def classification_ann(instruction, callback=False, dataset=None, text=[], ca_threshold=None, preprocess=True, callback_mode='min', drop=None, random_state=49, test_size=0.2, epochs=50, generate_plots=True, maximizer="val_accuracy", save_model=False, save_path=os.getcwd(), add_layer={}): ''' Body of the classification function used that is called in the neural network query if the data is categorical. :param many parameters: used to preprocess, tune, plot generation, and parameterizing the neural network trained. :return dictionary that holds all the information for the finished model. ''' if dataset is None: dataReader = DataReader(get_file()) else: dataReader = DataReader(dataset) logger("Reading in dataset") data = dataReader.data_generator() if drop is not None: data.drop(drop, axis=1, inplace=True) data, y, remove, full_pipeline = initial_preprocessor( data, instruction, preprocess, ca_threshold, text, test_size=test_size, random_state=random_state) logger("->", "Target column found: {}".format(remove)) # Needed to make a custom label encoder due to train test split changes # Can still be inverse transformed, just a bit of extra work y = pd.concat([y['train'], y['test']], axis=0) num_classes = len(np.unique(y)) if num_classes < 2: raise Exception("Number of classes must be greater than or equal to 2") X_train = data['train'] X_test = data['test'] if num_classes >= 2: # ANN needs target one hot encoded for classification one_hotencoder = OneHotEncoder() y = pd.DataFrame(one_hotencoder.fit_transform( np.reshape(y.values, (-1, 1))).toarray(), columns=one_hotencoder.get_feature_names()) y_train = y.iloc[:len(X_train)] y_test = y.iloc[len(X_train):] models = [] losses = [] accuracies = [] model_data = [] logger("Establishing callback function") # early stopping callback es = EarlyStopping(monitor=maximizer, mode='max', verbose=0, patience=5) callback_value = None if callback is not False: callback_value = [es] i = 0 model = get_keras_model_class(data, i, num_classes, add_layer) logger("Training initial model") history = model.fit(X_train, y_train, callbacks=callback_value, epochs=epochs, validation_data=(X_test, y_test), verbose=0) model_data.append(model) models.append(history) col_name = [[ "Initial number of layers ", "| Training Accuracy ", "| Test Accuracy " ]] col_width = max(len(word) for row in col_name for word in row) + 2 for row in col_name: print((" " * 2 * counter) + "| " + ("".join(word.ljust(col_width) for word in row)) + " |") values = [] values.append(str(len(model.layers))) values.append("| " + str(history.history['accuracy'][ len(history.history['val_accuracy']) - 1])) values.append("| " + str(history.history['val_accuracy'][ len(history.history['val_accuracy']) - 1])) datax = [] datax.append(values) for row in datax: print((" " * 2 * counter) + "| " + ("".join(word.ljust(col_width) for word in row)) + " |") # print((" " * 2 * counter)+ tabulate(datax, headers=col_name, tablefmt='orgtbl')) losses.append(history.history[maximizer][len(history.history[maximizer]) - 1]) accuracies.append( history.history['val_accuracy'][len(history.history['val_accuracy']) - 1]) # keeps running model and fit functions until the validation loss stops # decreasing logger("Testing number of layers") col_name = [[ "Current number of layers", "| Training Accuracy", "| Test Accuracy" ]] col_width = max(len(word) for row in col_name for word in row) + 2 for row in col_name: print((" " * 2 * counter) + "| " + ("".join(word.ljust(col_width) for word in row)) + " |") datax = [] # while all(x < y for x, y in zip(accuracies, accuracies[1:])): while (len(accuracies) <= 2 or accuracies[len(accuracies) - 1] > accuracies[len(accuracies) - 2]): model = get_keras_model_class(data, i, num_classes, add_layer) history = model.fit(X_train, y_train, callbacks=callback_value, epochs=epochs, validation_data=(X_test, y_test), verbose=0) values = [] datax = [] values.append(str(len(model.layers))) values.append("| " + str(history.history['accuracy'][ len(history.history['accuracy']) - 1])) values.append("| " + str(history.history['val_accuracy'][ len(history.history['val_accuracy']) - 1])) datax.append(values) for row in datax: print((" " * 2 * counter) + "| " + ("".join(word.ljust(col_width) for word in row)) + " |") del values, datax losses.append( history.history[maximizer][len(history.history[maximizer]) - 1]) accuracies.append(history.history['val_accuracy'][ len(history.history['val_accuracy']) - 1]) models.append(history) model_data.append(model) i += 1 # print((" " * 2 * counter)+ tabulate(datax, headers=col_name, tablefmt='orgtbl')) # del values, datax final_model = model_data[accuracies.index(max(accuracies))] final_hist = models[accuracies.index(max(accuracies))] print("") logger('->', "Best number of layers found: " + str(len(final_model.layers))) logger( '->', "Training Accuracy: " + str(final_hist.history['accuracy'][ len(final_hist.history['val_accuracy']) - 1])) logger( '->', "Test Accuracy: " + str(final_hist.history['val_accuracy'][ len(final_hist.history['val_accuracy']) - 1])) # genreates appropriate classification plots by feeding all information plots = {} if generate_plots: plots = generate_classification_plots(models[len(models) - 1]) if save_model: save(final_model, save_model, save_path) print("") logger("Stored model under 'classification_ANN' key") clearLog() K.clear_session() # stores the values and plots into the object dictionary return { 'id': generate_id(), "model": final_model, 'num_classes': num_classes, "plots": plots, "target": remove, "preprocessor": full_pipeline, "interpreter": one_hotencoder, 'test_data': { 'X': X_test, 'y': y_test }, 'losses': { 'training_loss': final_hist.history['loss'], 'val_loss': final_hist.history['val_loss'] }, 'accuracy': { 'training_accuracy': final_hist.history['accuracy'], 'validation_accuracy': final_hist.history['val_accuracy'] } }
