diff --git a/MANIFEST.in b/MANIFEST.in index d3140ed118..08f4df8a46 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -4,3 +4,5 @@ recursive-include autosklearn/metalearning/files *.txt include autosklearn/util/logging.yaml recursive-include autosklearn *.pyx include requirements.txt +recursive-include autosklearn/experimental/askl2_portfolios *.json +include autosklearn/experimental/askl2_training_data.json diff --git a/README.md b/README.md index dd53e9f140..93a4213cc7 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,28 @@ auto-sklearn is an automated machine learning toolkit and a drop-in replacement Find the documentation [here](http://automl.github.io/auto-sklearn/) +## Automated Machine Learning in four lines of code + +```python +import autosklearn.classification +cls = autosklearn.classification.AutoSklearnClassifier() +cls.fit(X_train, y_train) +predictions = cls.predict(X_test) +``` + +## Relevant publications + +Efficient and Robust Automated Machine Learning +Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter +Advances in Neural Information Processing Systems 28 (2015) +http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf + +Auto-Sklearn 2.0: The Next Generation +Authors: Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter +To appear + +## Status + Status for master branch [![Build Status](https://travis-ci.org/automl/auto-sklearn.svg?branch=master)](https://travis-ci.org/automl/auto-sklearn) diff --git a/autosklearn/__version__.py b/autosklearn/__version__.py index c9a99e5c71..05fe1cb59b 100644 --- a/autosklearn/__version__.py +++ b/autosklearn/__version__.py @@ -1,4 +1,4 @@ """Version information.""" # The following line *must* be the last in the module, exactly as formatted: -__version__ = "0.7.1" +__version__ = "0.8.0" diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 63dae3a4c3..58676e93dd 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -36,7 +36,7 @@ from autosklearn.util.hash import hash_array_or_matrix from autosklearn.metrics import f1_macro, accuracy, r2 from autosklearn.constants import MULTILABEL_CLASSIFICATION, MULTICLASS_CLASSIFICATION, \ - REGRESSION_TASKS, REGRESSION, BINARY_CLASSIFICATION + REGRESSION_TASKS, REGRESSION, BINARY_CLASSIFICATION, MULTIOUTPUT_REGRESSION def _model_predict(model, X, batch_size, logger, task): @@ -936,13 +936,16 @@ def _check_X(self, X): def _check_y(self, y): y = sklearn.utils.check_array(y, ensure_2d=False) - y = np.atleast_1d(y) - if y.ndim == 2 and y.shape[1] == 1: + + if y.ndim == 1: + return y + elif y.ndim == 2 and y.shape[1] == 1: warnings.warn("A column-vector y was passed when a 1d array was" " expected. Will change shape via np.ravel().", sklearn.utils.DataConversionWarning, stacklevel=2) y = np.ravel(y) + return y return y @@ -1097,6 +1100,9 @@ def predict_proba(self, X, batch_size=None, n_jobs=1): class AutoMLRegressor(BaseAutoML): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) + self._task_mapping = {'continuous-multioutput': MULTIOUTPUT_REGRESSION, + 'continuous': REGRESSION, + 'multiclass': REGRESSION} def fit( self, @@ -1110,17 +1116,20 @@ def fit( load_models: bool = True, ): X, y = super()._perform_input_checks(X, y) - _n_outputs = 1 if len(y.shape) == 1 else y.shape[1] - if _n_outputs > 1: - raise NotImplementedError( - 'Multi-output regression is not implemented.') + y_task = type_of_target(y) + task = self._task_mapping.get(y_task) + if task is None: + raise ValueError('Cannot work on data of type %s' % y_task) + if self._metric is None: self._metric = r2 + + self._n_outputs = 1 if len(y.shape) == 1 else y.shape[1] return super().fit( X, y, X_test=X_test, y_test=y_test, - task=REGRESSION, + task=task, feat_type=feat_type, dataset_name=dataset_name, only_return_configuration_space=only_return_configuration_space, diff --git a/autosklearn/constants.py b/autosklearn/constants.py index 85427f031f..60a025999e 100644 --- a/autosklearn/constants.py +++ b/autosklearn/constants.py @@ -4,8 +4,9 @@ MULTICLASS_CLASSIFICATION = 2 MULTILABEL_CLASSIFICATION = 3 REGRESSION = 4 +MULTIOUTPUT_REGRESSION = 5 -REGRESSION_TASKS = [REGRESSION] +REGRESSION_TASKS = [REGRESSION, MULTIOUTPUT_REGRESSION] CLASSIFICATION_TASKS = [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION] @@ -15,10 +16,12 @@ {BINARY_CLASSIFICATION: 'binary.classification', MULTICLASS_CLASSIFICATION: 'multiclass.classification', MULTILABEL_CLASSIFICATION: 'multilabel.classification', - REGRESSION: 'regression'} + REGRESSION: 'regression', + MULTIOUTPUT_REGRESSION: 'multioutput.regression'} STRING_TO_TASK_TYPES = \ {'binary.classification': BINARY_CLASSIFICATION, 'multiclass.classification': MULTICLASS_CLASSIFICATION, 'multilabel.classification': MULTILABEL_CLASSIFICATION, - 'regression': REGRESSION} + 'regression': REGRESSION, + 'multioutput.regression': MULTIOUTPUT_REGRESSION} diff --git a/autosklearn/data/xy_data_manager.py b/autosklearn/data/xy_data_manager.py index 3aaea4aa82..53c7513093 100644 --- a/autosklearn/data/xy_data_manager.py +++ b/autosklearn/data/xy_data_manager.py @@ -4,7 +4,7 @@ from scipy import sparse from autosklearn.constants import STRING_TO_TASK_TYPES, REGRESSION, BINARY_CLASSIFICATION, \ - MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION + MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION from autosklearn.data.abstract_data_manager import AbstractDataManager @@ -27,6 +27,7 @@ def __init__(self, X, y, X_test, y_test, task, feat_type, dataset_name): label_num = { REGRESSION: 1, BINARY_CLASSIFICATION: 2, + MULTIOUTPUT_REGRESSION: y.shape[-1], MULTICLASS_CLASSIFICATION: len(np.unique(y)), MULTILABEL_CLASSIFICATION: y.shape[-1] } diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 16d8394900..df6e69395c 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -766,7 +766,7 @@ def fit(self, X, y, X : array-like or sparse matrix of shape = [n_samples, n_features] The training input samples. - y : array-like, shape = [n_samples] + y : array-like, shape = [n_samples] or [n_samples, n_targets] The regression target. X_test : array-like or sparse matrix of shape = [n_samples, n_features] @@ -774,7 +774,7 @@ def fit(self, X, y, all models. This allows to evaluate the performance of Auto-sklearn over time. - y_test : array-like, shape = [n_samples] + y_test : array-like, shape = [n_samples] or [n_samples, n_targets] The regression target. Will be used to calculate the test error of all models. This allows to evaluate the performance of Auto-sklearn over time. @@ -799,7 +799,6 @@ def fit(self, X, y, target_type = type_of_target(y) if target_type in ['multiclass-multioutput', 'multilabel-indicator', - 'continuous-multioutput', 'unknown', ]: raise ValueError("regression with data of type %s is not" diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index 5a523ba0db..daa6c19a6b 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -15,6 +15,7 @@ REGRESSION_TASKS, MULTILABEL_CLASSIFICATION, MULTICLASS_CLASSIFICATION, + MULTIOUTPUT_REGRESSION ) from autosklearn.pipeline.implementations.util import ( convert_multioutput_multiclass_to_multilabel @@ -204,12 +205,19 @@ def _get_model(self): random_state=self.seed, init_params=self._init_params) else: - dataset_properties = { - 'task': self.task_type, - 'sparse': self.datamanager.info['is_sparse'] == 1, - 'multilabel': self.task_type == MULTILABEL_CLASSIFICATION, - 'multiclass': self.task_type == MULTICLASS_CLASSIFICATION, - } + if self.task_type in REGRESSION_TASKS: + dataset_properties = { + 'task': self.task_type, + 'sparse': self.datamanager.info['is_sparse'] == 1, + 'multioutput': self.task_type == MULTIOUTPUT_REGRESSION, + } + else: + dataset_properties = { + 'task': self.task_type, + 'sparse': self.datamanager.info['is_sparse'] == 