ium_444452/Scripts/train_neural_network.py
AdamOsiowy123 ac78319450
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mlflow fix3
2022-05-15 23:04:02 +02:00

121 lines
3.9 KiB
Python

#!/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()