#=============================================================================== # Copyright 2014 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #=============================================================================== # daal4py AdaBoost (Adaptive Boosting) scikit-learn-compatible estimator class import numpy as np import numbers from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.utils.validation import check_X_y, check_array, check_is_fitted from sklearn import preprocessing from sklearn.utils.multiclass import check_classification_targets import daal4py as d4p from .._utils import getFPType from sklearn import __version__ as sklearn_version try: from packaging.version import Version except ImportError: from distutils.version import LooseVersion as Version class AdaBoostClassifier(BaseEstimator, ClassifierMixin): def __init__(self, split_criterion='gini', max_tree_depth=1, min_observations_in_leaf_node=1, max_iterations=100, learning_rate=1.0, accuracy_threshold=0.01): self.split_criterion = split_criterion self.max_tree_depth = max_tree_depth self.min_observations_in_leaf_node = min_observations_in_leaf_node self.max_iterations = max_iterations self.learning_rate = learning_rate self.accuracy_threshold = accuracy_threshold def fit(self, X, y): if self.split_criterion not in ('gini', 'infoGain'): raise ValueError('Parameter "split_criterion" must be ' '"gini" or "infoGain".') if not isinstance(self.max_tree_depth, numbers.Integral) or \ self.max_tree_depth < 0: raise ValueError('Parameter "max_tree_depth" must be ' 'positive integer value or zero.') if not isinstance(self.min_observations_in_leaf_node, numbers.Integral) or \ self.min_observations_in_leaf_node <= 0: raise ValueError('Parameter "min_observations_in_leaf_node" must be ' 'non-zero positive integer value.') if not isinstance(self.max_iterations, numbers.Integral) or \ self.max_iterations <= 0: raise ValueError('Parameter "max_iterations" must be ' 'non-zero positive integer value.') if self.learning_rate <= 0: raise ValueError('Parameter "learning_rate" must be ' 'non-zero positive value.') # it is not clear why it is so but we will get error from # Intel(R) oneAPI Data Analytics # Library otherwise if self.accuracy_threshold < 0 and self.accuracy_threshold >= 1: raise ValueError('Parameter "accuracy_threshold" must be ' 'more or equal to 0 and less than 1.') # Check that X and y have correct shape X, y = check_X_y(X, y, y_numeric=False, dtype=[np.single, np.double]) check_classification_targets(y) # Encode labels le = preprocessing.LabelEncoder() le.fit(y) self.classes_ = le.classes_ y_ = le.transform(y) # Convert to 2d array y_ = y_.reshape((-1, 1)) self.n_classes_ = len(self.classes_) self.n_features_in_ = X.shape[1] # Classifier can't train when only one class is present. # Trivial case if self.n_classes_ == 1: return self # Define type of data fptype = getFPType(X) # Fit the model tr = d4p.decision_tree_classification_training( fptype=fptype, nClasses=self.n_classes_, # this parameter is strict upper bound in DAAL maxTreeDepth=self.max_tree_depth + 1, minObservationsInLeafNodes=self.min_observations_in_leaf_node, splitCriterion=self.split_criterion, pruning='none') pr = d4p.decision_tree_classification_prediction( fptype=fptype, nClasses=self.n_classes_) train_algo = d4p.adaboost_training( fptype=fptype, nClasses=self.n_classes_, weakLearnerTraining=tr, weakLearnerPrediction=pr, maxIterations=self.max_iterations, learningRate=self.learning_rate, accuracyThreshold=self.accuracy_threshold) train_result = train_algo.compute(X, y_) # Store the model self.daal_model_ = train_result.model # Return the classifier return self def predict(self, X): # Check is fit had been called if Version(sklearn_version) >= Version("0.22"): check_is_fitted(self) else: check_is_fitted(self, ['n_features_in_', 'n_classes_']) # Input validation X = check_array(X, dtype=[np.single, np.double]) if X.shape[1] != self.n_features_in_: raise ValueError('Shape of input is different from what was seen in `fit`') # Trivial case if self.n_classes_ == 1: return np.full(X.shape[0], self.classes_[0]) if not hasattr(self, 'daal_model_'): raise ValueError(( "The class {} instance does not have 'daal_model_' attribute set. " "Call 'fit' with appropriate arguments before using this method.").format( type(self).__name__)) # Define type of data fptype = getFPType(X) pr = d4p.decision_tree_classification_prediction(fptype=fptype, nClasses=self.n_classes_) # Prediction predict_algo = d4p.adaboost_prediction(fptype=fptype, nClasses=self.n_classes_, weakLearnerPrediction=pr) predict_result = predict_algo.compute(X, self.daal_model_) prediction = predict_result.prediction # in binary classification labels "-1, 1" are returned but "0, 1" are expected if self.n_classes_ == 2: prediction[prediction == -1] = 0 # Decode labels le = preprocessing.LabelEncoder() le.classes_ = self.classes_ return le.inverse_transform(prediction.ravel().astype(np.int64, copy=False))