212 lines
7.5 KiB
Python
212 lines
7.5 KiB
Python
import numpy as np
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import sys
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import os
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import torch
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import pandas as pd
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from torch import nn
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from torch.autograd import Variable
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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import torch.nn.functional as F
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from datetime import datetime
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from sklearn.metrics import precision_recall_fscore_support
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from sklearn.metrics import accuracy_score
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import matplotlib.pyplot as plt
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class Model(nn.Module):
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def __init__(self, input_dim):
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super(Model, self).__init__()
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self.layer1 = nn.Linear(input_dim, 50)
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self.layer2 = nn.Linear(50, 40)
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self.layer3 = nn.Linear(40, 3)
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def forward(self, x):
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x = F.relu(self.layer1(x))
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x = F.relu(self.layer2(x))
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x = F.softmax(self.layer3(x)) # To check with the loss function
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return x
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# funkcja usuwająca wiersze zawierające platformę "Stadia"
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def delete_stadia(games):
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index_list = []
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for i in range(0, len(games["platform"])):
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try:
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if games["platform"][i] == " Stadia":
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index_list.append(i)
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except:
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continue
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games.drop(index_list, inplace=True)
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return games.reset_index()
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# funkcja usuwająca wiersze zawierające "tbd" w kolumnie "user_review"
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def delete_tbd(games):
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index_list = []
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for i in range(0, len(games["platform"])):
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try:
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if games["user_review"][i] == 'tbd':
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index_list.append(i)
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except:
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continue
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games.drop(index_list, inplace=True)
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return games.reset_index()
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def delete_PC(games):
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index_list = []
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for i in range(0, len(games["platform"])):
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try:
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if games["platform"][i] == " PC":
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index_list.append(i)
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except:
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continue
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games.drop(index_list, inplace=True)
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return games.reset_index()
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# funkcja zmieniająca kolumnę "user_review" ze stringa na numeric
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def user_review_to_numeric(games):
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games["user_review"] = pd.to_numeric(games["user_review"])
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return games
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# funkcja normalizująca wartości w kolumnie "meta_score" i "user_review"
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def normalization(games):
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games['meta_score'] = games['meta_score'] / 100.0
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games['user_review'] = games['user_review'] / 10.0
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return games
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# old - 0
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# mid - 1
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# new - 2
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def platform_to_number(games):
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for i in range(0, len(games["platform"])):
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if games["platform"][i] == " PlayStation":
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games["platform"][i] = 0
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elif games["platform"][i] == " PlayStation 2":
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games["platform"][i] = 0
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elif games["platform"][i] == " PlayStation 3":
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games["platform"][i] = 1
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elif games["platform"][i] == " PlayStation 4":
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games["platform"][i] = 2
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elif games["platform"][i] == " PlayStation 5":
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games["platform"][i] = 2
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elif games["platform"][i] == " PlayStation Vita":
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games["platform"][i] = 1
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elif games["platform"][i] == " Xbox":
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games["platform"][i] = 0
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elif games["platform"][i] == " Xbox 360":
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games["platform"][i] = 1
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elif games["platform"][i] == " Xbox Series X":
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games["platform"][i] = 2
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elif games["platform"][i] == " Nintendo 64":
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games["platform"][i] = 0
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elif games["platform"][i] == " GameCube":
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games["platform"][i] = 0
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elif games["platform"][i] == " DS":
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games["platform"][i] = 0
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elif games["platform"][i] == " 3DS":
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games["platform"][i] = 1
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elif games["platform"][i] == " Wii":
