#!/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 H2OKLimeEstimator(H2OEstimator):
"""
k-LIME
"""
algo = "klime"
def __init__(self, **kwargs):
super(H2OKLimeEstimator, self).__init__()
self._parms = {}
names_list = {"model_id", "training_frame", "response_column", "ignored_columns", "max_k", "estimate_k",
"alpha", "min_cluster_size", "seed"}
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 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 max_k(self):
"""
Maximum number of clusters to be considered.
Type: ``int`` (default: ``20``).
"""
return self._parms.get("max_k")
@max_k.setter
def max_k(self, max_k):
assert_is_type(max_k, None, int)
self._parms["max_k"] = max_k
@property
def estimate_k(self):
"""
Automatically determine the number of clusters in an unsupervised manner.
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("estimate_k")
@estimate_k.setter
def estimate_k(self, estimate_k):
assert_is_type(estimate_k, None, bool)
self._parms["estimate_k"] = estimate_k
@property
def alpha(self):
"""
Balance between L1 and L2 regularization. Use alpha=0 to switch off L1 variable selection.
Type: ``float`` (default: ``0.5``).
"""
return self._parms.get("alpha")
@alpha.setter
def alpha(self, alpha):
assert_is_type(alpha, None, numeric)
self._parms["alpha"] = alpha
@property
def min_cluster_size(self):
"""
Required minimum cluster size to build a local regression model, smaller clusters will use a global model.
Type: ``int`` (default: ``20``).
"""
return self._parms.get("min_cluster_size")
@min_cluster_size.setter
def min_cluster_size(self, min_cluster_size):
assert_is_type(min_cluster_size, None, int)
self._parms["min_cluster_size"] = min_cluster_size
@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