95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
#!/usr/bin/python
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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 Sequential
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from keras import layers
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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 sacred.observers import MongoObserver
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from sacred.observers import FileStorageObserver
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from sacred import Experiment
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ex = Experiment(name='s444452_fake_job_classification_training', save_git_info=False)
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017',
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db_name='sacred'))
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ex.observers.append(FileStorageObserver('my_runs'))
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data_path = sys.argv[1]
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epochs = int(sys.argv[2])
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num_words = int(sys.argv[3])
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batch_size = int(sys.argv[4])
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pad_length = int(sys.argv[5])
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@ex.config
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def config():
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data_path = data_path
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epochs = epochs
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num_words = num_words
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batch_size = batch_size
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pad_length = pad_length
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@ex.capture
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def tokenize(x, x_train, 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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train_x = tokenizer.texts_to_sequences(x_train)
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vocabulary_length = len(tokenizer.word_index) + 1
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train_x = pad_sequences(train_x, padding='post', maxlen=pad_length)
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return train_x, vocabulary_length
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def save_model(model):
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# model_name = 'neural_net_' + datetime.datetime.today().strftime('%d-%b-%Y-%H:%M:%S')
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model_name = 'neural_net'
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model.save(os.path.join(os.getcwd(), 'model', model_name), save_format='h5', overwrite=True)
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ex.add_artifact(os.path.join(os.getcwd(), 'model', model_name))
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@ex.capture
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def train_model(model, x_train, y_train, epochs, batch_size):
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model.fit(x_train, y_train, epochs=epochs, verbose=False, batch_size=batch_size)
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@ex.capture
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def get_model(vocabulary_length, batch_size, pad_length):
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model = Sequential()
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model.add(layers.Embedding(input_dim=vocabulary_length,
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output_dim=batch_size,
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input_length=pad_length))
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model.add(layers.Flatten())
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model.add(layers.Dense(10, activation='relu'))
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model.add(layers.Dense(1, activation='sigmoid'))
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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return model
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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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@ex.main
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def main(data_path, num_words, epochs, batch_size, pad_length, _run):
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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, y_train = split_data(train_data)
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x_test, _ = split_data(test_data)
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x_train, vocab_size = tokenize(pd.concat([x_train, x_test]), x_train)
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model = get_model(vocab_size)
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train_model(model, x_train, y_train)
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save_model(model)
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ex.run()
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