H2O Module¶

h2o¶

h2o.h2o.as_list(data, use_pandas=True)[source]¶

Convert an H2O data object into a python-specific object.

WARNING: This will pull all data local!

If Pandas is available (and use_pandas is True), then pandas will be used to parse the data frame. Otherwise, a list-of-lists populated by character data will be returned (so the types of data will all be str).

Parameters:	data : H2OFrame An H2O data object. use_pandas : bool Try to use pandas for reading in the data. :return: List of list (Rows x Columns).

h2o.h2o.autoencoder(x, training_frame=None, model_id=None, overwrite_with_best_model=None, checkpoint=None, use_all_factor_levels=None, activation=None, hidden=None, epochs=None, train_samples_per_iteration=None, target_ratio_comm_to_comp=None, seed=None, adaptive_rate=None, rho=None, epsilon=None, rate=None, rate_annealing=None, rate_decay=None, momentum_start=None, momentum_ramp=None, momentum_stable=None, nesterov_accelerated_gradient=None, input_dropout_ratio=None, hidden_dropout_ratios=None, l1=None, l2=None, max_w2=None, initial_weight_distribution=None, initial_weight_scale=None, loss=None, distribution=None, tweedie_power=None, score_interval=None, score_training_samples=None, score_duty_cycle=None, classification_stop=None, regression_stop=None, quiet_mode=None, max_confusion_matrix_size=None, max_hit_ratio_k=None, balance_classes=None, class_sampling_factors=None, max_after_balance_size=None, diagnostics=None, variable_importances=None, fast_mode=None, ignore_const_cols=None, force_load_balance=None, replicate_training_data=None, single_node_mode=None, shuffle_training_data=None, sparse=None, col_major=None, average_activation=None, sparsity_beta=None, max_categorical_features=None, reproducible=None, export_weights_and_biases=None, max_runtime_secs=None)[source]¶

Build unsupervised auto encoder using H2O Deeplearning

Parameters:

x : H2OFrame An H2OFrame containing the predictors in the model. training_frame : H2OFrame (Optional) An H2OFrame. Only used to retrieve weights, offset, or nfolds columns, if they aren’t already provided in x. model_id : str (Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated. overwrite_with_best_model : bool Logical. If True, overwrite the final model with the best model found during training. Defaults to True. checkpoint : H2ODeepLearningModel “Model checkpoint (either key or H2ODeepLearningModel) to resume training with.” use_all_factor_levels : bool Logical. Use all factor levels of categorical variance. Otherwise the first factor level is omitted (without loss of accuracy). Useful for variable importances and auto-enabled for autoencoder. activation : str A string indicating the activation function to use. Must be either “Tanh”, “TanhWithDropout”, “Rectifier”, “RectifierWithDropout”, “Maxout”, or “MaxoutWithDropout” hidden : list Hidden layer sizes (e.g. c(100,100)) epochs : float How many times the dataset should be iterated (streamed), can be fractional train_samples_per_iteration : int Number of training samples (globally) per MapReduce iteration. Special values are: 0 one epoch; -1 all available data (e.g., replicated training data); or -2 auto-tuning (default) target_ratio_comm_to_comp : float Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration=-2 (auto-tuning). Higher values can lead to faster convergence. seed : int Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded adaptive_rate : bool Logical. Adaptive learning rate (ADAELTA) rho : float Adaptive learning rate time decay factor (similarity to prior updates) epsilon : float Adaptive learning rate parameter, similar to learn rate annealing during initial training phase. Typical values are between 1.0e-10 and 1.0e-4 rate : float Learning rate (higher => less stable, lower => slower convergence) rate_annealing : float Learning rate annealing: eqn{(rate)/(1 + rate_annealing*samples) rate_decay : float Learning rate decay factor between layers (N-th layer: eqn{rate*lpha^(N-1)) momentum_start : float Initial momentum at the beginning of training (try 0.5) momentum_ramp : int Number of training samples for which momentum increases momentum_stable : float Final momentum after the amp is over (try 0.99) nesterov_accelerated_gradient : bool Logical. Use Nesterov accelerated gradient (recommended) input_dropout_ratio : float A fraction of the features for each training row to be omitted from training in order to improve generalization (dimension sampling). hidden_dropout_ratios : float Input layer dropout ratio (can improve generalization) specify one value per hidden layer, defaults to 0.5 l1 : float L1 regularization (can add stability and improve generalization, causes many weights to become 0) l2: float L2 regularization (can add stability and improve generalization, causes many weights to be small) max_w2 : float Constraint for squared sum of incoming weights per unit (e.g. Rectifier) initial_weight_distribution : str Can be “Uniform”, “UniformAdaptive”, or “Normal” initial_weight_scale : str Uniform: -value ... value, Normal: stddev loss : str Loss function: “Automatic”, “CrossEntropy” (for classification only), “Quadratic”, “Absolute” (experimental) or “Huber” (experimental) distribution : str A character string. The distribution function of the response. Must be “AUTO”, “bernoulli”, “multinomial”, “poisson”, “gamma”, “tweedie”, “laplace”, “huber” or “gaussian” tweedie_power : float Tweedie power (only for Tweedie distribution, must be between 1 and 2) score_interval : int Shortest time interval (in secs) between model scoring score_training_samples : int Number of training set samples for scoring (0 for all) score_duty_cycle : float Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring) classification_stop : float Stopping criterion for classification error fraction on training data (-1 to disable) regression_stop : float Stopping criterion for regression error (MSE) on training data (-1 to disable) stopping_rounds : int Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve (by stopping_tolerance) for k=stopping_rounds scoring events. Can only trigger after at least 2k scoring events. Use 0 to disable. stopping_metric : str Metric to use for convergence checking, only for _stopping_rounds > 0 Can be one of “AUTO”, “MSE”. stopping_tolerance : float Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) quiet_mode : bool Enable quiet mode for less output to standard output max_confusion_matrix_size : int Max. size (number of classes) for confusion matrices to be shown max_hit_ratio_k : float Max number (top K) of predictions to use for hit ratio computation(for multi-class only, 0 to disable) balance_classes : bool Balance training data class counts via over/under-sampling (for imbalanced data) class_sampling_factors : list 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. max_after_balance_size : float Maximum relative size of the training data after balancing class counts (can be less than 1.0) diagnostics : bool Enable diagnostics for hidden layers variable_importances : bool Compute variable importances for input features (Gedeon method) - can be slow for large networks) fast_mode : bool Enable fast mode (minor approximations in back-propagation) ignore_const_cols : bool Ignore constant columns (no information can be gained anyway) force_load_balance : bool Force extra load balancing to increase training speed for small datasets (to keep all cores busy) replicate_training_data : bool Replicate the entire training dataset onto every node for faster training single_node_mode : bool Run on a single node for fine-tuning of model parameters shuffle_training_data : bool Enable shuffling of training data (recommended if training data is replicated and train_samples_per_iteration is close to eqn{numRows*numNodes sparse : bool Sparse data handling (Experimental) col_major : bool Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow down backpropagation (Experimental) average_activation : float Average activation for sparse auto-encoder (Experimental) sparsity_beta : float Sparsity regularization (Experimental) max_categorical_features : int Max. number of categorical features, enforced via hashing Experimental) reproducible : bool Force reproducibility on small data (will be slow - only uses 1 thread) export_weights_and_biases : bool Whether to export Neural Network weights and biases to H2O Frames” :return: H2OAutoEncoderModel

h2o.h2o.cluster_info()[source]¶

Display the current H2O cluster information.