1, + 'multilabel': self.task_type == MULTILABEL_CLASSIFICATION, + 'multiclass': self.task_type == MULTICLASS_CLASSIFICATION, + } model = self.model_class(config=self.configuration, dataset_properties=dataset_properties, random_state=self.seed, diff --git a/autosklearn/experimental/__init__.py b/autosklearn/experimental/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py new file mode 100644 index 0000000000..cbc94b2e9b --- /dev/null +++ b/autosklearn/experimental/askl2.py @@ -0,0 +1,297 @@ +import json +import multiprocessing +import os +import pickle +from typing import Any, Dict, Optional, Union + +from ConfigSpace import Configuration +import numpy as np +import pandas as pd + +from autosklearn.classification import AutoSklearnClassifier +import autosklearn.experimental.selector +from autosklearn.metrics import Scorer + + +CALLBACK_COUNTER = multiprocessing.Value('i', 0) + + +this_directory = os.path.abspath(os.path.dirname(__file__)) +selector_file = os.path.join(this_directory, 'selector.pkl') +training_data_file = os.path.join(this_directory, 'askl2_training_data.json') +with open(training_data_file) as fh: + training_data = json.load(fh) +metafeatures = pd.DataFrame(training_data['metafeatures']) +y_values = np.array(training_data['y_values']) +strategies = training_data['strategies'] +minima_for_methods = training_data['minima_for_methods'] +maxima_for_methods = training_data['maxima_for_methods'] +if not os.path.exists(selector_file): + selector = autosklearn.experimental.selector.OneVSOneSelector( + configuration=training_data['configuration'], + default_strategy_idx=strategies.index('RF_SH-eta4-i_holdout_iterative_es_if'), + rng=1, + ) + selector.fit( + X=metafeatures, + y=y_values, + methods=strategies, + minima=minima_for_methods, + maxima=maxima_for_methods, + ) + with open(selector_file, 'wb') as fh: + pickle.dump(selector, fh) + + +def get_smac_object_callback(portfolio, lock): + def get_smac_object( + scenario_dict, + seed, + ta, + ta_kwargs, + backend, + metalearning_configurations, + ): + from smac.facade.smac_ac_facade import SMAC4AC + from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost + from smac.scenario.scenario import Scenario + + scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob( + smac_run_id=seed if not scenario_dict['shared-model'] else '*', + ) + scenario = Scenario(scenario_dict) + + lock.acquire() + try: + global CALLBACK_COUNTER + print(CALLBACK_COUNTER.value, flush=True) + if CALLBACK_COUNTER.value == 0: + initial_configurations = [ + Configuration(configuration_space=scenario.cs, values=member) + for member in portfolio.values()] + else: + initial_configurations = [scenario.cs.sample_configuration(size=1)] + CALLBACK_COUNTER.value += 1 + finally: + lock.release() + + rh2EPM = RunHistory2EPM4LogCost + return SMAC4AC( + scenario=scenario, + rng=seed, + runhistory2epm=rh2EPM, + tae_runner=ta, + tae_runner_kwargs=ta_kwargs, + initial_configurations=initial_configurations, + run_id=seed, + ) + return get_smac_object + + +def get_sh_or_hb_object_callback(budget_type, bandit_strategy, eta, initial_budget, portfolio, + lock): + def get_smac_object( + scenario_dict, + seed, + ta, + ta_kwargs, + backend, + metalearning_configurations, + ): + from smac.facade.smac_ac_facade import SMAC4AC + from smac.intensification.successive_halving import SuccessiveHalving + from smac.intensification.hyperband import Hyperband + from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost + from smac.scenario.scenario import Scenario + + scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob( + smac_run_id=seed if not scenario_dict['shared-model'] else '*', + ) + scenario = Scenario(scenario_dict) + + lock.acquire() + try: + global CALLBACK_COUNTER + if CALLBACK_COUNTER.value == 0: + initial_configurations = [ + Configuration(configuration_space=scenario.cs, values=member) + for member in portfolio.values()] + else: + initial_configurations = [scenario.cs.sample_configuration(size=1)] + CALLBACK_COUNTER.value += 1 + finally: + lock.release() + + rh2EPM = RunHistory2EPM4LogCost + + ta_kwargs['budget_type'] = budget_type + + if bandit_strategy == 'sh': + bandit = SuccessiveHalving + elif bandit_strategy == 'hb': + bandit = Hyperband + else: + raise ValueError(bandit_strategy) + + smac4ac = SMAC4AC( + scenario=scenario, + rng=seed, + runhistory2epm=rh2EPM, + tae_runner=ta, + tae_runner_kwargs=ta_kwargs, + initial_configurations=initial_configurations, + run_id=seed, + intensifier=bandit, + intensifier_kwargs={ + 'initial_budget': initial_budget, + 'max_budget': 100, + 'eta': eta, + 'min_chall': 1}, + ) + smac4ac.solver.epm_chooser.min_samples_model = int( + len(scenario.cs.get_hyperparameters()) / 2 + ) + return smac4ac + return get_smac_object + + +class AutoSklearn2Classifier(AutoSklearnClassifier): + + def __init__( + self, + time_left_for_this_task: int = 3600, + ensemble_size: int = 50, + ensemble_nbest: Union[float, int] = 50, + max_models_on_disc: int = 50, + ensemble_memory_limit: int = 1024, + seed: int = 1, + ml_memory_limit: int = 3072, + tmp_folder: Optional[str] = None, + output_folder: Optional[str] = None, + delete_tmp_folder_after_terminate: bool = True, + delete_output_folder_after_terminate: bool = True, + shared_mode: bool = False, + n_jobs: Optional[int] = None, + disable_evaluator_output: bool = False, + smac_scenario_args: Optional[Dict[str, Any]] = None, + logging_config: Optional[Dict[str, Any]] = None, + metric: Optional[Scorer] = None, + ): + + include_estimators = [ + 'extra_trees', 'passive_aggressive', 'random_forest', 'sgd', 'gradient_boosting', + ] + include_preprocessors = ["no_preprocessing"] + super().__init__( + time_left_for_this_task=time_left_for_this_task, + initial_configurations_via_metalearning=0, + ensemble_size=ensemble_size, + ensemble_nbest=ensemble_nbest, + max_models_on_disc=max_models_on_disc, + ensemble_memory_limit=ensemble_memory_limit, + seed=seed, + ml_memory_limit=ml_memory_limit, + include_estimators=include_estimators, + exclude_estimators=None, + include_preprocessors=include_preprocessors, + exclude_preprocessors=None, + resampling_strategy=None, + resampling_strategy_arguments=None, + tmp_folder=tmp_folder, + output_folder=output_folder, + delete_tmp_folder_after_terminate=delete_tmp_folder_after_terminate, + delete_output_folder_after_terminate=delete_output_folder_after_terminate, + shared_mode=shared_mode, + n_jobs=n_jobs, + disable_evaluator_output=disable_evaluator_output, + get_smac_object_callback=None, + smac_scenario_args=smac_scenario_args, + logging_config=logging_config, + metadata_directory=None, + metric=metric, + ) + + def fit(self, X, y, + X_test=None, + y_test=None, + metric=None, + feat_type=None, + dataset_name=None): + + with open(selector_file, 'rb') as fh: + selector = pickle.load(fh) + + metafeatures = np.array([len(np.unique(y)), X.shape[1], X.shape[0]]) + selection = np.argmax(selector.predict(metafeatures)) + automl_policy = strategies[selection] + + setting = { + 'RF_None_holdout_iterative_es_if': { + 'resampling_strategy': 'holdout-iterative-fit', + 'fidelity': None, + }, + 'RF_None_3CV_iterative_es_if': { + 'resampling_strategy': 'cv-iterative-fit', + 'folds': 3, + 'fidelity': None, + }, + 'RF_None_5CV_iterative_es_if': { + 'resampling_strategy': 'cv-iterative-fit', + 'folds': 5, + 'fidelity': None, + }, + 'RF_None_10CV_iterative_es_if': { + 'resampling_strategy': 'cv-iterative-fit', + 'folds': 10, + 'fidelity': None, + }, + 'RF_SH-eta4-i_holdout_iterative_es_if': { + 'resampling_strategy': 'holdout-iterative-fit', + 'fidelity': 'SH', + }, + 'RF_SH-eta4-i_3CV_iterative_es_if': { + 'resampling_strategy': 'cv-iterative-fit', + 