#!/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 H2OSingularValueDecompositionEstimator(H2OEstimator):
"""
Singular Value Decomposition
"""
algo = "svd"
supervised_learning = False
def __init__(self,
model_id=None, # type: Optional[Union[None, str, H2OEstimator]]
training_frame=None, # type: Optional[Union[None, str, H2OFrame]]
validation_frame=None, # type: Optional[Union[None, str, H2OFrame]]
ignored_columns=None, # type: Optional[List[str]]
ignore_const_cols=True, # type: bool
score_each_iteration=False, # type: bool
transform="none", # type: Literal["none", "standardize", "normalize", "demean", "descale"]
svd_method="gram_s_v_d", # type: Literal["gram_s_v_d", "power", "randomized"]
nv=1, # type: int
max_iterations=1000, # type: int
seed=-1, # type: int
keep_u=True, # type: bool
u_name=None, # type: Optional[str]
use_all_factor_levels=True, # type: bool
max_runtime_secs=0.0, # type: float
export_checkpoints_dir=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 validation_frame: Id of the validation data frame.
Defaults to ``None``.
:type validation_frame: Union[None, str, H2OFrame], optional
: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 score_each_iteration: Whether to score during each iteration of model training.
Defaults to ``False``.
:type score_each_iteration: bool
:param transform: Transformation of training data
Defaults to ``"none"``.
:type transform: Literal["none", "standardize", "normalize", "demean", "descale"]
:param svd_method: Method for computing SVD (Caution: Randomized is currently experimental and unstable)
Defaults to ``"gram_s_v_d"``.
:type svd_method: Literal["gram_s_v_d", "power", "randomized"]
:param nv: Number of right singular vectors
Defaults to ``1``.
:type nv: int
:param max_iterations: Maximum iterations
Defaults to ``1000``.
:type max_iterations: int
:param seed: RNG seed for k-means++ initialization
Defaults to ``-1``.
:type seed: int
:param keep_u: Save left singular vectors?
Defaults to ``True``.
:type keep_u: bool
:param u_name: Frame key to save left singular vectors
Defaults to ``None``.
:type u_name: str, optional
:param use_all_factor_levels: Whether first factor level is included in each categorical expansion
Defaults to ``True``.
:type use_all_factor_levels: bool
: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 export_checkpoints_dir: Automatically export generated models to this directory.
Defaults to ``None``.
:type export_checkpoints_dir: str, optional
"""
super(H2OSingularValueDecompositionEstimator, self).__init__()
self._parms = {}
self._id = self._parms['model_id'] = model_id
self.training_frame = training_frame
self.validation_frame = validation_frame
self.ignored_columns = ignored_columns
self.ignore_const_cols = ignore_const_cols
self.score_each_iteration = score_each_iteration
self.transform = transform
self.svd_method = svd_method
self.nv = nv
self.max_iterations = max_iterations
self.seed = seed
self.keep_u = keep_u
self.u_name = u_name
self.use_all_factor_levels = use_all_factor_levels
self.max_runtime_secs = max_runtime_secs
self.export_checkpoints_dir = export_checkpoints_dir
self._parms["_rest_version"] = 99
@property
def training_frame(self):
"""
Id of the training data frame.
Type: ``Union[None, str, H2OFrame]``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator()
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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 validation_frame(self):
"""
Id of the validation data frame.
Type: ``Union[None, str, H2OFrame]``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> train, valid = arrests.split_frame(ratios=[.8])
>>> fit_h2o = H2OSingularValueDecompositionEstimator()
>>> fit_h2o.train(x=list(range(4)),
... training_frame=train,
... validation_frame=valid)
>>> fit_h2o
"""
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 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:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(ignore_const_cols=False,
... nv=4)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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``, defaults to ``False``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
... score_each_iteration=True)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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
Type: ``Literal["none", "standardize", "normalize", "demean", "descale"]``, defaults to ``"none"``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
... transform="standardize",
... max_iterations=2000)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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)
Type: ``Literal["gram_s_v_d", "power", "randomized"]``, defaults to ``"gram_s_v_d"``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(svd_method="power")
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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``, defaults to ``1``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
... transform="standardize",
... max_iterations=2000)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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``, defaults to ``1000``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
... transform="standardize",
... max_iterations=2000)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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``, defaults to ``-1``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, seed=-3)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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``, defaults to ``True``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(keep_u=False)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(u_name="fit_h2o")
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o.u_name
>>> fit_h2o
"""
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``, defaults to ``True``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(use_all_factor_levels=False)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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``, defaults to ``0.0``.
:examples:
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
... transform="standardize",
... max_runtime_secs=25)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o
"""
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 export_checkpoints_dir(self):
"""
Automatically export generated models to this directory.
Type: ``str``.
:examples:
>>> import tempfile
>>> from os import listdir
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> checkpoints_dir = tempfile.mkdtemp()
>>> fit_h2o = H2OSingularValueDecompositionEstimator(export_checkpoints_dir=checkpoints_dir,
... seed=-5)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> 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
[docs] def init_for_pipeline(self):
"""
Returns H2OSVD object which implements fit and transform method to be used in sklearn.Pipeline properly.
All parameters defined in self.__params, should be input parameters in H2OSVD.__init__ method.
:returns: H2OSVD object
:examples:
>>> from h2o.transforms.preprocessing import H2OScaler
>>> from h2o.estimators import H2ORandomForestEstimator
>>> from h2o.estimators import H2OSingularValueDecompositionEstimator
>>> from sklearn.pipeline import Pipeline
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> pipe = Pipeline([("standardize", H2OScaler()),
... ("svd", H2OSingularValueDecompositionEstimator(nv=3).init_for_pipeline()),
... ("rf", H2ORandomForestEstimator(seed=42,ntrees=50))])
>>> pipe.fit(arrests[1:], arrests[0])
"""
import inspect
from h2o.transforms.decomposition import H2OSVD
# check which parameters can be passed to H2OSVD init
var_names = list(dict(inspect.getmembers(H2OSVD.__init__.__code__))['co_varnames'])
parameters = {k: v for k, v in self._parms.items() if k in var_names}
return H2OSVD(**parameters)