main_hateval_a.py

### Prepare imports
from scripts_pipeline.PathsManagement import Var, PathsManagement as Paths
from scripts_pipeline.Experiment import Experiment
from scripts_pipeline.ClassifyNewData import ClassifyNewContestDataAfterExperiment
import itertools


def combinations_any_length(l):
    comb = []
    for i in range(len(l)):
        comb += itertools.combinations(l,i+1)
    return comb

def experiment_name(num_experiment, dataset_name, features, classifier, normalize_data, class_weight, use_grid_search, epochs, batch):
    return "experiment" + str(num_experiment) + Paths.util_names_separator(dataset_name, '_'.join(features), classifier,
                                                                    "normalize" + normalize_data,
                                                                    "weight" + str(class_weight),
                                                                    "use_grid_search" + str(use_grid_search),
                                                                    "epochs" + str(epochs),
                                                                    "batch" + str(batch))

##############
# Replication experiment
#############

FOLDS = 10

new_data_path = "/Users/paulafortuna/PycharmProjects/HatEval/original_datasets/public_development_en/test_en.tsv"
experiment_id = "extract_LSTMFeatures"
dataset = "hateval_en"

exp = Experiment(experiment_name=experiment_id,
                 dataset_name=dataset, # hateval_es hateval_en test hateval_en_my_division zeerak
                 apply_data_preprocessing=[],
                 features_to_extract=[Var.glove_twitter_200_en],  # Var.sentiment_vader, Var.hatebase, Var.glove_twitter_25_en[]
                 features_to_use=[Var.glove_twitter_200_en],
                 normalize_data=Var.none,  # Var.min_max, Var.normalize_gaussian , Var.normalize_linear_algebra
                 classifier_name=Var.LSTMFeatures,  # Var.LSTMFeatures Var.linear_svm, Var.CVgridSearchLSTM, Var.xgBoost, Var.LogisticRegressionClassifier, Var.RandomForest
                 consider_class_weight=False,
                 folds_cross_validation=FOLDS,
                 use_grid_search=False,
                 epochs=10,
                 batch=128
                 )
exp.start_experiment()

experiment_id = "other_features"
exp = Experiment(experiment_name=experiment_id,
                 dataset_name=dataset, # hateval_es hateval_en test hateval_en_my_division zeerak
                 apply_data_preprocessing=[],
                 features_to_extract=[Var.hatebase, Var.sentiment_vader],  # Var.sentiment_vader, Var.hatebase, Var.glove_twitter_25_en[]
                 features_to_use=[Var.hatebase, Var.sentiment_vader],
                 normalize_data=Var.none,  # Var.min_max, Var.normalize_gaussian , Var.normalize_linear_algebra
                 classifier_name=Var.xgBoost,  # Var.LSTMFeatures Var.linear_svm, Var.CVgridSearchLSTM, Var.xgBoost, Var.LogisticRegressionClassifier, Var.RandomForest
                 consider_class_weight=False,
                 folds_cross_validation=FOLDS,
                 use_grid_search=False,
                 epochs=10,
                 batch=128
                 )
exp.start_experiment()


def make_experiments():
    num_experiment = 0
    datasets_to_use = [dataset]
    for dataset_name in datasets_to_use:
        print(dataset_name)
        if num_experiment <20000:
            for features in combinations_any_length([Var.LSTMFeatures, Var.hatebase, Var.sentiment_vader]):
                for classifier in [Var.xgBoost]:
                    for normalize_data in [Var.none]:
                        for class_weight in [False]:
                            for use_grid_search in [True]:
                                num_experiment += 1
                                epochs = 10
                                batch = 128
                                exp_name = experiment_name(num_experiment, dataset_name, features, classifier, normalize_data,
                                                           class_weight, use_grid_search, epochs, batch)
                                print(exp_name)
                                exp = Experiment(experiment_name=exp_name,
                                                 dataset_name=dataset_name,
                                                 apply_data_preprocessing=[],
                                                 features_to_extract=[],
                                                 features_to_use=features,
                                                 normalize_data=normalize_data,
                                                 classifier_name=classifier,
                                                 consider_class_weight=class_weight,
                                                 folds_cross_validation=FOLDS,
                                                 use_grid_search=use_grid_search,
                                                 epochs=epochs,
                                                 batch=batch
                                                 )
                                exp.start_experiment()


make_experiments()


experiments_to_use = ["extract_LSTMFeatures",
                      "experiment1hateval_en_LSTMFeatures_xgBoost_normalizenone_weightFalse_use_grid_searchTrue_epochs10_batch128"
                      ]
for experiment_id in experiments_to_use:
    classify_new_data = ClassifyNewContestDataAfterExperiment(experiment_id)
    classify_new_data.start_classification(new_data_path, "test_new_data_hateval", True, "id")