Source code for h2o.estimators.stackedensemble

#!/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)
#

import ast
import json
import warnings

import h2o
from h2o.base import Keyed
from h2o.exceptions import H2OResponseError, H2ODeprecationWarning
from h2o.grid import H2OGridSearch
from h2o.job import H2OJob
from h2o.utils.shared_utils import quoted
from h2o.utils.typechecks import is_type
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 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("http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv", 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 >>> gbm = H2OGradientBoostingEstimator(nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True) >>> gbm.train(x=x, y=y, training_frame=train) >>> rf = H2ORandomForestEstimator(nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True) >>> rf.train(x=x, y=y, training_frame=train) >>> stack = H2OStackedEnsembleEstimator(model_id="ensemble", ... training_frame=train, ... validation_frame=test, ... base_models=[gbm.model_id, rf.model_id]) >>> stack.train(x=x, y=y, training_frame=train, validation_frame=test) >>> stack.model_performance() """ algo = "stackedensemble" supervised_learning = True _options_ = {'model_extensions': ['h2o.model.extensions.Fairness', 'h2o.model.extensions.Contributions']} def __init__(self, model_id=None, # type: Optional[Union[None, str, H2OEstimator]] training_frame=None, # type: Optional[Union[None, str, H2OFrame]] response_column=None, # type: Optional[str] validation_frame=None, # type: Optional[Union[None, str, H2OFrame]] blending_frame=None, # type: Optional[Union[None, str, H2OFrame]] base_models=[], # type: List[str] metalearner_algorithm="auto", # type: Literal["auto", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost"] metalearner_nfolds=0, # type: int metalearner_fold_assignment=None, # type: Optional[Literal["auto", "random", "modulo", "stratified"]] metalearner_fold_column=None, # type: Optional[str] metalearner_params=None, # type: Optional[dict] metalearner_transform="none", # type: Literal["none", "logit"] max_runtime_secs=0.0, # type: float weights_column=None, # type: Optional[str] offset_column=None, # type: Optional[str] custom_metric_func=None, # type: Optional[str] seed=-1, # type: int score_training_samples=10000, # type: int keep_levelone_frame=False, # type: bool export_checkpoints_dir=None, # type: Optional[str] auc_type="auto", # type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"] gainslift_bins=-1, # type: int ): """ :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 response_column: Response variable column. Defaults to ``None``. :type response_column: str, optional :param validation_frame: Id of the validation data frame. Defaults to ``None``. :type validation_frame: Union[None, str, H2OFrame], optional :param blending_frame: Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided) Defaults to ``None``. :type blending_frame: Union[None, str, H2OFrame], optional :param base_models: List of models or grids (or their ids) to ensemble/stack together. Grids are expanded to individual models. If not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be identical across models. Defaults to ``[]``. :type base_models: List[str] :param metalearner_algorithm: 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), 'deeplearning' (Deep Learning with default parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with default parameters), 'glm' (GLM with default parameters), 'naivebayes' (NaiveBayes with default parameters), or 'xgboost' (if available, XGBoost with default parameters). Defaults to ``"auto"``. :type metalearner_algorithm: Literal["auto", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost"] :param metalearner_nfolds: Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2). Defaults to ``0``. :type metalearner_nfolds: int :param metalearner_fold_assignment: 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. Defaults to ``None``. :type metalearner_fold_assignment: Literal["auto", "random", "modulo", "stratified"], optional :param metalearner_fold_column: Column with cross-validation fold index assignment per observation for cross- validation of the metalearner. Defaults to ``None``. :type metalearner_fold_column: str, optional :param metalearner_params: Parameters for metalearner algorithm Defaults to ``None``. :type metalearner_params: dict, optional :param metalearner_transform: Transformation used for the level one frame. Defaults to ``"none"``. :type metalearner_transform: Literal["none", "logit"] :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 weights_column: Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. Defaults to ``None``. :type weights_column: str, optional :param offset_column: Offset column. This will be added to the combination of columns before applying the link function. Defaults to ``None``. :type offset_column: str, optional :param custom_metric_func: Reference to custom evaluation function, format: `language:keyName=funcName` Defaults to ``None``. :type