import zipfile
import torch
import pandas as pd
import datetime
import numpy as np

from kaggle.api.kaggle_api_extended import KaggleApi
import torch.nn as nn
from torch.utils.data.dataset import random_split
from torch.utils.data import Dataset, TensorDataset
from sklearn import preprocessing


api = KaggleApi()
api.authenticate()
api.dataset_download_file('apoorvaappz/global-super-store-dataset',
                              file_name='Global_Superstore2.csv', path='./')

with zipfile.ZipFile('Global_Superstore2.csv.zip', 'r') as zipref:
    zipref.extractall('.')

data = pd.read_csv("Global_Superstore2.csv", header=0, sep=',')

data["Order Date"] = pd.to_datetime(data["Order Date"])
data = data.sort_values(by="Order Date")

#print(data)

byMonthsYears = {}
for index, row in data.iterrows():
    #datee = datetime.datetime.strptime(row['Order Date'], "%d-%m-%Y")
    #byMonthsYears.setdefault(datee.strftime("%m-%Y"), 0)
    #byMonthsYears[datee.strftime("%m-%Y")] += row['Sales']
    byMonthsYears.setdefault(row['Order Date'].strftime("%d-%m-%Y"), 0)
    byMonthsYears[row['Order Date'].strftime("%d-%m-%Y")] += row['Sales']
df = data.groupby('Order Date').agg({'Customer Name':'count', 'Sales': 'sum'}).reset_index().rename(columns={'Sales':'Sales sum', 'Customer Name':'Sales count'})

#normalizacja danych
flcols = df[['Sales count', 'Sales sum']].columns
x = df[['Sales count', 'Sales sum']].values
# min_max_scaler = preprocessing.MinMaxScaler()
max_abs_scaler = preprocessing.MaxAbsScaler()
# x_scaled = min_max_scaler.fit_transform(x)
x_scaled = max_abs_scaler.fit_transform(x)
normcols = pd.DataFrame(x_scaled, columns=flcols)
for col in flcols:
    df[col] = normcols[col]
#df.to_csv('mms_norm.csv')

x_tensor = torch.tensor(df['Sales sum'].values).float()
y_tensor = torch.tensor(df['Sales count'].values).float()

dataset = TensorDataset(x_tensor, y_tensor)

lengths = [int(len(dataset)*0.8), int(len(dataset)*0.2)]
train_dataset, val_dataset = random_split(dataset, lengths)

torch.save(train_dataset, 'train_dataset.pt')
torch.save(val_dataset, 'val_dataset.pt')