, return all cross-validated models; otherwise return the model specified by the key.
- Returns
A model or a list of models.
- Examples
>>> from h2o.estimators import H2OGradientBoostingEstimator >>> fr = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate_train.csv") >>> m = H2OGradientBoostingEstimator(nfolds=10, ... ntrees=10, ... keep_cross_validation_models=True) >>> m.train(x=list(range(2,fr.ncol)), y=1, training_frame=fr) >>> m.get_xval_models()
-
gini
(train=False, valid=False, xval=False)[source]¶ Get the Gini Coefficient(s).
If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.
- Parameters
train (bool) – If train is True, then return the Gini Coefficient value for the training data.
valid (bool) – If valid is True, then return the Gini Coefficient value for the validation data.
xval (bool) – If xval is True, then return the Gini Coefficient value for the cross validation data.
- Returns
The Gini Coefficient for the models in this grid.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.gini()
-
property
grid_id
¶ A key that identifies this grid search object in H2O.
- Examples
>>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator >>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv") >>> hyper_parameters = {'alpha': [0.01,0.5], ... 'lambda': [1e-5,1e-6]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_parameters) >>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data) >>> gs.grid_id
-
property
hyper_names
¶ Return the hyperparameter names.
- Examples
>>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator >>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv") >>> hyper_parameters = {'alpha': [0.01,0.5], ... 'lambda': [1e-5,1e-6]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_parameters) >>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data) >>> gs.hyper_names
-
is_cross_validated
()[source]¶ Return True if the model was cross-validated.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.is_cross_validated()
-
join
()[source]¶ Wait until grid finishes computing.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), hyper_params) >>> gs.start(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.join()
-
property
key
¶ - Returns
the unique key representing the object on the backend
-
logloss
(train=False, valid=False, xval=False)[source]¶ Get the Log Loss(s).
If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.
- Parameters
train (bool) – If train is True, then return the Log Loss value for the training data.
valid (bool) – If valid is True, then return the Log Loss value for the validation data.
xval (bool) – If xval is True, then return the Log Loss value for the cross validation data.
- Returns
The Log Loss for this binomial model.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.logloss()
-
mean_residual_deviance
(train=False, valid=False, xval=False)[source]¶ Get the Mean Residual Deviances(s).
If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.
- Parameters
train (bool) – If train is True, then return the Mean Residual Deviance value for the training data.
valid (bool) – If valid is True, then return the Mean Residual Deviance value for the validation data.
xval (bool) – If xval is True, then return the Mean Residual Deviance value for the cross validation data.
- Returns
The Mean Residual Deviance for this regression model.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.mean_residual_deviance()
-
property
model_ids
¶ Returns model ids.
- Examples
>>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator >>> training_data = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/logreg/benign.csv") >>> hyper_parameters = {'alpha': [0.01,0.5], ... 'lambda': [1e-5,1e-6]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_parameters) >>> gs.train(x=range(3)+range(4,11), y=3, training_frame=training_data) >>> gs.model_ids
-
model_performance
(test_data=None, train=False, valid=False, xval=False)[source]¶ Generate model metrics for this model on test_data.
- Parameters
test_data – Data set for which model metrics shall be computed against. All three of train, valid and xval arguments are ignored if test_data is not None.
train – Report the training metrics for the model.
valid – Report the validation metrics for the model.
xval – Report the validation metrics for the model.
- Returns
An object of class H2OModelMetrics.
- Examples
>>> from h2o.estimators import H2OGradientBoostingEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> data = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv") >>> test = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv") >>> x = data.columns >>> y = "response" >>> x.remove(y) >>> data[y] = data[y].asfactor() >>> test[y] = test[y].asfactor() >>> ss = data.split_frame(seed = 1) >>> train = ss[0] >>> valid = ss[1] >>> gbm_params1 = {'learn_rate': [0.01, 0.1], ... 'max_depth': [3, 5, 9], ... 'sample_rate': [0.8, 1.0], ... 'col_sample_rate': [0.2, 0.5, 1.0]} >>> gbm_grid1 = H2OGridSearch(model=H2OGradientBoostingEstimator, ... grid_id='gbm_grid1', ... hyper_params=gbm_params1) >>> gbm_grid1.train(x=x, y=y, ... training_frame=train, ... validation_frame=valid, ... ntrees=100, ... seed=1) >>> gbm_gridperf1 = gbm_grid1.get_grid(sort_by='auc', decreasing=True) >>> best_gbm1 = gbm_gridperf1.models[0] >>> best_gbm1.model_performance(test)
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mse
(train=False, valid=False, xval=False)[source]¶ Get the MSE(s).
