def _choose_classifier(job: Job):
if job.type == JobTypes.UPDATE.value:
classifier = _load_model(job.incremental_train)
# TODO: check if this instruction still makes sense
# are we updating a predictive_model with its own methods?
assert classifier[0].__class__.__name__ == job.method
else:
method, config = get_method_config(job)
config.pop('classification_method', None)
print("Using method {} with config {}".format(method, config))
if method == ClassificationMethods.KNN.value:
classifier = KNeighborsClassifier(**config)
elif method == ClassificationMethods.RANDOM_FOREST.value:
classifier = RandomForestClassifier(**config)
elif method == ClassificationMethods.DECISION_TREE.value:
classifier = DecisionTreeClassifier(**config)
elif method == ClassificationMethods.XGBOOST.value:
classifier = XGBClassifier(**config)
elif method == ClassificationMethods.MULTINOMIAL_NAIVE_BAYES.value:
classifier = MultinomialNB(**config)
elif method == ClassificationMethods.ADAPTIVE_TREE.value:
classifier = HAT(**config)
elif method == ClassificationMethods.HOEFFDING_TREE.value:
classifier = HoeffdingTree(**config)
elif method == ClassificationMethods.SGDCLASSIFIER.value:
classifier = SGDClassifier(**config)
elif method == ClassificationMethods.PERCEPTRON.value:
classifier = Perceptron(**config)
elif method == ClassificationMethods.NN.value:
config['encoding'] = job.encoding.value_encoding
config['is_binary_classifier'] = _check_is_binary_classifier(job.labelling.type)
classifier = NNClassifier(**config)
else:
raise ValueError("Unexpected classification method {}".format(method))
return classifier
def _choose_classifier(job: Job):
method, config = get_method_config(job)
config.pop('classification_method', None)
logger.info("Using method {} with config {}".format(method, config))
if method == ClassificationMethods.KNN.value:
classifier = KNeighborsClassifier(**config)
elif method == ClassificationMethods.RANDOM_FOREST.value:
classifier = RandomForestClassifier(**config)
elif method == ClassificationMethods.DECISION_TREE.value:
classifier = DecisionTreeClassifier(**config)
elif method == ClassificationMethods.XGBOOST.value:
classifier = XGBClassifier(**config)
elif method == ClassificationMethods.MULTINOMIAL_NAIVE_BAYES.value:
classifier = MultinomialNB(**config)
elif method == ClassificationMethods.ADAPTIVE_TREE.value:
classifier = HAT(**config)
elif method == ClassificationMethods.HOEFFDING_TREE.value:
classifier = HoeffdingTree(**config)
elif method == ClassificationMethods.SGDCLASSIFIER.value:
classifier = SGDClassifier(**config)
elif method == ClassificationMethods.PERCEPTRON.value:
classifier = Perceptron(**config)
elif method == ClassificationMethods.NN.value:
config['encoding'] = job.encoding.value_encoding
config['is_binary_classifier'] = _check_is_binary_classifier(
job.labelling.type)
classifier = NNClassifier(**config)
else:
raise ValueError("Unexpected classification method {}".format(method))
return classifier
def test_nn_classifier_simple_index_multiclass_no_exceptions(self):
encoding = create_test_encoding(value_encoding=ValueEncodings.SIMPLE_INDEX.value, prefix_length=2, padding=True)
labelling = create_test_labelling(label_type=LabelTypes.NEXT_ACTIVITY.value)
train_df = simple_index(self.train_log, labelling, encoding)
test_df = simple_index(self.test_log, labelling, encoding)
train_df, targets_df = self._drop_columns_and_split(train_df)
targets_df = targets_df.values.ravel()
test_df, _ = self._drop_columns_and_split(test_df)
config = self._get_nn_default_config(binary=False)
nn_classifier = NNClassifier(**config)
with HidePrints():
nn_classifier.fit(train_df, targets_df)
nn_classifier.predict(test_df)
nn_classifier.predict_proba(test_df)