function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def training_frame(self, training_frame):
assert_is_type(training_frame, None, H2OFrame)
self._parms["training_frame"] = training_frame | h2oai/h2o-dev | [
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] |
def validation_frame(self):
"""
Id of the validation data frame.
Type: ``H2OFrame``.
"""
return self._parms.get("validation_frame") | h2oai/h2o-dev | [
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] |
def validation_frame(self, validation_frame):
assert_is_type(validation_frame, None, H2OFrame)
self._parms["validation_frame"] = validation_frame | h2oai/h2o-dev | [
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] |
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") | h2oai/h2o-dev | [
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] |
def nfolds(self, nfolds):
assert_is_type(nfolds, None, int)
self._parms["nfolds"] = nfolds | h2oai/h2o-dev | [
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] |
def keep_cross_validation_models(self):
"""
Whether to keep the cross-validation models.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("keep_cross_validation_models") | h2oai/h2o-dev | [
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] |
def keep_cross_validation_models(self, keep_cross_validation_models):
assert_is_type(keep_cross_validation_models, None, bool)
self._parms["keep_cross_validation_models"] = keep_cross_validation_models | h2oai/h2o-dev | [
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] |
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") | h2oai/h2o-dev | [
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] |
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 | h2oai/h2o-dev | [
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] |
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") | h2oai/h2o-dev | [
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] |
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 | h2oai/h2o-dev | [
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] |
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") | h2oai/h2o-dev | [
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] |
def score_each_iteration(self, score_each_iteration):
assert_is_type(score_each_iteration, None, bool)
self._parms["score_each_iteration"] = score_each_iteration | h2oai/h2o-dev | [
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] |
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") | h2oai/h2o-dev | [
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] |
def fold_assignment(self, fold_assignment):
assert_is_type(fold_assignment, None, Enum("auto", "random", "modulo", "stratified"))
self._parms["fold_assignment"] = fold_assignment | h2oai/h2o-dev | [
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def fold_column(self):
"""
Column with cross-validation fold index assignment per observation.
Type: ``str``.
"""
return self._parms.get("fold_column") | h2oai/h2o-dev | [
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def fold_column(self, fold_column):
assert_is_type(fold_column, None, str)
self._parms["fold_column"] = fold_column | h2oai/h2o-dev | [
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def response_column(self):
"""
Response variable column.
Type: ``str``.
"""
return self._parms.get("response_column") | h2oai/h2o-dev | [
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def response_column(self, response_column):
assert_is_type(response_column, None, str)
self._parms["response_column"] = response_column | h2oai/h2o-dev | [
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def ignored_columns(self):
"""
Names of columns to ignore for training.
Type: ``List[str]``.
"""
return self._parms.get("ignored_columns") | h2oai/h2o-dev | [
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def ignored_columns(self, ignored_columns):
assert_is_type(ignored_columns, None, [str])
self._parms["ignored_columns"] = ignored_columns | h2oai/h2o-dev | [
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def ignore_const_cols(self):
"""
Ignore constant columns.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("ignore_const_cols") | h2oai/h2o-dev | [
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def ignore_const_cols(self, ignore_const_cols):
assert_is_type(ignore_const_cols, None, bool)
self._parms["ignore_const_cols"] = ignore_const_cols | h2oai/h2o-dev | [
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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") | h2oai/h2o-dev | [
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def offset_column(self, offset_column):
assert_is_type(offset_column, None, str)
self._parms["offset_column"] = offset_column | h2oai/h2o-dev | [
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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") | h2oai/h2o-dev | [
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] |
def weights_column(self, weights_column):
assert_is_type(weights_column, None, str)
self._parms["weights_column"] = weights_column | h2oai/h2o-dev | [
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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") | h2oai/h2o-dev | [
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] |
def stopping_rounds(self, stopping_rounds):
assert_is_type(stopping_rounds, None, int)
self._parms["stopping_rounds"] = stopping_rounds | h2oai/h2o-dev | [
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] |
def stopping_metric(self):
"""
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression). Note that custom
and custom_increasing can only be used in GBM and DRF with the Python client.