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games["platform"][i] = 0
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elif games["platform"][i] == " Wii U":
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games["platform"][i] = 1
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elif games["platform"][i] == " Switch":
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games["platform"][i] = 2
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elif games["platform"][i] == " PC":
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dt = datetime.strptime(games["release_date"][i], '%B %d, %Y')
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if (dt.year == 1995 or dt.year == 1996 or dt.year == 1997 or dt.year == 1998
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or dt.year == 1999 or dt.year == 2000 or dt.year == 2001 or dt.year == 2002
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or dt.year == 2003 or dt.year == 2004 or dt.year == 2005):
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games["platform"][i] = 0
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if (dt.year == 2006 or dt.year == 2007 or dt.year == 2008 or dt.year == 2009
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or dt.year == 2010 or dt.year == 2011 or dt.year == 2012 or dt.year == 2013
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or dt.year == 2014 or dt.year == 2015 or dt.year == 2016):
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games["platform"][i] = 1
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if (dt.year == 2017 or dt.year == 2018 or dt.year == 2019
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or dt.year == 2020 or dt.year == 2021):
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games["platform"][i] = 2
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# games["platform"][i] = 0
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elif games["platform"][i] == " Dreamcast":
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games["platform"][i] = 0
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elif games["platform"][i] == " Game Boy Advance":
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games["platform"][i] = 0
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elif games["platform"][i] == " PSP":
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games["platform"][i] = 1
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elif games["platform"][i] == " Xbox One":
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games["platform"][i] = 2
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return games
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def remove_list(games):
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for i in range(0, len(games)):
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games['platform'][i] = games['platform'][i][0]
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games['release_date'][i] = games['release_date'][i][0]
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games['meta_score'][i] = games['meta_score'][i][0]
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games['user_review'][i] = games['user_review'][i][0]
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return games
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platform = pd.read_csv('Data/all_games.train.csv', sep=',', usecols=[1], header=None).values.tolist()
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release_date = pd.read_csv('Data/all_games.train.csv', sep=',', usecols=[2], header=None).values.tolist()
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meta_score = pd.read_csv('Data/all_games.train.csv', sep=',', usecols=[4], header=None).values.tolist()
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user_review = pd.read_csv('Data/all_games.train.csv', sep=',', usecols=[5], header=None).values.tolist()
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games_test = {'platform': platform,
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'release_date': release_date,
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'meta_score': meta_score,
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'user_review': user_review}
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games_test = pd.DataFrame(games_test)
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games_test = remove_list(games_test)
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games_test = platform_to_number(games_test)
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games_test = delete_stadia(games_test)
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games_test = delete_tbd(games_test)
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games_test = user_review_to_numeric(games_test)
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games_test = normalization(games_test)
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labels_test_g = pd.DataFrame(games_test["platform"], dtype=np.int64)
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labels_test_g = labels_test_g.to_numpy()
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features_test_g = {'meta_score': games_test['meta_score'],
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'user_review': games_test['user_review']}
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features_test_g = pd.DataFrame(features_test_g, dtype=np.float64)
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features_test_g = features_test_g.to_numpy()
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# Training
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model = torch.load("games_model.pkl")
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# Prediction
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x_test = Variable(torch.from_numpy(features_test_g)).float()
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pred = model(x_test)
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pred = pred.detach().numpy()
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accuracy = accuracy_score(labels_test_g, np.argmax(pred, axis=1))
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pred = pd.DataFrame(pred)
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predicted = []
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expected = []
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for i in range(0, len(x_test)):
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predicted.append(np.argmax(model(x_test[i]).detach().numpy(), axis=0))
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expected.append(labels_test_g[i])
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for i in range(0, len(expected)):
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expected[i] = expected[i][0]
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precision, recall, fscore, support = precision_recall_fscore_support(expected, predicted, average="micro")
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res = f"Accuracy: {accuracy}, Precision: {precision}, Recall: {recall}, F-score: {fscore}"
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with open('metrics.txt', 'a+') as f:
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f.write(str(accuracy) + '\n')
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with open('metrics.txt') as f:
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accuracy_val = [float(line) for line in f if line]
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builds = list(range(1, len(accuracy_val) + 1))
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plt.xlabel('Build')
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plt.ylabel('Accuracy')
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plt.plot(builds, accuracy_val, label='Accuracy')
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plt.legend()
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plt.show()
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plt.savefig('metrics.png') |