Returns:	None

h2o.h2o.cluster_status()[source]¶

TODO: This isn’t really a cluster status... it’s a node status check for the node we’re connected to. This is possibly confusing because this can come back without warning, but if a user tries to do any remoteSend, they will get a “cloud sick warning”

Retrieve information on the status of the cluster running H2O. :return: None

h2o.h2o.create_frame(id=None, rows=10000, cols=10, randomize=True, value=0, real_range=100, categorical_fraction=0.2, factors=100, integer_fraction=0.2, integer_range=100, binary_fraction=0.1, binary_ones_fraction=0.02, missing_fraction=0.01, response_factors=2, has_response=False, seed=None)[source]¶

Data Frame Creation in H2O. Creates a data frame in H2O with real-valued, categorical, integer, and binary columns specified by the user.

Parameters:

id : str A string indicating the destination key. If empty, this will be auto-generated by H2O. rows : int The number of rows of data to generate. cols : int The number of columns of data to generate. Excludes the response column if has_response == True. randomize : bool A logical value indicating whether data values should be randomly generated. This must be TRUE if either categorical_fraction or integer_fraction is non-zero. value : int If randomize == FALSE, then all real-valued entries will be set to this value. real_range : float The range of randomly generated real values. categorical_fraction : float The fraction of total columns that are categorical. factors : int The number of (unique) factor levels in each categorical column. integer_fraction : float The fraction of total columns that are integer-valued. integer_range : list The range of randomly generated integer values. binary_fraction : float The fraction of total columns that are binary-valued. binary_ones_fraction : float The fraction of values in a binary column that are set to 1. missing_fraction : float The fraction of total entries in the data frame that are set to NA. response_factors : int If has_response == TRUE, then this is the number of factor levels in the response column. has_response : bool A logical value indicating whether an additional response column should be pre-pended to the final H2O data frame. If set to TRUE, the total number of columns will be cols+1. seed : int A seed used to generate random values when randomize = TRUE. :return: the H2OFrame that was created

h2o.h2o.deeplearning(x, y=None, validation_x=None, validation_y=None, training_frame=None, model_id=None, overwrite_with_best_model=None, validation_frame=None, checkpoint=None, autoencoder=None, use_all_factor_levels=None, activation=None, hidden=None, epochs=None, train_samples_per_iteration=None, target_ratio_comm_to_comp=None, seed=None, adaptive_rate=None, rho=None, epsilon=None, rate=None, rate_annealing=None, rate_decay=None, momentum_start=None, momentum_ramp=None, momentum_stable=None, nesterov_accelerated_gradient=None, input_dropout_ratio=None, hidden_dropout_ratios=None, l1=None, l2=None, max_w2=None, initial_weight_distribution=None, initial_weight_scale=None, loss=None, distribution=None, tweedie_power=None, score_interval=None, score_training_samples=None, score_validation_samples=None, score_duty_cycle=None, classification_stop=None, regression_stop=None, stopping_rounds=None, stopping_metric=None, stopping_tolerance=None, quiet_mode=None, max_confusion_matrix_size=None, max_hit_ratio_k=None, balance_classes=None, class_sampling_factors=None, max_after_balance_size=None, score_validation_sampling=None, diagnostics=None, variable_importances=None, fast_mode=None, ignore_const_cols=None, force_load_balance=None, replicate_training_data=None, single_node_mode=None, shuffle_training_data=None, sparse=None, col_major=None, average_activation=None, sparsity_beta=None, max_categorical_features=None, reproducible=None, export_weights_and_biases=None, offset_column=None, weights_column=None, nfolds=None, fold_column=None, fold_assignment=None, keep_cross_validation_predictions=None, max_runtime_secs=None)[source]¶

Build a supervised Deep Learning model Performs Deep Learning neural networks on an H2OFrame

Parameters:

