projectfile2_5.py

# -*- coding: utf-8 -*-
"""
Created on Mon Jun 24 17:40:02 2024

@author: shash
"""

import pandas as pd
import pickle
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt


file_path = 'D:/Submission of BTP Final Python code and its Datasets/Combined_table_list/combined_table_list.pkl'

# Load the DataFrame from the pickle file
with open(file_path, 'rb') as file:
    loaded_df = pickle.load(file)

# Verify that the DataFrame is loaded correctly
print(loaded_df)



# Encode the target variable
loaded_df['tilt/twist'] = loaded_df['tilt/twist'].map({'tilt': 0, 'twist': 1})

# Assign features to X and target to y
X=loaded_df.drop(columns=["file_name","ATOMS_id","type","tilt/twist"], axis=1) # Features
y = loaded_df['tilt/twist']  # Target variable

# Split the dataset into 75% training and 25% testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

# Calculate the min and max values for each column in the training set
train_min = X_train.min()
train_max = X_train.max()

# Function to normalize a dataset based on provided min and max values
def normalize(loaded_df, min_vals, max_vals):
    return (loaded_df - min_vals) / (max_vals - min_vals)

# Apply normalization to the training set
X_train_normalized = normalize(X_train, train_min, train_max)

# Apply the same normalization to the testing set using the training set's min and max values
X_test_normalized = normalize(X_test, train_min, train_max)


# Train the Logistic Regression model
log_reg = LogisticRegression(random_state=42)
log_reg.fit(X_train_normalized, y_train)

# Make predictions on the test set
y_pred = log_reg.predict(X_test_normalized)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")



# Compute the confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(conf_matrix)

# Create a DataFrame for the confusion matrix
conf_matrix_df = pd.DataFrame(conf_matrix, index=['Actual Tilt', 'Actual Twist'], columns=['Predicted Tilt', 'Predicted Twist'])

print("\nConfusion Matrix Table:")
print(conf_matrix_df)

# Create a heatmap for the confusion matrix
plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix_df, annot=True, fmt='d', cmap='Blues')
plt.title('Confusion Matrix')
plt.ylabel('Actual Label')
plt.xlabel('Predicted Label')
plt.show()