#!/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 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 anonomaly_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 anonomaly_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