#!/usr/bin/env python
# -*- encoding: utf-8 -*-
#
# This file is auto-generated by h2o-3/h2o-bindings/bin/gen_python.py
# Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
#
from __future__ import absolute_import, division, print_function, unicode_literals
from h2o.estimators.estimator_base import H2OEstimator
from h2o.exceptions import H2OValueError
from h2o.frame import H2OFrame
from h2o.utils.typechecks import assert_is_type, Enum, numeric
[docs]class H2ODeepLearningEstimator(H2OEstimator):
"""
Deep Learning
Build a Deep Neural Network model using CPUs
Builds a feed-forward multilayer artificial neural network on an H2OFrame
Examples
--------
>>> import h2o
>>> from h2o.estimators.deeplearning import H2ODeepLearningEstimator
>>> h2o.connect()
>>> 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)
"""
algo = "deeplearning"
def __init__(self, **kwargs):
super(H2ODeepLearningEstimator, self).__init__()
self._parms = {}
names_list = {"model_id", "training_frame", "validation_frame", "nfolds", "keep_cross_validation_predictions",
"keep_cross_validation_fold_assignment", "fold_assignment", "fold_column", "response_column",
"ignored_columns", "ignore_const_cols", "score_each_iteration", "weights_column", "offset_column",
"balance_classes", "class_sampling_factors", "max_after_balance_size",
"max_confusion_matrix_size", "max_hit_ratio_k", "checkpoint", "pretrained_autoencoder",
"overwrite_with_best_model", "use_all_factor_levels", "standardize", "activation", "hidden",
"epochs", "train_samples_per_iteration", "target_ratio_comm_to_comp", "seed", "adaptive_rate",
"rho", "epsilon", "rate", "rate_annealing", "rate_decay", "momentum_start", "momentum_ramp",
"momentum_stable", "nesterov_accelerated_gradient", "input_dropout_ratio",
"hidden_dropout_ratios", "l1", "l2", "max_w2", "initial_weight_distribution",
"initial_weight_scale", "initial_weights", "initial_biases", "loss", "distribution",
"quantile_alpha", "tweedie_power", "huber_alpha", "score_interval", "score_training_samples",
"score_validation_samples", "score_duty_cycle", "classification_stop", "regression_stop",
"stopping_rounds", "stopping_metric", "stopping_tolerance", "max_runtime_secs",
"score_validation_sampling", "diagnostics", "fast_mode", "force_load_balance",
"variable_importances", "replicate_training_data", "single_node_mode", "shuffle_training_data",
"missing_values_handling", "quiet_mode", "autoencoder", "sparse", "col_major",
"average_activation", "sparsity_beta", "max_categorical_features", "reproducible",
"export_weights_and_biases", "mini_batch_size", "categorical_encoding", "elastic_averaging",
"elastic_averaging_moving_rate", "elastic_averaging_regularization"}
if "Lambda" in kwargs: kwargs["lambda_"] = kwargs.pop("Lambda")
for pname, pvalue in kwargs.items():
if pname == 'model_id':
self._id = pvalue
self._parms["model_id"] = pvalue
elif pname in names_list:
# Using setattr(...) will invoke type-checking of the arguments
setattr(self, pname, pvalue)
else:
raise H2OValueError("Unknown parameter %s = %r" % (pname, pvalue))
if isinstance(self, H2OAutoEncoderEstimator): self._parms['autoencoder'] = True
@property
def training_frame(self):
"""
Id of the training data frame.
Type: ``H2OFrame``.
"""
return self._parms.get("training_frame")
@training_frame.setter
def training_frame(self, training_frame):
assert_is_type(training_frame, None, H2OFrame)
self._parms["training_frame"] = training_frame
@property
def validation_frame(self):
"""
Id of the validation data frame.
Type: ``H2OFrame``.
"""
return self._parms.get("validation_frame")
@validation_frame.setter
def validation_frame(self, validation_frame):
assert_is_type(validation_frame, None, H2OFrame)
self._parms["validation_frame"] = validation_frame
@property
def nfolds(self):
"""
Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type: ``int`` (default: ``0``).
