#!/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
import h2o
[docs]class H2ODeepWaterEstimator(H2OEstimator):
"""
Deep Water
Build a Deep Learning model using multiple native GPU backends
Builds a deep neural network on an H2OFrame containing various data sources
"""
algo = "deepwater"
def __init__(self, **kwargs):
super(H2ODeepWaterEstimator, self).__init__()
self._parms = {}
names_list = {"model_id", "checkpoint", "autoencoder", "training_frame", "validation_frame", "nfolds",
"balance_classes", "max_after_balance_size", "class_sampling_factors",
"keep_cross_validation_predictions", "keep_cross_validation_fold_assignment", "fold_assignment",
"fold_column", "response_column", "offset_column", "weights_column", "ignored_columns",
"score_each_iteration", "categorical_encoding", "overwrite_with_best_model", "epochs",
"train_samples_per_iteration", "target_ratio_comm_to_comp", "seed", "standardize",
"learning_rate", "learning_rate_annealing", "momentum_start", "momentum_ramp", "momentum_stable",
"distribution", "score_interval", "score_training_samples", "score_validation_samples",
"score_duty_cycle", "classification_stop", "regression_stop", "stopping_rounds",
"stopping_metric", "stopping_tolerance", "max_runtime_secs", "ignore_const_cols",
"shuffle_training_data", "mini_batch_size", "clip_gradient", "network", "backend", "image_shape",
"channels", "sparse", "gpu", "device_id", "cache_data", "network_definition_file",
"network_parameters_file", "mean_image_file", "export_native_parameters_prefix", "activation",
"hidden", "input_dropout_ratio", "hidden_dropout_ratios", "problem_type"}
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))
@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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 learning_rate(self):
"""
Learning rate (higher => less stable, lower => slower convergence).
Type: ``float`` (default: ``0.001``).
"""
return self._parms.get("learning_rate")
@learning_rate.setter
def learning_rate(self, learning_rate):
assert_is_type(learning_rate, None, numeric)
self._parms["learning_rate"] = learning_rate
@property
def learning_rate_annealing(self):
"""
Learning rate annealing: rate / (1 + rate_annealing * samples).
Type: ``float`` (default: ``1e-06``).
"""
return self._parms.get("learning_rate_annealing")
@learning_rate_annealing.setter
def learning_rate_annealing(self, learning_rate_annealing):
assert_is_type(learning_rate_annealing, None, numeric)
self._parms["learning_rate_annealing"] = learning_rate_annealing
@property
def momentum_start(self):
"""
Initial momentum at the beginning of training (try 0.5).
Type: ``float`` (default: ``0.9``).
"""
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: ``10000``).
"""
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: ``0.9``).
"""
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 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 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: ``0``).
"""
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 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 shuffle_training_data(self):
"""
Enable global shuffling of training data.
Type: ``bool`` (default: ``True``).
"""
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 mini_batch_size(self):
"""
Mini-batch size (smaller leads to better fit, larger can speed up and generalize better).
Type: ``int`` (default: ``32``).
"""
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 clip_gradient(self):
"""
Clip gradients once their absolute value is larger than this value.
Type: ``float`` (default: ``10``).
"""
return self._parms.get("clip_gradient")
@clip_gradient.setter
def clip_gradient(self, clip_gradient):
assert_is_type(clip_gradient, None, numeric)
self._parms["clip_gradient"] = clip_gradient
@property
def network(self):
"""
Network architecture.
One of: ``"auto"``, ``"user"``, ``"lenet"``, ``"alexnet"``, ``"vgg"``, ``"googlenet"``, ``"inception_bn"``,
``"resnet"`` (default: ``"auto"``).
"""
return self._parms.get("network")
@network.setter
def network(self, network):
assert_is_type(network, None, Enum("auto", "user", "lenet", "alexnet", "vgg", "googlenet", "inception_bn", "resnet"))
self._parms["network"] = network
@property
def backend(self):
"""
Deep Learning Backend.
One of: ``"mxnet"``, ``"caffe"``, ``"tensorflow"`` (default: ``"mxnet"``).
"""
return self._parms.get("backend")
@backend.setter
def backend(self, backend):
assert_is_type(backend, None, Enum("mxnet", "caffe", "tensorflow"))
self._parms["backend"] = backend
@property
def image_shape(self):
"""
Width and height of image.
Type: ``List[int]`` (default: ``[0, 0]``).
