.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()
.similarity() \
.diff_pos_count() \
.tfidf() \
.ont_hot_pos() \
.matching_lemma() \
.count_each_pos() \
.cosine() \
.jaccard() \
.difference_in_length()
model_trainer = ModelTrainer(german_config, german_config.logger)
german_classifier = WordSenseAlignmentClassifier(german_config,
feature_extractor,
model_trainer)
data = german_classifier.load_data().get_preprocessed_data()
feats = feature_extractor.extract(
data, feats_to_scale=['similarities', 'len_diff', 'pos_diff'])
feats = feature_extractor.keep_feats([
'similarities', 'cos_tfidf', 'ADP', 'DET', 'pos_diff', 'len_diff', 'PRON',
'CONJ', 'X', 'PROPN', 'NOUN', 'cos', 'ADJ', 'VERB', 'jaccard', 'PUNCT',
'noun', 'ADV', 'adjective'
])
x_trainset, x_testset = model_trainer.split_data(feats, 0.0)
with open(
'models/dutch_all_features_nonebalanceRandomForestClassifier20200329-1354.pickle',
'rb') as pickle_file:
clf = pickle.load(pickle_file)
predicted = clf.predict(x_trainset)
}
}
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']) \
.select_features(['target_word_synset_count',
'elmo_sim',
'simdiff_to_target',
'synsets_count_diff',
'lemma_match_normalized',
'token_count_norm_diff',
'len_diff',
'NOUN',
'VERB',
'PUNCT',
'pos_diff',
'CCONJ',
'semicol_count2_norm',
'ADP',
'ADJ',
'semicol_diff',
'max_depth_deptree_2',
'max_depth_deptree_1'])\
.train()
'max_depth': [30, 80],
'max_features': [2, 15, 'auto', None],
'min_samples_leaf': [3, 5],
'min_samples_split': [2, 5, 8],
'n_estimators': [500, 800],
'n_jobs': [10]
}
}
# 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([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)
.count_each_pos() \
.jaccard() \
.avg_count_synsets() \
.difference_in_length()\
.max_dependency_tree_depth() \
.target_word_synset_count()\
rf = {
'estimator': RandomForestClassifier(),
'parameters': {
'class_weight': ['balanced_subsample', 'balanced'],
'max_depth': [5, 10, 15],
'max_features': ['auto', 'sqrt', 'log2'],
'min_samples_leaf': [2],
'min_samples_split': [5, 10],
'n_estimators': [300, 1000],
'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(['similarities', 'len_diff']) \
.select_features(['cos_tfidf','jaccard','similarities','first_word_same','PART','noun','adjective','verb','target_word_synset_count','adverb','len_diff'])\
.train()
'min_samples_split': [5, 8],
'n_estimators': [500, 1000],
'n_jobs': [8]
}
}
# 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([rf])
german_classifier = WordSenseAlignmentClassifier(german_config,
feature_extractor,
model_trainer)
german_classifier.load_data() \
.extract_features(['similarities', 'len_diff', 'pos_diff']) \
.select_features(['similarities', 'cos_tfidf',
'ADP', 'DET', 'pos_diff', 'len_diff',
'PRON', 'CONJ','X', 'PROPN', 'NOUN', 'cos', 'ADJ', 'VERB', 'jaccard', 'PUNCT', 'noun', 'ADV', 'adjective'])\
.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)