"""
return self._parms.get("nfolds")
@nfolds.setter
def nfolds(self, nfolds):
assert_is_type(nfolds, None, int)
self._parms["nfolds"] = nfolds
@property
def keep_cross_validation_predictions(self):
"""
Whether to keep the predictions of the cross-validation models.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("keep_cross_validation_predictions")
@keep_cross_validation_predictions.setter
def keep_cross_validation_predictions(self, keep_cross_validation_predictions):
assert_is_type(keep_cross_validation_predictions, None, bool)
self._parms["keep_cross_validation_predictions"] = keep_cross_validation_predictions
@property
def keep_cross_validation_fold_assignment(self):
"""
Whether to keep the cross-validation fold assignment.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("keep_cross_validation_fold_assignment")
@keep_cross_validation_fold_assignment.setter
def keep_cross_validation_fold_assignment(self, keep_cross_validation_fold_assignment):
assert_is_type(keep_cross_validation_fold_assignment, None, bool)
self._parms["keep_cross_validation_fold_assignment"] = keep_cross_validation_fold_assignment
@property
def fold_assignment(self):
"""
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.
One of: ``"auto"``, ``"random"``, ``"modulo"``, ``"stratified"`` (default: ``"auto"``).
"""
return self._parms.get("fold_assignment")
@fold_assignment.setter
def fold_assignment(self, fold_assignment):
assert_is_type(fold_assignment, None, Enum("auto", "random", "modulo", "stratified"))
self._parms["fold_assignment"] = fold_assignment
@property
def fold_column(self):
"""
Column with cross-validation fold index assignment per observation.
Type: ``str``.
"""
return self._parms.get("fold_column")
@fold_column.setter
def fold_column(self, fold_column):
assert_is_type(fold_column, None, str)
self._parms["fold_column"] = fold_column
@property
def response_column(self):
"""
Response variable column.
Type: ``str``.
"""
return self._parms.get("response_column")
@response_column.setter
def response_column(self, response_column):
assert_is_type(response_column, None, str)
self._parms["response_column"] = response_column
@property
def ignored_columns(self):
"""
Names of columns to ignore for training.
Type: ``List[str]``.
"""
return self._parms.get("ignored_columns")
@ignored_columns.setter
def ignored_columns(self, ignored_columns):
assert_is_type(ignored_columns, None, [str])
self._parms["ignored_columns"] = ignored_columns
@property
def ignore_const_cols(self):
"""
Ignore constant columns.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("ignore_const_cols")
@ignore_const_cols.setter
def ignore_const_cols(self, ignore_const_cols):
assert_is_type(ignore_const_cols, None, bool)
self._parms["ignore_const_cols"] = ignore_const_cols
@property
def score_each_iteration(self):
"""
Whether to score during each iteration of model training.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("score_each_iteration")
@score_each_iteration.setter
def score_each_iteration(self, score_each_iteration):
assert_is_type(score_each_iteration, None, bool)
self._parms["score_each_iteration"] = score_each_iteration
@property
def weights_column(self):
"""
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.
Type: ``str``.
"""
return self._parms.get("weights_column")
@weights_column.setter
def weights_column(self, weights_column):
assert_is_type(weights_column, None, str)
self._parms["weights_column"] = weights_column
@property
def offset_column(self):
"""
Offset column. This will be added to the combination of columns before applying the link function.
Type: ``str``.
"""
return self._parms.get("offset_column")
@offset_column.setter
def offset_column(self, offset_column):
assert_is_type(offset_column, None, str)
self._parms["offset_column"] = offset_column
@property
def balance_classes(self):
"""
Balance training data class counts via over/under-sampling (for imbalanced data).
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("balance_classes")
@balance_classes.setter
def balance_classes(self, balance_classes):
assert_is_type(balance_classes, None, bool)
self._parms["balance_classes"] = balance_classes
@property
def class_sampling_factors(self):
"""
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]``.
"""
return self._parms.get("class_sampling_factors")
@class_sampling_factors.setter
def class_sampling_factors(self, class_sampling_factors):
assert_is_type(class_sampling_factors, None, [float])
self._parms["class_sampling_factors"] = class_sampling_factors
@property
def max_after_balance_size(self):
"""
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
balance_classes.
Type: ``float`` (default: ``5``).
"""
return self._parms.get("max_after_balance_size")
@max_after_balance_size.setter
def max_after_balance_size(self, max_after_balance_size):
assert_is_type(max_after_balance_size, None, float)
self._parms["max_after_balance_size"] = max_after_balance_size
@property
def max_confusion_matrix_size(self):
"""
[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.
