#!/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 H2OSingularValueDecompositionEstimator(H2OEstimator):
"""
Singular Value Decomposition
"""
algo = "svd"
def __init__(self, **kwargs):
super(H2OSingularValueDecompositionEstimator, self).__init__()
self._parms = {}
names_list = {"model_id", "training_frame", "validation_frame", "ignored_columns", "ignore_const_cols",
"score_each_iteration", "transform", "svd_method", "nv", "max_iterations", "seed", "keep_u",
"u_name", "use_all_factor_levels", "max_runtime_secs"}
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 validation_frame(self):
"""
Id of the validation data frame.
Type: ``H2OFrame``.
"""
return self._parms.get("validation_frame")
@validation_frame.setter
def validation_frame(self, validation_frame):
assert_is_type(validation_frame, None, H2OFrame)
self._parms["validation_frame"] = validation_frame
@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`` (default: ``True``).
"""
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 score_each_iteration(self):
"""
Whether to score during each iteration of model training.
Type: ``bool`` (default: ``False``).
"""
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 transform(self):
"""
Transformation of training data
One of: ``"none"``, ``"standardize"``, ``"normalize"``, ``"demean"``, ``"descale"`` (default: ``"none"``).
"""
return self._parms.get("transform")
@transform.setter
def transform(self, transform):
assert_is_type(transform, None, Enum("none", "standardize", "normalize", "demean", "descale"))
self._parms["transform"] = transform
@property
def svd_method(self):
"""
Method for computing SVD (Caution: Randomized is currently experimental and unstable)
One of: ``"gram_s_v_d"``, ``"power"``, ``"randomized"`` (default: ``"gram_s_v_d"``).
"""
return self._parms.get("svd_method")
@svd_method.setter
def svd_method(self, svd_method):
assert_is_type(svd_method, None, Enum("gram_s_v_d", "power", "randomized"))
self._parms["svd_method"] = svd_method
@property
def nv(self):
"""
Number of right singular vectors
Type: ``int`` (default: ``1``).
"""
return self._parms.get("nv")
@nv.setter
def nv(self, nv):
assert_is_type(nv, None, int)
self._parms["nv"] = nv
@property
def max_iterations(self):
"""
Maximum iterations
Type: ``int`` (default: ``1000``).
"""
return self._parms.get("max_iterations")
@max_iterations.setter
def max_iterations(self, max_iterations):
assert_is_type(max_iterations, None, int)
self._parms["max_iterations"] = max_iterations
@property
def seed(self):
"""
RNG seed for k-means++ initialization
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
@property
def keep_u(self):
"""
Save left singular vectors?
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("keep_u")
@keep_u.setter
def keep_u(self, keep_u):
assert_is_type(keep_u, None, bool)
self._parms["keep_u"] = keep_u
@property
def u_name(self):
"""
Frame key to save left singular vectors
Type: ``str``.
"""
return self._parms.get("u_name")
@u_name.setter
def u_name(self, u_name):
assert_is_type(u_name, None, str)
self._parms["u_name"] = u_name
@property
def use_all_factor_levels(self):
"""
Whether first factor level is included in each categorical expansion
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("use_all_factor_levels")
@use_all_factor_levels.setter
def use_all_factor_levels(self, use_all_factor_levels):
assert_is_type(use_all_factor_levels, None, bool)
self._parms["use_all_factor_levels"] = use_all_factor_levels
@property
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")
@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