Source code for h2o.estimators.svd

#!/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" 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)