Type: ``int`` (default: ``20``).
"""
return self._parms.get("max_confusion_matrix_size")
@max_confusion_matrix_size.setter
def max_confusion_matrix_size(self, max_confusion_matrix_size):
assert_is_type(max_confusion_matrix_size, None, int)
self._parms["max_confusion_matrix_size"] = max_confusion_matrix_size
@property
def max_hit_ratio_k(self):
"""
Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable).
Type: ``int`` (default: ``0``).
"""
return self._parms.get("max_hit_ratio_k")
@max_hit_ratio_k.setter
def max_hit_ratio_k(self, max_hit_ratio_k):
assert_is_type(max_hit_ratio_k, None, int)
self._parms["max_hit_ratio_k"] = max_hit_ratio_k
@property
def checkpoint(self):
"""
Model checkpoint to resume training with.
Type: ``str``.
"""
return self._parms.get("checkpoint")
@checkpoint.setter
def checkpoint(self, checkpoint):
assert_is_type(checkpoint, None, str, H2OEstimator)
self._parms["checkpoint"] = checkpoint
@property
def pretrained_autoencoder(self):
"""
Pretrained autoencoder model to initialize this model with.
Type: ``str``.
"""
return self._parms.get("pretrained_autoencoder")
@pretrained_autoencoder.setter
def pretrained_autoencoder(self, pretrained_autoencoder):
assert_is_type(pretrained_autoencoder, None, str, H2OEstimator)
self._parms["pretrained_autoencoder"] = pretrained_autoencoder
@property
def overwrite_with_best_model(self):
"""
If enabled, override the final model with the best model found during training.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("overwrite_with_best_model")
@overwrite_with_best_model.setter
def overwrite_with_best_model(self, overwrite_with_best_model):
assert_is_type(overwrite_with_best_model, None, bool)
self._parms["overwrite_with_best_model"] = overwrite_with_best_model
@property
def use_all_factor_levels(self):
"""
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`` (default: ``True``).
"""
return self._parms.get("use_all_factor_levels")
@use_all_factor_levels.setter
def use_all_factor_levels(self, use_all_factor_levels):
assert_is_type(use_all_factor_levels, None, bool)
self._parms["use_all_factor_levels"] = use_all_factor_levels
@property
def standardize(self):
"""
If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("standardize")
@standardize.setter
def standardize(self, standardize):
assert_is_type(standardize, None, bool)
self._parms["standardize"] = standardize
@property
def activation(self):
"""
Activation function.
One of: ``"tanh"``, ``"tanh_with_dropout"``, ``"rectifier"``, ``"rectifier_with_dropout"``, ``"maxout"``,
``"maxout_with_dropout"`` (default: ``"rectifier"``).
"""
return self._parms.get("activation")
@activation.setter
def activation(self, activation):
assert_is_type(activation, None, Enum("tanh", "tanh_with_dropout", "rectifier", "rectifier_with_dropout", "maxout", "maxout_with_dropout"))
self._parms["activation"] = activation
@property
def hidden(self):
"""
Hidden layer sizes (e.g. [100, 100]).
Type: ``List[int]`` (default: ``[200, 200]``).
"""
return self._parms.get("hidden")
@hidden.setter
def hidden(self, hidden):
assert_is_type(hidden, None, [int])
self._parms["hidden"] = hidden
@property
def epochs(self):
"""
How many times the dataset should be iterated (streamed), can be fractional.
Type: ``float`` (default: ``10``).
"""
return self._parms.get("epochs")
@epochs.setter
def epochs(self, epochs):
assert_is_type(epochs, None, numeric)
self._parms["epochs"] = epochs
@property
def train_samples_per_iteration(self):
"""
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`` (default: ``-2``).
"""
return self._parms.get("train_samples_per_iteration")
@train_samples_per_iteration.setter
def train_samples_per_iteration(self, train_samples_per_iteration):
assert_is_type(train_samples_per_iteration, None, int)
self._parms["train_samples_per_iteration"] = train_samples_per_iteration
@property
def target_ratio_comm_to_comp(self):
"""
Target ratio of communication overhead to computation. Only for multi-node operation and
train_samples_per_iteration = -2 (auto-tuning).
Type: ``float`` (default: ``0.05``).