x : H2OFrame An H2OFrame containing the predictors in the model. y : H2OFrame An H2OFrame of the response variable in the model. training_frame : H2OFrame (Optional) An H2OFrame. Only used to retrieve weights, offset, or nfolds columns, if they aren’t already provided in x. model_id : str (Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated. overwrite_with_best_model : bool Logical. If True, overwrite the final model with the best model found during training. Defaults to True. validation_frame : H2OFrame (Optional) An H2OFrame object indicating the validation dataset used to construct the confusion matrix. If left blank, this defaults to the training data when nfolds = 0 checkpoint : H2ODeepLearningModel “Model checkpoint (either key or H2ODeepLearningModel) to resume training with.” autoencoder : bool Enable auto-encoder for model building. use_all_factor_levels : bool Logical. Use all factor levels of categorical variance. Otherwise the first factor level is omitted (without loss of accuracy). Useful for variable importances and auto-enabled for autoencoder. activation : str A string indicating the activation function to use. Must be either “Tanh”, “TanhWithDropout”, “Rectifier”, “RectifierWithDropout”, “Maxout”, or “MaxoutWithDropout” hidden : list Hidden layer sizes (e.g. c(100,100)) epochs : float How many times the dataset should be iterated (streamed), can be fractional train_samples_per_iteration : int Number of training samples (globally) per MapReduce iteration. Special values are: 0 one epoch; -1 all available data (e.g., replicated training data); or -2 auto-tuning (default) target_ratio_comm_to_comp : float Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration=-2 (auto-tuning). Higher values can lead to faster convergence. seed : int Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded adaptive_rate : bool Logical. Adaptive learning rate (ADAELTA) rho : float Adaptive learning rate time decay factor (similarity to prior updates) epsilon : float Adaptive learning rate parameter, similar to learn rate annealing during initial training phase. Typical values are between 1.0e-10 and 1.0e-4 rate : float Learning rate (higher => less stable, lower => slower convergence) rate_annealing : float Learning rate annealing: eqn{(rate)/(1 + rate_annealing*samples) rate_decay : float Learning rate decay factor between layers (N-th layer: eqn{rate*lpha^(N-1)) momentum_start : float Initial momentum at the beginning of training (try 0.5) momentum_ramp : float Number of training samples for which momentum increases momentum_stable : float Final momentum after the amp is over (try 0.99) nesterov_accelerated_gradient : bool Logical. Use Nesterov accelerated gradient (recommended) input_dropout_ratio : float A fraction of the features for each training row to be omitted from training in order to improve generalization (dimension sampling). hidden_dropout_ratios : float Input layer dropout ratio (can improve generalization) specify one value per hidden layer, defaults to 0.5 l1 : float L1 regularization (can add stability and improve generalization, causes many weights to become 0) l2 : float L2 regularization (can add stability and improve generalization, causes many weights to be small) max_w2 : float Constraint for squared sum of incoming weights per unit (e.g. Rectifier) initial_weight_distribution : str Can be “Uniform”, “UniformAdaptive”, or “Normal” initial_weight_scale : str Uniform: -value ... value, Normal: stddev loss : str Loss function: “Automatic”, “CrossEntropy” (for classification only), “Quadratic”, “Absolute” (experimental) or “Huber” (experimental) distribution : str A character string. The distribution function of the response. Must be “AUTO”, “bernoulli”, “multinomial”, “poisson”, “gamma”, “tweedie”, “laplace”, “huber” or “gaussian” tweedie_power : float Tweedie power (only for Tweedie distribution, must be between 1 and 2) score_interval : int Shortest time interval (in secs) between model scoring score_training_samples : int Number of training set samples for scoring (0 for all) score_validation_samples : int Number of validation set samples for scoring (0 for all) score_duty_cycle : float Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring) classification_stop : float Stopping criterion for classification error fraction on training data (-1 to disable) regression_stop : float Stopping criterion for regression error (MSE) on training data (-1 to disable) stopping_rounds : int Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve (by stopping_tolerance) for k=stopping_rounds scoring events. Can only trigger after at least 2k scoring events. Use 0 to disable. stopping_metric : str Metric to use for convergence checking, only for _stopping_rounds > 0 Can be one of “AUTO”, “deviance”, “logloss”, “MSE”, “AUC”, “r2”, “misclassification”. stopping_tolerance : float Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) quiet_mode : bool Enable quiet mode for less output to standard output max_confusion_matrix_size : int Max. size (number of classes) for confusion matrices to be shown max_hit_ratio_k : float Max number (top K) of predictions to use for hit ratio computation(for multi-class only, 0 to disable) balance_classes : bool Balance training data class counts via over/under-sampling (for imbalanced data) class_sampling_factors : list 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. max_after_balance_size : float Maximum relative size of the training data after balancing class counts (can be less than 1.0) score_validation_sampling : Method used to sample validation dataset for scoring diagnostics : bool Enable diagnostics for hidden layers variable_importances : bool Compute variable importances for input features (Gedeon method) - can be slow for large networks) fast_mode : bool Enable fast mode (minor approximations in back-propagation) ignore_const_cols : bool Ignore constant columns (no information can be gained anyway) force_load_balance : bool Force extra load balancing to increase training speed for small datasets (to keep all cores busy) replicate_training_data : bool Replicate the entire training dataset onto every node for faster training single_node_mode : bool Run on a single node for fine-tuning of model parameters shuffle_training_data : bool Enable shuffling of training data (recommended if training data is replicated and train_samples_per_iteration is close to eqn{numRows*numNodes sparse : bool Sparse data handling (more efficient for data with lots of 0 values) col_major : bool Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow down backpropagation (Experimental) average_activation : float Average activation for sparse auto-encoder (Experimental) sparsity_beta : bool Sparsity regularization (Experimental) max_categorical_features : int Max. number of categorical features, enforced via hashing Experimental) reproducible : bool Force reproducibility on small data (will be slow - only uses 1 thread) export_weights_and_biases : bool Whether to export Neural Network weights and biases to H2O Frames” offset_column : H2OFrame Specify the offset column. weights_column : H2OFrame Specify the weights column. nfolds : int (Optional) Number of folds for cross-validation. If nfolds >= 2, then validation must remain empty. fold_column : H2OFrame (Optional) Column with cross-validation fold index assignment per observation fold_assignment : str Cross-validation fold assignment scheme, if fold_column is not specified Must be “AUTO”, “Random” or “Modulo” keep_cross_validation_predictions : bool Whether to keep the predictions of the cross-validation models :return: Return a new classifier or regression model.

h2o.h2o.download_all_logs(dirname='.', filename=None)[source]¶

Download H2O Log Files to Disk

Parameters:	dirname : str, optional A character string indicating the directory that the log file should be saved in. filename : str, optional A string indicating the name that the CSV file should be
Returns:	Path of logs written.

h2o.h2o.download_csv(data, filename)[source]¶

Download an H2O data set to a CSV file on the local disk.

Warning: Files located on the H2O server may be very large! Make sure you have enough hard drive space to accommodate the entire file.

Parameters:	data : H2OFrame An H2OFrame object to be downloaded. filename : str A string indicating the name that the CSV file should be should be saved to.

h2o.h2o.download_pojo(model, path='', get_jar=True)[source]¶

Download the POJO for this model to the directory specified by path (no trailing slash!). If path is “”, then dump to screen.

Parameters:	model : H2OModel Retrieve this model’s scoring POJO. path : str An absolute path to the directory where POJO should be saved. get_jar : bool Retrieve the h2o-genmodel.jar also.

h2o.h2o.export_file(frame, path, force=False)[source]¶

Export a given H2OFrame to a path on the machine this python session is currently connected to. To view the current session, call h2o.cluster_info().

Parameters:	frame : H2OFrame The Frame to save to disk. path : str The path to the save point on disk. force : bool Overwrite any preexisting file with the same path

h2o.h2o.frame(frame_id, exclude='')[source]¶

Retrieve metadata for an id that points to a Frame.

Parameters:	frame_id : str A pointer to a Frame in H2O.
Returns:	Python dict containing the frame meta-information

h2o.h2o.frames()[source]¶

Retrieve all the Frames.

Returns:	Meta information on the frames

h2o.h2o.gbm(x, y, validation_x=None, validation_y=None, training_frame=None, model_id=None, distribution=None, tweedie_power=None, ntrees=None, max_depth=None, min_rows=None, learn_rate=None, sample_rate=None, col_sample_rate=None, col_sample_rate_per_tree=None, nbins=None, nbins_top_level=None, nbins_cats=None, validation_frame=None, balance_classes=None, max_after_balance_size=None, seed=None, build_tree_one_node=None, nfolds=None, fold_column=None, fold_assignment=None, keep_cross_validation_predictions=None, score_each_iteration=None, offset_column=None, weights_column=None, do_future=None, checkpoint=None, stopping_rounds=None, stopping_metric=None, stopping_tolerance=None, max_runtime_secs=None)[source]¶

Builds gradient boosted classification trees, and gradient boosted regression trees on a parsed data set. The default distribution function will guess the model type based on the response column typerun properly the response column must be an numeric for “gaussian” or an enum for “bernoulli” or “multinomial”.

