AdamOsiowy123
2f59e13059
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107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
#!/usr/bin/python
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import glob
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import os
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import sys
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import pandas as pd
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from keras.models import load_model
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from keras.preprocessing.text import Tokenizer
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from keras.preprocessing.sequence import pad_sequences
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from sklearn.metrics import precision_score, recall_score, f1_score
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import logging
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import matplotlib.pyplot as plt
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logging.getLogger("tensorflow").setLevel(logging.ERROR)
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build_number = ''
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data_path = ''
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num_words = 0
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epochs = 0
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batch_size = 0
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pad_length = 0
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def tokenize(x, x_test):
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global pad_length, num_words
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tokenizer = Tokenizer(num_words=num_words)
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tokenizer.fit_on_texts(x)
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test_x = tokenizer.texts_to_sequences(x_test)
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vocabulary_length = len(tokenizer.word_index) + 1
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test_x = pad_sequences(test_x, padding='post', maxlen=pad_length)
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return test_x, vocabulary_length
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def evaluate_and_save(model, x, y, abs_path):
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global build_number
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loss, accuracy = model.evaluate(x, y, verbose=False)
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y_predicted = (model.predict(x) >= 0.5).astype(int)
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evaluation_file_path = os.path.join(abs_path, 'neural_network_evaluation.csv')
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with open(evaluation_file_path, 'a+') as f:
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result = f'{build_number},{accuracy},{loss},{precision_score(y, y_predicted)},{recall_score(y, y_predicted)},{f1_score(y, y_predicted)}'
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f.write(result + '\n')
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def generate_and_save_comparison(abs_path):
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evaluation_file_path = os.path.join(abs_path, 'neural_network_evaluation.csv')
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df = pd.read_csv(evaluation_file_path, sep=',', header=None,
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names=['build_number', 'Accuracy', 'Loss', 'Precision', 'Recall', 'F1'])
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fig = plt.figure(figsize=(16 * .6, 9 * .6))
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ax = fig.add_subplot(111)
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ax.set_title('Evaluation')
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X = df['build_number']
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ax.set_xlabel('build_number')
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ax.set_xticks(df['build_number'])
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for metrics, color in zip(['Accuracy', 'Loss', 'Precision', 'Recall', 'F1'],
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['green', 'red', 'blue', 'brown', 'magenta']):
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ax.plot(X, df[metrics], color=color, lw=1, label=f'{metrics}')
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ax.legend()
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plt.savefig(os.path.join(abs_path, 'evaluation.png'), format='png')
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return ax
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def load_trained_model():
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# glob_pattern = os.path.join(os.getcwd(), 'model', 'neural_net_*')
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glob_pattern = os.path.join(os.getcwd(), 'model', 'neural_net')
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models = glob.glob(glob_pattern)
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models = [os.path.split(x)[1] for x in models]
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# model_name = sorted(models, key=lambda x: datetime.datetime.strptime(x[11:], '%d-%b-%Y-%H:%M:%S'),
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# reverse=True)[0]
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return load_model(os.path.join(os.getcwd(), 'model', models[0]))
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def split_data(data):
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x = data['tokens']
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y = data['fraudulent']
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return x, y
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def load_data(data_path, filename) -> pd.DataFrame:
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return pd.read_csv(os.path.join(data_path, filename))
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def read_params():
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global build_number, data_path, num_words, epochs, batch_size, pad_length
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build_number = sys.argv[1]
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data_path, num_words, epochs, batch_size, pad_length = sys.argv[2].split(',')
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num_words = int(num_words)
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epochs = int(epochs)
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batch_size = int(batch_size)
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pad_length = int(pad_length)
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def main():
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read_params()
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global data_path
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abs_data_path = os.path.abspath(data_path)
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train_data = load_data(abs_data_path, 'train_data.csv')
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test_data = load_data(abs_data_path, 'test_data.csv')
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x_train, _ = split_data(train_data)
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x_test, y_test = split_data(test_data)
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x_test, _ = tokenize(pd.concat([x_train, x_test]), x_test)
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model = load_trained_model()
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evaluate_and_save(model, x_test, y_test, abs_data_path)
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generate_and_save_comparison(abs_data_path)
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if __name__ == '__main__':
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main()
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