"""
return self._parms.get("image_shape")
@image_shape.setter
def image_shape(self, image_shape):
assert_is_type(image_shape, None, [int])
self._parms["image_shape"] = image_shape
@property
def channels(self):
"""
Number of (color) channels.
Type: ``int`` (default: ``3``).
"""
return self._parms.get("channels")
@channels.setter
def channels(self, channels):
assert_is_type(channels, None, int)
self._parms["channels"] = channels
@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 gpu(self):
"""
Whether to use a GPU (if available).
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("gpu")
@gpu.setter
def gpu(self, gpu):
assert_is_type(gpu, None, bool)
self._parms["gpu"] = gpu
@property
def device_id(self):
"""
Device IDs (which GPUs to use).
Type: ``List[int]`` (default: ``[0]``).
"""
return self._parms.get("device_id")
@device_id.setter
def device_id(self, device_id):
assert_is_type(device_id, None, [int])
self._parms["device_id"] = device_id
@property
def cache_data(self):
"""
Whether to cache the data in memory (automatically disabled if data size is too large).
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("cache_data")
@cache_data.setter
def cache_data(self, cache_data):
assert_is_type(cache_data, None, bool)
self._parms["cache_data"] = cache_data
@property
def network_definition_file(self):
"""
Path of file containing network definition (graph, architecture).
Type: ``str``.
"""
return self._parms.get("network_definition_file")
@network_definition_file.setter
def network_definition_file(self, network_definition_file):
assert_is_type(network_definition_file, None, str)
self._parms["network_definition_file"] = network_definition_file
@property
def network_parameters_file(self):
"""
Path of file containing network (initial) parameters (weights, biases).
Type: ``str``.
"""
return self._parms.get("network_parameters_file")
@network_parameters_file.setter
def network_parameters_file(self, network_parameters_file):
assert_is_type(network_parameters_file, None, str)
self._parms["network_parameters_file"] = network_parameters_file
@property
def mean_image_file(self):
"""
Path of file containing the mean image data for data normalization.
Type: ``str``.
"""
return self._parms.get("mean_image_file")
@mean_image_file.setter
def mean_image_file(self, mean_image_file):
assert_is_type(mean_image_file, None, str)
self._parms["mean_image_file"] = mean_image_file
@property
def export_native_parameters_prefix(self):
"""
Path (prefix) where to export the native model parameters after every iteration.
Type: ``str``.
"""
return self._parms.get("export_native_parameters_prefix")
@export_native_parameters_prefix.setter
def export_native_parameters_prefix(self, export_native_parameters_prefix):
assert_is_type(export_native_parameters_prefix, None, str)
self._parms["export_native_parameters_prefix"] = export_native_parameters_prefix
@property
def activation(self):
"""
Activation function. Only used if no user-defined network architecture file is provided, and only for
problem_type=dataset.
One of: ``"rectifier"``, ``"tanh"``.
"""
return self._parms.get("activation")
@activation.setter
def activation(self, activation):
assert_is_type(activation, None, Enum("rectifier", "tanh"))
self._parms["activation"] = activation
@property
def hidden(self):
"""
Hidden layer sizes (e.g. [200, 200]). Only used if no user-defined network architecture file is provided, and
only for problem_type=dataset.
Type: ``List[int]``.
"""
return self._parms.get("hidden")
@hidden.setter
def hidden(self, hidden):
assert_is_type(hidden, None, [int])
self._parms["hidden"] = hidden
@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 problem_type(self):
"""
Problem type, auto-detected by default. If set to image, the H2OFrame must contain a string column containing
the path (URI or URL) to the images in the first column. If set to text, the H2OFrame must contain a string
column containing the text in the first column. If set to dataset, Deep Water behaves just like any other H2O
Model and builds a model on the provided H2OFrame (non-String columns).
One of: ``"auto"``, ``"image"``, ``"dataset"`` (default: ``"auto"``).
"""
return self._parms.get("problem_type")
@problem_type.setter
def problem_type(self, problem_type):
assert_is_type(problem_type, None, Enum("auto", "image", "dataset"))
self._parms["problem_type"] = problem_type
# Ask the H2O server whether a Deep Water model can be built (depends on availability of native backends)
[docs] @staticmethod
def available():
"""Returns True if a deep water model can be built, or False otherwise."""
builder_json = h2o.api("GET /3/ModelBuilders", data={"algo": "deepwater"})
visibility = builder_json["model_builders"]["deepwater"]["visibility"]
if visibility == "Experimental":
print("Cannot build a Deep Water model - no backend found.")
return False
else:
return True