def run_tc3_0():
global dataset, feature_extraction_method, classifiers, experiment_controller
dataset = Dataset.arxiv_metadata
feature_extraction_method = FeatureExtractionMethod.BOW
classifiers = [ClassificationMethod.Gradient_Boosting_Machines]
experiment_controller = ExperimentController('tc#3.0', '3')
experiment_controller.set_variables(dataset,
feature_extraction_method,
classifiers,
should_load_embedding_model=False)
experiment_controller.run_experiment()
def run_tc0_0():
global dataset, feature_extraction_method, classifiers, experiment_controller
dataset = Dataset.ds20newsgroups
feature_extraction_method = FeatureExtractionMethod.BOW
classifiers = [
ClassificationMethod.Naive_Bayes_Classifier,
# ClassificationMethod.Logistic_Regression,
ClassificationMethod.Support_Vector_Machines,
ClassificationMethod.SVM_with_SGD
]
experiment_controller = ExperimentController('tc#0.0', '1')
experiment_controller.set_variables(dataset, feature_extraction_method,
classifiers)
experiment_controller.run_experiment()
def run_tc3_4():
global dataset, feature_extraction_method, classifiers, experiment_controller
dataset = Dataset.arxiv_metadata
feature_extraction_method = FeatureExtractionMethod.FASTTEXT
classifiers = [
ClassificationMethod.Logistic_Regression,
ClassificationMethod.Support_Vector_Machines,
ClassificationMethod.SVM_with_SGD
]
experiment_controller = ExperimentController('tc#3.4', '2')
experiment_controller.set_variables(dataset,
feature_extraction_method,
classifiers,
should_load_embedding_model=False)
experiment_controller.run_experiment()
def run_tc0_2():
global dataset, feature_extraction_method, classifiers, experiment_controller
dataset = Dataset.ds20newsgroups
feature_extraction_method = FeatureExtractionMethod.WORD2VEC
classifiers = [
ClassificationMethod.Naive_Bayes_Classifier,
ClassificationMethod.Logistic_Regression,
ClassificationMethod.Support_Vector_Machines,
ClassificationMethod.SVM_with_SGD
]
experiment_controller = ExperimentController('tc#0.2', '1')
experiment_controller.set_variables(dataset,
feature_extraction_method,
classifiers,
should_load_embedding_model=True)
experiment_controller.run_experiment()
def run_tc3_1():
global dataset, feature_extraction_method, classifiers, experiment_controller
dataset = Dataset.arxiv_metadata
feature_extraction_method = FeatureExtractionMethod.TF_IDF
classifiers = [
ClassificationMethod.Naive_Bayes_Classifier,
ClassificationMethod.Logistic_Regression,
ClassificationMethod.Support_Vector_Machines,
ClassificationMethod.SVM_with_SGD,
ClassificationMethod.Gradient_Boosting_Machines
]
experiment_controller = ExperimentController('tc#3.1', '2')
experiment_controller.set_variables(dataset,
feature_extraction_method,
classifiers,
should_load_embedding_model=False)
experiment_controller.run_experiment()