One of: ``"auto"``, ``"deviance"``, ``"logloss"``, ``"mse"``, ``"rmse"``, ``"mae"``, ``"rmsle"``, ``"auc"``,
``"lift_top_group"``, ``"misclassification"``, ``"mean_per_class_error"``, ``"custom"``, ``"custom_increasing"``
(default: ``"auto"``).
"""
return self._parms.get("stopping_metric") | h2oai/h2o-dev | [
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] |
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", "custom", "custom_increasing"))
self._parms["stopping_metric"] = stopping_metric | h2oai/h2o-dev | [
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] |
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") | h2oai/h2o-dev | [
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def stopping_tolerance(self, stopping_tolerance):
assert_is_type(stopping_tolerance, None, numeric)
self._parms["stopping_tolerance"] = stopping_tolerance | h2oai/h2o-dev | [
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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") | h2oai/h2o-dev | [
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def max_runtime_secs(self, max_runtime_secs):
assert_is_type(max_runtime_secs, None, numeric)
self._parms["max_runtime_secs"] = max_runtime_secs | h2oai/h2o-dev | [
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def seed(self):
"""
Seed for pseudo random number generator (if applicable)
Type: ``int`` (default: ``-1``).
"""
return self._parms.get("seed") | h2oai/h2o-dev | [
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def seed(self, seed):
assert_is_type(seed, None, int)
self._parms["seed"] = seed | h2oai/h2o-dev | [
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def distribution(self):
"""
Distribution function
One of: ``"auto"``, ``"bernoulli"``, ``"multinomial"``, ``"gaussian"``, ``"poisson"``, ``"gamma"``,
``"tweedie"``, ``"laplace"``, ``"quantile"``, ``"huber"`` (default: ``"auto"``).
"""
return self._parms.get("distribution") | h2oai/h2o-dev | [
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def distribution(self, distribution):
assert_is_type(distribution, None, Enum("auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"))
self._parms["distribution"] = distribution | h2oai/h2o-dev | [
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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") | h2oai/h2o-dev | [
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def tweedie_power(self, tweedie_power):
assert_is_type(tweedie_power, None, numeric)
self._parms["tweedie_power"] = tweedie_power | h2oai/h2o-dev | [
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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") | h2oai/h2o-dev | [
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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 | h2oai/h2o-dev | [
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def quiet_mode(self):
"""
Enable quiet mode
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("quiet_mode") | h2oai/h2o-dev | [
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def quiet_mode(self, quiet_mode):
assert_is_type(quiet_mode, None, bool)
self._parms["quiet_mode"] = quiet_mode | h2oai/h2o-dev | [
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def export_checkpoints_dir(self):
"""
Automatically export generated models to this directory.
Type: ``str``.
"""
return self._parms.get("export_checkpoints_dir") | h2oai/h2o-dev | [
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def export_checkpoints_dir(self, export_checkpoints_dir):
assert_is_type(export_checkpoints_dir, None, str)
self._parms["export_checkpoints_dir"] = export_checkpoints_dir | h2oai/h2o-dev | [
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def ntrees(self):
"""
(same as n_estimators) Number of trees.
Type: ``int`` (default: ``50``).
"""
return self._parms.get("ntrees") | h2oai/h2o-dev | [
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def ntrees(self, ntrees):
assert_is_type(ntrees, None, int)
self._parms["ntrees"] = ntrees | h2oai/h2o-dev | [
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def max_depth(self):
"""
Maximum tree depth.
Type: ``int`` (default: ``6``).
"""
return self._parms.get("max_depth") | h2oai/h2o-dev | [
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def max_depth(self, max_depth):
assert_is_type(max_depth, None, int)
self._parms["max_depth"] = max_depth | h2oai/h2o-dev | [
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def min_rows(self):
"""
(same as min_child_weight) Fewest allowed (weighted) observations in a leaf.
Type: ``float`` (default: ``1``).
"""
return self._parms.get("min_rows") | h2oai/h2o-dev | [
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def min_rows(self, min_rows):
assert_is_type(min_rows, None, numeric)
self._parms["min_rows"] = min_rows | h2oai/h2o-dev | [
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def min_child_weight(self):
"""
(same as min_rows) Fewest allowed (weighted) observations in a leaf.
