Ml-model/test-1.py at main · Arpit192005/Ml-model · GitHub

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import pandas as pd
import numpy as np
import os

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

import joblib
import matplotlib.pyplot as plt

# ==============================
# Load Dataset
# ==============================
script_dir = os.path.dirname(os.path.abspath(__file__))
csv_path = os.path.join(script_dir, "fault_detection_ltspice_dataset_12000.csv")
data = pd.read_csv(csv_path)

# Inputs and Output
X = data.drop(["fault_type","circuit"], axis=1)
y = data["fault_type"]

# ==============================
# Convert labels to numbers
# ==============================
encoder = LabelEncoder()
y_encoded = encoder.fit_transform(y)

# save label mapping
label_mapping = dict(zip(encoder.classes_, encoder.transform(encoder.classes_)))
print("Label Mapping:\n", label_mapping)

# ==============================
# Train Test Split
# ==============================
X_train, X_test, y_train, y_test = train_test_split(
    X, y_encoded,
    test_size=0.2,
    random_state=700
)

# ==============================
# Feature Scaling
# ==============================
scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# ==============================
# Neural Network Model
# ==============================
model = MLPClassifier(

    hidden_layer_sizes=(128,64),

    activation='relu',

    solver='adam',

    max_iter=1500,

    early_stopping=True,

    validation_fraction=0.2,

    random_state=42
)

# ==============================
# Train Model
# ==============================
model.fit(X_train, y_train)

# ==============================
# Test Model
# ==============================
y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

print("\nAccuracy:", accuracy)

print("\nClassification Report:")
print(classification_report(y_test, y_pred))

# ==============================
# Confusion Matrix Plot
# ==============================
cm = confusion_matrix(y_test, y_pred)

plt.figure()
plt.imshow(cm)
plt.title("Confusion Matrix")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.show()

# ==============================
# Save model
# ==============================
joblib.dump(model,"fault_detection_model.pkl")
joblib.dump(scaler,"scaler.pkl")

print("\nModel saved successfully")