Parameters:

x : H2OFrame An H2OFrame containing the predictors in the model. y : H2OFrame An H2OFrame of the response variable in the model. training_frame : H2OFrame (Optional) An H2OFrame. Only used to retrieve weights, offset, or nfolds columns, if they aren’t already provided in x. model_id : str (Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated. distribution : str A character string. The distribution function of the response. Must be “AUTO”, “bernoulli”, “multinomial”, “poisson”, “gamma”, “tweedie”, “laplace” or “gaussian” tweedie_power : float Tweedie power (only for Tweedie distribution, must be between 1 and 2) ntrees : int A non-negative integer that determines the number of trees to grow. max_depth : int Maximum depth to grow the tree. min_rows : int Minimum number of rows to assign to terminal nodes. learn_rate : float Learning rate (from 0.0 to 1.0) sample_rate : float Row sample rate (from 0.0 to 1.0) col_sample_rate : float Column sample rate (from 0.0 to 1.0) col_sample_rate_per_tree : float Column sample rate per tree (from 0.0 to 1.0) nbins : int For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point. nbins_top_level : int 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. nbins_cats : int For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. validation_frame : H2OFrame An H2OFrame object indicating the validation dataset used to contruct the confusion matrix. If left blank, this defaults to the training data when nfolds = 0 balance_classes : bool logical, indicates whether or not to balance training data class counts via over/under-sampling (for imbalanced data) max_after_balance_size : float Maximum relative size of the training data after balancing class counts (can be less than 1.0). Ignored if balance_classes is False, which is the default behavior. seed : int Seed for random numbers (affects sampling when balance_classes=T) build_tree_one_node : bool Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. nfolds : int (Optional) Number of folds for cross-validation. If nfolds >= 2, then validation must remain empty. fold_column : H2OFrame (Optional) Column with cross-validation fold index assignment per observation fold_assignment : str Cross-validation fold assignment scheme, if fold_column is not specified Must be “AUTO”, “Random” or “Modulo” keep_cross_validation_predictions : bool Whether to keep the predictions of the cross-validation models score_each_iteration : bool Attempts to score each tree. offset_column : H2OFrame Specify the offset column. weights_column : H2OFrame Specify the weights column. stopping_rounds : int Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve (by stopping_tolerance) for k=stopping_rounds scoring events. Can only trigger after at least 2k scoring events. Use 0 to disable. stopping_metric : str Metric to use for convergence checking, only for _stopping_rounds > 0 Can be one of “AUTO”, “deviance”, “logloss”, “MSE”, “AUC”, “r2”, “misclassification”. stopping_tolerance : float Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) :return: A new classifier or regression model.

h2o.h2o.get_frame(frame_id)[source]¶

Obtain a handle to the frame in H2O with the frame_id key.

Returns:	H2OFrame

h2o.h2o.get_future_model(future_model)[source]¶

Waits for the future model to finish building, and then returns the model.

Parameters:	future_model : H2OModelFuture an H2OModelFuture object
Returns:	H2OEstimator

h2o.h2o.get_model(model_id)[source]¶

Return the specified model

Parameters:	model_id : str The model identification in h2o
Returns:	Subclass of H2OEstimator

h2o.h2o.get_timezone()[source]¶

Get the Time Zone on the H2O Cloud

Returns:	the time zone (string)

h2o.h2o.glm(x, y, validation_x=None, validation_y=None, training_frame=None, model_id=None, validation_frame=None, max_iterations=None, beta_epsilon=None, solver=None, standardize=None, family=None, link=None, tweedie_variance_power=None, tweedie_link_power=None, alpha=None, prior=None, lambda_search=None, nlambdas=None, lambda_min_ratio=None, beta_constraints=None, offset_column=None, weights_column=None, nfolds=None, fold_column=None, fold_assignment=None, keep_cross_validation_predictions=None, intercept=None, Lambda=None, max_active_predictors=None, do_future=None, checkpoint=None, max_runtime_secs=None)[source]¶

Build a Generalized Linear Model Fit a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.

Parameters:

x : H2OFrame An H2OFrame containing the predictors in the model. y : H2OFrame An H2OFrame of the response variable in the model. training_frame : H2OFrame (Optional) An H2OFrame. Only used to retrieve weights, offset, or nfolds columns, if they aren’t already provided in x. model_id : str (Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated. validation_frame : H2OFrame An H2OFrame object containing the variables in the model. max_iterations : int A non-negative integer specifying the maximum number of iterations. beta_epsilon : int A non-negative number specifying the magnitude of the maximum difference between the coefficient estimates from successive iterations. Defines the convergence criterion for h2o.glm. solver : str A character string specifying the solver used: IRLSM (supports more features), L_BFGS (scales better for datasets with many columns) standardize : bool A logical value indicating whether the numeric predictors should be standardized to have a mean of 0 and a variance of 1 prior to training the models. family : str A character string specifying the distribution of the model: gaussian, binomial, poisson, gamma, tweedie. link : str A character string specifying the link function. The default is the canonical link for the family. The supported links for each of the family specifications are: “gaussian”: “identity”, “log”, “inverse” “binomial”: “logit”, “log” “poisson”: “log”, “identity” “gamma”: “inverse”, “log”, “identity” “tweedie”: “tweedie” tweedie_variance_power : int numeric specifying the power for the variance function when family = “tweedie”. tweedie_link_power : int A numeric specifying the power for the link function when family = “tweedie”. alpha : float A numeric in [0, 1] specifying the elastic-net mixing parameter. The elastic-net penalty is defined to be: eqn{P(lpha,eta) = (1-lpha)/2||eta||_2^2 + lpha||eta||_1 = sum_j [(1-lpha)/2 eta_j^2 + lpha|eta_j|], making alpha = 1 the lasso penalty and alpha = 0 the ridge penalty. Lambda : float A non-negative shrinkage parameter for the elastic-net, which multiplies eqn{P(lpha,eta) in the objective function. When Lambda = 0, no elastic-net penalty is applied and ordinary generalized linear models are fit. prior : float (Optional) A numeric specifying the prior probability of class 1 in the response when family = “binomial”. The default prior is the observational frequency of class 1. lambda_search : bool A logical value indicating whether to conduct a search over the space of lambda values starting from the lambda max, given lambda is interpreted as lambda minself. nlambdas : int The number of lambda values to use when lambda_search = TRUE. lambda_min_ratio : float Smallest value for lambda as a fraction of lambda.max. By default if the number of observations is greater than the the number of variables then lambda_min_ratio = 0.0001; if the number of observations is less than the number of variables then lambda_min_ratio = 0.01. beta_constraints : H2OFrame A data.frame or H2OParsedData object with the columns [“names”, “lower_bounds”, “upper_bounds”, “beta_given”], where each row corresponds to a predictor in the GLM. “names” contains the predictor names, “lower”/”upper_bounds”, are the lower and upper bounds of beta, and “beta_given” is some supplied starting values for the offset_column : H2OFrame Specify the offset column. weights_column : H2OFrame Specify the weights column. nfolds : int (Optional) Number of folds for cross-validation. If nfolds >= 2, then validation must remain empty. fold_column : H2OFrame (Optional) Column with cross-validation fold index assignment per observation fold_assignment : str Cross-validation fold assignment scheme, if fold_column is not specified Must be “AUTO”, “Random” or “Modulo” keep_cross_validation_predictions : bool Whether to keep the predictions of the cross-validation models intercept : bool Logical, include constant term (intercept) in the model max_active_predictors : int (Optional) Convergence criteria for number of predictors when using L1 penalty. Returns: 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-specifc 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.

