Delete new_Word2Vec_train.py

dump_scripts/new_Word2Vec_train.py

-from gensim.models import Word2Vec
-from project_utilities import my_datasets
-from projectsettings import DefaultConfig
-import numpy as np
-from custom_models.classifiers.ML_classifiers import ITMultinomialLogisticRegression
-from custom_models.feature_selection_extraction.algorithmic_feature_extraction_selection import TFIDF_Model
-from project_utilities.evaluators import DetailedConfusionMatrix, AccuracyPerClass
-# Load Dataset
-dataset = my_datasets.ITSupportDatasetBuilder(
-    f"{DefaultConfig.absolute_project_root_path()}/project_utilities/Datasets/ITSupport_Tickets.csv",
-    f"{DefaultConfig.absolute_project_root_path()}/project_utilities/Datasets/ITSupport_Tickets_High_Prio.csv",
-    f"{DefaultConfig.absolute_project_root_path()}/project_utilities/Datasets/synonym_IT_tickets.csv") \
-    .with_summaries_and_descriptions_combined() \
-    .with_overall_priority_column() \
-    .with_pre_processed_descriptions() \
-    .build().corpus
-dataset['Description'] = dataset['Description'].apply(lambda x: x.split(' '))
-# Split dataset into test and train
-X_train_str, X_test_str, y_train, y_test = TFIDF_Model.split_dataset(0.1, dataset['Description'].tolist(),
-                                                                     dataset['Priority'].tolist())
-
-
-
-# Create and train the Word2Vec model
-model = Word2Vec(sentences=X_train_str, vector_size=250, window=5, min_count=3, workers=16)
-
-# Save the model
-#model.save("word2vec.model")
-
-def get_vectors(texts):
-    X_vectors = []
-    for sentence in texts:
-        sentence_vectors = []
-        for word in sentence:
-            if word in model.wv:
-                sentence_vectors.append(model.wv[word])
-            else:
-                # Handle words not in the vocabulary
-                sentence_vectors.append(np.zeros(model.vector_size))
-        X_vectors.append(np.mean(sentence_vectors, axis=0))
-    X_vectors = np.array(X_vectors)
-    return X_vectors
-
-X_vectors = get_vectors(X_train_str)
-X_test_vectors = get_vectors(X_test_str)
-
-logreg = ITMultinomialLogisticRegression()
-logreg.train_model(X_vectors, y_train)
-
-pred = logreg.make_predictions(X_test_vectors)
-# Represent accuracies
-confusion_matrix = DetailedConfusionMatrix(pred, y_test, ['P5', 'P4', 'P3', 'P2', 'P1'])
-confusion_matrix.plot_confusion_matrix(fullscreen_requested=True)
-
-apc = AccuracyPerClass(pred, y_test, ['P5', 'P4', 'P3', 'P2', 'P1'])
-apc.plot_confusion_matrix()
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