260 lines
8.9 KiB
Python
260 lines
8.9 KiB
Python
import numpy as np
|
|
import torch
|
|
import pandas as pd
|
|
from torch import nn
|
|
from torch.autograd import Variable
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.metrics import accuracy_score
|
|
import torch.nn.functional as F
|
|
from datetime import datetime
|
|
|
|
|
|
class Model(nn.Module):
|
|
def __init__(self, input_dim):
|
|
super(Model, self).__init__()
|
|
self.layer1 = nn.Linear(input_dim, 50)
|
|
self.layer2 = nn.Linear(50, 40)
|
|
self.layer3 = nn.Linear(40, 3)
|
|
|
|
def forward(self, x):
|
|
x = F.relu(self.layer1(x))
|
|
x = F.relu(self.layer2(x))
|
|
x = F.softmax(self.layer3(x)) # To check with the loss function
|
|
return x
|
|
|
|
# funkcja usuwająca wiersze zawierające platformę "Stadia"
|
|
def delete_stadia(games):
|
|
index_list = []
|
|
for i in range(0, len(games["platform"])):
|
|
try:
|
|
if games["platform"][i] == " Stadia":
|
|
index_list.append(i)
|
|
except:
|
|
continue
|
|
games.drop(index_list, inplace=True)
|
|
return games.reset_index()
|
|
|
|
# funkcja usuwająca wiersze zawierające "tbd" w kolumnie "user_review"
|
|
def delete_tbd(games):
|
|
index_list = []
|
|
for i in range(0, len(games["platform"])):
|
|
try:
|
|
if games["user_review"][i] == "tbd":
|
|
index_list.append(i)
|
|
except:
|
|
continue
|
|
games.drop(index_list, inplace=True)
|
|
return games.reset_index()
|
|
|
|
def delete_PC(games):
|
|
index_list = []
|
|
for i in range(0, len(games["platform"])):
|
|
try:
|
|
if games["platform"][i] == " PC":
|
|
index_list.append(i)
|
|
except:
|
|
continue
|
|
games.drop(index_list, inplace=True)
|
|
return games.reset_index()
|
|
|
|
# funkcja zmieniająca kolumnę "user_review" ze stringa na numeric
|
|
def user_review_to_numeric(games):
|
|
games["user_review"] = pd.to_numeric(games["user_review"])
|
|
return games
|
|
|
|
# funkcja normalizująca wartości w kolumnie "meta_score" i "user_review"
|
|
def normalization(games):
|
|
games['meta_score'] = games['meta_score'] / 100.0
|
|
games['user_review'] = games['user_review'] / 10.0
|
|
return games
|
|
|
|
# PlayStation - 0
|
|
# PlayStation 2 - 1
|
|
# PlayStation 3 - 2
|
|
# PlayStation 4 - 3
|
|
# PlayStation 5 - 4
|
|
# PlayStation Vita - 5
|
|
# Xbox - 6
|
|
# Xbox 360 - 7
|
|
# Xbox Series X - 8
|
|
# Nintendo 64 - 9
|
|
# GameCube - 10
|
|
# DS - 11
|
|
# 3DS - 12
|
|
# Wii - 13
|
|
# Wii U - 14
|
|
# Switch - 15
|
|
# PC - 16
|
|
# Dreamcast - 17
|
|
# Game Boy Advance - 18
|
|
# PSP - 19
|
|
# Xbox One - 20
|
|
|
|
# def platform_to_number(games):
|
|
# for i in range(0, len(games["platform"])):
|
|
#
|
|
# if games["platform"][i] == " PlayStation":
|
|
# games["platform"][i] = 0
|
|
# elif games["platform"][i] == " PlayStation 2":
|
|
# games["platform"][i] = 1
|
|
# elif games["platform"][i] == " PlayStation 3":
|
|
# games["platform"][i] = 2
|
|
# elif games["platform"][i] == " PlayStation 4":
|
|
# games["platform"][i] = 3
|
|
# elif games["platform"][i] == " PlayStation 5":
|
|
# games["platform"][i] = 4
|
|
# elif games["platform"][i] == " PlayStation Vita":
|
|
# games["platform"][i] = 5
|
|
# elif games["platform"][i] == " Xbox":
|
|
# games["platform"][i] = 6
|
|
# elif games["platform"][i] == " Xbox 360":
|
|
# games["platform"][i] = 7
|
|
# elif games["platform"][i] == " Xbox Series X":
|
|
# games["platform"][i] = 8
|
|
# elif games["platform"][i] == " Nintendo 64":
|
|
# games["platform"][i] = 9
|
|
# elif games["platform"][i] == " GameCube":
|
|
# games["platform"][i] = 10
|
|
# elif games["platform"][i] == " DS":
|
|
# games["platform"][i] = 11
|
|
# elif games["platform"][i] == " 3DS":
|
|
# games["platform"][i] = 12
|
|
# elif games["platform"][i] == " Wii":
|
|
# games["platform"][i] = 13
|
|
# elif games["platform"][i] == " Wii U":
|
|
# games["platform"][i] = 14
|
|
# elif games["platform"][i] == " Switch":
|
|
# games["platform"][i] = 15
|
|
# elif games["platform"][i] == " PC":
|
|
# games["platform"][i] = 16
|
|
# elif games["platform"][i] == " Dreamcast":
|
|
# games["platform"][i] = 17
|
|
# elif games["platform"][i] == " Game Boy Advance":
|
|
# games["platform"][i] = 18
|
|
# elif games["platform"][i] == " PSP":
|
|
# games["platform"][i] = 19
|
|
