ium_487194/evaluate.py

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from sklearn.preprocessing import StandardScaler
import torch.nn.functional as F

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

data = pd.read_csv("./Sales.csv")

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)
X_test = X_test.astype(np.float32)
y_test = y_test.astype(np.float32)
X_test=torch.FloatTensor(X_test)
y_test=torch.LongTensor(y_test)

model = torch.load("classificationn_model.pt")

def calculate_predictions(model, X):
    with torch.no_grad():
        outputs = model(X)
        _, predicted = torch.max(outputs.data, 1)
    return predicted

y_pred = calculate_predictions(model, X_test)
y_pred_np = y_pred.numpy()

np.savetxt("predictions.txt", y_pred_np, fmt='%d')

accuracy = accuracy_score(y_test.numpy(), y_pred_np)
f1 = f1_score(y_test.numpy(), y_pred_np, average='micro')
precision = precision_score(y_test.numpy(), y_pred_np, average='micro')
recall = recall_score(y_test.numpy(), y_pred_np, average='micro')

with open("metrics.txt", "w") as f:
    f.write(f"Accuracy: {accuracy}\n")
    f.write(f"F1 Score: {f1}\n")
    f.write(f"Precision: {precision}\n")
    f.write(f"Recall: {recall}\n")