LR.py

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LogisticRegression

import Utils
from Utils import RawData, Plotter
from sklearn.model_selection import train_test_split, learning_curve
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler

# Taking clean data from RawData() class
df = RawData().df

# Separating features and label
X = df[RawData().feature_cols]  # Features
y = df.music_genre  # Target variable

# Separating train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)

# Initializing LogisticRegression modals with multinomial algorithm
lr = LogisticRegression(max_iter=len(X_train), multi_class="multinomial")

# Applying standardization
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Training model with train sets
lr.fit(X_train, y_train)

# Testing model with test sets
y_pred = lr.predict(X_test)

# Printing accuracy score
print("Accuracy:", accuracy_score(y_test, y_pred))

# Plot Common Graphs
Plotter().plot_cofusion_matrix(y_test, y_pred, "Logistic Regression")
Plotter().plot_traning_curves(X, y, lr, "Logistic Regression")


# Plot Iteration-Accuracy Graph
def iter_error():
    global X_train, X_test
    error = list()
    for i in range(1, 100, 2):
        lr = LogisticRegression(max_iter=i)

        lr.fit(X_train, y_train)

        pred_i = lr.predict(X_test)
        error.append(np.mean(pred_i != y_test))

    plt.figure(figsize=(12, 6))
    plt.plot(range(1, 100, 2), error, color='red', linestyle='dashed', marker='o',
             markerfacecolor='blue', markersize=10)
    plt.title('Error Rate iteration Value')
    plt.xlabel('iteration Value')
    plt.ylabel('Mean Error')
    plt.show()


iter_error()