Delete main.py

main.py

-#import ITSupportTicketPrioritisers.FromCSV_TFIDF_KerasCNN_ToCSV
-#import ITSupportTicketPrioritisers.DefaultDatasets_TFIDF_KerasCNN_ToCSV
-#import ITSupportTicketPrioritisers.NoDataset_TFIDF_KerasCNN
-#import ITSupportTicketPrioritisers.DefaultDatasets_TFIDF_SKLearnLogReg_ToCSV
-
-from sklearn.preprocessing import LabelEncoder
-from keras.utils import to_categorical
-from custom_models.classifiers.DL_classifiers import KerasCNN
-from custom_models.feature_selection_extraction.algorithmic_feature_extraction_selection import TFIDF_Model
-from project_utilities.evaluators import DetailedConfusionMatrix, AccuracyPerClass
-from project_utilities.model_interaction import SKLearnModelFileInteraction, KerasModelFileInteraction
-from project_utilities import predictionformats
-from project_utilities.my_datasets import ITSupportDatasetBuilder
-from projectsettings import DefaultConfig
-from keras.preprocessing.text import Tokenizer
-from keras.utils import pad_sequences, to_categorical
-from keras.models import Sequential
-from keras.layers import Dense, Flatten, Embedding, Conv1D, MaxPooling1D, Dropout, LSTM
-from project_utilities import my_datasets
-from keras.callbacks import EarlyStopping
-from project_utilities import evaluators
-import pandas as pd
-import tensorflow as tf
-from tensorflow import keras
-from keras import layers
-from keras.models import Sequential
-from keras.layers import Dense, Dropout
-from keras.optimizers import Adam
-from keras.regularizers import l2
-
-# Load Dataset
-dataset = 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") \
-    .with_summaries_and_descriptions_combined() \
-    .with_overall_priority_column() \
-    .with_pre_processed_descriptions() \
-    .build().corpus
-
-# Load Pre-configured TF-IDF
-TFIDF_model = TFIDF_Model()
-TFIDF_model.from_file(
-    f'{DefaultConfig.absolute_project_root_path()}/custom_models/preconfigured_models/tfidf_model.joblib',
-    SKLearnModelFileInteraction())
-
-# 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())
-
-X_train_tfidf = TFIDF_model.vectorize_descriptions(X_train_str)
-X_test_tfidf = TFIDF_model.vectorize_descriptions(X_test_str)
-
-# Encode class labels
-encoder = LabelEncoder()
-encoder.fit(['P5', 'P4', 'P3', 'P2', 'P1'])
-
-y_train = encoder.transform(y_train)
-y_val = encoder.transform(y_test)
-y_train = to_categorical(y_train)
-y_val = to_categorical(y_val)
-
-input_dim = X_train_tfidf.shape[1]
-
-model = Sequential()
-model.add(Dense(1024, activation='relu', input_shape=(input_dim,), kernel_regularizer=l2(0.01)))
-model.add(Dropout(0.60))
-model.add(Dense(512, activation='relu'))
-model.add(Dropout(0.50))
-model.add(Dense(256, activation='relu'))
-model.add(Dropout(0.40))
-model.add(Dense(5, activation='softmax'))
-
-# Compile model
-opt = Adam(lr=0.001)
-model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=opt)
-
-# Train model
-from keras.callbacks import EarlyStopping
-
-# Define early stopping callback
-early_stopping = EarlyStopping(monitor='val_loss', patience=5)
-
-model.fit(X_train_tfidf, y_train, epochs=50, batch_size=50, validation_data=(X_test_tfidf, y_val), callbacks=[early_stopping])
-model.save('CNN_model_larger_regularised.h5')
-print("finished")
-
-'''# Make predictions
-encoded_predictions = CNN_model.make_predictions(X_test)
-decoded_predictions = encoder.inverse_transform(encoded_predictions.argmax(axis=1))
-
-# Represent accuracies
-confusion_matrix = DetailedConfusionMatrix(decoded_predictions, y_test, ['P5', 'P4', 'P3', 'P2', 'P1'])
-confusion_matrix.plot_confusion_matrix(fullscreen_requested=True)
-
-apc = AccuracyPerClass(decoded_predictions, y_test, ['P5', 'P4', 'P3', 'P2', 'P1'])
-apc.plot_confusion_matrix()'''
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