# elif games["platform"][i] == " Xbox One":
|
|
# games["platform"][i] = 20
|
|
#
|
|
# return games
|
|
|
|
|
|
# old - 0
|
|
# mid - 1
|
|
# new - 2
|
|
|
|
def platform_to_number(games):
|
|
for i in range(0, len(games["platform"])):
|
|
|
|
if games["platform"][i] == " PlayStation":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " PlayStation 2":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " PlayStation 3":
|
|
games["platform"][i] = 1
|
|
elif games["platform"][i] == " PlayStation 4":
|
|
games["platform"][i] = 2
|
|
elif games["platform"][i] == " PlayStation 5":
|
|
games["platform"][i] = 2
|
|
elif games["platform"][i] == " PlayStation Vita":
|
|
games["platform"][i] = 1
|
|
elif games["platform"][i] == " Xbox":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " Xbox 360":
|
|
games["platform"][i] = 1
|
|
elif games["platform"][i] == " Xbox Series X":
|
|
games["platform"][i] = 2
|
|
elif games["platform"][i] == " Nintendo 64":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " GameCube":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " DS":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " 3DS":
|
|
games["platform"][i] = 1
|
|
elif games["platform"][i] == " Wii":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " Wii U":
|
|
games["platform"][i] = 1
|
|
elif games["platform"][i] == " Switch":
|
|
games["platform"][i] = 2
|
|
elif games["platform"][i] == " PC":
|
|
dt = datetime.strptime(games["release_date"][i], '%B %d, %Y')
|
|
if (dt.year == 1995 or dt.year == 1996 or dt.year == 1997 or dt.year == 1998
|
|
or dt.year == 1999 or dt.year == 2000 or dt.year == 2001 or dt.year == 2002
|
|
or dt.year == 2003 or dt.year == 2004 or dt.year == 2005):
|
|
games["platform"][i] = 0
|
|
if (dt.year == 2006 or dt.year == 2007 or dt.year == 2008 or dt.year == 2009
|
|
or dt.year == 2010 or dt.year == 2011 or dt.year == 2012 or dt.year == 2013
|
|
or dt.year == 2014 or dt.year == 2015 or dt.year == 2016):
|
|
games["platform"][i] = 1
|
|
if (dt.year == 2017 or dt.year == 2018 or dt.year == 2019
|
|
or dt.year == 2020 or dt.year == 2021):
|
|
games["platform"][i] = 2
|
|
|
|
# games["platform"][i] = 0
|
|
elif games["platform"][i] == " Dreamcast":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " Game Boy Advance":
|
|
games["platform"][i] = 0
|
|
elif games["platform"][i] == " PSP":
|
|
games["platform"][i] = 1
|
|
elif games["platform"][i] == " Xbox One":
|
|
games["platform"][i] = 2
|
|
|
|
return games
|
|
|
|
|
|
games = pd.read_csv('/dane/all_games.csv', sep=',')
|
|
games = platform_to_number(games)
|
|
games = delete_stadia(games)
|
|
games = delete_tbd(games)
|
|
games = user_review_to_numeric(games)
|
|
games = normalization(games)
|
|
games.drop(['level_0', 'index'], axis='columns', inplace=True)
|
|
|
|
labels_g = pd.DataFrame(games["platform"], dtype=np.int64)
|
|
labels_g = labels_g.to_numpy()
|
|
features_g = {'meta_score': games['meta_score'],
|
|
'user_review': games['user_review']}
|
|
features_g = pd.DataFrame(features_g, dtype=np.float64)
|
|
features_g = features_g.to_numpy()
|
|
|
|
features_train_g, features_test_g, labels_train_g, labels_test_g = train_test_split(features_g,
|
|
labels_g,
|
|
random_state=1,
|
|
shuffle=True)
|
|
|
|
# Training
|
|
model = Model(features_train_g.shape[1])
|
|
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
|
|
loss_fn = nn.CrossEntropyLoss()
|
|
epochs = 1000
|
|
|
|
def print_(loss):
|
|
print ("The loss calculated: ", loss)
|
|
|
|
|
|
# Not using dataloader
|
|
x_train, y_train = Variable(torch.from_numpy(features_train_g)).float(), Variable(torch.from_numpy(labels_train_g)).long()
|
|
for epoch in range(1, epochs + 1):
|
|
print("Epoch #", epoch)
|
|
y_pred = model(x_train)
|
|
|
|
loss = loss_fn(y_pred, y_train.squeeze(-1))
|
|
print_(loss.item())
|
|
|
|
# Zero gradients
|
|
optimizer.zero_grad()
|
|
loss.backward() # Gradients
|
|
optimizer.step() # Update
|
|
|
|
# Prediction
|
|
x_test = Variable(torch.from_numpy(features_test_g)).float()
|
|
pred = model(x_test)
|
|
|
|
pred = pred.detach().numpy()
|
|
|
|
print("The accuracy is", accuracy_score(labels_test_g, np.argmax(pred, axis=1)))
|
|
pred = pd.DataFrame(pred)
|
|
pred.to_csv('result.csv') |