#!/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 H2OGeneralizedLowRankEstimator(H2OEstimator):
"""
Generalized Low Rank Modeling
Builds a generalized low rank model of a H2O dataset.
"""
algo = "glrm"
def __init__(self, **kwargs):
super(H2OGeneralizedLowRankEstimator, self).__init__()
self._parms = {}
names_list = {"model_id", "training_frame", "validation_frame", "ignored_columns", "ignore_const_cols",
"score_each_iteration", "loading_name", "transform", "k", "loss", "loss_by_col",
"loss_by_col_idx", "multi_loss", "period", "regularization_x", "regularization_y", "gamma_x",
"gamma_y", "max_iterations", "max_updates", "init_step_size", "min_step_size", "seed", "init",
"svd_method", "user_y", "user_x", "expand_user_y", "impute_original", "recover_svd",
"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))
self._parms["_rest_version"] = 3
@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 loading_name(self):
"""
Frame key to save resulting X
Type: ``str``.
"""
return self._parms.get("loading_name")
@loading_name.setter
def loading_name(self, loading_name):
assert_is_type(loading_name, None, str)
self._parms["loading_name"] = loading_name
@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 k(self):
"""
Rank of matrix approximation
Type: ``int`` (default: ``1``).
"""
return self._parms.get("k")
@k.setter
def k(self, k):
assert_is_type(k, None, int)
self._parms["k"] = k
@property
def loss(self):
"""
Numeric loss function
One of: ``"quadratic"``, ``"absolute"``, ``"huber"``, ``"poisson"``, ``"hinge"``, ``"logistic"``, ``"periodic"``
(default: ``"quadratic"``).
"""
return self._parms.get("loss")
@loss.setter
def loss(self, loss):
assert_is_type(loss, None, Enum("quadratic", "absolute", "huber", "poisson", "hinge", "logistic", "periodic"))
self._parms["loss"] = loss
@property
def loss_by_col(self):
"""
Loss function by column (override)
Type: ``List[Enum["quadratic", "absolute", "huber", "poisson", "hinge", "logistic", "periodic", "categorical",
"ordinal"]]``.
"""
return self._parms.get("loss_by_col")
@loss_by_col.setter
def loss_by_col(self, loss_by_col):
assert_is_type(loss_by_col, None, [Enum("quadratic", "absolute", "huber", "poisson", "hinge", "logistic", "periodic", "categorical", "ordinal")])
self._parms["loss_by_col"] = loss_by_col
@property
def loss_by_col_idx(self):
"""
Loss function by column index (override)
Type: ``List[int]``.
"""
return self._parms.get("loss_by_col_idx")
@loss_by_col_idx.setter
def loss_by_col_idx(self, loss_by_col_idx):
assert_is_type(loss_by_col_idx, None, [int])
self._parms["loss_by_col_idx"] = loss_by_col_idx
@property
def multi_loss(self):
"""
Categorical loss function
One of: ``"categorical"``, ``"ordinal"`` (default: ``"categorical"``).
"""
return self._parms.get("multi_loss")
@multi_loss.setter
def multi_loss(self, multi_loss):
assert_is_type(multi_loss, None, Enum("categorical", "ordinal"))
self._parms["multi_loss"] = multi_loss
@property
def period(self):
"""
Length of period (only used with periodic loss function)
Type: ``int`` (default: ``1``).
"""
return self._parms.get("period")
@period.setter
def period(self, period):
assert_is_type(period, None, int)
self._parms["period"] = period
@property
def regularization_x(self):
"""
Regularization function for X matrix
One of: ``"none"``, ``"quadratic"``, ``"l2"``, ``"l1"``, ``"non_negative"``, ``"one_sparse"``,
``"unit_one_sparse"``, ``"simplex"`` (default: ``"none"``).
"""
return self._parms.get("regularization_x")
@regularization_x.setter
def regularization_x(self, regularization_x):
assert_is_type(regularization_x, None, Enum("none", "quadratic", "l2", "l1", "non_negative", "one_sparse", "unit_one_sparse", "simplex"))
self._parms["regularization_x"] = regularization_x
@property
def regularization_y(self):
"""
Regularization function for Y matrix
One of: ``"none"``, ``"quadratic"``, ``"l2"``, ``"l1"``, ``"non_negative"``, ``"one_sparse"``,
``"unit_one_sparse"``, ``"simplex"`` (default: ``"none"``).
