ium_434695/sacred2.py

#! /usr/bin/python3
import sys
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 tensorflow.keras.models import Sequential
from sacred.observers import FileStorageObserver
from sacred.observers import MongoObserver

ex = Experiment("ium_s434695", interactive=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():
    train_size_param = 0.8
    test_size_param = 0.2

@ex.capture
def prepare_model(train_size_param, test_size_param, _run):
    _run.info["prepare_model_ts"] = str(datetime.now())

    url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
    r = requests.get(url, allow_redirects=True)

    open('vgsales.csv', 'wb').write(r.content)
    df = pd.read_csv('vgsales.csv')



    def regression_model():
        model = Sequential()
        model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
        model.add(Dense(64,activation = "relu"))
        model.add(Dense(1,activation = "relu"))
        
        model.compile(optimizer = "adam", loss = "mean_squared_error")
        return model

    df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
    df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
    df

    y = df.Nintendo

    df=((df-df.min())/(df.max()-df.min()))

    x = df.drop(['Nintendo'],axis = 1)

    x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)

    model = regression_model()
    model.fit(x_train, y_train, epochs = 600, verbose = 1)

    y_pred = model.predict(x_test)

    y_pred[:5]

    y_pred = np.around(y_pred, decimals=0)

    y_pred[:5]

    return(classification_report(y_test,y_pred))

@ex.main
def my_main(train_size_param, test_size_param):
    print(prepare_model())


r = ex.run()
ex.add_artifact("vgsales_model/saved_model/saved_model.pb")