"""
return self._parms.get("target_ratio_comm_to_comp")
@target_ratio_comm_to_comp.setter
def target_ratio_comm_to_comp(self, target_ratio_comm_to_comp):
assert_is_type(target_ratio_comm_to_comp, None, numeric)
self._parms["target_ratio_comm_to_comp"] = target_ratio_comm_to_comp
@property
def seed(self):
"""
Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded.
Type: ``int`` (default: ``-1``).
"""
return self._parms.get("seed")
@seed.setter
def seed(self, seed):
assert_is_type(seed, None, int)
self._parms["seed"] = seed
@property
def adaptive_rate(self):
"""
Adaptive learning rate.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("adaptive_rate")
@adaptive_rate.setter
def adaptive_rate(self, adaptive_rate):
assert_is_type(adaptive_rate, None, bool)
self._parms["adaptive_rate"] = adaptive_rate
@property
def rho(self):
"""
Adaptive learning rate time decay factor (similarity to prior updates).
Type: ``float`` (default: ``0.99``).
"""
return self._parms.get("rho")
@rho.setter
def rho(self, rho):
assert_is_type(rho, None, numeric)
self._parms["rho"] = rho
@property
def epsilon(self):
"""
Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress).
Type: ``float`` (default: ``1e-08``).
"""
return self._parms.get("epsilon")
@epsilon.setter
def epsilon(self, epsilon):
assert_is_type(epsilon, None, numeric)
self._parms["epsilon"] = epsilon
@property
def rate(self):
"""
Learning rate (higher => less stable, lower => slower convergence).
Type: ``float`` (default: ``0.005``).
"""
return self._parms.get("rate")
@rate.setter
def rate(self, rate):
assert_is_type(rate, None, numeric)
self._parms["rate"] = rate
@property
def rate_annealing(self):
"""
Learning rate annealing: rate / (1 + rate_annealing * samples).
Type: ``float`` (default: ``1e-06``).
"""
return self._parms.get("rate_annealing")
@rate_annealing.setter
def rate_annealing(self, rate_annealing):
assert_is_type(rate_annealing, None, numeric)
self._parms["rate_annealing"] = rate_annealing
@property
def rate_decay(self):
"""
Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1).
Type: ``float`` (default: ``1``).
"""
return self._parms.get("rate_decay")
@rate_decay.setter
def rate_decay(self, rate_decay):
assert_is_type(rate_decay, None, numeric)
self._parms["rate_decay"] = rate_decay
@property
def momentum_start(self):
"""
Initial momentum at the beginning of training (try 0.5).
Type: ``float`` (default: ``0``).
"""
return self._parms.get("momentum_start")
@momentum_start.setter
def momentum_start(self, momentum_start):
assert_is_type(momentum_start, None, numeric)
self._parms["momentum_start"] = momentum_start
@property
def momentum_ramp(self):
"""
Number of training samples for which momentum increases.
Type: ``float`` (default: ``1000000``).
"""
return self._parms.get("momentum_ramp")
@momentum_ramp.setter
def momentum_ramp(self, momentum_ramp):
assert_is_type(momentum_ramp, None, numeric)
self._parms["momentum_ramp"] = momentum_ramp
@property
def momentum_stable(self):
"""
Final momentum after the ramp is over (try 0.99).
Type: ``float`` (default: ``0``).
"""
return self._parms.get("momentum_stable")
@momentum_stable.setter
def momentum_stable(self, momentum_stable):
assert_is_type(momentum_stable, None, numeric)
self._parms["momentum_stable"] = momentum_stable
@property
def nesterov_accelerated_gradient(self):
"""
Use Nesterov accelerated gradient (recommended).
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("nesterov_accelerated_gradient")
@nesterov_accelerated_gradient.setter
def nesterov_accelerated_gradient(self, nesterov_accelerated_gradient):
assert_is_type(nesterov_accelerated_gradient, None, bool)
self._parms["nesterov_accelerated_gradient"] = nesterov_accelerated_gradient
@property
def input_dropout_ratio(self):
"""
Input layer dropout ratio (can improve generalization, try 0.1 or 0.2).
Type: ``float`` (default: ``0``).
"""
return self._parms.get("input_dropout_ratio")
@input_dropout_ratio.setter
def input_dropout_ratio(self, input_dropout_ratio):
assert_is_type(input_dropout_ratio, None, numeric)
self._parms["input_dropout_ratio"] = input_dropout_ratio
@property
def hidden_dropout_ratios(self):
"""
Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.
