Source code for h2o.estimators.stackedensemble

#!/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, is_type

[docs]class H2OStackedEnsembleEstimator(H2OEstimator): """ Stacked Ensemble Builds a stacked ensemble (aka "super learner") machine learning method that uses two or more H2O learning algorithms to improve predictive performance. It is a loss-based supervised learning method that finds the optimal combination of a collection of prediction algorithms.This method supports regression and binary classification. Examples -------- >>> import h2o >>> h2o.init() >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> col_types = ["numeric", "numeric", "numeric", "enum", "enum", "numeric", "numeric", "numeric", "numeric"] >>> data = h2o.import_file("", col_types=col_types) >>> train, test = data.split_frame(ratios=[.8], seed=1) >>> x = ["CAPSULE","GLEASON","RACE","DPROS","DCAPS","PSA","VOL"] >>> y = "AGE" >>> nfolds = 5 >>> my_gbm = H2OGradientBoostingEstimator(nfolds=nfolds, fold_assignment="Modulo", keep_cross_validation_predictions=True) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(nfolds=nfolds, fold_assignment="Modulo", keep_cross_validation_predictions=True) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack = H2OStackedEnsembleEstimator(model_id="my_ensemble", training_frame=train, validation_frame=test, base_models=[my_gbm.model_id, my_rf.model_id]) >>> stack.train(x=x, y=y, training_frame=train, validation_frame=test) >>> stack.model_performance() """ algo = "stackedensemble" def __init__(self, **kwargs): super(H2OStackedEnsembleEstimator, self).__init__() self._parms = {} names_list = {"model_id", "training_frame", "response_column", "validation_frame", "base_models", "metalearner_algorithm", "metalearner_nfolds", "metalearner_fold_assignment", "metalearner_fold_column", "keep_levelone_frame"} 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"] = 99 @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 response_column(self): """ Response variable column. Type: ``str``. """ return self._parms.get("response_column") @response_column.setter def response_column(self, response_column): assert_is_type(response_column, None, str) self._parms["response_column"] = response_column @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 base_models(self): """ List of models (or model ids) to ensemble/stack together. Models must have been cross-validated using nfolds > 1, and folds must be identical across models. Type: ``List[str]`` (default: ``[]``). """ return self._parms.get("base_models") @base_models.setter def base_models(self, base_models): if is_type(base_models,[H2OEstimator]): base_models = [b.model_id for b in base_models] self._parms["base_models"] = base_models else: assert_is_type(base_models, None, [str]) self._parms["base_models"] = base_models @property def metalearner_algorithm(self): """ Type of algorithm to use as the metalearner. Options include 'AUTO' (GLM with non negative weights; if validation_frame is present, a lambda search is performed), 'glm' (GLM with default parameters), 'gbm' (GBM with default parameters), 'drf' (Random Forest with default parameters), or 'deeplearning' (Deep Learning with default parameters). One of: ``"auto"``, ``"glm"``, ``"gbm"``, ``"drf"``, ``"deeplearning"`` (default: ``"auto"``). """ return self._parms.get("metalearner_algorithm") @metalearner_algorithm.setter def metalearner_algorithm(self, metalearner_algorithm): assert_is_type(metalearner_algorithm, None, Enum("auto", "glm", "gbm", "drf", "deeplearning")) self._parms["metalearner_algorithm"] = metalearner_algorithm @property def metalearner_nfolds(self): """ Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2). Type: ``int`` (default: ``0``). """ return self._parms.get("metalearner_nfolds") @metalearner_nfolds.setter def metalearner_nfolds(self, metalearner_nfolds): assert_is_type(metalearner_nfolds, None, int) self._parms["metalearner_nfolds"] = metalearner_nfolds @property def metalearner_fold_assignment(self): """ Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is currently set to Random). The 'Stratified' option will stratify the folds based on the response variable, for classification problems. One of: ``"auto"``, ``"random"``, ``"modulo"``, ``"stratified"``. """ return self._parms.get("metalearner_fold_assignment") @metalearner_fold_assignment.setter def metalearner_fold_assignment(self, metalearner_fold_assignment): assert_is_type(metalearner_fold_assignment, None, Enum("auto", "random", "modulo", "stratified")) self._parms["metalearner_fold_assignment"] = metalearner_fold_assignment @property def metalearner_fold_column(self): """ Column with cross-validation fold index assignment per observation for cross-validation of the metalearner. Type: ``str``. """ return self._parms.get("metalearner_fold_column") @metalearner_fold_column.setter def metalearner_fold_column(self, metalearner_fold_column): assert_is_type(metalearner_fold_column, None, str) self._parms["metalearner_fold_column"] = metalearner_fold_column @property def keep_levelone_frame(self): """ Keep level one frame used for metalearner training. Type: ``bool`` (default: ``False``). """ return self._parms.get("keep_levelone_frame") @keep_levelone_frame.setter def keep_levelone_frame(self, keep_levelone_frame): assert_is_type(keep_levelone_frame, None, bool) self._parms["keep_levelone_frame"] = keep_levelone_frame