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main_offenseval_a.py
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main_offenseval_a.py
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### 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))
##############
# offenseval experiment
#############
FOLDS = 10
new_data_path = "/Users/paulafortuna/PycharmProjects/HatEval/original_datasets/OffensEval/testset-taska.tsv"
experiment_id = "extract_LSTMFeatures"
dataset = "offenseval"
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()
experiments_to_use = ["extract_LSTMFeatures",
"experiment5offenseval_LSTMFeatures_sentiment_vader_xgBoost_normalizenone_weightFalse_use_grid_searchTrue_epochs10_batch128",
"experiment7offenseval_LSTMFeatures_hatebase_sentiment_vader_xgBoost_normalizenone_weightFalse_use_grid_searchTrue_epochs10_batch128",
"experiment1offenseval_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_offenseval", True, "id")