Source code for h2o.estimators.svd

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# This file is auto-generated by h2o-3/h2o-bindings/bin/
# Copyright 2016;  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. 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