h2o.h2o.glrm(x, validation_x=None, training_frame=None, validation_frame=None, k=None, max_iterations=None, max_updates=None, transform=None, seed=None, ignore_const_cols=None, loss=None, multi_loss=None, loss_by_col=None, loss_by_col_idx=None, regularization_x=None, regularization_y=None, gamma_x=None, gamma_y=None, init_step_size=None, min_step_size=None, init=None, svd_method=None, user_y=None, user_x=None, expand_user_y=None, impute_original=None, recover_svd=None, max_runtime_secs=None)[source]¶

Builds a generalized low rank model of a H2O dataset.

Parameters:

k : int The rank of the resulting decomposition. This must be between 1 and the number of columns in the training frame inclusive. max_iterations : int The maximum number of iterations to run the optimization loop. Each iteration consists of an update of the X matrix, followed by an update of the Y matrix. max_updates : int The maximum number of updates of X or Y to run. Each update consists of an update of either the X matrix or the Y matrix. For example, if max_updates = 1 and max_iterations = 1, the algorithm will initialize X and Y, update X once, and terminate without updating Y. transform : str A character string that indicates how the training data should be transformed before running GLRM. Possible values are “NONE”: for no transformation, “DEMEAN”: for subtracting the mean of each column, “DESCALE”: for dividing by the standard deviation of each column, “STANDARDIZE”: for demeaning and descaling, and “NORMALIZE”: for demeaning and dividing each column by its range (max - min). seed : int (Optional) Random seed used to initialize the X and Y matrices. ignore_const_cols : bool (Optional) A logical value indicating whether to ignore constant columns in the training frame. A column is constant if all of its non-missing values are the same value. loss : str A character string indicating the default loss function for numeric columns. Possible values are “Quadratic” (default), “Absolute”, “Huber”, “Poisson”, “Hinge”, and “Logistic”. multi_loss : str A character string indicating the default loss function for enum columns. Possible values are “Categorical” and “Ordinal”. loss_by_col : str (Optional) A list of strings indicating the loss function for specific columns by corresponding index in loss_by_col_idx. Will override loss for numeric columns and multi_loss for enum columns. loss_by_col_idx : str (Optional) A list of column indices to which the corresponding loss functions in loss_by_col are assigned. Must be zero indexed. regularization_x : str A character string indicating the regularization function for the X matrix. Possible values are “None” (default), “Quadratic”, “L2”, “L1”, “NonNegative”, “OneSparse”, “UnitOneSparse”, and “Simplex”. regularization_y : str A character string indicating the regularization function for the Y matrix. Possible values are “None” (default), “Quadratic”, “L2”, “L1”, “NonNegative”, “OneSparse”, “UnitOneSparse”, and “Simplex”. gamma_x : float The weight on the X matrix regularization term. gamma_y : float The weight on the Y matrix regularization term. init_step_size : float Initial step size. Divided by number of columns in the training frame when calculating the proximal gradient update. The algorithm begins at init_step_size and decreases the step size at each iteration until a termination condition is reached. min_step_size : float Minimum step size upon which the algorithm is terminated. init : str A character string indicating how to select the initial X and Y matrices. Possible values are “Random”: for initialization to a random array from the standard normal distribution, “PlusPlus”: for initialization using the clusters from k-means++ initialization, “SVD”: for initialization using the first k (approximate) right singular vectors, and “User”: user-specified initial X and Y frames (must set user_y and user_x arguments). svd_method : str A character string that indicates how SVD should be calculated during initialization. Possible values are “GramSVD”: distributed computation of the Gram matrix followed by a local SVD using the JAMA package, “Power”: computation of the SVD using the power iteration method, “Randomized”: approximate SVD by projecting onto a random subspace. user_x : H2OFrame (Optional) An H2OFrame object specifying the initial X matrix. Only used when init = “User”. user_y : H2OFrame (Optional) An H2OFrame object specifying the initial Y matrix. Only used when init = “User”. expand_user_y : bool A logical value indicating whether the categorical columns of the initial Y matrix should be one-hot expanded. Only used when init = “User” and user_y is specified. impute_original : bool A logical value indicating whether to reconstruct the original training data by reversing the transformation during prediction. Model metrics are calculated with respect to the original data. recover_svd : bool A logical value indicating whether the singular values and eigenvectors should be recovered during post-processing of the generalized low rank decomposition. :return: a new dim reduction model

h2o.h2o.import_file(path=None, destination_frame='', parse=True, header=(-1, 0, 1), sep='', col_names=None, col_types=None, na_strings=None)[source]¶

Have H2O import a dataset into memory. The path to the data must be a valid path for each node in the H2O cluster. If some node in the H2O cluster cannot see the file, then an exception will be thrown by the H2O cluster.

Parameters:

path : str A path specifying the location of the data to import. destination_frame : str, optional The unique hex key assigned to the imported file. If none is given, a key will automatically be generated. parse : bool, optional A logical value indicating whether the file should be parsed after import. header : int, optional -1 means the first line is data, 0 means guess, 1 means first line is header. sep : str, optional The field separator character. Values on each line of the file are separated by this character. If sep = “”, the parser will automatically detect the separator. col_names : list, optional A list of column names for the file. col_types : list or dict, optional A list of types or a dictionary of column names to types to specify whether columns should be forced to a certain type upon import parsing. If a list, the types for elements that are None will be guessed. The possible types a column may have are: “unknown” - this will force the column to be parsed as all NA “uuid” - the values in the column must be true UUID or will be parsed as NA “string” - force the column to be parsed as a string “numeric” - force the column to be parsed as numeric. H2O will handle the compression of the numeric data in the optimal manner. “enum” - force the column to be parsed as a categorical column. “time” - force the column to be parsed as a time column. H2O will attempt to parse the following list of date time formats. date: “yyyy-MM-dd” “yyyy MM dd” “dd-MMM-yy” “dd MMM yy” time: “HH:mm:ss” “HH:mm:ss:SSS” “HH:mm:ss:SSSnnnnnn” “HH.mm.ss” “HH.mm.ss.SSS” “HH.mm.ss.SSSnnnnnn” Times can also contain “AM” or “PM”. na_strings : list or dict, optional A list of strings, or a list of lists of strings (one list per column), or a dictionary of column names to strings which are to be interpreted as missing values.

