Source code for h2o.estimators.isolation_forest

#!/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 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 H2OIsolationForestEstimator(H2OEstimator): """ Isolation Forest Builds an Isolation Forest model. Isolation Forest algorithm samples the training frame and in each iteration builds a tree that partitions the space of the sample observations until it isolates each observation. Length of the path from root to a leaf node of the resulting tree is used to calculate the anomaly score. Anomalies are easier to isolate and their average tree path is expected to be shorter than paths of regular observations. """ algo = "isolationforest" supervised_learning = False _options_ = {'model_extensions': ['h2o.model.extensions.Trees']} def __init__(self, model_id=None, # type: Optional[Union[None, str, H2OEstimator]] training_frame=None, # type: Optional[Union[None, str, H2OFrame]] score_each_iteration=False, # type: bool score_tree_interval=0, # type: int ignored_columns=None, # type: Optional[List[str]] ignore_const_cols=True, # type: bool ntrees=50, # type: int max_depth=8, # type: int min_rows=1.0, # type: float max_runtime_secs=0.0, # type: float seed=-1, # type: int build_tree_one_node=False, # type: bool mtries=-1, # type: int sample_size=256, # type: int sample_rate=-1.0, # type: float col_sample_rate_change_per_level=1.0, # type: float col_sample_rate_per_tree=1.0, # type: float categorical_encoding="auto", # type: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"] stopping_rounds=0, # type: int stopping_metric="auto", # type: Literal["auto", "anomaly_score", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "misclassification", "mean_per_class_error"] stopping_tolerance=0.01, # type: float export_checkpoints_dir=None, # type: Optional[str] contamination=-1.0, # type: float validation_frame=None, # type: Optional[Union[None, str, H2OFrame]] validation_response_column=None, # type: Optional[str] ): """ :param model_id: Destination id for this model; auto-generated if not specified. Defaults to ``None``. :type model_id: Union[None, str, H2OEstimator], optional :param training_frame: Id of the training data frame. Defaults to ``None``. :type training_frame: Union[None, str, H2OFrame], optional :param score_each_iteration: Whether to score during each iteration of model training. Defaults to ``False``. :type score_each_iteration: bool :param score_tree_interval: Score the model after every so many trees. Disabled if set to 0. Defaults to ``0``. :type score_tree_interval: int :param ignored_columns: Names of columns to ignore for training. Defaults to ``None``. :type ignored_columns: List[str], optional :param ignore_const_cols: Ignore constant columns. Defaults to ``True``. :type ignore_const_cols: bool :param ntrees: Number of trees. Defaults to ``50``. :type ntrees: int :param max_depth: Maximum tree depth (0 for unlimited). Defaults to ``8``. :type max_depth: int :param min_rows: Fewest allowed (weighted) observations in a leaf. Defaults to ``1.0``. :type min_rows: float :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to ``0.0``. :type max_runtime_secs: float :param seed: Seed for pseudo random number generator (if applicable) Defaults to ``-1``. :type seed: int :param build_tree_one_node: Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. Defaults to ``False``. :type build_tree_one_node: bool :param mtries: Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3. Defaults to ``-1``. :type mtries: int :param sample_size: Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored. Defaults to ``256``. :type sample_size: int :param sample_rate: Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead. Defaults to ``-1.0``. :type sample_rate: float :param col_sample_rate_change_per_level: Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) Defaults to ``1.0``. :type col_sample_rate_change_per_level: float :param col_sample_rate_per_tree: Column sample rate per tree (from 0.0 to 1.0) Defaults to ``1.0``. :type col_sample_rate_per_tree: float :param categorical_encoding: Encoding scheme for categorical features Defaults to ``"auto"``. :type categorical_encoding: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"] :param stopping_rounds: 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) Defaults to ``0``. :type stopping_rounds: int :param stopping_metric: Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Defaults to ``"auto"``. :type stopping_metric: Literal["auto", "anomaly_score", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "misclassification", "mean_per_class_error"] :param stopping_tolerance: Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to ``0.01``. :type stopping_tolerance: float :param export_checkpoints_dir: Automatically export generated models to this directory. Defaults to ``None``. :type export_checkpoints_dir: str, optional :param contamination: Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined). Defaults to ``-1.0``. :type contamination: float :param validation_frame: Id of the validation data frame. Defaults to ``None``. :type validation_frame: Union[None, str, H2OFrame], optional :param validation_response_column: (experimental) Name of the response column in the validation frame. Response column should be binary and indicate not anomaly/anomaly. Defaults to ``None``. :type validation_response_column: str, optional """ super(H2OIsolationForestEstimator, self).