.similarity_diff_to_target()\
.max_dependency_tree_depth() \
.target_word_synset_count()\
.token_count_norm_diff()\
.semicol_count()\
.elmo_similarity()
rf = {
'estimator': RandomForestClassifier(),
'parameters': {
'bootstrap': [True],
'class_weight': ['balanced', 'balanced_subsample', 'None'],
'max_depth': [5, 10, 30, 50, 80],
'max_features': [2, 10, 15, 'auto', 'sqrt', 'log2'],
'min_samples_leaf': [2, 5, 10],
'min_samples_split': [2, 5, 10, 20],
'n_estimators': [500, 800, 1000, 1500],
'n_jobs': [8]
}
}
model_trainer = ModelTrainer(english_config.testset_ratio,
english_config.logger)
model_trainer.add_estimators([rf])
english_classifier = WordSenseAlignmentClassifier(english_config,
feature_extractor,
model_trainer)
english_classifier.load_data() \
.extract_features(['len_diff', 'pos_diff']) \
.train()
'parameters': {
'bootstrap': [True],
'class_weight': ['balanced', 'balanced_subsample','None'],
'max_depth': [30, 50, 80],
'max_features': [2, 10, 15, 'auto', 'sqrt', 'log2', None],
'min_samples_leaf': [3, 5],
'min_samples_split': [2, 5, 8],
'n_estimators': [500, 800],
'n_jobs':[-1]
}
}
# rf = {
# 'estimator': RandomForestClassifier(),
# 'parameters': {
# 'bootstrap': [True],
# 'max_depth': [30, 50],
# 'max_features': [None],
# 'min_samples_leaf': [3, 5],
# 'min_samples_split': [2, 5, 8],
# 'n_estimators': [500, 600]
# }
# }
dt = {'estimator': DecisionTreeClassifier(), 'parameters': {}}
model_trainer = ModelTrainer(german_config.testset_ratio, german_config.logger)
model_trainer.add_estimators([lr, svm_model, rf])
german_classifier = WordSenseAlignmentClassifier(german_config, feature_extractor, model_trainer)
german_classifier.load_data() \
.extract_features(['similarities', 'len_diff', 'pos_diff']) \
.train(with_testset=True)
logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO"))
if __name__ == '__main__':
configure()
english_config = ClassifierConfig('en_core_web_lg', "english", 'data/train', balancing_strategy="none",
testset_ratio=0.2, with_wordnet= True, dataset='english_nuig', logger = 'en_nuig')
feature_extractor = FeatureExtractor() \
.first_word() \
.similarity() \
.diff_pos_count() \
.tfidf() \
.ont_hot_pos() \
.matching_lemma() \
.count_each_pos() \
.cosine() \
.jaccard() \
.avg_count_synsets() \
.difference_in_length()
dt = {'estimator': DecisionTreeClassifier(), 'parameters': {}}
model_trainer = ModelTrainer(english_config.testset_ratio, english_config.logger)
model_trainer.add_estimators([dt])
english_classifier = WordSenseAlignmentClassifier(english_config, feature_extractor, model_trainer)
english_classifier.load_data() \
.extract_features(['similarities', 'len_diff', 'pos_diff']) \
.train(with_testset=True)
.tfidf() \
.ont_hot_pos() \
.matching_lemma() \
.count_each_pos() \
.cosine() \
.jaccard() \
.avg_count_synsets() \
.difference_in_length()\
.similarity_diff_to_target()\
.max_dependency_tree_depth() \
.target_word_synset_count()
svm_model = {
'estimator': SVC(),
'parameters': {
'C': [3, 5, 10],
'kernel': ['rbf', 'linear', 'poly', 'sigmoid'],
'degree':[3, 5, 10],
'gamma':['scale', 'auto'],
'shrinking':[True, False],
'class_weight':['balanced'],
'decision_function_shape':['ovr','ovo'],
}
}
model_trainer = ModelTrainer(english_config.testset_ratio, english_config.logger)
model_trainer.add_estimators([svm_model])
english_classifier = WordSenseAlignmentClassifier(english_config, feature_extractor, model_trainer)
english_classifier.load_data() \
.extract_features(['similarities', 'len_diff', 'pos_diff', 'max_depth_deptree_1', 'max_depth_deptree_2', 'synset_count_1','synset_count_2', 'target_word_synset_count']) \
.train(with_testset=True)