custom_metric_func: str, optional :param seed: Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number) Defaults to ``-1``. :type seed: int :param score_training_samples: Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples, enter 0. Defaults to ``10000``. :type score_training_samples: int :param keep_levelone_frame: Keep level one frame used for metalearner training. Defaults to ``False``. :type keep_levelone_frame: bool :param export_checkpoints_dir: Automatically export generated models to this directory. Defaults to ``None``. :type export_checkpoints_dir: str, optional :param auc_type: Set default multinomial AUC type. Defaults to ``"auto"``. :type auc_type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"] :param gainslift_bins: Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to ``-1``. :type gainslift_bins: int """ super(H2OStackedEnsembleEstimator, self).__init__() self._parms = {} self._id = self._parms['model_id'] = model_id self.training_frame = training_frame self.response_column = response_column self.validation_frame = validation_frame self.blending_frame = blending_frame self.base_models = base_models self.metalearner_algorithm = metalearner_algorithm self.metalearner_nfolds = metalearner_nfolds self.metalearner_fold_assignment = metalearner_fold_assignment self.metalearner_fold_column = metalearner_fold_column self.metalearner_params = metalearner_params self.metalearner_transform = metalearner_transform self.max_runtime_secs = max_runtime_secs self.weights_column = weights_column self.offset_column = offset_column self.custom_metric_func = custom_metric_func self.seed = seed self.score_training_samples = score_training_samples self.keep_levelone_frame = keep_levelone_frame self.export_checkpoints_dir = export_checkpoints_dir self.auc_type = auc_type self.gainslift_bins = gainslift_bins self._parms["_rest_version"] = 99 @property def training_frame(self): """ Id of the training data frame. Type: ``Union[None, str, H2OFrame]``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, valid = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_fold_assignment="Random") >>> stack_blend.train(x=x, y=y, training_frame=train, validation_frame=valid) >>> stack_blend.model_performance(blend).auc() """ 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 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: ``Union[None, str, H2OFrame]``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, valid = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_fold_assignment="Random") >>> stack_blend.train(x=x, y=y, training_frame=train, validation_frame=valid) >>> stack_blend.model_performance(blend).auc() """ 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 blending_frame(self): """ Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided) Type: ``Union[None, str, H2OFrame]``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc() """ return self._parms.get("blending_frame") @blending_frame.setter def blending_frame(self, blending_frame): self._parms["blending_frame"] = H2OFrame._validate(blending_frame, 'blending_frame') @property def base_models(self): """ List of models or grids (or their ids) to ensemble/stack together. Grids are expanded to individual models. If not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be identical across models. Type: ``List[str]``, defaults to ``[]``. :examples: >>> 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("http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv", 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 >>> gbm = H2OGradientBoostingEstimator(nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True) >>> gbm.train(x=x, y=y, training_frame=train) >>> rf = H2ORandomForestEstimator(nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True) >>> rf.train(x=x, y=y, training_frame=train) >>> stack = H2OStackedEnsembleEstimator(model_id="ensemble", ... training_frame=train, ... validation_frame=test, ... base_models=[gbm.model_id, rf.model_id]) >>> stack.train(x=x, y=y, training_frame=train, validation_frame=test) >>> stack.model_performance() """ base_models = self.actual_params.get("base_models", []) base_models = [base_model["name"] for base_model in base_models] if len(base_models) == 0: base_models = self._parms.get("base_models") return base_models @base_models.setter def base_models(self, base_models): def _get_id(something): if isinstance(something, Keyed): return something.key return something if not is_type(base_models, list): base_models = [base_models] if is_type(base_models, [H2OEstimator, H2OGridSearch, str]): base_models = [_get_id(b) for b in base_models] self._parms["base_models"] = base_models else: assert_is_type(base_models, None) @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), 'deeplearning' (Deep Learning with default parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with default parameters), 'glm' (GLM with default parameters), 'naivebayes' (NaiveBayes with default parameters), or 'xgboost' (if available, XGBoost with default parameters). Type: ``Literal["auto", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost"]``, defaults to ``"auto"``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_algorithm="gbm") >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc() """ return self._parms.get("metalearner_algorithm") @metalearner_algorithm.setter def metalearner_algorithm(self, metalearner_algorithm): assert_is_type(metalearner_algorithm, None, Enum("auto", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost")) 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``, defaults to ``0``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_nfolds=3) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc() """ 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. Type: ``Literal["auto", "random", "modulo", "stratified"]``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_fold_assignment="Random") >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc() """ 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``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_test_5k.csv") >>> fold_column = "fold_id" >>> train[fold_column] = train.kfold_column(n_folds=3, seed=1) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> x.remove(fold_column) >>> train[y] = train[y].asfactor() >>> test[y] = test[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... metalearner_fold_column=fold_column, ... metalearner_params=dict(keep_cross_validation_models=True)) >>> stack.train(x=x, y=y, training_frame=train) >>> stack.model_performance().auc() """ 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 metalearner_params(self): """ Parameters for metalearner algorithm Type: ``dict``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> gbm_params = {"ntrees" : 100, "max_depth" : 6} >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... metalearner_algorithm="gbm", ... metalearner_params=gbm_params) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc() """ if self._parms.get("metalearner_params") != None: metalearner_params_dict = ast.literal_eval(self._parms.get("metalearner_params")) for k in metalearner_params_dict: if len(metalearner_params_dict[k]) == 1: #single parameter metalearner_params_dict[k] = metalearner_params_dict[k][0] return metalearner_params_dict else: return self._parms.get("metalearner_params") @metalearner_params.setter def metalearner_params(self, metalearner_params): assert_is_type(metalearner_params, None, dict) if metalearner_params is not None and metalearner_params != "": for k in metalearner_params: if ("[" and "]") not in str(metalearner_params[k]): metalearner_params[k] = [metalearner_params[k]] self._parms["metalearner_params"] = str(json.dumps(metalearner_params)) else: self._parms["metalearner_params"] = None @property def metalearner_transform(self): """ Transformation used for the level one frame. Type: ``Literal["none", "logit"]``, defaults to ``"none"``. """ return self._parms.get("metalearner_transform") @metalearner_transform.setter def metalearner_transform(self, metalearner_transform): assert_is_type(metalearner_transform, None, Enum("none", "logit")) self._parms["metalearner_transform"] = metalearner_transform @property def max_runtime_secs(self): """ Maximum allowed runtime in seconds for model training. Use 0 to disable. Type: ``float``, defaults to ``0.0``. """ 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 @property def weights_column(self): """ Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. Type: ``str``. """ return self._parms.get("weights_column") @weights_column.setter def weights_column(self, weights_column): assert_is_type(weights_column, None, str) self._parms["weights_column"] = weights_column @property def offset_column(self): """ Offset column. This will be added to the combination of columns before applying the link function. Type: ``str``. """ return self._parms.get("offset_column") @offset_column.setter def offset_column(self, offset_column): assert_is_type(offset_column, None, str) self._parms["offset_column"] = offset_column @property def custom_metric_func(self): """ Reference to custom evaluation function, format: `language:keyName=funcName` Type: ``str``. """ return self._parms.get("custom_metric_func") @custom_metric_func.setter def custom_metric_func(self, custom_metric_func): assert_is_type(custom_metric_func, None, str) self._parms["custom_metric_func"] = custom_metric_func @property def seed(self): """ Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number) Type: ``int``, defaults to ``-1``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... metalearner_fold_assignment="Random") >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc() """ return self._parms.get("seed") @seed.setter def seed(self, seed): assert_is_type(seed, None, int) self._parms["seed"] = seed @property def score_training_samples(self): """ Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples, enter 0. Type: ``int``, defaults to ``10000``. """ return self._parms.get("score_training_samples") @score_training_samples.setter def score_training_samples(self, score_training_samples): assert_is_type(score_training_samples, None, int) self._parms["score_training_samples"] = score_training_samples @property def keep_levelone_frame(self): """ Keep level one frame used for metalearner training. Type: ``bool``, defaults to ``False``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=1, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... keep_levelone_frame=True) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.model_performance(blend).auc() """ 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 @property def export_checkpoints_dir(self): """ Automatically export generated models to this directory. Type: ``str``. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> import tempfile >>> from os import listdir >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> checkpoints_dir = tempfile.mkdtemp() >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... export_checkpoints_dir=checkpoints_dir) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> 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 @property def auc_type(self): """ Set default multinomial AUC type. Type: ``Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]``, defaults to ``"auto"``. """ return self._parms.get("auc_type") @auc_type.setter def auc_type(self, auc_type): assert_is_type(auc_type, None, Enum("auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo")) self._parms["auc_type"] = auc_type @property def gainslift_bins(self): """ Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Type: ``int``, defaults to ``-1``. """ return self._parms.get("gainslift_bins") @gainslift_bins.setter def gainslift_bins(self, gainslift_bins): assert_is_type(gainslift_bins, None, int) self._parms["gainslift_bins"] = gainslift_bins
[docs] def metalearner(self): """Print the metalearner of an H2OStackedEnsembleEstimator. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... keep_levelone_frame=True) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.metalearner() """ def _get_item(self, key): warnings.warn( "The usage of stacked_ensemble.metalearner()['name'] will be deprecated. " "Metalearner now returns the metalearner object. If you need to get the " "'name' please use stacked_ensemble.metalearner().model_id", H2ODeprecationWarning ) if key == "name": return self.model_id raise NotImplementedError model = self._model_json["output"] if "metalearner" in model and model["metalearner"] is not None: metalearner = h2o.get_model(model["metalearner"]["name"]) metalearner.__class__.__getitem__ = _get_item return metalearner print("No metalearner for this model")
[docs] def levelone_frame_id(self): """Fetch the levelone_frame_id for an H2OStackedEnsembleEstimator. :examples: >>> from h2o.estimators.random_forest import H2ORandomForestEstimator >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator >>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv") >>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234) >>> x = train.columns >>> y = "response" >>> x.remove(y) >>> train[y] = train[y].asfactor() >>> blend[y] = blend[y].asfactor() >>> nfolds = 3 >>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ... ntrees=10, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_gbm.train(x=x, y=y, training_frame=train) >>> my_rf = H2ORandomForestEstimator(ntrees=50, ... nfolds=nfolds, ... fold_assignment="Modulo", ... keep_cross_validation_predictions=True, ... seed=1) >>> my_rf.train(x=x, y=y, training_frame=train) >>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], ... seed=1, ... keep_levelone_frame=True) >>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend) >>> stack_blend.levelone_frame_id() """ model = self._model_json["output"] if "levelone_frame_id" in model and model["levelone_frame_id"] is not None: return model["levelone_frame_id"] print("No levelone_frame_id for this model")
def stacking_strategy(self): model = self._model_json["output"] if "stacking_strategy" in model and model["stacking_strategy"] is not None: return model["stacking_strategy"] print("No stacking strategy for this model") # Override train method to support blending
[docs] def train(self, x=None, y=None, training_frame=None, blending_frame=None, verbose=False, **kwargs): has_training_frame = training_frame is not None or self.training_frame is not None blending_frame = H2OFrame._validate(blending_frame, 'blending_frame', required=not has_training_frame) if not has_training_frame: training_frame = blending_frame # used to bypass default checks in super class and backend and to guarantee default metrics sup = super(self.__class__, self) def extend_parms(parms): if blending_frame is not None: parms['blending_frame'] = blending_frame if self.metalearner_fold_column is not None: parms['ignored_columns'].remove(quoted(self.metalearner_fold_column)) parms = sup._make_parms(x, y, training_frame, extend_parms_fn=extend_parms, **kwargs) sup._train(parms, verbose=verbose) if self.metalearner() is None: raise H2OResponseError("Meta learner didn't get to be trained in time. " "Try increasing max_runtime_secs or setting it to 0 (unlimited).") return self