'folds': 3, + 'fidelity': 'SH', + }, + 'RF_SH-eta4-i_5CV_iterative_es_if': { + 'resampling_strategy': 'cv-iterative-fit', + 'folds': 5, + 'fidelity': 'SH', + }, + 'RF_SH-eta4-i_10CV_iterative_es_if': { + 'resampling_strategy': 'cv-iterative-fit', + 'folds': 10, + 'fidelity': 'SH', + } + }[automl_policy] + + resampling_strategy = setting['resampling_strategy'] + if resampling_strategy == 'cv-iterative-fit': + resampling_strategy_kwargs = {'folds': setting['folds']} + else: + resampling_strategy_kwargs = None + + portfolio_file = os.path.join(this_directory, 'askl2_portfolios', '%s.json' % automl_policy) + with open(portfolio_file) as fh: + portfolio_json = json.load(fh) + portfolio = portfolio_json['portfolio'] + + lock = multiprocessing.Lock() + if setting['fidelity'] == 'SH': + smac_callback = get_sh_or_hb_object_callback('iterations', 'sh', 4, 5.0, portfolio, + lock) + else: + smac_callback = get_smac_object_callback(portfolio, lock) + + self.resampling_strategy = resampling_strategy + self.resampling_strategy_arguments = resampling_strategy_kwargs + self.get_smac_object_callback = smac_callback + return super().fit( + X=X, + y=y, + X_test=X_test, + y_test=y_test, + feat_type=feat_type, + dataset_name=dataset_name, + ) diff --git a/autosklearn/experimental/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/askl2_portfolios/RF_None_10CV_iterative_es_if.json new file mode 100755 index 0000000000..0443e4cfea --- /dev/null +++ b/autosklearn/experimental/askl2_portfolios/RF_None_10CV_iterative_es_if.json @@ -0,0 +1,1113 @@ +{ + "portfolio": { + "51852cc": { + "balancing:strategy": "weighting", + "classifier:__choice__": "extra_trees", + "data_preprocessing:categorical_transformer:categorical_encoding:__choice__": "one_hot_encoding", + "data_preprocessing:categorical_transformer:category_coalescence:__choice__": "minority_coalescer", + "data_preprocessing:numerical_transformer:imputation:strategy": "median", + "data_preprocessing:numerical_transformer:rescaling:__choice__": "minmax", + "feature_preprocessor:__choice__": "no_preprocessing", + "classifier:extra_trees:bootstrap": "False", + "classifier:extra_trees:criterion": "gini", + "classifier:extra_trees:max_depth": "None", + "classifier:extra_trees:max_features": 0.9692051682427812, + "classifier:extra_trees:max_leaf_nodes": "None", + "classifier:extra_trees:min_impurity_decrease": 0.0, + "classifier:extra_trees:min_samples_leaf": 4, + "classifier:extra_trees:min_samples_split": 18, + "classifier:extra_trees:min_weight_fraction_leaf": 0.0, + "data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.029580715482237837 + }, + "09ab365": { + "balancing:strategy": "weighting", + "classifier:__choice__": "gradient_boosting", + "data_preprocessing:categorical_transformer:categorical_encoding:__choice__": "one_hot_encoding", + "data_preprocessing:categorical_transformer:category_coalescence:__choice__": "minority_coalescer", + "data_preprocessing:numerical_transformer:imputation:strategy": "most_frequent", + "data_preprocessing:numerical_transformer:rescaling:__choice__": "robust_scaler", + "feature_preprocessor:__choice__": "no_preprocessing", + "classifier:gradient_boosting:early_stop": "off", + "classifier:gradient_boosting:l2_regularization": 0.2687219517447521, + "classifier:gradient_boosting:learning_rate": 0.1985233620671305, + "classifier:gradient_boosting:loss": "auto", + "classifier:gradient_boosting:max_bins": 255, + "classifier:gradient_boosting:max_depth": "None", + "classifier:gradient_boosting:max_leaf_nodes": 38, + "classifier:gradient_boosting:min_samples_leaf": 14, + "classifier:gradient_boosting:scoring": "loss", + "classifier:gradient_boosting:tol": 1e-07, + "data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction": 0.005568862236215643, + "data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max": 0.9775940902447804, + "data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min": 0.2926657237964259 + }, + "5eacac7": { + "balancing:strategy": "weighting", + "classifier:__choice__": "passive_aggressive", + "data_preprocessing:categorical_transformer:categorical_encoding:__choice__": "one_hot_encoding", + "data_preprocessing:categorical_transformer:category_coalescence:__choice__": "no_coalescense", + "data_preprocessing:numerical_transformer:imputation:strategy": "most_frequent", + "data_preprocessing:numerical_transformer:rescaling:__choice__": "minmax", + "feature_preprocessor:__choice__": "no_preprocessing", + "classifier:passive_aggressive:C": 5.069434712194951, + "classifier:passive_aggressive:average": "True", + "classifier:passive_aggressive:fit_intercept": "True", + "classifier:passive_aggressive:loss": "hinge", + "classifier:passive_aggressive:tol": 0.00010829786375778913 + }, + "371b2fe": { + "balancing:strategy": "weighting", + "classifier:__choice__": "random_forest", + "data_preprocessing:categorical_transformer:categorical_encoding:__choice__": "one_hot_encoding", + "data_preprocessing:categorical_transformer:category_coalescence:__choice__": "no_coalescense", + "data_preprocessing:numerical_transformer:imputation:strategy": "mean", + "data_preprocessing:numerical_transformer:rescaling:__choice__": "none", + "feature_preprocessor:__choice__": "no_preprocessing", + "classifier:random_forest:bootstrap": "True", + "classifier:random_forest:criterion": "gini", + "classifier:random_forest:max_depth": "None", + "classifier:random_forest:max_features": 0.658459788114925, + "classifier:random_forest:max_leaf_nodes": "None", + 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0000000000..13364f9a29 --- /dev/null +++ b/autosklearn/experimental/selector.py @@ -0,0 +1,134 @@ +import numpy as np +import sklearn.ensemble + + +class OneVSOneSelector: + def __init__(self, configuration, default_strategy_idx, rng): + self.configuration = configuration + self.default_strategy_idx = default_strategy_idx + self.rng = rng + self.models = None + self.target_indices = None + self.selectors_ = None + self.weights_ = {} + self.X_train = None + + def fit(self, X, y, methods, minima, maxima): + self.X_train = X.copy() + target_indices = np.array(list(range(y.shape[1]))) + models = dict() + weights = dict() + for i in range(len(target_indices)): + models[i] = dict() + weights[i] = dict() + for j in range(i + 1, len(target_indices)): + y_i_j = y[:, i] < y[:, j] + min_i = np.array([minima[methods[i]][task_id] for task_id in X.index]) + max_i = np.array([maxima[methods[i]][task_id] for task_id in X.index]) + min_j = np.array([minima[methods[j]][task_id] for task_id in X.index]) + max_j = np.array([maxima[methods[j]][task_id] for task_id in X.index]) + + minimum = np.minimum(min_i, min_j) + maximum = np.maximum(max_i, max_j) + diff = maximum - minimum + diff[diff == 0] = 1 + normalized_y_i = (y[:, i].copy() - minimum) / diff + normalized_y_j = (y[:, j].copy() - minimum) / diff + + weights_i_j = np.abs(normalized_y_i - normalized_y_j) + if np.all([target == y_i_j[0] for target in y_i_j]): + n_zeros = int(np.ceil(len(y_i_j) / 2)) + n_ones = int(np.floor(len(y_i_j) / 2)) + base_model = sklearn.dummy.DummyClassifier( + strategy='constant', constant=y_i_j[0], + ) + base_model.fit( + X.values, + np.array(([[0]] * n_zeros) + ([[1]] * n_ones)).flatten(), + sample_weight=weights_i_j, + ) + else: + base_model = sklearn.ensemble.RandomForestClassifier( + random_state=self.rng, + n_estimators=500, + oob_score=True, + bootstrap=True, + min_samples_split=self.configuration['min_samples_split'], + min_samples_leaf=self.configuration['min_samples_leaf'], + max_features=int(np.rint(X.shape[1] ** self.configuration['max_features'])), + ) + base_model.fit(X.values, y_i_j, sample_weight=weights_i_j) + models[i][j] = base_model + weights[i][j] = weights_i_j + self.models = models + self.weights_ = weights + self.target_indices = target_indices + + def predict(self, X): + + if self.default_strategy_idx is not None: + use_prediction = False + counter = 0 + te = X.copy().flatten() + assert len(te) == 3 + for _, tr in self.X_train.iterrows(): + tr = tr.to_numpy() + if tr[0] >= te[0] and tr[1] >= te[1] and tr[2] >= te[2]: + counter += 1 + + if counter > 0: + use_prediction = True + + if not use_prediction: + print('Using Backup selector') + return np.array( + [1 if i == self.default_strategy_idx else 0 for i in self.target_indices] + ) + print('Using no backup selector') + + X = X.reshape((1, -1)) + + raw_predictions = dict() + for i in range(len(self.target_indices)): + for j in range(i + 1, len(self.target_indices)): + raw_predictions[(i, j)] = self.models[i][j].predict(X) + + predictions = [] + for x_idx in range(X.shape[0]): + wins = np.zeros(self.target_indices.shape) + for i in range(len(self.target_indices)): + for j in range(i + 1, len(self.target_indices)): + prediction = raw_predictions[(i, j)][x_idx] + if prediction == 1: + wins[i] += 1 + else: + wins[j] += 1 + wins = wins / np.sum(wins) + predictions.append(wins) + predictions = np.array([np.array(prediction) for prediction in predictions]) + return predictions + + def predict_oob(self, X): + + raw_predictions = dict() + for i in range(len(self.target_indices)): + for j in range(i + 1, len(self.target_indices)): + rp = self.models[i][j].oob_decision_function_.copy() + rp[np.isnan(rp)] = 0 + rp = np.nanargmax(rp, axis=1) + raw_predictions[(i, j)] = rp + + predictions = [] + for x_idx in range(X.shape[0]): + wins = np.zeros(self.target_indices.shape) + for i in range(len(self.target_indices)): + for j in range(i + 1, len(self.target_indices)): + prediction = raw_predictions[(i, j)][x_idx] + if prediction == 1: + wins[i] += 1 + else: + wins[j] += 1 + wins = wins / np.sum(wins) + predictions.append(wins) + predictions = np.array([np.array(prediction) for prediction in predictions]) + return predictions diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py index def49dbdef..41f8453096 100644 --- a/autosklearn/metrics/__init__.py +++ b/autosklearn/metrics/__init__.py @@ -60,6 +60,8 @@ def __call__(self, y_true, y_pred, sample_weight=None): elif type_true == 'multilabel-indicator': y_pred[y_pred > 0.5] = 1.0 y_pred[y_pred <= 0.5] = 0.0 + elif type_true == 'continuous-multioutput': + pass else: raise ValueError(type_true) diff --git a/autosklearn/pipeline/components/base.py b/autosklearn/pipeline/components/base.py index d7ff10a444..d82bf70b9d 100644 --- a/autosklearn/pipeline/components/base.py +++ b/autosklearn/pipeline/components/base.py @@ -44,8 +44,8 @@ def add_component(self, obj): properties = set(classifier.get_properties()) should_be_there = {'shortname', 'name', 'handles_regression', 'handles_classification', 'handles_multiclass', - 'handles_multilabel', 'is_deterministic', - 'input', 'output'} + 'handles_multilabel', 'handles_multioutput', + 'is_deterministic', 'input', 'output'} for property in properties: if property not in should_be_there: raise ValueError('Property %s must not be specified for ' @@ -54,7 +54,8 @@ def add_component(self, obj): (property, name, str(should_be_there))) for property in should_be_there: if property not in properties: - raise ValueError('Property %s not specified for algorithm %s') + raise ValueError('Property %s not specified for algorithm %s' % + (property, name)) self.components[name] = classifier @@ -292,7 +293,7 @@ def predict(self, X): Returns ------- - array, shape = (n_samples,) + array, shape = (n_samples,) or shape = (n_samples, n_targets) Returns the predicted values Notes diff --git a/autosklearn/pipeline/components/classification/adaboost.py b/autosklearn/pipeline/components/classification/adaboost.py index bd094f2f2d..31567aaeae 100644 --- a/autosklearn/pipeline/components/classification/adaboost.py +++ b/autosklearn/pipeline/components/classification/adaboost.py @@ -57,6 +57,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/bernoulli_nb.py b/autosklearn/pipeline/components/classification/bernoulli_nb.py index eda85e3950..9bb2f8c590 100644 --- a/autosklearn/pipeline/components/classification/bernoulli_nb.py +++ b/autosklearn/pipeline/components/classification/bernoulli_nb.py @@ -53,6 +53,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/decision_tree.py b/autosklearn/pipeline/components/classification/decision_tree.py index a00157322f..045e5c3e44 100644 --- a/autosklearn/pipeline/components/classification/decision_tree.py +++ b/autosklearn/pipeline/components/classification/decision_tree.py @@ -84,6 +84,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index b89a8ac6df..83f8e1943d 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -115,6 +115,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/gaussian_nb.py b/autosklearn/pipeline/components/classification/gaussian_nb.py index 27a0f3f154..cae1733baf 100644 --- a/autosklearn/pipeline/components/classification/gaussian_nb.py +++ b/autosklearn/pipeline/components/classification/gaussian_nb.py @@ -49,6 +49,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index 7057a05196..8ce851dd22 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -154,6 +154,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/k_nearest_neighbors.py b/autosklearn/pipeline/components/classification/k_nearest_neighbors.py index 5c98104826..6901451f11 100644 --- a/autosklearn/pipeline/components/classification/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/classification/k_nearest_neighbors.py @@ -48,6 +48,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/lda.py b/autosklearn/pipeline/components/classification/lda.py index 9adf2ab6a2..c6fd67542b 100644 --- a/autosklearn/pipeline/components/classification/lda.py +++ b/autosklearn/pipeline/components/classification/lda.py @@ -70,6 +70,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/liblinear_svc.py b/autosklearn/pipeline/components/classification/liblinear_svc.py index 43cad4b861..9c625139f5 100644 --- a/autosklearn/pipeline/components/classification/liblinear_svc.py +++ b/autosklearn/pipeline/components/classification/liblinear_svc.py @@ -82,6 +82,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': False, 'input': (SPARSE, DENSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/libsvm_svc.py b/autosklearn/pipeline/components/classification/libsvm_svc.py index 760dd1a5cd..97c55be49d 100644 --- a/autosklearn/pipeline/components/classification/libsvm_svc.py +++ b/autosklearn/pipeline/components/classification/libsvm_svc.py @@ -115,6 +115,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/multinomial_nb.py b/autosklearn/pipeline/components/classification/multinomial_nb.py index 9754874dbb..e678bd4c77 100644 --- a/autosklearn/pipeline/components/classification/multinomial_nb.py +++ b/autosklearn/pipeline/components/classification/multinomial_nb.py @@ -68,6 +68,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, SIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 3e0b0c8d3b..5fb1f1bbf7 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -139,6 +139,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/qda.py b/autosklearn/pipeline/components/classification/qda.py index 514860b7b1..7405b21fae 100644 --- a/autosklearn/pipeline/components/classification/qda.py +++ b/autosklearn/pipeline/components/classification/qda.py @@ -64,6 +64,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/random_forest.py b/autosklearn/pipeline/components/classification/random_forest.py index 79de7ae26e..c2f4e9779a 100644 --- a/autosklearn/pipeline/components/classification/random_forest.py +++ b/autosklearn/pipeline/components/classification/random_forest.py @@ -123,6 +123,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/classification/sgd.py b/autosklearn/pipeline/components/classification/sgd.py index 4ea6f600c3..6875541824 100644 --- a/autosklearn/pipeline/components/classification/sgd.py +++ b/autosklearn/pipeline/components/classification/sgd.py @@ -143,6 +143,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py index 4d44b51e97..e4efef69e5 100644 --- a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py +++ b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py @@ -98,6 +98,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'handles_sparse': True, 'handles_dense': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py index d04e46dbaa..99c4ae28bf 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/no_encoding.py @@ -22,6 +22,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'handles_sparse': True, 'handles_dense': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py index ab8f30ec25..8f5aa67c29 100644 --- a/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py +++ b/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py @@ -38,6 +38,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, # TODO find out of this is right! 'handles_sparse': True, 'handles_dense': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py b/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py index 1a84313a45..2aa0b670fc 100644 --- a/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py +++ b/autosklearn/pipeline/components/data_preprocessing/category_shift/category_shift.py @@ -42,6 +42,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out of this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py index b3c86db82a..ff1eb5bd0e 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/categorical_imputation.py @@ -41,6 +41,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out of this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py b/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py index c6b0de6d33..17b25c609e 100644 --- a/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py +++ b/autosklearn/pipeline/components/data_preprocessing/imputation/numerical_imputation.py @@ -37,6 +37,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out if this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py index 3d89d873e5..c4446f2345 100644 --- a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py +++ b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/minority_coalescer.py @@ -38,6 +38,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, # TODO find out of this is right! 'handles_sparse': True, 'handles_dense': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py index 1ef19d3174..20612ec94c 100644 --- a/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py +++ b/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/no_coalescense.py @@ -25,6 +25,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'handles_sparse': True, 'handles_dense': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py index d82152c05e..42cb02ff17 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/minmax.py @@ -22,6 +22,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out if this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/none.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/none.py index 9b1ed35cf4..f74ea229b3 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/none.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/none.py @@ -27,6 +27,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out if this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py index 0e628d82fe..0db3261b37 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py @@ -24,6 +24,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out if this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py index 80ef3f118d..cf8980d000 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py @@ -28,6 +28,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out if this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py index e782995d89..b4070d9a01 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py @@ -25,6 +25,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out if this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py b/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py index e5b98927c3..da5cbe7303 100644 --- a/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py +++ b/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py @@ -24,6 +24,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, # TODO find out if this is right! 'handles_sparse': True, diff --git a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py index 46ac916c61..b454e3ed45 100644 --- a/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py +++ b/autosklearn/pipeline/components/data_preprocessing/variance_threshold/variance_threshold.py @@ -32,6 +32,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'handles_sparse': True, 'handles_dense': True, diff --git a/autosklearn/pipeline/components/feature_preprocessing/__init__.py b/autosklearn/pipeline/components/feature_preprocessing/__init__.py index 388b59080e..30697a33d2 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/__init__.py +++ b/autosklearn/pipeline/components/feature_preprocessing/__init__.py @@ -72,6 +72,9 @@ def get_available_components(self, dataset_properties=None, elif target_type == 'regression': if entry.get_properties()['handles_regression'] is False: continue + if dataset_properties.get('multioutput') is True and \ + entry.get_properties()['handles_multioutput'] is False: + continue else: raise ValueError('Unknown target type %s' % target_type) diff --git a/autosklearn/pipeline/components/feature_preprocessing/densifier.py b/autosklearn/pipeline/components/feature_preprocessing/densifier.py index 628f829af3..ab14cd7fcb 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/densifier.py +++ b/autosklearn/pipeline/components/feature_preprocessing/densifier.py @@ -26,6 +26,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (SPARSE, UNSIGNED_DATA), 'output': (DENSE, INPUT)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py index 9dae96fed9..622180af8f 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py @@ -98,6 +98,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py index c8e59d8178..e8e28a2736 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py @@ -97,6 +97,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py index 9d10909ad1..549d708506 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py +++ b/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py @@ -59,6 +59,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': False, 'input': (DENSE, UNSIGNED_DATA), 'output': (INPUT, UNSIGNED_DATA)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py b/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py index 53ac83fa91..e23ff1b865 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py +++ b/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py @@ -52,6 +52,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py index 47bdd6f48d..d2bdd88e82 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py @@ -67,6 +67,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': False, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (DENSE, UNSIGNED_DATA)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py index c285cc43ce..00a641323a 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py +++ b/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py @@ -44,6 +44,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (SPARSE, DENSE, UNSIGNED_DATA), 