Type: ``List[float]``.
"""
return self._parms.get("hidden_dropout_ratios")
@hidden_dropout_ratios.setter
def hidden_dropout_ratios(self, hidden_dropout_ratios):
assert_is_type(hidden_dropout_ratios, None, [numeric])
self._parms["hidden_dropout_ratios"] = hidden_dropout_ratios
@property
def l1(self):
"""
L1 regularization (can add stability and improve generalization, causes many weights to become 0).
Type: ``float`` (default: ``0``).
"""
return self._parms.get("l1")
@l1.setter
def l1(self, l1):
assert_is_type(l1, None, numeric)
self._parms["l1"] = l1
@property
def l2(self):
"""
L2 regularization (can add stability and improve generalization, causes many weights to be small.
Type: ``float`` (default: ``0``).
"""
return self._parms.get("l2")
@l2.setter
def l2(self, l2):
assert_is_type(l2, None, numeric)
self._parms["l2"] = l2
@property
def max_w2(self):
"""
Constraint for squared sum of incoming weights per unit (e.g. for Rectifier).
Type: ``float`` (default: ``3.4028235e+38``).
"""
return self._parms.get("max_w2")
@max_w2.setter
def max_w2(self, max_w2):
assert_is_type(max_w2, None, float)
self._parms["max_w2"] = max_w2
@property
def initial_weight_distribution(self):
"""
Initial weight distribution.
One of: ``"uniform_adaptive"``, ``"uniform"``, ``"normal"`` (default: ``"uniform_adaptive"``).
"""
return self._parms.get("initial_weight_distribution")
@initial_weight_distribution.setter
def initial_weight_distribution(self, initial_weight_distribution):
assert_is_type(initial_weight_distribution, None, Enum("uniform_adaptive", "uniform", "normal"))
self._parms["initial_weight_distribution"] = initial_weight_distribution
@property
def initial_weight_scale(self):
"""
Uniform: -value...value, Normal: stddev.
Type: ``float`` (default: ``1``).
"""
return self._parms.get("initial_weight_scale")
@initial_weight_scale.setter
def initial_weight_scale(self, initial_weight_scale):
assert_is_type(initial_weight_scale, None, numeric)
self._parms["initial_weight_scale"] = initial_weight_scale
@property
def initial_weights(self):
"""
A list of H2OFrame ids to initialize the weight matrices of this model with.
Type: ``List[H2OFrame]``.
"""
return self._parms.get("initial_weights")
@initial_weights.setter
def initial_weights(self, initial_weights):
assert_is_type(initial_weights, None, [H2OFrame, None])
self._parms["initial_weights"] = initial_weights
@property
def initial_biases(self):
"""
A list of H2OFrame ids to initialize the bias vectors of this model with.
Type: ``List[H2OFrame]``.
"""
return self._parms.get("initial_biases")
@initial_biases.setter
def initial_biases(self, initial_biases):
assert_is_type(initial_biases, None, [H2OFrame, None])
self._parms["initial_biases"] = initial_biases
@property
def loss(self):
"""
Loss function.
One of: ``"automatic"``, ``"cross_entropy"``, ``"quadratic"``, ``"huber"``, ``"absolute"``, ``"quantile"``
(default: ``"automatic"``).
"""
return self._parms.get("loss")
@loss.setter
def loss(self, loss):
assert_is_type(loss, None, Enum("automatic", "cross_entropy", "quadratic", "huber", "absolute", "quantile"))
self._parms["loss"] = loss
@property
def distribution(self):
"""
Distribution function
One of: ``"auto"``, ``"bernoulli"``, ``"multinomial"``, ``"gaussian"``, ``"poisson"``, ``"gamma"``,
``"tweedie"``, ``"laplace"``, ``"quantile"``, ``"huber"`` (default: ``"auto"``).
"""
return self._parms.get("distribution")
@distribution.setter
def distribution(self, distribution):
assert_is_type(distribution, None, Enum("auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"))
self._parms["distribution"] = distribution
@property
def quantile_alpha(self):
"""
Desired quantile for Quantile regression, must be between 0 and 1.
Type: ``float`` (default: ``0.5``).
