import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, plot_tree
from sklearn.metrics import accuracy_score, roc_curve, confusion_matrix
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import cross_val_score

# Load the Carseats data
MusicSpotify = pd.read_csv('music_spotify.csv')
MusicSpotify = MusicSpotify.drop(columns=['X', 'artist', 'song_title'])
#print(MusicSpotify.head())

label_encoder = LabelEncoder()
label_encoder.fit_transform(MusicSpotify['target'])

# Data division
X = MusicSpotify.drop(columns=['target']) #dropping Sales, because class variable High is made of variable Sales.
y = MusicSpotify['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

# Tree training (no cost complexity pruning)
tree_music_spotify = DecisionTreeClassifier(ccp_alpha=0.00, min_samples_split=2)
tree_music_spotify.fit(X_train, y_train)

pred_rbf = tree_music_spotify.predict(X_test)


if False:
    # Plot the Decision Tree with matplotlib
    feature_names = X_train.columns.tolist()  # Assuming X_train is a DataFrame
    class_names = y_train.unique().astype(str)
    plt.figure(figsize=(12, 8))
    plot_tree(tree_music_spotify, filled=True, feature_names=feature_names, class_names=class_names, rounded=True, fontsize=10)
    plt.show()

# Tree information and visualization
path = tree_music_spotify.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
#path = tree_music_spotify.cost_complexity_pruning_path(X_train, y_train)
if False:
    fig, ax = plt.subplots()
    ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
    ax.set_xlabel("effective alpha")
    ax.set_ylabel("total impurity of leaves")
    ax.set_title("Total Impurity vs effective alpha for training set")
    plt.grid()
    plt.show()

if False:
   # Vary the hyperparameter (e.g., max depth)
    depth_values = range(1, 25)
    cv_scores = []

    for depth in depth_values:
        dt_classifier = DecisionTreeClassifier(max_depth=depth, random_state=20)
        scores = cross_val_score(dt_classifier, X, y, cv=10, scoring='accuracy')
        cv_scores.append(np.mean(scores))

    # Plot the cross-validated error
    plt.plot(depth_values, 1 - np.array(cv_scores), marker='o')
    plt.xlabel('Tree depth')
    plt.ylabel('Cross-validated error rate')
    plt.title('Cross-validated error vs. tree depth')
    plt.grid(True)
    plt.show()

if False:
    ccp_alphas_collect=[]
    accuracy_collect=[]
    for ccp_alpha in ccp_alphas:
        tree = DecisionTreeClassifier(ccp_alpha=ccp_alpha)
        tree.fit(X_train, y_train)
        accuracy = tree.score(X_test, y_test)
        ccp_alphas_collect.append(ccp_alpha)
        accuracy_collect.append(accuracy)

    plt.plot(np.array(ccp_alphas_collect),np.array(accuracy_collect))
    plt.grid()
    plt.xlim(0, 0.03)
    plt.xlabel('CP alpha')
    plt.ylabel('Accuracy')
    plt.show()

if False:
    plt.figure(figsize=(12, 6))
    plot_tree(tree_music_spotify, filled=True, feature_names=X.columns.tolist())
    plt.show()

# Prediction and accuracy
pred = tree_music_spotify.predict(X_test)
accuracy = accuracy_score(y_test, pred) * 100
print("Unprunned:")
print(f"Accuracy: {accuracy:.2f}%")
print("10-fold cross-validation score: ", cross_val_score(tree_music_spotify, X, y, cv=10).mean())
print("Confusion Matrix:\n", confusion_matrix(pred, y_test))

# Pruning
music_spotify_prunned = DecisionTreeClassifier(ccp_alpha=0.006, random_state=20)
music_spotify_prunned.fit(X_train, y_train)
pred = music_spotify_prunned.predict(X_test)
print("Prunned:")
print(f"Accuracy: {accuracy_score(y_test, pred) * 100:.2f}%")
print("10-fold cross-validation score: ", cross_val_score(music_spotify_prunned, X, y, cv=10).mean())
print("Confusion Matrix:\n", confusion_matrix(pred, y_test))

if False:
    plt.figure(figsize=(12, 6))
    plot_tree(music_spotify_prunned, filled=True, feature_names=X.columns.tolist())
    plt.show()

if True:
    feat_importances = pd.DataFrame(tree_music_spotify.feature_importances_, index=X_train.columns, columns=["Importance"])
    feat_importances.sort_values(by='Importance', ascending=False, inplace=True)
    feat_importances.plot(kind='bar', figsize=(8,6))
    plt.show()