'output': (INPUT, UNSIGNED_DATA)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py index 79508ef0a1..6e6de1a998 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py +++ b/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py @@ -70,6 +70,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': False, + 'handles_multioutput': False, 'input': (SPARSE, DENSE, UNSIGNED_DATA), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py b/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py index ca75151643..a7d732d733 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py +++ b/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py @@ -27,6 +27,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (SPARSE, DENSE, UNSIGNED_DATA), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py index 00dfb1164e..d450d8f09f 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py +++ b/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py @@ -73,6 +73,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (SPARSE, DENSE, data_type), 'output': (INPUT, UNSIGNED_DATA)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/pca.py b/autosklearn/pipeline/components/feature_preprocessing/pca.py index 3f44c35950..ae992520fa 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/pca.py +++ b/autosklearn/pipeline/components/feature_preprocessing/pca.py @@ -44,6 +44,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, # TODO document that we have to be very careful 'is_deterministic': False, 'input': (DENSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/components/feature_preprocessing/polynomial.py b/autosklearn/pipeline/components/feature_preprocessing/polynomial.py index dad57c8321..478040c497 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/polynomial.py +++ b/autosklearn/pipeline/components/feature_preprocessing/polynomial.py @@ -43,6 +43,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py index 00f00f4006..a5e9ff1b8c 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py +++ b/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py @@ -73,6 +73,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (SPARSE, SIGNED_DATA)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py index 9331f16866..12cbe7c206 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py @@ -86,6 +86,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (SPARSE, DENSE, data_type), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py index 78d8cf5e86..90be07a25f 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py @@ -36,6 +36,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/select_rates.py b/autosklearn/pipeline/components/feature_preprocessing/select_rates.py index 7dcef08c8b..7406244ea9 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/select_rates.py +++ b/autosklearn/pipeline/components/feature_preprocessing/select_rates.py @@ -89,6 +89,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (SPARSE, DENSE, data_type), 'output': (INPUT,)} diff --git a/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py b/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py index c2b71fc098..e19527e617 100644 --- a/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py +++ b/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py @@ -39,6 +39,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (SPARSE, UNSIGNED_DATA), 'output': (DENSE, INPUT)} diff --git a/autosklearn/pipeline/components/regression/__init__.py b/autosklearn/pipeline/components/regression/__init__.py index d473fd50c2..b9c85da0f2 100644 --- a/autosklearn/pipeline/components/regression/__init__.py +++ b/autosklearn/pipeline/components/regression/__init__.py @@ -27,11 +27,14 @@ def get_components(cls): return components @classmethod - def get_available_components(cls, data_prop, + def get_available_components(cls, + dataset_properties=None, include=None, exclude=None): available_comp = cls.get_components() components_dict = OrderedDict() + if dataset_properties is None: + dataset_properties = {} if include is not None and exclude is not None: raise ValueError( @@ -57,11 +60,14 @@ def get_available_components(cls, data_prop, if entry.get_properties()['handles_regression'] is False: continue + if dataset_properties.get('multioutput') is True and \ + entry.get_properties()['handles_multioutput'] is False: + continue components_dict[name] = entry return components_dict - def get_hyperparameter_search_space(self, dataset_properties, + def get_hyperparameter_search_space(self, dataset_properties=None, default=None, include=None, exclude=None): @@ -72,7 +78,7 @@ def get_hyperparameter_search_space(self, dataset_properties, # Compile a list of all estimator objects for this problem available_estimators = self.get_available_components( - data_prop=dataset_properties, + dataset_properties=dataset_properties, include=include, exclude=exclude) diff --git a/autosklearn/pipeline/components/regression/adaboost.py b/autosklearn/pipeline/components/regression/adaboost.py index 4c398f3bb1..9af0df2bdc 100644 --- a/autosklearn/pipeline/components/regression/adaboost.py +++ b/autosklearn/pipeline/components/regression/adaboost.py @@ -48,6 +48,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS, )} diff --git a/autosklearn/pipeline/components/regression/ard_regression.py b/autosklearn/pipeline/components/regression/ard_regression.py index f19fba12ee..dd642e6098 100644 --- a/autosklearn/pipeline/components/regression/ard_regression.py +++ b/autosklearn/pipeline/components/regression/ard_regression.py @@ -63,6 +63,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': False, 'prefers_data_normalized': True, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/components/regression/decision_tree.py b/autosklearn/pipeline/components/regression/decision_tree.py index ce8d2666ae..f458fbb9a5 100644 --- a/autosklearn/pipeline/components/regression/decision_tree.py +++ b/autosklearn/pipeline/components/regression/decision_tree.py @@ -72,6 +72,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': True, 'is_deterministic': False, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index b3b2c36349..e7ff30b7f7 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -116,6 +116,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/regression/gaussian_process.py b/autosklearn/pipeline/components/regression/gaussian_process.py index 1d09683101..84a7fde238 100644 --- a/autosklearn/pipeline/components/regression/gaussian_process.py +++ b/autosklearn/pipeline/components/regression/gaussian_process.py @@ -53,6 +53,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index d24f16c1a2..b2f259c6a9 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -103,6 +103,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': False, 'prefers_data_normalized': False, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py index f58b99d34e..c8e92985ac 100644 --- a/autosklearn/pipeline/components/regression/k_nearest_neighbors.py +++ b/autosklearn/pipeline/components/regression/k_nearest_neighbors.py @@ -40,6 +40,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/regression/liblinear_svr.py b/autosklearn/pipeline/components/regression/liblinear_svr.py index 79c502122e..043ef2ec82 100644 --- a/autosklearn/pipeline/components/regression/liblinear_svr.py +++ b/autosklearn/pipeline/components/regression/liblinear_svr.py @@ -58,6 +58,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': False, 'input': (SPARSE, DENSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/autosklearn/pipeline/components/regression/libsvm_svr.py b/autosklearn/pipeline/components/regression/libsvm_svr.py index 641261ec29..6be08d87ad 100644 --- a/autosklearn/pipeline/components/regression/libsvm_svr.py +++ b/autosklearn/pipeline/components/regression/libsvm_svr.py @@ -109,6 +109,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': False, 'prefers_data_normalized': True, 'is_deterministic': True, 'input': (SPARSE, DENSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index 47f1dc9215..e3482e4ea2 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -98,6 +98,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': True, 'prefers_data_normalized': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/components/regression/ridge_regression.py b/autosklearn/pipeline/components/regression/ridge_regression.py index fbfbac04fd..3da5a435df 100644 --- a/autosklearn/pipeline/components/regression/ridge_regression.py +++ b/autosklearn/pipeline/components/regression/ridge_regression.py @@ -43,6 +43,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': True, 'prefers_data_normalized': True, 'is_deterministic': True, 'input': (SPARSE, DENSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index 718f6eea08..c8b91fb931 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -132,6 +132,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'handles_sparse': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), diff --git a/autosklearn/pipeline/constants.py b/autosklearn/pipeline/constants.py index f0f7e1df07..924baa185a 100644 --- a/autosklearn/pipeline/constants.py +++ b/autosklearn/pipeline/constants.py @@ -4,8 +4,9 @@ MULTICLASS_CLASSIFICATION = 2 MULTILABEL_CLASSIFICATION = 3 REGRESSION = 4 +MULTIOUTPUT_REGRESSION = 5 -REGRESSION_TASKS = [REGRESSION] +REGRESSION_TASKS = [REGRESSION, MULTIOUTPUT_REGRESSION] CLASSIFICATION_TASKS = [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION] @@ -15,21 +16,23 @@ {BINARY_CLASSIFICATION: "binary.classification", MULTICLASS_CLASSIFICATION: "multiclass.classification", MULTILABEL_CLASSIFICATION: "multilabel.classification", - REGRESSION: "regression"} + REGRESSION: "regression", + MULTIOUTPUT_REGRESSION: "multioutput.regression"} STRING_TO_TASK_TYPES = \ {"binary.classification": BINARY_CLASSIFICATION, "multiclass.classification": MULTICLASS_CLASSIFICATION, "multilabel.classification": MULTILABEL_CLASSIFICATION, - "regression": REGRESSION} + "regression": REGRESSION, + "multioutput.regression": MULTIOUTPUT_REGRESSION} -DENSE = 5 -SPARSE = 6 -PREDICTIONS = 7 -INPUT = 8 +DENSE = 6 +SPARSE = 7 +PREDICTIONS = 8 +INPUT = 9 -SIGNED_DATA = 9 -UNSIGNED_DATA = 10 +SIGNED_DATA = 10 +UNSIGNED_DATA = 11 DATASET_PROPERTIES_TO_STRING = \ {DENSE: 'dense', diff --git a/autosklearn/pipeline/regression.py b/autosklearn/pipeline/regression.py index 3f71e2bc55..bf2cd0490a 100644 --- a/autosklearn/pipeline/regression.py +++ b/autosklearn/pipeline/regression.py @@ -1,4 +1,3 @@ -from collections import OrderedDict import copy from itertools import product @@ -97,20 +96,6 @@ def predict(self, X, batch_size=None): y[y < (0.5 * self.y_min_)] = 0.5 * self.y_min_ return y - def get_available_components(self, available_comp, data_prop, inc, exc): - components_dict = OrderedDict() - for name in available_comp: - if inc is not None and name not in inc: - continue - elif exc is not None and name in exc: - continue - entry = available_comp[name] - - if not entry.get_properties()['handles_regression']: - continue - components_dict[name] = entry - return components_dict - def _get_hyperparameter_search_space(self, include=None, exclude=None, dataset_properties=None): """Return the configuration space for the CASH problem. diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 8290d9df4a..5d493ae0a6 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -16,7 +16,8 @@ import autosklearn.metalearning from autosklearn.constants import MULTILABEL_CLASSIFICATION, \ BINARY_CLASSIFICATION, TASK_TYPES_TO_STRING, CLASSIFICATION_TASKS, \ - REGRESSION_TASKS, MULTICLASS_CLASSIFICATION, REGRESSION + REGRESSION_TASKS, MULTICLASS_CLASSIFICATION, REGRESSION, \ + MULTIOUTPUT_REGRESSION from autosklearn.metalearning.mismbo import suggest_via_metalearning from autosklearn.data.abstract_data_manager import AbstractDataManager from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash @@ -71,7 +72,8 @@ def _calculate_metafeatures(data_feat_type, data_info_task, basename, if data_info_task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION if data_info_task in [MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION, - MULTILABEL_CLASSIFICATION, REGRESSION]: + MULTILABEL_CLASSIFICATION, REGRESSION, + MULTIOUTPUT_REGRESSION]: logger.info('Start calculating metafeatures for %s', basename) result = calculate_all_metafeatures_with_labels( x_train, y_train, categorical=categorical, diff --git a/autosklearn/util/pipeline.py b/autosklearn/util/pipeline.py index 8486b8a104..e5d2954b29 100755 --- a/autosklearn/util/pipeline.py +++ b/autosklearn/util/pipeline.py @@ -1,6 +1,7 @@ # -*- encoding: utf-8 -*- from autosklearn.constants import CLASSIFICATION_TASKS, REGRESSION_TASKS, BINARY_CLASSIFICATION, \ - MULTILABEL_CLASSIFICATION, REGRESSION, MULTICLASS_CLASSIFICATION + MULTILABEL_CLASSIFICATION, REGRESSION, MULTICLASS_CLASSIFICATION, \ + MULTIOUTPUT_REGRESSION from autosklearn.pipeline.classification import SimpleClassificationPipeline from autosklearn.pipeline.regression import SimpleRegressionPipeline @@ -53,11 +54,21 @@ def get_configuration_space(info, def _get_regression_configuration_space(info, include, exclude): + task_type = info['task'] sparse = False + multioutput = False + if task_type == MULTIOUTPUT_REGRESSION: + multioutput = True + + dataset_properties = { + 'multioutput': multioutput, + 'sparse': sparse + } + if info['is_sparse'] == 1: sparse = True configuration_space = SimpleRegressionPipeline( - dataset_properties={'sparse': sparse}, + dataset_properties=dataset_properties, include=include, exclude=exclude ).get_hyperparameter_search_space() diff --git a/ci_scripts/create_doc.sh b/ci_scripts/create_doc.sh index d817cfce2a..4e562c7564 100644 --- a/ci_scripts/create_doc.sh +++ b/ci_scripts/create_doc.sh @@ -13,10 +13,6 @@ if ! [[ -z ${DOCPUSH+x} ]]; then # $1 is the branch name # $2 is the global variable where we set the script status - if ! { [ $1 = "master" ] || [ $1 = "development" ]; }; then - { echo "Not one of the allowed branches"; exit 0; } - fi - # delete any previous documentation folder if [ -d doc/$1 ]; then rm -rf doc/$1 @@ -25,6 +21,10 @@ if ! [[ -z ${DOCPUSH+x} ]]; then # create the documentation cd doc && make html 2>&1 + if ! { [ $1 = "master" ] || [ $1 = "development" ]; }; then + { echo "Not one of the allowed branches"; exit 0; } + fi + # create directory with branch name # the documentation for dev/stable from git will be stored here mkdir $1 diff --git a/doc/api.rst b/doc/api.rst index 6c0d34952f..6397548763 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -17,6 +17,10 @@ Classification :members: :inherited-members: show_models, fit_ensemble, refit, sprint_statistics +.. autoclass:: autosklearn.experimental.AutoSklearn2Classifier + :inherited-members: show_models, fit_ensemble, refit, sprint_statistics, fit, predict, predict_proba + + ~~~~~~~~~~ Regression ~~~~~~~~~~ diff --git a/doc/index.rst b/doc/index.rst index 4af101979b..d27c423d00 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -23,6 +23,17 @@ the technology behind *auto-sklearn* by reading our paper published at `NIPS 2015 `_ . +.. topic:: NEW: Auto-sklearn 2.0 + + Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself + and contains a multitude of improvements which speed up the fitting the AutoML system. + +*auto-sklearn 2.0* works the same way as regular *auto-sklearn* and you can use it via + + >>> from autosklearn.experimental.askl2 import AutoSklearn2Classifier + +A paper describing our advances will be available on arXiv.org soon. + Example ******* @@ -81,6 +92,22 @@ reference to the following paper: url = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf} } +If you are using Auto-sklearn 2.0, please also cite + + + Auto-Sklearn 2.0: The Next Generation, Feurer *et al.