"""
return self._parms.get("quantile_alpha")
@quantile_alpha.setter
def quantile_alpha(self, quantile_alpha):
assert_is_type(quantile_alpha, None, numeric)
self._parms["quantile_alpha"] = quantile_alpha
@property
def tweedie_power(self):
"""
Tweedie power for Tweedie regression, must be between 1 and 2.
Type: ``float`` (default: ``1.5``).
"""
return self._parms.get("tweedie_power")
@tweedie_power.setter
def tweedie_power(self, tweedie_power):
assert_is_type(tweedie_power, None, numeric)
self._parms["tweedie_power"] = tweedie_power
@property
def huber_alpha(self):
"""
Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
Type: ``float`` (default: ``0.9``).
"""
return self._parms.get("huber_alpha")
@huber_alpha.setter
def huber_alpha(self, huber_alpha):
assert_is_type(huber_alpha, None, numeric)
self._parms["huber_alpha"] = huber_alpha
@property
def score_interval(self):
"""
Shortest time interval (in seconds) between model scoring.
Type: ``float`` (default: ``5``).
"""
return self._parms.get("score_interval")
@score_interval.setter
def score_interval(self, score_interval):
assert_is_type(score_interval, None, numeric)
self._parms["score_interval"] = score_interval
@property
def score_training_samples(self):
"""
Number of training set samples for scoring (0 for all).
Type: ``int`` (default: ``10000``).
"""
return self._parms.get("score_training_samples")
@score_training_samples.setter
def score_training_samples(self, score_training_samples):
assert_is_type(score_training_samples, None, int)
self._parms["score_training_samples"] = score_training_samples
@property
def score_validation_samples(self):
"""
Number of validation set samples for scoring (0 for all).
Type: ``int`` (default: ``0``).
"""
return self._parms.get("score_validation_samples")
@score_validation_samples.setter
def score_validation_samples(self, score_validation_samples):
assert_is_type(score_validation_samples, None, int)
self._parms["score_validation_samples"] = score_validation_samples
@property
def score_duty_cycle(self):
"""
Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring).
Type: ``float`` (default: ``0.1``).
"""
return self._parms.get("score_duty_cycle")
@score_duty_cycle.setter
def score_duty_cycle(self, score_duty_cycle):
assert_is_type(score_duty_cycle, None, numeric)
self._parms["score_duty_cycle"] = score_duty_cycle
@property
def classification_stop(self):
"""
Stopping criterion for classification error fraction on training data (-1 to disable).
Type: ``float`` (default: ``0``).
"""
return self._parms.get("classification_stop")
@classification_stop.setter
def classification_stop(self, classification_stop):
assert_is_type(classification_stop, None, numeric)
self._parms["classification_stop"] = classification_stop
@property
def regression_stop(self):
"""
Stopping criterion for regression error (MSE) on training data (-1 to disable).
Type: ``float`` (default: ``1e-06``).
"""
return self._parms.get("regression_stop")
@regression_stop.setter
def regression_stop(self, regression_stop):
assert_is_type(regression_stop, None, numeric)
self._parms["regression_stop"] = regression_stop
@property
def stopping_rounds(self):
"""
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`` (default: ``5``).
"""
return self._parms.get("stopping_rounds")
@stopping_rounds.setter
def stopping_rounds(self, stopping_rounds):
assert_is_type(stopping_rounds, None, int)
self._parms["stopping_rounds"] = stopping_rounds
@property
def stopping_metric(self):
"""
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)
One of: ``"auto"``, ``"deviance"``, ``"logloss"``, ``"mse"``, ``"rmse"``, ``"mae"``, ``"rmsle"``, ``"auc"``,
``"lift_top_group"``, ``"misclassification"``, ``"mean_per_class_error"`` (default: ``"auto"``).
"""
return self._parms.get("stopping_metric")
@stopping_metric.setter
def stopping_metric(self, stopping_metric):
assert_is_type(stopping_metric, None, Enum("auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "lift_top_group", "misclassification", "mean_per_class_error"))
self._parms["stopping_metric"] = stopping_metric
@property
def stopping_tolerance(self):
"""
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
Type: ``float`` (default: ``0``).
"""
return self._parms.get("stopping_tolerance")
@stopping_tolerance.setter
def stopping_tolerance(self, stopping_tolerance):
assert_is_type(stopping_tolerance, None, numeric)
self._parms["stopping_tolerance"] = stopping_tolerance
@property
def max_runtime_secs(self):
"""
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type: ``float`` (default: ``0``).
