def run(): # Load image paths in the dataset training_image_path_list, testing_image_path_list = load_image_path_list() # Load features training_image_feature_dict = load_features(training_image_path_list) testing_image_feature_dict = load_features(testing_image_path_list) # Load training labels training_names, training_labels, testing_names = load_csv_files() # Convert data to suitable form for training/testing phase X_train = get_attributes(training_image_feature_dict, training_names) Y_train = training_labels X_test = get_attributes(testing_image_feature_dict, testing_names) # Generate prediction list prediction_list = [] for trial_index in range(11): print("Working on trial NO.{:d}".format(trial_index + 1)) current_prediction = keras_NN.generate_prediction(X_train, Y_train, X_test) prediction_list.append(current_prediction) # Generate ensemble prediction ensemble_prediction, _ = stats.mode(prediction_list) ensemble_prediction = np.squeeze(ensemble_prediction) # Create submission file submission_file_name = "Aurora_" + str(int(time.time())) + ".csv" file_content = pd.DataFrame({"Id": testing_names, "Prediction": ensemble_prediction}) file_content.to_csv(submission_file_name, index=False, header=True) print("All done!")
def run(): # Load image paths in the dataset training_image_path_list, testing_image_path_list = load_image_path_list() # Load features training_image_feature_dict = load_features(training_image_path_list) testing_image_feature_dict = load_features(testing_image_path_list) # Load training labels training_names, training_labels, testing_names = load_csv_files() # Convert data to suitable form for training/testing phase X_train = get_attributes(training_image_feature_dict, training_names) Y_train = training_labels X_test = get_attributes(testing_image_feature_dict, testing_names) # Generate prediction list prediction_list = [] for trial_index in range(11): print("Working on trial NO.{:d}".format(trial_index + 1)) current_prediction = keras_NN.generate_prediction( X_train, Y_train, X_test) prediction_list.append(current_prediction) # Generate ensemble prediction ensemble_prediction, _ = stats.mode(prediction_list) ensemble_prediction = np.squeeze(ensemble_prediction) # Create submission file submission_file_name = "Aurora_" + str(int(time.time())) + ".csv" file_content = pd.DataFrame({ "Id": testing_names, "Prediction": ensemble_prediction }) file_content.to_csv(submission_file_name, index=False, header=True) print("All done!")
import time # Read data set from file X_train = pd.read_csv("./input/X_train.csv", skiprows=0).as_matrix()[:, 1:] multi_Y_train = pd.read_csv("./input/y_train.csv", skiprows=0).as_matrix()[:, 1:] X_test = pd.read_csv("./input/X_test.csv", skiprows=0).as_matrix()[:, 1:] multi_Y_test = [] # Generate prediction for each angle for current_column in range(multi_Y_train.shape[1]): Y_train = multi_Y_train[:, current_column] prediction = keras_NN.generate_prediction(X_train, Y_train, X_test, True, layer_size=512, layer_num=5, nb_epoch=100) multi_Y_test.append(np.reshape(prediction, (-1, 1))) # Create submission file multi_Y_test = np.hstack(multi_Y_test) ID = np.arange(X_train.shape[0] + 1, X_train.shape[0] + multi_Y_test.shape[0] + 1) submission_file_name = "Aurora_" + str(int(time.time())) + ".csv" submission_file_DataFrame = pd.DataFrame({ "Id": ID, "Angle1": multi_Y_test[:, 0], "Angle2": multi_Y_test[:, 1] })
import keras_NN import numpy as np import pandas as pd import time # Read data set from file X_train = pd.read_csv("./input/X_train.csv", skiprows=0).as_matrix()[:, 1:] multi_Y_train = pd.read_csv("./input/y_train.csv", skiprows=0).as_matrix()[:, 1:] X_test = pd.read_csv("./input/X_test.csv", skiprows=0).as_matrix()[:, 1:] multi_Y_test = [] # Generate prediction for each angle for current_column in range(multi_Y_train.shape[1]): Y_train = multi_Y_train[:, current_column] prediction = keras_NN.generate_prediction(X_train, Y_train, X_test, True, layer_size=512, layer_num=5, nb_epoch=100) multi_Y_test.append(np.reshape(prediction, (-1, 1))) # Create submission file multi_Y_test = np.hstack(multi_Y_test) ID = np.arange(X_train.shape[0] + 1, X_train.shape[0] + multi_Y_test.shape[0] + 1) submission_file_name = "Aurora_" + str(int(time.time())) + ".csv" submission_file_DataFrame = pd.DataFrame({"Id": ID, "Angle1": multi_Y_test[:, 0], "Angle2": multi_Y_test[:, 1]}) submission_file_DataFrame.to_csv(submission_file_name, index=False, header=True) print("All done!")