Type: ``float`` (default: ``1``).
"""
return self._parms.get("min_child_weight") | h2oai/h2o-dev | [
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] |
def min_child_weight(self, min_child_weight):
assert_is_type(min_child_weight, None, numeric)
self._parms["min_child_weight"] = min_child_weight | h2oai/h2o-dev | [
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def learn_rate(self):
"""
(same as eta) Learning rate (from 0.0 to 1.0)
Type: ``float`` (default: ``0.3``).
"""
return self._parms.get("learn_rate") | h2oai/h2o-dev | [
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def learn_rate(self, learn_rate):
assert_is_type(learn_rate, None, numeric)
self._parms["learn_rate"] = learn_rate | h2oai/h2o-dev | [
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def eta(self):
"""
(same as learn_rate) Learning rate (from 0.0 to 1.0)
Type: ``float`` (default: ``0.3``).
"""
return self._parms.get("eta") | h2oai/h2o-dev | [
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def eta(self, eta):
assert_is_type(eta, None, numeric)
self._parms["eta"] = eta | h2oai/h2o-dev | [
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def sample_rate(self):
"""
(same as subsample) Row sample rate per tree (from 0.0 to 1.0)
Type: ``float`` (default: ``1``).
"""
return self._parms.get("sample_rate") | h2oai/h2o-dev | [
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def sample_rate(self, sample_rate):
assert_is_type(sample_rate, None, numeric)
self._parms["sample_rate"] = sample_rate | h2oai/h2o-dev | [
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def subsample(self):
"""
(same as sample_rate) Row sample rate per tree (from 0.0 to 1.0)
Type: ``float`` (default: ``1``).
"""
return self._parms.get("subsample") | h2oai/h2o-dev | [
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def subsample(self, subsample):
assert_is_type(subsample, None, numeric)
self._parms["subsample"] = subsample | h2oai/h2o-dev | [
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def col_sample_rate(self):
"""
(same as colsample_bylevel) Column sample rate (from 0.0 to 1.0)
Type: ``float`` (default: ``1``).
"""
return self._parms.get("col_sample_rate") | h2oai/h2o-dev | [
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def col_sample_rate(self, col_sample_rate):
assert_is_type(col_sample_rate, None, numeric)
self._parms["col_sample_rate"] = col_sample_rate | h2oai/h2o-dev | [
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def colsample_bylevel(self):
"""
(same as col_sample_rate) Column sample rate (from 0.0 to 1.0)
Type: ``float`` (default: ``1``).
"""
return self._parms.get("colsample_bylevel") | h2oai/h2o-dev | [
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def colsample_bylevel(self, colsample_bylevel):
assert_is_type(colsample_bylevel, None, numeric)
self._parms["colsample_bylevel"] = colsample_bylevel | h2oai/h2o-dev | [
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def col_sample_rate_per_tree(self):
"""
(same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0)
Type: ``float`` (default: ``1``).
"""
return self._parms.get("col_sample_rate_per_tree") | h2oai/h2o-dev | [
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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 | h2oai/h2o-dev | [
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def colsample_bytree(self):
"""
(same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0)
Type: ``float`` (default: ``1``).
"""
return self._parms.get("colsample_bytree") | h2oai/h2o-dev | [
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def colsample_bytree(self, colsample_bytree):
assert_is_type(colsample_bytree, None, numeric)
self._parms["colsample_bytree"] = colsample_bytree | h2oai/h2o-dev | [
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def max_abs_leafnode_pred(self):
"""
(same as max_delta_step) Maximum absolute value of a leaf node prediction
Type: ``float`` (default: ``0``).
"""
return self._parms.get("max_abs_leafnode_pred") | h2oai/h2o-dev | [
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def max_abs_leafnode_pred(self, max_abs_leafnode_pred):
assert_is_type(max_abs_leafnode_pred, None, float)
self._parms["max_abs_leafnode_pred"] = max_abs_leafnode_pred | h2oai/h2o-dev | [
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def max_delta_step(self):
"""
(same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction
Type: ``float`` (default: ``0``).