"""
return self._parms.get("max_runtime_secs")
@max_runtime_secs.setter
def max_runtime_secs(self, max_runtime_secs):
assert_is_type(max_runtime_secs, None, numeric)
self._parms["max_runtime_secs"] = max_runtime_secs
@property
def score_validation_sampling(self):
"""
Method used to sample validation dataset for scoring.
One of: ``"uniform"``, ``"stratified"`` (default: ``"uniform"``).
"""
return self._parms.get("score_validation_sampling")
@score_validation_sampling.setter
def score_validation_sampling(self, score_validation_sampling):
assert_is_type(score_validation_sampling, None, Enum("uniform", "stratified"))
self._parms["score_validation_sampling"] = score_validation_sampling
@property
def diagnostics(self):
"""
Enable diagnostics for hidden layers.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("diagnostics")
@diagnostics.setter
def diagnostics(self, diagnostics):
assert_is_type(diagnostics, None, bool)
self._parms["diagnostics"] = diagnostics
@property
def fast_mode(self):
"""
Enable fast mode (minor approximation in back-propagation).
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("fast_mode")
@fast_mode.setter
def fast_mode(self, fast_mode):
assert_is_type(fast_mode, None, bool)
self._parms["fast_mode"] = fast_mode
@property
def force_load_balance(self):
"""
Force extra load balancing to increase training speed for small datasets (to keep all cores busy).
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("force_load_balance")
@force_load_balance.setter
def force_load_balance(self, force_load_balance):
assert_is_type(force_load_balance, None, bool)
self._parms["force_load_balance"] = force_load_balance
@property
def variable_importances(self):
"""
Compute variable importances for input features (Gedeon method) - can be slow for large networks.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("variable_importances")
@variable_importances.setter
def variable_importances(self, variable_importances):
assert_is_type(variable_importances, None, bool)
self._parms["variable_importances"] = variable_importances
@property
def replicate_training_data(self):
"""
Replicate the entire training dataset onto every node for faster training on small datasets.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("replicate_training_data")
@replicate_training_data.setter
def replicate_training_data(self, replicate_training_data):
assert_is_type(replicate_training_data, None, bool)
self._parms["replicate_training_data"] = replicate_training_data
@property
def single_node_mode(self):
"""
Run on a single node for fine-tuning of model parameters.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("single_node_mode")
@single_node_mode.setter
def single_node_mode(self, single_node_mode):
assert_is_type(single_node_mode, None, bool)
self._parms["single_node_mode"] = single_node_mode
@property
def shuffle_training_data(self):
"""
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`` (default: ``False``).
"""
return self._parms.get("shuffle_training_data")
@shuffle_training_data.setter
def shuffle_training_data(self, shuffle_training_data):
assert_is_type(shuffle_training_data, None, bool)
self._parms["shuffle_training_data"] = shuffle_training_data
@property
def missing_values_handling(self):
"""
Handling of missing values. Either MeanImputation or Skip.
One of: ``"mean_imputation"``, ``"skip"`` (default: ``"mean_imputation"``).
"""
return self._parms.get("missing_values_handling")
@missing_values_handling.setter
def missing_values_handling(self, missing_values_handling):
assert_is_type(missing_values_handling, None, Enum("mean_imputation", "skip"))
self._parms["missing_values_handling"] = missing_values_handling
@property
def quiet_mode(self):
"""
Enable quiet mode for less output to standard output.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("quiet_mode")
@quiet_mode.setter
def quiet_mode(self, quiet_mode):
assert_is_type(quiet_mode, None, bool)
self._parms["quiet_mode"] = quiet_mode
@property
def autoencoder(self):
"""
Auto-Encoder.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("autoencoder")
@autoencoder.setter
def autoencoder(self, autoencoder):
assert_is_type(autoencoder, None, bool)
self._parms["autoencoder"] = autoencoder
@property
def sparse(self):
"""
Sparse data handling (more efficient for data with lots of 0 values).
