def test_train_and_predict(self): # Load data set. X = DataFrame(RANDOM_CLASSIFICATION_TEST_CASE['X'], columns=['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10']) y = DataFrame(RANDOM_CLASSIFICATION_TEST_CASE['y']) random_state = RANDOM_CLASSIFICATION_TEST_CASE['random_state'] expected_y_pred_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['y_predicted'] expected_str_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['str'] expected_hyperparams_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['hyperparams'] expected_params_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['params'] expected_descriptions_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['description'] # Generate train/test split. X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state) # Iterate through SUPPORTED_ALGORITHMS. for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS: log.info('Testing %s classifier...' % algorithm) # Train model. hyperparams = {'algorithm': algorithm, 'random_state': random_state} # Default to stochastic search for expensive algorithms. if algorithm in [SupervisedClassifier.RANDOM_FOREST]: hyperparams['hyperparam_strategy'] = SupervisedClassifier.STOCHASTIC_SEARCH # Test ability to force hyperparam values. hyperparams['max_depth'] = 2 hyperparams['n_estimators'] = 5 hyperparams['min_samples_leaf'] = 1 hyperparams['min_samples_split'] = 0.2 else: hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH classifier = SupervisedClassifier([0, 1], hyperparams) classifier.train(X_train, y_train) # Test str(). expected_str = expected_str_by_algorithm[algorithm] actual_str = str(classifier) self.assertEqual(expected_str, actual_str) # Test hyperparameters. expected_hyperparams = expected_hyperparams_by_algorithm[algorithm] actual_hyperparams = classifier.hyperparams() self._assert_equal_hyperparams(expected_hyperparams, actual_hyperparams) # Test model parameters. expected_params = expected_params_by_algorithm[algorithm] actual_params = classifier.params() self.assertEqualDict(expected_params, actual_params) # Test model description. expected_description = expected_descriptions_by_algorithm[algorithm] actual_description = classifier.description() self.assertEqual(expected_description, actual_description) # Test prediction values. expected_y_pred = expected_y_pred_by_algorithm[algorithm] log.debug('expected_y_pred: %s' % expected_y_pred) actual_y_pred = classifier.predict(X_test) log.debug('actual_y_pred: %s' % actual_y_pred) self.assertEqualList(expected_y_pred, actual_y_pred)
class BifurcatedSupervisedClassifier: BIFURCATION = 'bifurcation' EQUAL = '==' LTE = '<=' GTE = '>=' SUPPORTED_BIFURCATION_STRATEGIES = [EQUAL, GTE, LTE] def __init__(self, classes, hyperparams): if hyperparams[ 'bifurcation_strategy'] not in BifurcatedSupervisedClassifier.SUPPORTED_BIFURCATION_STRATEGIES: raise ValueError('Bifurcation strategy %s not supported.' % hyperparams['bifurcation_strategy']) self._classes = classes self._hyperparams = hyperparams # Note that if we don't pass a copies of hyperparams, then we won't # be able to change hyperparams independently in the two classifiers. self._sc_true = SupervisedClassifier(classes, hyperparams.copy()) self._sc_false = SupervisedClassifier(classes, hyperparams.copy()) def __repr__(self): bs = self._build_bifurcation_str() classes_str = str(self._classes) hyperparams_str = "hyperparams={'algorithm': %s, 'bifurcator': %s, 'bifurcation_strategy': %s, 'bifurcation_threshold': %s, 'random_state': %s}" % ( self._hyperparams['algorithm'], self._hyperparams['bifurcator'], self._hyperparams['bifurcation_strategy'], self._hyperparams['bifurcation_value'], self._hyperparams['random_state']) s = "BifurcatedSupervisedClassifier(%s, %s)" % (classes_str, hyperparams_str) return s __str__ = __repr__ def _build_bifurcation_str(self): args = (self._hyperparams['bifurcator'], self._hyperparams['bifurcation_strategy'], self._hyperparams['bifurcation_value']) return '%s %s %s' % args def fetch_bifurcation_masks(self, X): log.debug('bifurcator: %s' % self._hyperparams['bifurcator']) log.debug('bifurcation_strategy: %s' % BifurcatedSupervisedClassifier.EQUAL) log.debug('bifurcation_value: %s' % self._hyperparams['bifurcation_value']) if self._hyperparams[ 'bifurcation_strategy'] is BifurcatedSupervisedClassifier.EQUAL: true_mask = X[self._hyperparams['bifurcator']].astype( float) == self._hyperparams['bifurcation_value'] false_mask = X[self._hyperparams['bifurcator']].astype( float) != self._hyperparams['bifurcation_value'] elif self._hyperparams[ 'bifurcation_strategy'] is BifurcatedSupervisedClassifier.LTE: true_mask = X[self._hyperparams['bifurcator']].astype( float) <= self._hyperparams['bifurcation_value'] false_mask = X[self._hyperparams['bifurcator']].astype( float) > self._hyperparams['bifurcation_value'] elif self._hyperparams[ 'bifurcation_strategy'] is BifurcatedSupervisedClassifier.GTE: true_mask = X[self._hyperparams['bifurcator']].astype( float) >= self._hyperparams['bifurcation_value'] false_mask = X[self._hyperparams['bifurcator']].astype( float) < self._hyperparams['bifurcation_value'] log.debug('X[%s].value_counts(): %s' % (self._hyperparams['bifurcator'], X[self._hyperparams['bifurcator']].value_counts())) log.debug('true_mask.value_counts(): %s' % true_mask.value_counts()) log.debug('false_mask.value_counts(): %s' % false_mask.value_counts()) return true_mask, false_mask def description(self): args = (self._hyperparams['algorithm'].upper().replace('-', '_'), self._build_bifurcation_str(), self._sc_true.description(), self._sc_false.description()) return 'BIFURCATED_%s(%s, true=%s, false=%s)' % args def hyperparams(self): hyperparams = { 'model_true': self._sc_true.hyperparams(), 'model_false': self._sc_false.hyperparams() } return hyperparams def params(self): params = { 'bifurcator': self._hyperparams['bifurcator'], 'bifurcation_strategy': self._hyperparams['bifurcation_strategy'], 'bifurcation_value': self._hyperparams['bifurcation_value'], 'model_true': self._sc_true.description(), 'model_false': self._sc_false.description() } return params def train(self, X_train, y_train): true_mask, false_mask = self.fetch_bifurcation_masks(X_train) # Train sc_true. X_train_true = X_train[true_mask] y_train_true = y_train[true_mask] status_true = self._sc_true.train(X_train_true, y_train_true) if status_true == SupervisedClassifier.INSUFFICIENT_SAMPLES: return status_true # Train sc_true. X_train_false = X_train[false_mask] y_train_false = y_train[false_mask] status_false = self._sc_false.train(X_train_false, y_train_false) if status_false == SupervisedClassifier.INSUFFICIENT_SAMPLES: return status_false return SupervisedClassifier.TRAINED def _stitch_disjoint_row(self, row): if pd.isnull(row['y_pred_true']): val = row['y_pred_false'] else: val = row['y_pred_true'] return val def _stitch_prob_0(self, row): if pd.isnull(row['y_pred_prob_true_0']): val = row['y_pred_prob_false_0'] else: val = row['y_pred_prob_true_0'] return val def _stitch_prob_1(self, row): if pd.isnull(row['y_pred_prob_true_1']): val = row['y_pred_prob_false_1'] else: val = row['y_pred_prob_true_1'] return val def _predict_label_or_probability(self, X_test, probability=None): true_mask, false_mask = self.fetch_bifurcation_masks(X_test) # Predict X_test_true. X_test_true = X_test[true_mask] if probability: y_pred_true = self._sc_true.predict_probability(X_test_true) else: y_pred_true = self._sc_true.predict(X_test_true) log.debug('y_pred_true: %s' % y_pred_true) # Predict X_test_false. X_test_false = X_test[false_mask] if probability: y_pred_false = self._sc_false.predict_probability(X_test_false) else: y_pred_false = self._sc_false.predict(X_test_false) log.debug('y_pred_false: %s' % y_pred_false) # Stitch results. if probability: column_names = ['y_pred_true_0', 'y_pred_true_1'] else: column_names = ['y_pred_true'] y_pred_true_df = DataFrame(y_pred_true, index=X_test_true.index, \ columns=column_names) log.debug('y_pred_true_df: %s' % y_pred_true_df) if probability: column_names = ['y_pred_false_0', 'y_pred_false_1'] else: column_names = ['y_pred_false'] y_pred_false_df = DataFrame(y_pred_false, index=X_test_false.index, \ columns=column_names) log.debug('y_pred_false_df: %s' % y_pred_false_df) true_mask_df = DataFrame(true_mask) mask_plus_true = true_mask_df.merge(y_pred_true_df, how='left', \ left_index=True, right_index=True) mask_plus_true_plus_false = mask_plus_true.merge(y_pred_false_df, \ how='left', left_index=True, right_index=True) mask_plus_true_plus_false['y_pred'] = mask_plus_true_plus_false.apply( self._stitch_disjoint_row, axis=1) log.debug('mask_plus_false: %s' % mask_plus_true_plus_false) y_pred = mask_plus_true_plus_false['y_pred'].values return y_pred def predict(self, X_test): true_mask, false_mask = self.fetch_bifurcation_masks(X_test) # Predict X_test_true. X_test_true = X_test[true_mask] y_pred_true = self._sc_true.predict(X_test_true) log.debug('y_pred_true: %s' % y_pred_true) # Predict X_test_false. X_test_false = X_test[false_mask] y_pred_false = self._sc_false.predict(X_test_false) log.debug('y_pred_false: %s' % y_pred_false) # Stitch results. column_names = ['y_pred_true'] y_pred_true_df = DataFrame(y_pred_true, index=X_test_true.index, \ columns=column_names) log.debug('y_pred_true_df: %s' % y_pred_true_df) column_names = ['y_pred_false'] y_pred_false_df = DataFrame(y_pred_false, index=X_test_false.index, \ columns=column_names) log.debug('y_pred_false_df: %s' % y_pred_false_df) true_mask_df = DataFrame(true_mask) mask_plus_true = true_mask_df.merge(y_pred_true_df, how='left', \ left_index=True, right_index=True) mask_plus_true_plus_false = mask_plus_true.merge(y_pred_false_df, \ how='left', left_index=True, right_index=True) mask_plus_true_plus_false['y_pred'] = mask_plus_true_plus_false.apply( self._stitch_disjoint_row, axis=1) log.debug('mask_plus_false: %s' % mask_plus_true_plus_false) y_pred = mask_plus_true_plus_false['y_pred'].values return y_pred def predict_probability(self, X_test): true_mask, false_mask = self.fetch_bifurcation_masks(X_test) # Predict X_test_true. X_test_true = X_test[true_mask] y_pred_prob_true = self._sc_true.predict_probability(X_test_true) log.debug('y_pred_prob_true: %s' % y_pred_prob_true) # Predict X_test_false. X_test_false = X_test[false_mask] y_pred_prob_false = self._sc_false.predict_probability(X_test_false) log.debug('y_pred_prob_false: %s' % y_pred_prob_false) # Stitch results. column_names = ['y_pred_prob_true_0', 'y_pred_prob_true_1'] y_pred_prob_true_df = DataFrame(y_pred_prob_true, index=X_test_true.index, \ columns=column_names) log.debug('y_pred_prob_true_df: %s' % y_pred_prob_true_df) column_names = ['y_pred_prob_false_0', 'y_pred_prob_false_1'] y_pred_prob_false_df = DataFrame(y_pred_prob_false, index=X_test_false.index, \ columns=column_names) log.debug('y_pred_prob_false_df: %s' % y_pred_prob_false_df) true_mask_df = DataFrame(true_mask) mask_plus_true = true_mask_df.merge(y_pred_prob_true_df, how='left', \ left_index=True, right_index=True) composite = mask_plus_true.merge(y_pred_prob_false_df, \ how='left', left_index=True, right_index=True) composite['y_pred_prob_0'] = composite.apply(self._stitch_prob_0, axis=1) composite['y_pred_prob_1'] = composite.apply(self._stitch_prob_1, axis=1) log.debug('composite: %s' % composite) y_pred_prob = composite[['y_pred_prob_0', 'y_pred_prob_1']].values log.debug(y_pred_prob) return y_pred_prob