ium_426206/dlgssdpytorch copy.py

import zipfile
import torch
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import datetime
import numpy as np

from kaggle.api.kaggle_api_extended import KaggleApi
import torch.nn as nn
import torch.optim as optim
from torch.utils.data.dataset import random_split
from torch.utils.data import Dataset, TensorDataset, DataLoader
from torchviz import make_dot
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')
exit()
# fig, ax = plt.subplots()
# fig.set_figheight(15)
# fig.set_figwidth(20)
# ax.scatter(df['Month and Year'], df['Sum of sales'])
#plt.show()
# # Data Generation
# np.random.seed(42)
# x = np.random.rand(100, 1)
# y = 1 + 2 * x + .1 * np.random.randn(100, 1)

# # Shuffles the indices
# idx = np.arange(100)
# np.random.shuffle(idx)

# # Uses first 80 random indices for train
# train_idx = idx[:80]
# # Uses the remaining indices for validation
# val_idx = idx[80:]

# # Generates train and validation sets
# x_train, y_train = x[train_idx], y[train_idx]
# x_val, y_val = x[val_idx], y[val_idx]
# x_tensor = torch.from_numpy(x_train).float()
# y_tensor = torch.from_numpy(y_train).float()

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

dataset = TensorDataset(x_tensor, y_tensor)


#torch.manual_seed(42)
lengths = [int(len(dataset)*0.8), int(len(dataset)*0.2)]
train_dataset, val_dataset = random_split(dataset, lengths)

train_loader = DataLoader(dataset=train_dataset)
val_loader = DataLoader(dataset=val_dataset)


class LayerLinearRegression(nn.Module):
    def __init__(self):
        super().__init__()
        # Instead of our custom parameters, we use a Linear layer with single input and single output
        self.linear = nn.Linear(1, 1)
                
    def forward(self, x):
        # Now it only takes a call to the layer to make predictions
        return self.linear(x)
    
model = LayerLinearRegression()
# Checks model's parameters
#print(model.state_dict())   

lr = 1e-3
n_epochs = 100

loss_fn = nn.MSELoss(reduction='mean')
optimizer = optim.SGD(model.parameters(), lr=lr)

def make_train_step(model, loss_fn, optimizer):
    # Builds function that performs a step in the train loop
    def train_step(x, y):
        # Sets model to TRAIN mode
        model.train()
        # Makes predictions
        yhat = model(x)
        # Computes loss
        loss = loss_fn(y, yhat)
        # Computes gradients
        loss.backward()
        # Updates parameters and zeroes gradients
        optimizer.step()
        optimizer.zero_grad()
        # Returns the loss
        return loss.item()
    
    # Returns the function that will be called inside the train loop
    return train_step

# Creates the train_step function for our model, loss function and optimizer
train_step = make_train_step(model, loss_fn, optimizer)
training_losses = []
validation_losses = []
print(model.state_dict())   
# For each epoch...
for epoch in range(n_epochs):
    losses = []
    # Uses loader to fetch one mini-batch for training
    for x_batch, y_batch in train_loader:
        # NOW, sends the mini-batch data to the device
        # so it matches location of the MODEL
        # x_batch = x_batch.to(device)
        # y_batch = y_batch.to(device)
        # One stpe of training
        loss = train_step(x_batch, y_batch)
        losses.append(loss)
    training_loss = np.mean(losses)
    training_losses.append(training_loss)
        
    # After finishing training steps for all mini-batches,
    # it is time for evaluation!
        
    # We tell PyTorch to NOT use autograd...
    # Do you remember why?
    with torch.no_grad():
        val_losses = []
        # Uses loader to fetch one mini-batch for validation
        for x_val, y_val in val_loader:
            # Again, sends data to same device as model
            # x_val = x_val.to(device)
            # y_val = y_val.to(device)
            
            # What is that?!
            model.eval()
            # Makes predictions
            yhat = model(x_val)
            # Computes validation loss
            val_loss = loss_fn(y_val, yhat)
            val_losses.append(val_loss.item())
        validation_loss = np.mean(val_losses)
        validation_losses.append(validation_loss)

    print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")

# Checks model's parameters
print(model.state_dict())
print(np.mean(losses))
print(np.mean(val_losses))