Returns:

A new H2OFrame instance.

h2o.h2o.init(ip='localhost', port=54321, start_h2o=True, enable_assertions=True, license=None, nthreads=-1, max_mem_size=None, min_mem_size=None, ice_root=None, strict_version_check=True, proxy=None, https=False, insecure=False, username=None, password=None, max_mem_size_GB=None, min_mem_size_GB=None, proxies=None, size=None)[source]¶

Initiate an H2O connection to the specified ip and port.

Parameters:

ip : str A string representing the hostname or IP address of the server where H2O is running. port : int A port, default is 54321 start_h2o : bool A boolean dictating whether this module should start the H2O jvm. An attempt is made anyways if _connect fails. enable_assertions : bool If start_h2o, pass -ea as a VM option. license : str If not None, is a path to a license file. nthreads : int Number of threads in the thread pool. This relates very closely to the number of CPUs used. -1 means use all CPUs on the host. A positive integer specifies the number of CPUs directly. This value is only used when Python starts H2O. max_mem_size : int Maximum heap size (jvm option Xmx) in gigabytes. min_mem_size : int Minimum heap size (jvm option Xms) in gigabytes. ice_root : str A temporary directory (default location is determined by tempfile.mkdtemp()) to hold H2O log files. strict_version_check : bool Setting this to False is unsupported and should only be done when advised by technical support. proxy : dict A dictionary with keys ‘ftp’, ‘http’, ‘https’ and values that correspond to a proxy path. https : bool Set this to True to use https instead of http. insecure : bool Set this to True to disable SSL certificate checking. username : str Username to login with. password : str Password to login with. max_mem_size_GB : DEPRECATED Use max_mem_size instead. min_mem_size_GB : DEPRECATED Use min_mem_size instead. proxies : DEPRECATED Use proxy instead. size : DEPRECATED Size is deprecated.

Examples

Using the ‘proxy’ parameter

>>> import h2o
>>> import urllib
>>> proxy_dict = urllib.getproxies()
>>> h2o.init(proxy=proxy_dict)
Starting H2O JVM and connecting: ............... Connection successful!

h2o.h2o.interaction(data, factors, pairwise, max_factors, min_occurrence, destination_frame=None)[source]¶

Categorical Interaction Feature Creation in H2O. Creates a frame in H2O with n-th order interaction features between categorical columns, as specified by the user.

Parameters:

data : H2OFrame the H2OFrame that holds the target categorical columns. factors : list factors Factor columns (either indices or column names). pairwise : bool Whether to create pairwise interactions between factors (otherwise create one higher-order interaction). Only applicable if there are 3 or more factors. max_factors : int Max. number of factor levels in pair-wise interaction terms (if enforced, one extra catch-all factor will be made) min_occurrence : int Min. occurrence threshold for factor levels in pair-wise interaction terms destination_frame : str A string indicating the destination key. If empty, this will be auto-generated by H2O.

Returns:

H2OFrame

h2o.h2o.kmeans(x, validation_x=None, k=None, model_id=None, max_iterations=None, standardize=None, init=None, seed=None, nfolds=None, fold_column=None, fold_assignment=None, training_frame=None, validation_frame=None, user_points=None, ignored_columns=None, score_each_iteration=None, keep_cross_validation_predictions=None, ignore_const_cols=None, checkpoint=None, max_runtime_secs=None)[source]¶

Performs k-means clustering on an H2O dataset.

Parameters:

x : H2OFrame The data columns on which k-means operates. k : int The number of clusters. Must be between 1 and 1e7 inclusive. k may be omitted if the user specifies the initial centers in the init parameter. If k is not omitted, in this case, then it should be equal to the number of user-specified centers. model_id : str (Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated. max_iterations : int The maximum number of iterations allowed. Must be between 0 and 1e6 inclusive. standardize : bool Indicates whether the data should be standardized before running k-means. init : str A character string that selects the initial set of k cluster centers. Possible values are “Random”: for random initialization, “PlusPlus”: for k-means plus initialization, or “Furthest”: for initialization at the furthest point from each successive center. Additionally, the user may specify a the initial centers as a matrix, data.frame, H2OFrame, or list of vectors. For matrices, data.frames, and H2OFrames, each row of the respective structure is an initial center. For lists of vectors, each vector is an initial center. seed : int (Optional) Random seed used to initialize the cluster centroids. nfolds : int (Optional) Number of folds for cross-validation. If nfolds >= 2, then validation must remain empty. fold_column : H2OFrame (Optional) Column with cross-validation fold index assignment per observation fold_assignment : str Cross-validation fold assignment scheme, if fold_column is not specified Must be “AUTO”, “Random” or “Modulo” :return: An instance of H2OClusteringModel.

h2o.h2o.lazy_import(path)[source]¶

Import a single file or collection of files.

Parameters:	path : str A path to a data file (remote or local).

h2o.h2o.list_timezones()[source]¶

Get a list of all the timezones

Returns:	the time zones (as an H2OFrame)

h2o.h2o.load_model(path)[source]¶

Load a saved H2O model from disk. Example:

>>> path = h2o.save_model(my_model,dir=my_path)
>>> h2o.load_model(path)                         # use the result of save_model

Parameters:	path : str The full path of the H2O Model to be imported. :return: the model

h2o.h2o.log_and_echo(message)[source]¶

Log a message on the server-side logs This is helpful when running several pieces of work one after the other on a single H2O cluster and you want to make a notation in the H2O server side log where one piece of work ends and the next piece of work begins.

Sends a message to H2O for logging. Generally used for debugging purposes.

Parameters:	message : str A character string with the message to write to the log.

h2o.h2o.ls()[source]¶

List Keys on an H2O Cluster

Returns:	A list of keys in the current H2O instance.

h2o.h2o.naive_bayes(x, y, validation_x=None, validation_y=None, training_frame=None, validation_frame=None, laplace=None, threshold=None, eps=None, compute_metrics=None, offset_column=None, weights_column=None, balance_classes=None, max_after_balance_size=None, nfolds=None, fold_column=None, fold_assignment=None, keep_cross_validation_predictions=None, checkpoint=None, max_runtime_secs=None)[source]¶

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.