"""
return self._parms.get("regularization_y")
@regularization_y.setter
def regularization_y(self, regularization_y):
assert_is_type(regularization_y, None, Enum("none", "quadratic", "l2", "l1", "non_negative", "one_sparse", "unit_one_sparse", "simplex"))
self._parms["regularization_y"] = regularization_y
@property
def gamma_x(self):
"""
Regularization weight on X matrix
Type: ``float`` (default: ``0``).
"""
return self._parms.get("gamma_x")
@gamma_x.setter
def gamma_x(self, gamma_x):
assert_is_type(gamma_x, None, numeric)
self._parms["gamma_x"] = gamma_x
@property
def gamma_y(self):
"""
Regularization weight on Y matrix
Type: ``float`` (default: ``0``).
"""
return self._parms.get("gamma_y")
@gamma_y.setter
def gamma_y(self, gamma_y):
assert_is_type(gamma_y, None, numeric)
self._parms["gamma_y"] = gamma_y
@property
def max_iterations(self):
"""
Maximum number of 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 max_updates(self):
"""
Maximum number of updates, defaults to 2*max_iterations
Type: ``int`` (default: ``2000``).
"""
return self._parms.get("max_updates")
@max_updates.setter
def max_updates(self, max_updates):
assert_is_type(max_updates, None, int)
self._parms["max_updates"] = max_updates
@property
def init_step_size(self):
"""
Initial step size
Type: ``float`` (default: ``1``).
"""
return self._parms.get("init_step_size")
@init_step_size.setter
def init_step_size(self, init_step_size):
assert_is_type(init_step_size, None, numeric)
self._parms["init_step_size"] = init_step_size
@property
def min_step_size(self):
"""
Minimum step size
Type: ``float`` (default: ``0.0001``).
"""
return self._parms.get("min_step_size")
@min_step_size.setter
def min_step_size(self, min_step_size):
assert_is_type(min_step_size, None, numeric)
self._parms["min_step_size"] = min_step_size
@property
def seed(self):
"""
RNG seed for 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 init(self):
"""
Initialization mode
One of: ``"random"``, ``"svd"``, ``"plus_plus"``, ``"user"`` (default: ``"plus_plus"``).
"""
return self._parms.get("init")
@init.setter
def init(self, init):
assert_is_type(init, None, Enum("random", "svd", "plus_plus", "user"))
self._parms["init"] = init
@property
def svd_method(self):
"""
Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable)
One of: ``"gram_s_v_d"``, ``"power"``, ``"randomized"`` (default: ``"randomized"``).
"""
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 user_y(self):
"""
User-specified initial Y
Type: ``H2OFrame``.
"""
return self._parms.get("user_y")
@user_y.setter
def user_y(self, user_y):
assert_is_type(user_y, None, H2OFrame)
self._parms["user_y"] = user_y
@property
def user_x(self):
"""
User-specified initial X
Type: ``H2OFrame``.
"""
return self._parms.get("user_x")
@user_x.setter
def user_x(self, user_x):
assert_is_type(user_x, None, H2OFrame)
self._parms["user_x"] = user_x
@property
def expand_user_y(self):
"""
Expand categorical columns in user-specified initial Y
Type: ``bool`` (default: ``True``).
"""
return self._parms.get("expand_user_y")
@expand_user_y.setter
def expand_user_y(self, expand_user_y):
assert_is_type(expand_user_y, None, bool)
self._parms["expand_user_y"] = expand_user_y
@property
def impute_original(self):
"""
Reconstruct original training data by reversing transform
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("impute_original")
@impute_original.setter
def impute_original(self, impute_original):
assert_is_type(impute_original, None, bool)
self._parms["impute_original"] = impute_original
@property
def recover_svd(self):
"""
Recover singular values and eigenvectors of XY
Type: ``bool`` (default: ``False``).
"""
return self._parms.get("recover_svd")
@recover_svd.setter
def recover_svd(self, recover_svd):
assert_is_type(recover_svd, None, bool)
self._parms["recover_svd"] = recover_svd
@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