147 lines
5.0 KiB
Python
147 lines
5.0 KiB
Python
import tensorflow as tf
|
|
import os
|
|
import pandas as pd
|
|
import numpy as np
|
|
import csv
|
|
from sklearn.model_selection import train_test_split
|
|
import sys
|
|
from sacred.observers import MongoObserver
|
|
from sacred.observers import FileStorageObserver
|
|
from sacred import Experiment
|
|
|
|
ex = Experiment("sacred_scopes", interactive=True, save_git_info=False)
|
|
#ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2022@127.0.0.1:27017',db_name='sacred'))
|
|
ex.observers.append(FileStorageObserver('training'))
|
|
epochs = int(sys.argv[1])
|
|
|
|
@ex.config
|
|
def my_config():
|
|
epoch = epochs
|
|
layerDenseRelu = 256
|
|
layerDropout = 0.01
|
|
layerDenseSoftMax = 1000.0
|
|
|
|
#ex.add_config("config.json")
|
|
|
|
@ex.capture
|
|
def prepare_data():
|
|
steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
|
|
steam.isnull().values.any()
|
|
steam['userId'] = steam.userId.astype(str)
|
|
purchaseCount = steam[steam["behavior"] != "play"]["game"].value_counts()
|
|
playCount = steam[steam["behavior"] != "purchase"]["game"].value_counts()
|
|
|
|
playerPurchaseCount = steam[steam["behavior"] != "play"]["userId"].value_counts()
|
|
playerPlayCount = steam[steam["behavior"] != "purchase"]["userId"].value_counts()
|
|
|
|
steam = steam[steam['behavior'] != 'purchase']
|
|
steam = steam.groupby("game").filter(lambda x: len(x)>10)
|
|
size=int(len(steam)/10)
|
|
|
|
meanGame = steam[steam["behavior"] != "purchase"].groupby("game").mean()
|
|
meanGame = meanGame.to_dict()
|
|
meanGame = meanGame['hoursPlayed']
|
|
|
|
purchaseCount = purchaseCount.to_dict()
|
|
playCount = playCount.to_dict()
|
|
playerPurchaseCount = playerPurchaseCount.to_dict()
|
|
playerPlayCount = playerPlayCount.to_dict()
|
|
|
|
steam['meanTime'] = 0;
|
|
steam['purchaseCount'] = 0;
|
|
steam['playCount'] = 0;
|
|
steam['playerPurchaseCount'] =0;
|
|
steam['playerPlayCount'] =0;
|
|
steam['playPercent'] =0;
|
|
|
|
for i in steam.index:
|
|
steam.at[i,'meanTime'] = meanGame[steam.at[i,'game']]
|
|
steam.at[i,'purchaseCount'] = purchaseCount[steam.at[i,'game']]
|
|
steam.at[i,'playCount'] = playCount[steam.at[i,'game']]
|
|
steam.at[i,'playerPurchaseCount'] = playerPurchaseCount[steam.at[i,'userId']]
|
|
steam.at[i,'playerPlayCount'] = playerPlayCount[steam.at[i,'userId']]
|
|
steam.at[i,'playPercent'] = playerPlayCount[steam.at[i,'userId']]/playerPurchaseCount[steam.at[i,'userId']]
|
|
|
|
steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
|
|
steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])
|
|
|
|
games = {}
|
|
for i in steam['game']:
|
|
games[i] = 0
|
|
|
|
j=0
|
|
for key,game in games.items():
|
|
games[key]=j
|
|
j=j+1
|
|
|
|
for i in steam['game']:
|
|
i = games[i]
|
|
|
|
invGames = {v: k for k, v in games.items()}
|
|
|
|
x_train = steam_train[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
|
|
y_train = steam_train['game']
|
|
|
|
x_test = steam_test[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
|
|
y_test = steam_test['game']
|
|
|
|
|
|
x_train = np.array(x_train)
|
|
y_train = np.array(y_train)
|
|
x_test = np.array(x_test)
|
|
y_test = np.array(y_test)
|
|
|
|
with open('xtest.csv','w',encoding='UTF-8',newline='') as xtest:
|
|
writer = csv.writer(xtest)
|
|
for i in x_test:
|
|
writer.writerow(i)
|
|
|
|
for i,j in enumerate(y_train):
|
|
y_train[i] = games[j]
|
|
|
|
for i,j in enumerate(y_test):
|
|
y_test[i] = games[j]
|
|
y_train = np.array(y_train).astype(np.float32)
|
|
y_test = np.array(y_test).astype(np.float32)
|
|
np.savetxt("ytest.csv",y_test,delimiter=",",fmt='%d')
|
|
return x_train, y_train, x_test, y_test, invGames
|
|
|
|
@ex.main
|
|
def my_main(epoch,layerDenseRelu,layerDropout,layerDenseSoftMax,_run):
|
|
x_train, y_train, x_test, y_test, invGames = prepare_data()
|
|
model = tf.keras.models.Sequential([
|
|
tf.keras.layers.Flatten(input_shape=(5,1)),
|
|
tf.keras.layers.Dense(layerDenseRelu, activation='relu'),
|
|
tf.keras.layers.Dropout(layerDropout),
|
|
tf.keras.layers.Dense(layerDenseSoftMax, activation='softmax')
|
|
])
|
|
|
|
model.compile(optimizer='adam',
|
|
loss='sparse_categorical_crossentropy',
|
|
metrics=['accuracy'])
|
|
|
|
|
|
model.fit(x_train, y_train, epochs=epoch)
|
|
evaluation = model.evaluate(x_test, y_test)
|
|
_run.log_scalar("training.loss", evaluation[0])
|
|
_run.log_scalar("training.accuracy", evaluation[1])
|
|
|
|
prediction = model.predict(x_test)
|
|
classes_x=np.argmax(prediction,axis=1)
|
|
|
|
rows = []
|
|
|
|
for j,i in enumerate(classes_x):
|
|
row = [invGames[i],invGames[y_test[j]]]
|
|
rows.append(row)
|
|
with open('results.csv','w',encoding='UTF-8',newline='') as f:
|
|
writer = csv.writer(f)
|
|
writer.writerow(["predicted", "expected"])
|
|
for row in rows:
|
|
writer.writerow(row)
|
|
|
|
model.save('./model')
|
|
ex.add_artifact('./model/saved_model.pb')
|
|
|
|
|
|
ex.run() |