Parameters:

laplace : int A positive number controlling Laplace smoothing. The default zero disables smoothing. threshold : float The minimum standard deviation to use for observations without enough data. Must be at least 1e-10. eps : float A threshold cutoff to deal with numeric instability, must be positive. compute_metrics : bool A logical value indicating whether model metrics should be computed. Set to FALSE to reduce the runtime of the algorithm. training_frame : H2OFrame Training Frame validation_frame : H2OFrame Validation Frame offset_column : H2OFrame Specify the offset column. weights_column : H2OFrame Specify the weights column. nfolds : int (Optional) Number of folds for cross-validation. If nfolds >= 2, then validation must remain empty. fold_column : H2OFrame (Optional) Column with cross-validation fold index assignment per observation fold_assignment : str Cross-validation fold assignment scheme, if fold_column is not specified Must be “AUTO”, “Random” or “Modulo” keep_cross_validation_predictions : bool Whether to keep the predictions of the cross-validation models :return: Returns an H2OBinomialModel if the response has two categorical levels, H2OMultinomialModel otherwise.

h2o.h2o.no_progress()[source]¶

Disable the progress bar from flushing to stdout. The completed progress bar is printed when a job is complete so as to demarcate a log file.

h2o.h2o.ou()[source]¶

Where is my baguette!?

Returns:	The name of the baguette. oh uhr uhr huhr

h2o.h2o.parse_raw(setup, id=None, first_line_is_header=(-1, 0, 1))[source]¶

Used in conjunction with lazy_import and parse_setup in order to make alterations before parsing.

Parameters:	setup : dict Result of h2o.parse_setup id : str, optional An id for the frame. first_line_is_header : int, optional -1,0,1 if the first line is to be used as the header
Returns:	H2OFrame

h2o.h2o.parse_setup(raw_frames, destination_frame='', header=(-1, 0, 1), separator='', column_names=None, column_types=None, na_strings=None)[source]¶

During parse setup, the H2O cluster will make several guesses about the attributes of the data. This method allows a user to perform corrective measures by updating the returning dictionary from this method. This dictionary is then fed into parse_raw to produce the H2OFrame instance.

Parameters:

raw_frames : H2OFrame A collection of imported file frames destination_frame : str, optional The unique hex key assigned to the imported file. If none is given, a key will automatically be generated. parse : bool, optional A logical value indicating whether the file should be parsed after import. header : int, optional -1 means the first line is data, 0 means guess, 1 means first line is header. sep : str, optional The field separator character. Values on each line of the file are separated by this character. If sep = “”, the parser will automatically detect the separator. col_names : list, optional A list of column names for the file. col_types : list or dict, optional A list of types or a dictionary of column names to types to specify whether columns should be forced to a certain type upon import parsing. If a list, the types for elements that are None will be guessed. The possible types a column may have are: “unknown” - this will force the column to be parsed as all NA “uuid” - the values in the column must be true UUID or will be parsed as NA “string” - force the column to be parsed as a string “numeric” - force the column to be parsed as numeric. H2O will handle the compression of the numeric data in the optimal manner. “enum” - force the column to be parsed as a categorical column. “time” - force the column to be parsed as a time column. H2O will attempt to parse the following list of date time formats. date: “yyyy-MM-dd” “yyyy MM dd” “dd-MMM-yy” “dd MMM yy” time: “HH:mm:ss” “HH:mm:ss:SSS” “HH:mm:ss:SSSnnnnnn” “HH.mm.ss” “HH.mm.ss.SSS” “HH.mm.ss.SSSnnnnnn” Times can also contain “AM” or “PM”. A list of strings, or a list of lists of strings (one list per column), or a dictionary of column names to strings which are to be interpreted as missing values.

Returns:

A dictionary is returned containing all of the guesses made by the H2O back end.

h2o.h2o.prcomp(x, validation_x=None, k=None, model_id=None, max_iterations=None, transform=None, seed=None, use_all_factor_levels=None, training_frame=None, validation_frame=None, pca_method=None, max_runtime_secs=None)[source]¶

Principal components analysis of a H2O dataset.

Parameters:

k : int The number of principal components to be computed. This must be between 1 and min(ncol(training_frame), nrow(training_frame)) inclusive. model_id : str (Optional) The unique hex key assigned to the resulting model. Automatically generated if none is provided. max_iterations : int The maximum number of iterations to run each power iteration loop. Must be between 1 and 1e6 inclusive. transform : str A character string that indicates how the training data should be transformed before running PCA. Possible values are “NONE”: for no transformation, “DEMEAN”: for subtracting the mean of each column, “DESCALE”: for dividing by the standard deviation of each column, “STANDARDIZE”: for demeaning and descaling, and “NORMALIZE”: for demeaning and dividing each column by its range (max - min). seed : int (Optional) Random seed used to initialize the right singular vectors at the beginning of each power method iteration. use_all_factor_levels : bool (Optional) A logical value indicating whether all factor levels should be included in each categorical column expansion. If FALSE, the indicator column corresponding to the first factor level of every categorical variable will be dropped. Defaults to FALSE. pca_method : str A character string that indicates how PCA should be calculated. Possible values are “GramSVD”: distributed computation of the Gram matrix followed by a local SVD using the JAMA package, “Power”: computation of the SVD using the power iteration method, “Randomized”: approximate SVD by projecting onto a random subspace, “GLRM”: fit a generalized low rank model with an l2 loss function (no regularization) and solve for the SVD using local matrix algebra. :return: a new dim reduction model

h2o.h2o.random_forest(x, y, validation_x=None, validation_y=None, training_frame=None, model_id=None, mtries=None, sample_rate=None, col_sample_rate_per_tree=None, build_tree_one_node=None, ntrees=None, max_depth=None, min_rows=None, nbins=None, nbins_top_level=None, nbins_cats=None, binomial_double_trees=None, validation_frame=None, balance_classes=None, max_after_balance_size=None, seed=None, offset_column=None, weights_column=None, nfolds=None, fold_column=None, fold_assignment=None, keep_cross_validation_predictions=None, score_each_iteration=None, checkpoint=None, stopping_rounds=None, stopping_metric=None, stopping_tolerance=None, max_runtime_secs=None)[source]¶