If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.
- Parameters
train (bool) – If train is True, then return the MSE value for the training data.
valid (bool) – If valid is True, then return the MSE value for the validation data.
xval (bool) – If xval is True, then return the MSE value for the cross validation data.
- Returns
The MSE for this regression model.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.mse()
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normmul
()[source]¶ Normalization/Standardization multipliers for numeric predictors.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.normmul()
-
normsub
()[source]¶ Normalization/Standardization offsets for numeric predictors.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.normsub()
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null_degrees_of_freedom
(train=False, valid=False, xval=False)[source]¶ Retreive the null degress of freedom if this model has the attribute, or None otherwise.
- Parameters
train (bool) – Get the null dof for the training set. If both train and valid are False, then train is selected by default.
valid (bool) – Get the null dof for the validation set. If both train and valid are True, then train is selected by default.
xval (bool) – Get the null dof for the cross-validated models.
- Returns
the null dof, or None if it is not present.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.null_degrees_of_freedom()
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null_deviance
(train=False, valid=False, xval=False)[source]¶ Retreive the null deviance if this model has the attribute, or None otherwise.
- Parameters
train (bool) – Get the null deviance for the training set. If both train and valid are False, then train is selected by default.
valid (bool) – Get the null deviance for the validation set. If both train and valid are True, then train is selected by default.
xval (bool) – Get the null deviance for the cross-validated models.
- Returns
the null deviance, or None if it is not present.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.null_deviance()
-
pprint_coef
()[source]¶ Pretty print the coefficents table (includes normalized coefficients).
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.pprint_coef()
-
predict
(test_data)[source]¶ Predict on a dataset.
- Parameters
test_data (H2OFrame) – Data to be predicted on.
- Returns
H2OFrame filled with predictions.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.predict(benign)
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r2
(train=False, valid=False, xval=False)[source]¶ Return the R^2 for this regression model.
The R^2 value is defined to be
1 - MSE/var
, wherevar
is computed assigma^2
.If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.
- Parameters
train (bool) – If train is True, then return the R^2 value for the training data.
valid (bool) – If valid is True, then return the R^2 value for the validation data.
xval (bool) – If xval is True, then return the R^2 value for the cross validation data.
- Returns
The R^2 for this regression model.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.r2()
-
residual_degrees_of_freedom
(train=False, valid=False, xval=False)[source]¶ Retreive the residual degress of freedom if this model has the attribute, or None otherwise.
- Parameters
train (bool) – Get the residual dof for the training set. If both train and valid are False, then train is selected by default.
valid (bool) – Get the residual dof for the validation set. If both train and valid are True, then train is selected by default.
xval (bool) – Get the residual dof for the cross-validated models.
- Returns
the residual degrees of freedom, or None if they are not present.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.residual_degrees_of_freedom()
-
residual_deviance
(train=False, valid=False, xval=False)[source]¶ Retreive the residual deviance if this model has the attribute, or None otherwise.
- Parameters
train (bool) – Get the residual deviance for the training set. If both train and valid are False, then train is selected by default.
valid (bool) – Get the residual deviance for the validation set. If both train and valid are True, then train is selected by default.
xval (bool) – Get the residual deviance for the cross-validated models.
- Returns
the residual deviance, or None if it is not present.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.residual_deviance()
-
respmul
()[source]¶ Normalization/Standardization multipliers for numeric response.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.respmul()
-
respsub
()[source]¶ Normalization/Standardization offsets for numeric response.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.respsub()
-
resume
(recovery_dir=None, **kwargs)[source]¶ Resume previously stopped grid training.
