82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.optim as optim
|
||
|
|
import torch.nn.functional as F
|
||
|
|
from sklearn.model_selection import train_test_split
|
||
|
|
from sklearn.metrics import accuracy_score
|
||
|
|
from sklearn.preprocessing import StandardScaler
|
||
|
|
import pandas as pd
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
# Wczytanie danych
|
||
|
|
data = pd.read_csv("Sales.csv")
|
||
|
|
|
||
|
|
# Przygotowanie danych
|
||
|
|
data["Profit_Category"] = pd.cut(data["Profit"], bins=[-np.inf, 500, 1000, np.inf], labels=[0, 1, 2])
|
||
|
|
bike = data.loc[:, ['Customer_Age', 'Customer_Gender', 'Country','State', 'Product_Category', 'Sub_Category', 'Profit_Category']]
|
||
|
|
bikes = pd.get_dummies(bike, columns=['Country', 'State', 'Product_Category', 'Sub_Category', 'Customer_Gender'])
|
||
|
|
X = bikes.drop('Profit_Category', axis=1).values
|
||
|
|
y = bikes['Profit_Category'].values
|
||
|
|
X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)
|
||
|
|
scaler = StandardScaler()
|
||
|
|
X = scaler.fit_transform(X)
|
||
|
|
#### Tworzenie tensorów
|
||
|
|
X_train = X_train.astype(np.float32)
|
||
|
|
X_test = X_test.astype(np.float32)
|
||
|
|
y_train = y_train.astype(np.float32)
|
||
|
|
y_test = y_test.astype(np.float32)
|
||
|
|
X_train=torch.FloatTensor(X_train)
|
||
|
|
X_test=torch.FloatTensor(X_test)
|
||
|
|
y_train=torch.LongTensor(y_train)
|
||
|
|
y_test=torch.LongTensor(y_test)
|
||
|
|
|
||
|
|
#### Model
|
||
|
|
|
||
|
|
class ANN_Model(nn.Module):
|
||
|
|
def __init__(self,input_features=82,hidden1=20,hidden2=20,out_features=3):
|
||
|
|
super().__init__()
|
||
|
|
self.f_connected1=nn.Linear(input_features,hidden1)
|
||
|
|
self.f_connected2=nn.Linear(hidden1,hidden2)
|
||
|
|
self.out=nn.Linear(hidden2,out_features)
|
||
|
|
def forward(self, x):
|
||
|
|
x=F.relu(self.f_connected1(x))
|
||
|
|
x=F.relu(self.f_connected2(x))
|
||
|
|
x=self.out(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
torch.manual_seed(20)
|
||
|
|
model=ANN_Model()
|
||
|
|
model.parameters
|
||
|
|
|
||
|
|
def calculate_accuracy(model, X, y):
|
||
|
|
with torch.no_grad():
|
||
|
|
outputs = model(X)
|
||
|
|
_, predicted = torch.max(outputs.data, 1)
|
||
|
|
total = y.size(0)
|
||
|
|
correct = (predicted == y).sum().item()
|
||
|
|
accuracy = correct / total * 100
|
||
|
|
return accuracy
|
||
|
|
|
||
|
|
loss_function = nn.CrossEntropyLoss()
|
||
|
|
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
|
||
|
|
|
||
|
|
epochs = 100
|
||
|
|
final_losses = []
|
||
|
|
accuracy_list = []
|
||
|
|
|
||
|
|
for i in range(epochs):
|
||
|
|
i = i + 1
|
||
|
|
y_pred = model(X_train)
|
||
|
|
loss = loss_function(y_pred, y_train)
|
||
|
|
final_losses.append(loss)
|
||
|
|
|
||
|
|
train_accuracy = calculate_accuracy(model, X_train, y_train)
|
||
|
|
test_accuracy = calculate_accuracy(model, X_test, y_test)
|
||
|
|
print(f"Epoch: {i}, Loss: {loss.item()}, Train Accuracy: {train_accuracy}%, Test Accuracy: {test_accuracy}%")
|
||
|
|
optimizer.zero_grad()
|
||
|
|
loss.backward()
|
||
|
|
optimizer.step()
|
||
|
|
|
||
|
|
torch.save(model,"classificationn_model.pt")
|