ium_434742/sacred-mongoobserver.py

import sys
from keras.backend import mean
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Input, Dense, Activation,Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping
from keras.models import Sequential
from sacred import Experiment
from sacred.observers import MongoObserver

ex = Experiment("434742-mongo", interactive=False, save_git_info=False)
ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))

@ex.config
def my_config():
    epochs = 10
    batch_size = 16

@ex.capture
def prepare_model(epochs, batch_size):
    # odczytanie danych z plików
    avocado_train = pd.read_csv('avocado_train.csv')
    avocado_test = pd.read_csv('avocado_test.csv')
    avocado_validate = pd.read_csv('avocado_validate.csv')


    # podzial na X i y
    X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
    y_train = avocado_train[['type']]
    X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
    y_test = avocado_test[['type']]

    print(X_train.shape[1])
    # keras model
    model = Sequential()
    model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
    model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))

    early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)

    # kompilacja
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    # trenowanie modelu
    model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))

    # predykcja
    prediction = model.predict(X_test)

    # ewaluacja
    rmse = mean_squared_error(y_test, prediction)

    # zapisanie modelu
    model.save('avocado_model.h5')

    return rmse



@ex.automain
def my_main(epochs, batch_size):
    print(prepare_model())

ex.run()
ex.add_artifact('avocado_model.h5')