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("sparse")
@sparse.setter
def sparse(self, sparse):
assert_is_type(sparse, None, bool)
self._parms["sparse"] = sparse
@property
def col_major(self):
"""
#DEPRECATED Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow
down backpropagation.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("col_major")
@col_major.setter
def col_major(self, col_major):
assert_is_type(col_major, None, bool)
self._parms["col_major"] = col_major
@property
def average_activation(self):
"""
Average activation for sparse auto-encoder. #Experimental
Type: ``float`` (default: ``0``).
"""
return self._parms.get("average_activation")
@average_activation.setter
def average_activation(self, average_activation):
assert_is_type(average_activation, None, numeric)
self._parms["average_activation"] = average_activation
@property
def sparsity_beta(self):
"""
Sparsity regularization. #Experimental
Type: ``float`` (default: ``0``).
"""
return self._parms.get("sparsity_beta")
@sparsity_beta.setter
def sparsity_beta(self, sparsity_beta):
assert_is_type(sparsity_beta, None, numeric)
self._parms["sparsity_beta"] = sparsity_beta
@property
def max_categorical_features(self):
"""
Max. number of categorical features, enforced via hashing. #Experimental
Type: ``int`` (default: ``2147483647``).
"""
return self._parms.get("max_categorical_features")
@max_categorical_features.setter
def max_categorical_features(self, max_categorical_features):
assert_is_type(max_categorical_features, None, int)
self._parms["max_categorical_features"] = max_categorical_features
@property
def reproducible(self):
"""
Force reproducibility on small data (will be slow - only uses 1 thread).
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("reproducible")
@reproducible.setter
def reproducible(self, reproducible):
assert_is_type(reproducible, None, bool)
self._parms["reproducible"] = reproducible
@property
def export_weights_and_biases(self):
"""
Whether to export Neural Network weights and biases to H2O Frames.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("export_weights_and_biases")
@export_weights_and_biases.setter
def export_weights_and_biases(self, export_weights_and_biases):
assert_is_type(export_weights_and_biases, None, bool)
self._parms["export_weights_and_biases"] = export_weights_and_biases
@property
def mini_batch_size(self):
"""
Mini-batch size (smaller leads to better fit, larger can speed up and generalize better).
Type: ``int`` (default: ``1``).
"""
return self._parms.get("mini_batch_size")
@mini_batch_size.setter
def mini_batch_size(self, mini_batch_size):
assert_is_type(mini_batch_size, None, int)
self._parms["mini_batch_size"] = mini_batch_size
@property
def categorical_encoding(self):
"""
Encoding scheme for categorical features
One of: ``"auto"``, ``"enum"``, ``"one_hot_internal"``, ``"one_hot_explicit"``, ``"binary"``, ``"eigen"``,
``"label_encoder"``, ``"sort_by_response"``, ``"enum_limited"`` (default: ``"auto"``).
"""
return self._parms.get("categorical_encoding")
@categorical_encoding.setter
def categorical_encoding(self, categorical_encoding):
assert_is_type(categorical_encoding, None, Enum("auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"))
self._parms["categorical_encoding"] = categorical_encoding
@property
def elastic_averaging(self):
"""
Elastic averaging between compute nodes can improve distributed model convergence. #Experimental
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("elastic_averaging")
@elastic_averaging.setter
def elastic_averaging(self, elastic_averaging):
assert_is_type(elastic_averaging, None, bool)
self._parms["elastic_averaging"] = elastic_averaging
@property
def elastic_averaging_moving_rate(self):
"""
Elastic averaging moving rate (only if elastic averaging is enabled).
Type: ``float`` (default: ``0.9``).
"""
return self._parms.get("elastic_averaging_moving_rate")
@elastic_averaging_moving_rate.setter
def elastic_averaging_moving_rate(self, elastic_averaging_moving_rate):
assert_is_type(elastic_averaging_moving_rate, None, numeric)
self._parms["elastic_averaging_moving_rate"] = elastic_averaging_moving_rate
@property
def elastic_averaging_regularization(self):
"""
Elastic averaging regularization strength (only if elastic averaging is enabled).
Type: ``float`` (default: ``0.001``).
"""
return self._parms.get("elastic_averaging_regularization")
@elastic_averaging_regularization.setter
def elastic_averaging_regularization(self, elastic_averaging_regularization):
assert_is_type(elastic_averaging_regularization, None, numeric)
self._parms["elastic_averaging_regularization"] = elastic_averaging_regularization
[docs]class H2OAutoEncoderEstimator(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)
"""
pass