__init__() self._parms = {} self._id = self._parms['model_id'] = model_id self.training_frame = training_frame self.score_each_iteration = score_each_iteration self.score_tree_interval = score_tree_interval self.ignored_columns = ignored_columns self.ignore_const_cols = ignore_const_cols self.ntrees = ntrees self.max_depth = max_depth self.min_rows = min_rows self.max_runtime_secs = max_runtime_secs self.seed = seed self.build_tree_one_node = build_tree_one_node self.mtries = mtries self.sample_size = sample_size self.sample_rate = sample_rate self.col_sample_rate_change_per_level = col_sample_rate_change_per_level self.col_sample_rate_per_tree = col_sample_rate_per_tree self.categorical_encoding = categorical_encoding self.stopping_rounds = stopping_rounds self.stopping_metric = stopping_metric self.stopping_tolerance = stopping_tolerance self.export_checkpoints_dir = export_checkpoints_dir self.contamination = contamination self.validation_frame = validation_frame self.validation_response_column = validation_response_column @property def training_frame(self): """ Id of the training data frame. Type: ``Union[None, str, H2OFrame]``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance() """ return self._parms.get("training_frame") @training_frame.setter def training_frame(self, training_frame): self._parms["training_frame"] = H2OFrame._validate(training_frame, 'training_frame') @property def score_each_iteration(self): """ Whether to score during each iteration of model training. Type: ``bool``, defaults to ``False``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(score_each_iteration=True, ... ntrees=55, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance() """ 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``, defaults to ``0``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(score_tree_interval=5, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance() """ 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 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``, defaults to ``True``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year","const_1","const_2"] >>> cars["const_1"] = 6 >>> cars["const_2"] = 7 >>> train, valid = cars.split_frame(ratios=[.8], seed=1234) >>> cars_if = H2OIsolationForestEstimator(seed=1234, ... ignore_const_cols=True) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance() """ 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 ntrees(self): """ Number of trees. Type: ``int``, defaults to ``50``. :examples: >>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") >>> predictors = titanic.columns >>> tree_num = [20, 50, 80, 110, 140, 170, 200] >>> label = ["20", "50", "80", "110", "140", "170", "200"] >>> for key, num in enumerate(tree_num): ... titanic_if = H2OIsolationForestEstimator(ntrees=num, ... seed=1234) ... titanic_if.train(x=predictors, ... training_frame=titanic) ... print(label[key], 'training score', titanic_if.mse(train=True)) """ 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 (0 for unlimited). Type: ``int``, defaults to ``8``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(max_depth=2, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance() """ 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``, defaults to ``1.0``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(min_rows=16, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance() """ 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 max_runtime_secs(self): """ Maximum allowed runtime in seconds for model training. Use 0 to disable. Type: ``float``, defaults to ``0.0``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(max_runtime_secs=10, ... ntrees=10000, ... max_depth=10, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance() """ 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``, defaults to ``-1``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> isofor_w_seed = H2OIsolationForestEstimator(seed=1234) >>> isofor_w_seed.train(x=predictors, ... training_frame=airlines) >>> isofor_wo_seed = H2OIsolationForestEstimator() >>> isofor_wo_seed.train(x=predictors, ... training_frame=airlines) >>> isofor_w_seed.model_performance() >>> isofor_wo_seed.model_performance() """ 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``, defaults to ``False``. :examples: >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") >>> predictors = ["displacement","power","weight","acceleration","year"] >>> cars_if = H2OIsolationForestEstimator(build_tree_one_node=True, ... seed=1234) >>> cars_if.train(x=predictors, ... training_frame=cars) >>> cars_if.model_performance() """ 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 (number of predictors)/3. Type: ``int``, defaults to ``-1``. :examples: >>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data") >>> predictors = covtype.columns[0:54] >>> cov_if = H2OIsolationForestEstimator(mtries=30, seed=1234) >>> cov_if.train(x=predictors, ... training_frame=covtype) >>> cov_if.model_performance() """ return self._parms.get("mtries") @mtries.setter def mtries(self, mtries): assert_is_type(mtries, None, int) self._parms["mtries"] = mtries @property def sample_size(self): """ Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored. Type: ``int``, defaults to ``256``. :examples: >>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_train.csv") >>> isofor_model = H2OIsolationForestEstimator(sample_size=5, ... ntrees=7) >>> isofor_model.train(training_frame=train) >>> isofor_model.model_performance() """ return self._parms.get("sample_size") @sample_size.setter def sample_size(self, sample_size): assert_is_type(sample_size, None, int) self._parms["sample_size"] = sample_size @property def sample_rate(self): """ Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead. Type: ``float``, defaults to ``-1.0``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(sample_rate=.7, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance() """ 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 col_sample_rate_change_per_level(self): """ Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) Type: ``float``, defaults to ``1.0``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(col_sample_rate_change_per_level=.9, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance() """ 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``, defaults to ``1.0``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(col_sample_rate_per_tree=.7, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance() """ 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 categorical_encoding(self): """ Encoding scheme for categorical features Type: ``Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]``, defaults to ``"auto"``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> encoding = "one_hot_explicit" >>> airlines_if = H2OIsolationForestEstimator(categorical_encoding=encoding, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance() """ 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 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``, defaults to ``0``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance() """ 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 and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Type: ``Literal["auto", "anomaly_score", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "misclassification", "mean_per_class_error"]``, defaults to ``"auto"``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance() """ return self._parms.get("stopping_metric") @stopping_metric.setter def stopping_metric(self, stopping_metric): assert_is_type(stopping_metric, None, Enum("auto", "anomaly_score", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "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``, defaults to ``0.01``. :examples: >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] >>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto", ... stopping_rounds=3, ... stopping_tolerance=1e-2, ... seed=1234) >>> airlines_if.train(x=predictors, ... training_frame=airlines) >>> airlines_if.model_performance() """ 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 export_checkpoints_dir(self): """ Automatically export generated models to this directory. Type: ``str``. :examples: >>> import tempfile >>> from os import listdir >>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex") >>> predictors = ["DayofMonth", "DayOfWeek"] >>> checkpoints_dir = tempfile.mkdtemp() >>> air_if = H2OIsolationForestEstimator(max_depth=3, ... seed=1234, ... export_checkpoints_dir=checkpoints_dir) >>> air_if.train(x=predictors, ... training_frame=airlines) >>> len(listdir(checkpoints_dir)) """ return self._parms.get("export_checkpoints_dir") @export_checkpoints_dir.setter def export_checkpoints_dir(self, export_checkpoints_dir): assert_is_type(export_checkpoints_dir, None, str) self._parms["export_checkpoints_dir"] = export_checkpoints_dir @property def contamination(self): """ Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined). Type: ``float``, defaults to ``-1.0``. """ return self._parms.get("contamination") @contamination.setter def contamination(self, contamination): assert_is_type(contamination, None, numeric) self._parms["contamination"] = contamination @property def validation_frame(self): """ Id of the validation data frame. Type: ``Union[None, str, H2OFrame]``. """ return self._parms.get("validation_frame") @validation_frame.setter def validation_frame(self, validation_frame): self._parms["validation_frame"] = H2OFrame._validate(validation_frame, 'validation_frame') @property def validation_response_column(self): """ (experimental) Name of the response column in the validation frame. Response column should be binary and indicate not anomaly/anomaly. Type: ``str``. """ return self._parms.get("validation_response_column") @validation_response_column.setter def validation_response_column(self, validation_response_column): assert_is_type(validation_response_column, None, str) self._parms["validation_response_column"] = validation_response_column