- Parameters
recovery_dir – When specified, the grid and all necessary data (frames, models) will be saved to this directory (use HDFS or other distributed file-system). Should the cluster crash during training, the grid can be reloaded from this directory via
h2o.load_grid
, and training can be resumed.
-
scoring_history
()[source]¶ Retrieve model scoring history.
- Returns
Score history (H2OTwoDimTable)
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.scoring_history()
-
show
()[source]¶ Print models sorted by metric.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.show()
-
sort_by
(metric, increasing=True)[source]¶ grid.sort_by() is deprecated; use grid.get_grid() instead
Deprecated since 2016-12-12, use grid.get_grid() instead.
-
sorted_metric_table
()[source]¶ Retrieve summary table of an H2O Grid Search.
- Returns
The summary table as an H2OTwoDimTable or a Pandas DataFrame.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.sorted_metric_table()
-
start
(x, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, **params)[source]¶ Asynchronous model build by specifying the predictor columns, response column, and any additional frame-specific values.
To block for results, call
join()
.- Parameters
x – A list of column names or indices indicating the predictor columns.
y – An index or a column name indicating the response column.
training_frame – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
offset_column – The name or index of the column in training_frame that holds the offsets.
fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
weights_column – The name or index of the column in training_frame that holds the per-row weights.
validation_frame – H2OFrame with validation data to be scored on while training.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), hyper_params) >>> gs.start(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.join()
-
summary
(header=True)[source]¶ Print a detailed summary of the explored models.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.summary()
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train
(x=None, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, **params)[source]¶ Train the model synchronously (i.e. do not return until the model finishes training).
To train asynchronously call
start()
.- Parameters
x – A list of column names or indices indicating the predictor columns.
y – An index or a column name indicating the response column.
training_frame – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
offset_column – The name or index of the column in training_frame that holds the offsets.
fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
weights_column – The name or index of the column in training_frame that holds the per-row weights.
validation_frame – H2OFrame with validation data to be scored on while training.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance)
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varimp
(use_pandas=False)[source]¶ Pretty print the variable importances, or return them in a list/pandas DataFrame.
- Parameters
use_pandas (bool) – If True, then the variable importances will be returned as a pandas data frame.
- Returns
A dictionary of lists or Pandas DataFrame instances.
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> insurance = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") >>> insurance["offset"] = insurance["Holders"].log() >>> insurance["Group"] = insurance["Group"].asfactor() >>> insurance["Age"] = insurance["Age"].asfactor() >>> insurance["District"] = insurance["District"].asfactor() >>> hyper_params = {'huber_alpha': [0.2,0.5], ... 'quantile_alpha': [0.2,0.6]} >>> from h2o.estimators import H2ODeepLearningEstimator >>> gs = H2OGridSearch(H2ODeepLearningEstimator(epochs=5), ... hyper_params) >>> gs.train(x=list(range(3)),y="Claims", training_frame=insurance) >>> gs.varimp(use_pandas=True)
-
weights
(matrix_id=0)[source]¶ Return the frame for the respective weight matrix.
- Param
matrix_id: an integer, ranging from 0 to number of layers, that specifies the weight matrix to return.
- Returns
an H2OFrame which represents the weight matrix identified by matrix_id
- Examples
>>> from h2o.estimators import H2ODeepLearningEstimator >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv") >>> hh = H2ODeepLearningEstimator(hidden=[], ... loss="CrossEntropy", ... export_weights_and_biases=True) >>> hh.train(x=list(range(4)), y=4, training_frame=iris) >>> hh.weights(0)
-
xval_keys
()[source]¶ Model keys for the cross-validated model.
- Examples
>>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> from h2o.grid.grid_search import H2OGridSearch >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv") >>> y = 3 >>> x = [4,5,6,7,8,9,10,11] >>> hyper_params = {'alpha': [0.01,0.3,0.5], ... 'lambda': [1e-5, 1e-6, 1e-7]} >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), ... hyper_params) >>> gs.train(x=x,y=y, training_frame=benign) >>> gs.xval_keys()
-