def classification_ann(instruction, dataset=None, text=None, ca_threshold=None, preprocess=True, callback_mode='min', drop=None, random_state=49, test_size=0.2, epochs=50, generate_plots=True, maximizer="val_loss", save_model=True, save_path=os.getcwd()): global currLog logger("Reading in dataset...") dataReader = DataReader(dataset) data = dataReader.data_generator() if drop is not None: data.drop(drop, axis=1, inplace=True) data, y, remove, full_pipeline = initial_preprocesser( data, instruction, preprocess, ca_threshold, text) logger("->", "Target Column Found: {}".format(remove)) # Needed to make a custom label encoder due to train test split changes # Can still be inverse transformed, just a bit of extra work y = pd.concat([y['train'], y['test']], axis=0) num_classes = len(np.unique(y)) X_train = data['train'] X_test = data['test'] # ANN needs target one hot encoded for classification one_hot_encoder = OneHotEncoder() y = pd.DataFrame(one_hot_encoder.fit_transform( np.reshape(y.values, (-1, 1))).toarray(), columns=one_hot_encoder.get_feature_names()) y_train = y.iloc[:len(X_train)] y_test = y.iloc[len(X_train):] models = [] losses = [] accuracies = [] model_data = [] logger("Establishing callback function...") # early stopping callback es = EarlyStopping(monitor=maximizer, mode='min', verbose=0, patience=5) i = 0 model = get_keras_model_class(data, i, num_classes) logger("Training initial model...") history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), callbacks=[es], verbose=0) model_data.append(model) models.append(history) col_name = [[ "Initial number of layers ", "| Training Loss ", "| Test Loss " ]] col_width = max(len(word) for row in col_name for word in row) + 2 for row in col_name: print((" " * 2 * counter) + "| " + ("".join(word.ljust(col_width) for word in row)) + " |") values = [] values.append(str(len(model.layers))) values.append( "| " + str(history.history['loss'][len(history.history['val_loss']) - 1])) values.append( "| " + str(history.history['val_loss'][len(history.history['val_loss']) - 1])) datax = [] datax.append(values) for row in datax: print((" " * 2 * counter) + "| " + ("".join(word.ljust(col_width) for word in row)) + " |") #print((" " * 2 * counter)+ tabulate(datax, headers=col_name, tablefmt='orgtbl')) losses.append(history.history[maximizer][len(history.history[maximizer]) - 1]) # keeps running model and fit functions until the validation loss stops # decreasing logger("Testing number of layers...") col_name = [["Current number of layers", "| Training Loss", "| Test Loss"]] col_width = max(len(word) for row in col_name for word in row) + 2 for row in col_name: print((" " * 2 * counter) + "| " + ("".join(word.ljust(col_width) for word in row)) + " |") datax = [] while (all(x > y for x, y in zip(losses, losses[1:]))): model = get_keras_model_class(data, i, num_classes) history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), callbacks=[es], verbose=0) values = [] datax = [] values.append(str(len(model.layers))) values.append( "| " + str(history.history['loss'][len(history.history['val_loss']) - 1])) values.append("| " + str(history.history['val_loss'][ len(history.history['val_loss']) - 1])) datax.append(values) for row in datax: print((" " * 2 * counter) + "| " + ("".join(word.ljust(col_width) for word in row)) + " |") losses.append( history.history[maximizer][len(history.history[maximizer]) - 1]) accuracies.append(history.history['val_accuracy'][ len(history.history['val_accuracy']) - 1]) i += 1 #print((" " * 2 * counter)+ tabulate(datax, headers=col_name, tablefmt='orgtbl')) #del values, datax final_model = model_data[losses.index(min(losses))] final_hist = models[losses.index(min(losses))] print("") logger('->', "Best number of layers found: " + str(len(final_model.layers))) logger( '->', "Training Accuracy: " + str(final_hist.history['accuracy'][ len(final_hist.history['val_accuracy']) - 1])) logger( '->', "Test Accuracy: " + str(final_hist.history['val_accuracy'][ len(final_hist.history['val_accuracy']) - 1])) # genreates appropriate classification plots by feeding all information plots = generate_classification_plots(models[len(models) - 1], data, y, model, X_test, y_test) if save_model: save(final_model, save_model) print("") logger("Stored model under 'classification_ANN' key") # stores the values and plots into the object dictionary return { 'id': generate_id(), "model": final_model, 'num_classes': num_classes, "plots": plots, "target": remove, "preprocesser": full_pipeline, "interpreter": one_hot_encoder, 'losses': { 'training_loss': final_hist.history['loss'], 'val_loss': final_hist.history['val_loss'] }, 'accuracy': { 'training_accuracy': final_hist.history['accuracy'], 'validation_accuracy': final_hist.history['val_accuracy'] } }