projectfile2_14.py

# -*- coding: utf-8 -*-
"""
Created on Fri Aug 23 16:21:17 2024

@author: shash
"""


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



# Path to save the pickle file
pickle_file_path = 'combined_df.pkl'

# Loading the saved DataFrame
with open(pickle_file_path, 'rb') as file:
    loaded_df = pickle.load(file)



# 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","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)

# Function to train and evaluate SVM with a given value of C
def evaluate_svm(C, X_train, y_train, X_test, y_test):
    svm = SVC(C=C, random_state=42)
    svm.fit(X_train, y_train)
    y_pred = svm.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    return accuracy, y_pred

# Hyperparameter tuning for C
C_values = [1000]
accuracies = []
y_preds = []

for C in C_values:
    accuracy, y_pred = evaluate_svm(C, X_train_normalized, y_train, X_test_normalized, y_test)
    accuracies.append(accuracy)
    y_preds.append(y_pred)
    print(f"Accuracy for C={C}: {accuracy}")

# Find the index of the best accuracy
best_index = accuracies.index(max(accuracies))
best_C = C_values[best_index]
best_accuracy = accuracies[best_index]
best_y_pred = y_preds[best_index]

print(f"\nBest C value: {best_C} with accuracy: {best_accuracy}")