#!/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
import h2o
import warnings
from h2o.exceptions import H2ODeprecationWarning
from h2o.utils.metaclass import deprecated_property
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 H2OTargetEncoderEstimator(H2OEstimator):
"""
TargetEncoder
"""
algo = "targetencoder"
param_names = {"model_id", "training_frame", "fold_column", "response_column", "ignored_columns",
"keep_original_categorical_columns", "blending", "inflection_point", "smoothing",
"data_leakage_handling", "noise", "seed"}
def __init__(self, **kwargs):
super(H2OTargetEncoderEstimator, self).__init__()
self._parms = {}
for pname, pvalue in kwargs.items():
if pname == 'model_id':
self._id = pvalue
self._parms["model_id"] = pvalue
elif pname in self._deprecated_params_:
setattr(self, pname, pvalue) # property handles the redefinition
elif pname in self.param_names:
# 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.
Type: ``H2OFrame``.
:examples:
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35,
... smoothing=25,
... blending=True)
>>> titanic_te.train(x=predictors,
... y=response,
... training_frame=titanic)
>>> titanic_te
"""
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 fold_column(self):
"""
Column with cross-validation fold index assignment per observation.
Type: ``str``.
:examples:
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35,
... smoothing=25,
... blending=True)
>>> titanic_te.train(x=predictors,
... y=response,
... training_frame=titanic)
>>> titanic_te
"""
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 keep_original_categorical_columns(self):
"""
If true, the original non-encoded categorical features will remain in the result frame.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("keep_original_categorical_columns")
@keep_original_categorical_columns.setter
def keep_original_categorical_columns(self, keep_original_categorical_columns):
assert_is_type(keep_original_categorical_columns, None, bool)
self._parms["keep_original_categorical_columns"] = keep_original_categorical_columns
@property
def blending(self):
"""
If true, enables blending of posterior probabilities (computed for a given categorical value) with prior
probabilities (computed on the entire set). This allows to mitigate the effect of categorical values with small
cardinality. The blending effect can be tuned using the `inflection_point` and `smoothing` parameters.
Type: ``bool`` (default: ``False``).
:examples:
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35,
... smoothing=25,
... blending=True)
>>> titanic_te.train(x=predictors,
... y=response,
... training_frame=titanic)
>>> titanic_te
"""
return self._parms.get("blending")
@blending.setter
def blending(self, blending):
assert_is_type(blending, None, bool)
self._parms["blending"] = blending
@property
def inflection_point(self):
"""
Inflection point of the sigmoid used to blend probabilities (see `blending` parameter). For a given categorical
value, if it appears less that `inflection_point` in a data sample, then the influence of the posterior
probability will be smaller than the prior.
Type: ``float`` (default: ``10``).
:examples:
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35,
... smoothing=25,
... blending=True)
>>> titanic_te.train(x=predictors,
... y=response,
... training_frame=titanic)
>>> titanic_te
"""
return self._parms.get("inflection_point")
@inflection_point.setter
def inflection_point(self, inflection_point):
assert_is_type(inflection_point, None, numeric)
self._parms["inflection_point"] = inflection_point
@property
def smoothing(self):
"""
Smoothing factor corresponds to the inverse of the slope at the inflection point on the sigmoid used to blend
probabilities (see `blending` parameter). If smoothing tends towards 0, then the sigmoid used for blending turns
into a Heaviside step function.
Type: ``float`` (default: ``20``).
:examples:
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35,
... smoothing=25,
... blending=True)
>>> titanic_te.train(x=predictors,
... y=response,
... training_frame=titanic)
>>> titanic_te
"""
return self._parms.get("smoothing")
@smoothing.setter
def smoothing(self, smoothing):
assert_is_type(smoothing, None, numeric)
self._parms["smoothing"] = smoothing
@property
def data_leakage_handling(self):
"""
Data leakage handling strategy used to generate the encoding. Supported options are: 1) "none" (default) - no
holdout, using the entire training frame. 2) "leave_one_out" - current row's response value is subtracted from
the per-level frequencies pre-calculated on the entire training frame. 3) "k_fold" - encodings for a fold are
generated based on out-of-fold data.
One of: ``"leave_one_out"``, ``"k_fold"``, ``"none"`` (default: ``"none"``).
:examples:
>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35,
... smoothing=25,
... data_leakage_handling="k_fold",
... blending=True)
>>> titanic_te.train(x=predictors,
... y=response,
... training_frame=titanic)
>>> titanic_te
"""
return self._parms.get("data_leakage_handling")
@data_leakage_handling.setter
def data_leakage_handling(self, data_leakage_handling):
assert_is_type(data_leakage_handling, None, Enum("leave_one_out", "k_fold", "none"))
self._parms["data_leakage_handling"] = data_leakage_handling
@property
def noise(self):
"""
The amount of noise to add to the encoded column. Use 0 to disable noise, and -1 (=AUTO) to let the algorithm
determine a reasonable amount of noise.
Type: ``float`` (default: ``0.01``).
"""
return self._parms.get("noise")
@noise.setter
def noise(self, noise):
assert_is_type(noise, None, numeric)
self._parms["noise"] = noise
@property
def seed(self):
"""
Seed used to generate the noise. By default, the seed is chosen randomly.
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
_deprecated_params_ = ['k', 'f', 'noise_level']
k = deprecated_property('k', inflection_point)
f = deprecated_property('f', smoothing)
noise_level = deprecated_property('noise_level', noise)