*, to appear (2020). + + Bibtex entry:: + + @article{ASKL2, + title = {Auto-Sklearn 2.0}, + author = {Feurer, Matthias and Eggensperger, Katharina and + Falkner, Stefan and Lindauer, Marius and Hutter, Frank}, + booktitle = {Advances in Neural Information Processing Systems 28}, + year = {2020}, + journal = {arXiv:2006.???? [cs.LG]}, + } + Contributing ************ diff --git a/doc/releases.rst b/doc/releases.rst index 45b51717df..8eb2a20005 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -11,6 +11,12 @@ Releases ======== +Version 0.8 +=========== + +* ADD #803: multi-output regression +* ADD #893: new Auto-sklearn mode Auto-sklearn 2.0 + Version 0.7.1 ============= diff --git a/examples/example_extending_classification.py b/examples/example_extending_classification.py index dc298c1e2f..978533a770 100644 --- a/examples/example_extending_classification.py +++ b/examples/example_extending_classification.py @@ -78,6 +78,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': False, # Both input and output must be tuple(iterable) 'input': [DENSE, SIGNED_DATA, UNSIGNED_DATA], diff --git a/examples/example_extending_preprocessor.py b/examples/example_extending_preprocessor.py index 434beed983..ff85975c80 100644 --- a/examples/example_extending_preprocessor.py +++ b/examples/example_extending_preprocessor.py @@ -70,6 +70,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, UNSIGNED_DATA, SIGNED_DATA), 'output': (DENSE, UNSIGNED_DATA, SIGNED_DATA)} diff --git a/examples/example_extending_regression.py b/examples/example_extending_regression.py index c078ae19b1..0f106c18b8 100644 --- a/examples/example_extending_regression.py +++ b/examples/example_extending_regression.py @@ -62,6 +62,7 @@ def get_properties(dataset_properties=None): 'handles_classification': False, 'handles_multiclass': False, 'handles_multilabel': False, + 'handles_multioutput': True, 'is_deterministic': True, 'input': (SPARSE, DENSE, UNSIGNED_DATA, SIGNED_DATA), 'output': (PREDICTIONS,)} diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index 14f2064570..a0e0ff7a87 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -20,6 +20,7 @@ from autosklearn.automl import AutoMLClassifier, AutoML from autosklearn.util.backend import Backend, BackendContext from autosklearn.constants import BINARY_CLASSIFICATION +from autosklearn.experimental.askl2 import AutoSklearn2Classifier sys.path.append(os.path.dirname(__file__)) from base import Base # noqa (E402: module level import not at top of file) @@ -187,17 +188,8 @@ def test_type_of_target(self, mock_estimator): # Test that regressor raises error for illegal target types. reg = AutoSklearnRegressor() - # Illegal target types for regression: multiclass-multioutput, - # multilabel-indicator, continuous-multioutput. - self.assertRaisesRegex( - ValueError, - "regression with data of type" - " multiclass-multioutput is not supported", - reg.fit, - X=X, - y=y_multiclass_multioutput, - ) - + # Illegal target types for regression: multilabel-indicator + # multiclass-multioutput self.assertRaisesRegex( ValueError, "regression with data of type" @@ -210,12 +202,15 @@ def test_type_of_target(self, mock_estimator): self.assertRaisesRegex( ValueError, "regression with data of type" - " continuous-multioutput is not supported", + " multiclass-multioutput is not supported", reg.fit, X=X, - y=y_continuous_multioutput, + y=y_multiclass_multioutput, ) - # Legal target types: continuous, binary, multiclass + + # Legal target types: continuous, multiclass, + # continuous-multioutput, + # binary try: reg.fit(X, y_continuous) except ValueError: @@ -223,16 +218,22 @@ def test_type_of_target(self, mock_estimator): "continuous targets") try: - reg.fit(X, y_binary) + reg.fit(X, y_multiclass) except ValueError: self.fail("reg.fit() raised ValueError while fitting " - "binary targets") + "multiclass targets") try: - reg.fit(X, y_multiclass) + reg.fit(X, y_continuous_multioutput) except ValueError: self.fail("reg.fit() raised ValueError while fitting " - "multiclass targets") + "continuous_multioutput targets") + + try: + reg.fit(X, y_binary) + except ValueError: + self.fail("reg.fit() raised ValueError while fitting " + "binary targets") def test_fit_pSMAC(self): tmp = os.path.join(self.test_dir, '..', '.tmp_estimator_fit_pSMAC') @@ -751,5 +752,22 @@ def test_regression_methods_returns_self(self): self.assertIs(automl, automl_refitted) +class AutoSklearn2ClassifierTest(unittest.TestCase): + # Currently this class only tests that the methods of AutoSklearnClassifier + # which should return self actually return self. + def test_classification_methods_returns_self(self): + X_train, y_train, X_test, y_test = putil.get_dataset('iris') + automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0,) + + automl_fitted = automl.fit(X_train, y_train) + self.assertIs(automl, automl_fitted) + + automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5) + self.assertIs(automl, automl_ensemble_fitted) + + automl_refitted = automl.refit(X_train.copy(), y_train.copy()) + self.assertIs(automl, automl_refitted) + + if __name__ == "__main__": unittest.main() diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 2e0bc7454c..34f4ada66a 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -38,6 +38,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (PREDICTIONS,)} @@ -57,6 +58,7 @@ def get_properties(dataset_properties=None): 'handles_classification': True, 'handles_multiclass': True, 'handles_multilabel': True, + 'handles_multioutput': False, 'is_deterministic': True, 'input': (DENSE, SPARSE, UNSIGNED_DATA), 'output': (INPUT,)} diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 1efad2e65d..4a5e28cbe5 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -1,10 +1,12 @@ import copy import resource import sys +import tempfile import traceback import unittest import unittest.mock +from joblib import Memory import numpy as np import sklearn.datasets import sklearn.decomposition @@ -48,6 +50,7 @@ def test_io_dict(self): self.assertIn('handles_classification', props) self.assertIn('handles_multiclass', props) self.assertIn('handles_multilabel', props) + self.assertIn('handles_multioutput', props) self.assertFalse(props['handles_classification']) self.assertFalse(props['handles_multiclass']) self.assertFalse(props['handles_multilabel']) @@ -90,6 +93,38 @@ def test_configurations_sparse(self): self._test_configurations(cs, make_sparse=True, dataset_properties=dataset_properties) + def test_multioutput(self): + cache = Memory(cachedir=tempfile.gettempdir()) + cached_func = cache.cache( + sklearn.datasets.make_regression + ) + X, Y = cached_func( + n_samples=250, + n_features=20, + n_informative=9, + n_targets=4, + bias=0.5, + effective_rank=10, + tail_strength=0.4, + noise=0.3, + shuffle=True, + coef=False, + random_state=1 + ) + X_train = X[:200, :] + Y_train = Y[:200, :] + X_test = X[200:, :] + Y_test = Y[200:, :] + + data = {'X_train': X_train, 'Y_train': Y_train, + 'X_test': X_test, 'Y_test': Y_test} + + dataset_properties = {'multioutput': True} + cs = SimpleRegressionPipeline(dataset_properties=dataset_properties).\ + get_hyperparameter_search_space() + self._test_configurations(cs, data=data, + dataset_properties=dataset_properties) + def _test_configurations(self, configurations_space, make_sparse=False, data=None, dataset_properties=None): # Use a limit of ~4GiB