ium_444452/Scripts/evaluate_neural_network.py

#!/usr/bin/python
import glob
import os
import sys
import pandas as pd
from keras.models import load_model
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.metrics import precision_score, recall_score, f1_score
import matplotlib.pyplot as plt
from sacred.observers import MongoObserver
from sacred.observers import FileStorageObserver
from sacred import Experiment
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_evaluation', 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'))

build_number = sys.argv[1]
data_path = sys.argv[2]
epochs = int(sys.argv[3])
num_words = int(sys.argv[4])
batch_size = int(sys.argv[5])
pad_length = int(sys.argv[6])


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


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


@ex.capture
def evaluate_and_save(model, x, y, abs_path, build_number, _run):
    loss, accuracy = model.evaluate(x, y, verbose=False)
    y_predicted = (model.predict(x) >= 0.5).astype(int)
    evaluation_file_path = os.path.join(abs_path, 'neural_network_evaluation.csv')
    with open(evaluation_file_path, 'a+') as f:
        precision = precision_score(y, y_predicted)
        recall = recall_score(y, y_predicted)
        f1 = f1_score(y, y_predicted)
        result = f'{build_number},{accuracy},{loss},{precision},{recall},{f1}'
        f.write(result + '\n')
        mlflow.log_metric("accuracy", accuracy)
        mlflow.log_metric("loss", loss)
        mlflow.log_metric("precision", precision)
        mlflow.log_metric("recall", recall)
        mlflow.log_metric("f1_score", f1)
    # ex.log_scalar("loss", loss)
    _run.log_scalar("training.loss", loss)
    # ex.log_scalar("accuracy", accuracy)
    _run.log_scalar("training.accuracy", accuracy)
    ex.add_artifact(evaluation_file_path)


def generate_and_save_comparison(abs_path):
    evaluation_file_path = os.path.join(abs_path, 'neural_network_evaluation.csv')
    df = pd.read_csv(evaluation_file_path, sep=',', header=None,
                     names=['build_number', 'Accuracy', 'Loss', 'Precision', 'Recall', 'F1'])
    df = df[df.build_number != 0]
    fig = plt.figure(figsize=(16 * .6, 9 * .6))
    ax = fig.add_subplot(111)
    ax.set_title('Evaluation')
    X = df['build_number']
    ax.set_xlabel('build_number')
    ax.set_xticks(df['build_number'])
    for metrics, color in zip(['Accuracy', 'Loss', 'Precision', 'Recall', 'F1'],
                              ['green', 'red', 'blue', 'brown', 'magenta']):
        ax.plot(X, df[metrics], color=color, lw=1, label=f'{metrics}')
    ax.legend()
    plt.savefig(os.path.join(abs_path, 'evaluation.png'), format='png')
    ex.add_artifact(os.path.join(abs_path, 'evaluation.png'))
    return ax


def load_trained_model():
    # glob_pattern = os.path.join(os.getcwd(), 'model', 'neural_net_*')
    glob_pattern = os.path.join(os.getcwd(), 'model', 'neural_net')
    models = glob.glob(glob_pattern)
    models = [os.path.split(x)[1] for x in models]
    # model_name = sorted(models, key=lambda x: datetime.datetime.strptime(x[11:], '%d-%b-%Y-%H:%M:%S'),
    #                     reverse=True)[0]
    return load_model(os.path.join(os.getcwd(), 'model', models[0]))


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(build_number, 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("build_number", build_number)
        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, _ = split_data(train_data)
        x_test, y_test = split_data(test_data)
        x_test, _ = tokenize(pd.concat([x_train, x_test]), x_test)
        model = load_trained_model()
        evaluate_and_save(model, x_test, y_test, abs_data_path)
        generate_and_save_comparison(abs_data_path)


ex.run()