ium_434695/sacred1.py

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#! /usr/bin/python3
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import sys
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import pandas as pd
import numpy as np
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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
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from sacred.observers import FileStorageObserver
from sacred import Experiment
from datetime import datetime
import os
ex = Experiment("ium_s434695", interactive=False)
ex.observers.append(FileStorageObserver('ium_s434695/my_runs'))
@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")