#!/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 H2ORandomForestEstimator(H2OEstimator):
"""
Distributed Random Forest
"""
algo = "drf"
def __init__(self, **kwargs):
super(H2ORandomForestEstimator, self).__init__()
self._parms = {}
names_list = {"model_id", "training_frame", "validation_frame", "nfolds", "keep_cross_validation_predictions",
"keep_cross_validation_fold_assignment", "score_each_iteration", "score_tree_interval",
"fold_assignment", "fold_column", "response_column", "ignored_columns", "ignore_const_cols",
"offset_column", "weights_column", "balance_classes", "class_sampling_factors",
"max_after_balance_size", "max_confusion_matrix_size", "max_hit_ratio_k", "ntrees", "max_depth",
"min_rows", "nbins", "nbins_top_level", "nbins_cats", "r2_stopping", "stopping_rounds",
"stopping_metric", "stopping_tolerance", "max_runtime_secs", "seed", "build_tree_one_node",
"mtries", "sample_rate", "sample_rate_per_class", "binomial_double_trees", "checkpoint",
"col_sample_rate_change_per_level", "col_sample_rate_per_tree", "min_split_improvement",
"histogram_type", "categorical_encoding"}
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 training_frame(self):
"""
Id of the training data frame (Not required, to allow initial validation of model parameters).
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 N-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 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 score_tree_interval(self):
"""
Score the model after every so many trees. Disabled if set to 0.
Type: ``int`` (default: ``0``).
"""
return self._parms.get("score_tree_interval")
@score_tree_interval.setter
def score_tree_interval(self, score_tree_interval):
assert_is_type(score_tree_interval, None, int)
self._parms["score_tree_interval"] = score_tree_interval
@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 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.
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 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 ntrees(self):
"""
Number of trees.
Type: ``int`` (default: ``50``).
"""
return self._parms.get("ntrees")
@ntrees.setter
def ntrees(self, ntrees):
assert_is_type(ntrees, None, int)
self._parms["ntrees"] = ntrees
@property
def max_depth(self):
"""
Maximum tree depth.
Type: ``int`` (default: ``20``).
"""
return self._parms.get("max_depth")
@max_depth.setter
def max_depth(self, max_depth):
assert_is_type(max_depth, None, int)
self._parms["max_depth"] = max_depth
@property
def min_rows(self):
"""
Fewest allowed (weighted) observations in a leaf.
Type: ``float`` (default: ``1``).
"""
return self._parms.get("min_rows")
@min_rows.setter
def min_rows(self, min_rows):
assert_is_type(min_rows, None, numeric)
self._parms["min_rows"] = min_rows
@property
def nbins(self):
"""
For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
Type: ``int`` (default: ``20``).
"""
return self._parms.get("nbins")
@nbins.setter
def nbins(self, nbins):
assert_is_type(nbins, None, int)
self._parms["nbins"] = nbins
@property
def nbins_top_level(self):
"""
For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease
by factor of two per level
Type: ``int`` (default: ``1024``).
"""
return self._parms.get("nbins_top_level")
@nbins_top_level.setter
def nbins_top_level(self, nbins_top_level):
assert_is_type(nbins_top_level, None, int)
self._parms["nbins_top_level"] = nbins_top_level
@property
def nbins_cats(self):
"""
For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher
values can lead to more overfitting.
Type: ``int`` (default: ``1024``).
"""
return self._parms.get("nbins_cats")
@nbins_cats.setter
def nbins_cats(self, nbins_cats):
assert_is_type(nbins_cats, None, int)
self._parms["nbins_cats"] = nbins_cats
@property
def r2_stopping(self):
"""
r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and
stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or
exceeds this
Type: ``float`` (default: ``1.797693135e+308``).
"""
return self._parms.get("r2_stopping")
@r2_stopping.setter
def r2_stopping(self, r2_stopping):
assert_is_type(r2_stopping, None, numeric)
self._parms["r2_stopping"] = r2_stopping
@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: ``0``).
"""
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: ``0.001``).