Build a Big Data Random Forest Model Builds a Random Forest Model on an H2OFrame

Parameters:

x : H2OFrame An H2OFrame containing the predictors in the model. y : H2OFrame An H2OFrame of the response variable in the model. training_frame : H2OFrame (Optional) An H2OFrame. Only used to retrieve weights, offset, or nfolds columns, if they aren’t already provided in x. model_id : str (Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated. mtries : int 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 number of predictors. sample_rate : float Row sample rate (from 0.0 to 1.0) col_sample_rate_per_tree : float Column sample rate per tree (from 0.0 to 1.0) build_tree_one_node : bool Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. ntrees : int A nonnegative integer that determines the number of trees to grow. max_depth : int Maximum depth to grow the tree. min_rows : int Minimum number of rows to assign to teminal nodes. nbins : int For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point. nbins_top_level : int 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. nbins_cats : int For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. binomial_double_trees : bool or binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy. validation_frame : H2OFrame An H2OFrame object containing the variables in the model. balance_classes : bool logical, indicates whether or not to balance training data class counts via over/under-sampling (for imbalanced data) max_after_balance_size : float Maximum relative size of the training data after balancing class counts (can be less than 1.0). Ignored if balance_classes is False, which is the default behavior. seed : int Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded offset_column : H2OFrame Specify the offset column. weights_column : H2OFrame Specify the weights column. nfolds : int (Optional) Number of folds for cross-validation. If nfolds >= 2, then validation must remain empty. fold_column : H2OFrame (Optional) Column with cross-validation fold index assignment per observation fold_assignment : str Cross-validation fold assignment scheme, if fold_column is not specified Must be “AUTO”, “Random” or “Modulo” keep_cross_validation_predictions : bool Whether to keep the predictions of the cross-validation models score_each_iteration : bool Attempts to score each tree. stopping_rounds : int Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve (by stopping_tolerance) for k=stopping_rounds scoring events. Can only trigger after at least 2k scoring events. Use 0 to disable. stopping_metric : str Metric to use for convergence checking, only for _stopping_rounds > 0 Can be one of “AUTO”, “deviance”, “logloss”, “MSE”, “AUC”, “r2”, “misclassification”. stopping_tolerance : float Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) :return: A new classifier or regression model.

h2o.h2o.rapids(expr)[source]¶

Execute a Rapids expression.

Parameters:	expr : str The rapids expression (ascii string).
Returns:	The JSON response (as a python dictionary) of the Rapids execution

h2o.h2o.remove(x)[source]¶

Remove object(s) from H2O.

Parameters:	x : H2OFrame, H2OEstimator, or basestring, or a list/tuple of those things. The object(s) or unique id(s) pointing to the object(s) to be removed.

h2o.h2o.remove_all()[source]¶

Remove all objects from H2O.

h2o.h2o.save_model(model, path='', force=False)[source]¶

Save an H2O Model Object to Disk.

Parameters:	model : H2OModel The model object to save. path : str A path to save the model at (hdfs, s3, local) force : bool Overwrite destination directory in case it exists or throw exception if set to false. :return: the path of the saved model (string)

h2o.h2o.set_timezone(tz)[source]¶

Set the Time Zone on the H2O Cloud

Parameters:	tz : str The desired timezone. :return: None

h2o.h2o.show_progress()[source]¶

Enable the progress bar. (Progress bar is enabled by default).

h2o.h2o.shutdown(conn=None, prompt=True)[source]¶

Shut down the specified instance. All data will be lost. This method checks if H2O is running at the specified IP address and port, and if it is, shuts down that H2O instance.

Parameters:	conn : H2OConnection An H2OConnection object containing the IP address and port of the server running H2O. prompt : bool A logical value indicating whether to prompt the user before shutting down the H2O server. :return: None

h2o.h2o.start_glm_job(x, y, validation_x=None, validation_y=None, **kwargs)[source]¶

Build a Generalized Linear Model Note: this function is the same as glm(), but it doesn’t block on model-build. Instead, it returns and H2OModelFuture object immediately. The model can be retrieved from the H2OModelFuture object with get_future_model().

Returns:	H2OModelFuture

h2o.h2o.svd(x, validation_x=None, training_frame=None, validation_frame=None, nv=None, max_iterations=None, transform=None, seed=None, use_all_factor_levels=None, svd_method=None, max_runtime_secs=None)[source]¶

Singular value decomposition of a H2O dataset.

Parameters:

nv : int The number of right singular vectors to be computed. This must be between 1 and min(ncol(training_frame), snrow(training_frame)) inclusive. max_iterations : int The maximum number of iterations to run each power iteration loop. Must be between 1 and 1e6 inclusive.max_iterations The maximum number of iterations to run each power iteration loop. Must be between 1 and 1e6 inclusive. transform : str A character string that indicates how the training data should be transformed before running SVD. Possible values are “NONE”: for no transformation, “DEMEAN”: for subtracting the mean of each column, “DESCALE”: for dividing by the standard deviation of each column, “STANDARDIZE”: for demeaning and descaling, and “NORMALIZE”: for demeaning and dividing each column by its range (max - min). seed : int (Optional) Random seed used to initialize the right singular vectors at the beginning of each power method iteration. use_all_factor_levels : bool (Optional) A logical value indicating whether all factor levels should be included in each categorical column expansion. If FALSE, the indicator column corresponding to the first factor level of every categorical variable will be dropped. Defaults to TRUE. svd_method : str A character string that indicates how SVD should be calculated. Possible values are “GramSVD”: distributed computation of the Gram matrix followed by a local SVD using the JAMA package, “Power”: computation of the SVD using the power iteration method, “Randomized”: approximate SVD by projecting onto a random subspace. :return: a new dim reduction model

h2o.h2o.upload_file(path, destination_frame='', header=(-1, 0, 1), sep='', col_names=None, col_types=None, na_strings=None)[source]¶

Upload a dataset at the path given from the local machine to the H2O cluster.

Parameters:

path : str A path specifying the location of the data to upload. destination_frame : str, optional The unique hex key assigned to the imported file. If none is given, a key will automatically be generated. header : int, optional -1 means the first line is data, 0 means guess, 1 means first line is header. sep : str, optional The field separator character. Values on each line of the file are separated by this character. If sep = “”, the parser will automatically detect the separator. col_names : list, optional A list of column names for the file. col_types : list or dict, optional A list of types or a dictionary of column names to types to specify whether columns should be forced to a certain type upon import parsing. If a list, the types for elements that are None will be guessed. The possible types a column may have are: “unknown” - this will force the column to be parsed as all NA “uuid” - the values in the column must be true UUID or will be parsed as NA “string” - force the column to be parsed as a string “numeric” - force the column to be parsed as numeric. H2O will handle the compression of the numeric data in the optimal manner. “enum” - force the column to be parsed as a categorical column. “time” - force the column to be parsed as a time column. H2O will attempt to parse the following list of date time formats. date: “yyyy-MM-dd” “yyyy MM dd” “dd-MMM-yy” “dd MMM yy” time: “HH:mm:ss” “HH:mm:ss:SSS” “HH:mm:ss:SSSnnnnnn” “HH.mm.ss” “HH.mm.ss.SSS” “HH.mm.ss.SSSnnnnnn” Times can also contain “AM” or “PM”. na_strings : list or dict, optional A list of strings, or a list of lists of strings (one list per column), or a dictionary of column names to strings which are to be interpreted as missing values.

Returns:

A new H2OFrame instance.

Examples

>>> import h2o as ml
>>> ml.upload_file(path="/path/to/local/data", destination_frame="my_local_data")
...