import numpy as np
import sys
import os
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

EPOCHS = int(sys.argv[1])

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

def remove_list(games):
    for i in range(0, len(games)):
        games['platform'][i] = games['platform'][i][0]
        games['release_date'][i] = games['release_date'][i][0]
        games['meta_score'][i] = games['meta_score'][i][0]
        games['user_review'][i] = games['user_review'][i][0]
    return games

        # games = pd.read_csv('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()

platform = pd.read_csv('all_games.train.csv', sep=',', usecols=[1], header=None).values.tolist()
release_date = pd.read_csv('all_games.train.csv', sep=',', usecols=[2], header=None).values.tolist()
meta_score = pd.read_csv('all_games.train.csv', sep=',', usecols=[4], header=None).values.tolist()
user_review = pd.read_csv('all_games.train.csv', sep=',', usecols=[5], header=None).values.tolist()

games_train = {'platform': platform,
     'release_date': release_date,
     'meta_score': meta_score,
     'user_review': user_review}
games_train = pd.DataFrame(games_train)

games_test = {'platform': platform,
     'release_date': release_date,
     'meta_score': meta_score,
     'user_review': user_review}
games_test = pd.DataFrame(games_test)

games_train = remove_list(games_train)
games_train = platform_to_number(games_train)
games_train = delete_stadia(games_train)
games_train = delete_tbd(games_train)
games_train = user_review_to_numeric(games_train)
games_train = normalization(games_train)

games_test = remove_list(games_test)
games_test = platform_to_number(games_test)
games_test = delete_stadia(games_test)
games_test = delete_tbd(games_test)
games_test = user_review_to_numeric(games_test)
games_test = normalization(games_test)

labels_train_g = pd.DataFrame(games_train["platform"], dtype=np.int64)
labels_train_g = labels_train_g.to_numpy()
features_train_g = {'meta_score': games_train['meta_score'],
              'user_review': games_train['user_review']}
features_train_g = pd.DataFrame(features_train_g, dtype=np.float64)
features_train_g = features_train_g.to_numpy()

labels_test_g = pd.DataFrame(games_test["platform"], dtype=np.int64)
labels_test_g = labels_test_g.to_numpy()
features_test_g = {'meta_score': games_test['meta_score'],
              'user_review': games_test['user_review']}
features_test_g = pd.DataFrame(features_test_g, dtype=np.float64)
features_test_g = features_test_g.to_numpy()

# Training
model = Model(features_train_g.shape[1])
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
loss_fn = nn.CrossEntropyLoss()
epochs = EPOCHS

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')

# save model
torch.save(model, "games_model.pkl")