"""
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 seed(self):
"""
Seed for pseudo random number generator (if applicable)
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 build_tree_one_node(self):
"""
Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("build_tree_one_node")
@build_tree_one_node.setter
def build_tree_one_node(self, build_tree_one_node):
assert_is_type(build_tree_one_node, None, bool)
self._parms["build_tree_one_node"] = build_tree_one_node
@property
def mtries(self):
"""
Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for
classification and p/3 for regression (where p is the # of predictors
Type: ``int`` (default: ``-1``).
"""
return self._parms.get("mtries")
@mtries.setter
def mtries(self, mtries):
assert_is_type(mtries, None, int)
self._parms["mtries"] = mtries
@property
def sample_rate(self):
"""
Row sample rate per tree (from 0.0 to 1.0)
Type: ``float`` (default: ``0.6320000291``).
"""
return self._parms.get("sample_rate")
@sample_rate.setter
def sample_rate(self, sample_rate):
assert_is_type(sample_rate, None, numeric)
self._parms["sample_rate"] = sample_rate
@property
def sample_rate_per_class(self):
"""
Row sample rate per tree per class (from 0.0 to 1.0)
Type: ``List[float]``.
"""
return self._parms.get("sample_rate_per_class")
@sample_rate_per_class.setter
def sample_rate_per_class(self, sample_rate_per_class):
assert_is_type(sample_rate_per_class, None, [numeric])
self._parms["sample_rate_per_class"] = sample_rate_per_class
@property
def binomial_double_trees(self):
"""
For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("binomial_double_trees")
@binomial_double_trees.setter
def binomial_double_trees(self, binomial_double_trees):
assert_is_type(binomial_double_trees, None, bool)
self._parms["binomial_double_trees"] = binomial_double_trees
@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 col_sample_rate_change_per_level(self):
"""
Relative change of the column sampling rate for every level (from 0.0 to 2.0)
Type: ``float`` (default: ``1``).
"""
return self._parms.get("col_sample_rate_change_per_level")
@col_sample_rate_change_per_level.setter
def col_sample_rate_change_per_level(self, col_sample_rate_change_per_level):
assert_is_type(col_sample_rate_change_per_level, None, numeric)
self._parms["col_sample_rate_change_per_level"] = col_sample_rate_change_per_level
@property
def col_sample_rate_per_tree(self):
"""
Column sample rate per tree (from 0.0 to 1.0)
Type: ``float`` (default: ``1``).
"""
return self._parms.get("col_sample_rate_per_tree")
@col_sample_rate_per_tree.setter
def col_sample_rate_per_tree(self, col_sample_rate_per_tree):
assert_is_type(col_sample_rate_per_tree, None, numeric)
self._parms["col_sample_rate_per_tree"] = col_sample_rate_per_tree
@property
def min_split_improvement(self):
"""
Minimum relative improvement in squared error reduction for a split to happen
Type: ``float`` (default: ``1e-05``).
"""
return self._parms.get("min_split_improvement")
@min_split_improvement.setter
def min_split_improvement(self, min_split_improvement):
assert_is_type(min_split_improvement, None, numeric)
self._parms["min_split_improvement"] = min_split_improvement
@property
def histogram_type(self):
"""
What type of histogram to use for finding optimal split points
One of: ``"auto"``, ``"uniform_adaptive"``, ``"random"``, ``"quantiles_global"``, ``"round_robin"`` (default:
``"auto"``).
"""
return self._parms.get("histogram_type")
@histogram_type.setter
def histogram_type(self, histogram_type):
assert_is_type(histogram_type, None, Enum("auto", "uniform_adaptive", "random", "quantiles_global", "round_robin"))
self._parms["histogram_type"] = histogram_type
@property
def categorical_encoding(self):
"""
Encoding scheme for categorical features
One of: ``"auto"``, ``"enum"``, ``"one_hot_internal"``, ``"one_hot_explicit"``, ``"binary"``, ``"eigen"``
(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"))
self._parms["categorical_encoding"] = categorical_encoding