ium_444452/Scripts/train_neural_network.py

#!/usr/bin/python
import os
import sys
import warnings

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras import layers
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sacred.observers import MongoObserver
from sacred.observers import FileStorageObserver
from sacred import Experiment
from mlflow.models.signature import infer_signature
import mlflow
import logging

logging.basicConfig(level=logging.WARN)
logger = logging.getLogger(__name__)

mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment("s444452")

ex = Experiment(name='s444452_fake_job_classification_training', save_git_info=False)
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017',
                                  db_name='sacred'))
ex.observers.append(FileStorageObserver('my_runs'))

data_path = sys.argv[1]
epochs = int(sys.argv[2])
num_words = int(sys.argv[3])
batch_size = int(sys.argv[4])
pad_length = int(sys.argv[5])


@ex.config
def config():
    data_path = data_path
    epochs = epochs
    num_words = num_words
    batch_size = batch_size
    pad_length = pad_length


@ex.capture
def tokenize(x, x_train, pad_length, num_words):
    tokenizer = Tokenizer(num_words=num_words)
    tokenizer.fit_on_texts(x)
    train_x = tokenizer.texts_to_sequences(x_train)
    vocabulary_length = len(tokenizer.word_index) + 1
    train_x = pad_sequences(train_x, padding='post', maxlen=pad_length)
    return train_x, vocabulary_length


def save_model(model):
    # model_name = 'neural_net_' + datetime.datetime.today().strftime('%d-%b-%Y-%H:%M:%S')
    model_name = 'neural_net'
    model.save(os.path.join(os.getcwd(), 'model', model_name), save_format='h5', overwrite=True)
    ex.add_artifact(os.path.join(os.getcwd(), 'model', model_name))


@ex.capture
def train_model(model, x_train, y_train, epochs, batch_size):
    model.fit(x_train, y_train, epochs=epochs, verbose=False, batch_size=batch_size)


@ex.capture
def get_model(vocabulary_length, batch_size, pad_length):
    model = Sequential()
    model.add(layers.Embedding(input_dim=vocabulary_length,
                               output_dim=batch_size,
                               input_length=pad_length))
    model.add(layers.Flatten())
    model.add(layers.Dense(10, activation='relu'))
    model.add(layers.Dense(1, activation='sigmoid'))
    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])
    return model


def split_data(data):
    x = data['tokens']
    y = data['fraudulent']
    return x, y


def load_data(data_path, filename) -> pd.DataFrame:
    return pd.read_csv(os.path.join(data_path, filename))


@ex.main
def main(data_path, num_words, epochs, batch_size, pad_length, _run):
    with mlflow.start_run() as mlflow_run:
        print("MLflow run experiment_id: {0}".format(mlflow_run.info.experiment_id))
        print("MLflow run artifact_uri: {0}".format(mlflow_run.info.artifact_uri))
        mlflow.log_param("data_path", data_path)
        mlflow.log_param("num_words", num_words)
        mlflow.log_param("epochs", epochs)
        mlflow.log_param("batch_size", batch_size)
        mlflow.log_param("pad_length", pad_length)

        abs_data_path = os.path.abspath(data_path)
        train_data = load_data(abs_data_path, 'train_data.csv')
        test_data = load_data(abs_data_path, 'test_data.csv')
        x_train, y_train = split_data(train_data)
        x_test, _ = split_data(test_data)
        x_train, vocab_size = tokenize(pd.concat([x_train, x_test]), x_train)
        model = get_model(vocab_size)
        train_model(model, x_train, y_train)
        save_model(model)

        signature = infer_signature(x_train, y_train)
        input_example = np.array(x_test[:20])
        mlflow.keras.log_model(model, "model", signature=signature, input_example=input_example)


warnings.filterwarnings("ignore")
ex.run()