"""
return self._parms.get("max_delta_step") | h2oai/h2o-dev | [
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def max_delta_step(self, max_delta_step):
assert_is_type(max_delta_step, None, float)
self._parms["max_delta_step"] = max_delta_step | h2oai/h2o-dev | [
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def monotone_constraints(self):
"""
A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a
decreasing constraint.
Type: ``dict``.
"""
return self._parms.get("monotone_constraints") | h2oai/h2o-dev | [
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def monotone_constraints(self, monotone_constraints):
assert_is_type(monotone_constraints, None, dict)
self._parms["monotone_constraints"] = monotone_constraints | h2oai/h2o-dev | [
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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") | h2oai/h2o-dev | [
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1393862887
] |
def score_tree_interval(self, score_tree_interval):
assert_is_type(score_tree_interval, None, int)
self._parms["score_tree_interval"] = score_tree_interval | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def min_split_improvement(self):
"""
(same as gamma) Minimum relative improvement in squared error reduction for a split to happen
Type: ``float`` (default: ``0``).
"""
return self._parms.get("min_split_improvement") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def min_split_improvement(self, min_split_improvement):
assert_is_type(min_split_improvement, None, float)
self._parms["min_split_improvement"] = min_split_improvement | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def gamma(self):
"""
(same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen
Type: ``float`` (default: ``0``).
"""
return self._parms.get("gamma") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def gamma(self, gamma):
assert_is_type(gamma, None, float)
self._parms["gamma"] = gamma | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def nthread(self):
"""
Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads
parameter). Defaults to maximum available
Type: ``int`` (default: ``-1``).
"""
return self._parms.get("nthread") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def nthread(self, nthread):
assert_is_type(nthread, None, int)
self._parms["nthread"] = nthread | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def max_bins(self):
"""
For tree_method=hist only: maximum number of bins
Type: ``int`` (default: ``256``).
"""
return self._parms.get("max_bins") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def max_bins(self, max_bins):
assert_is_type(max_bins, None, int)
self._parms["max_bins"] = max_bins | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def max_leaves(self):
"""
For tree_method=hist only: maximum number of leaves
Type: ``int`` (default: ``0``).
"""
return self._parms.get("max_leaves") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def max_leaves(self, max_leaves):
assert_is_type(max_leaves, None, int)
self._parms["max_leaves"] = max_leaves | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def min_sum_hessian_in_leaf(self):
"""
For tree_method=hist only: the mininum sum of hessian in a leaf to keep splitting
Type: ``float`` (default: ``100``).
"""
return self._parms.get("min_sum_hessian_in_leaf") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def min_sum_hessian_in_leaf(self, min_sum_hessian_in_leaf):
assert_is_type(min_sum_hessian_in_leaf, None, float)
self._parms["min_sum_hessian_in_leaf"] = min_sum_hessian_in_leaf | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def min_data_in_leaf(self):
"""
For tree_method=hist only: the mininum data in a leaf to keep splitting
Type: ``float`` (default: ``0``).
"""
return self._parms.get("min_data_in_leaf") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def min_data_in_leaf(self, min_data_in_leaf):
assert_is_type(min_data_in_leaf, None, float)
self._parms["min_data_in_leaf"] = min_data_in_leaf | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def sample_type(self):
"""
For booster=dart only: sample_type
One of: ``"uniform"``, ``"weighted"`` (default: ``"uniform"``).
"""
return self._parms.get("sample_type") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def sample_type(self, sample_type):
assert_is_type(sample_type, None, Enum("uniform", "weighted"))
self._parms["sample_type"] = sample_type | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def normalize_type(self):
"""
For booster=dart only: normalize_type
One of: ``"tree"``, ``"forest"`` (default: ``"tree"``).
"""
return self._parms.get("normalize_type") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def normalize_type(self, normalize_type):
assert_is_type(normalize_type, None, Enum("tree", "forest"))
self._parms["normalize_type"] = normalize_type | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def rate_drop(self):
"""
For booster=dart only: rate_drop (0..1)
Type: ``float`` (default: ``0``).
"""
return self._parms.get("rate_drop") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def rate_drop(self, rate_drop):
assert_is_type(rate_drop, None, float)
self._parms["rate_drop"] = rate_drop | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def one_drop(self):
"""
For booster=dart only: one_drop
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("one_drop") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
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