import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns 
import librosa.display

music_data = pd.read_csv('file.csv') 
music_data.head(5)

music_data['label'].value_counts()

path = 'drive/MyDrive/genres_original/disco/disco.00001.wav'
plt.figure(figsize=(14, 5)) 
x, sr = librosa.load(path) 
librosa.display.waveshow(x, sr=sr) 

print("Disco")

path = 'drive/MyDrive/genres_original/jazz/jazz.00001.wav'
plt.figure(figsize=(14, 5)) 
x, sr = librosa.load(path) 
librosa.display.waveshow(x, sr=sr) 

print("Jazz")

from sklearn import preprocessing 
label_encoder = preprocessing.LabelEncoder() 
music_data['label'] = label_encoder.fit_transform(music_data['label'])

X = music_data.drop(['label','filename'],axis=1) 
y = music_data['label']
cols = X.columns 
minmax = preprocessing.MinMaxScaler() 
np_scaled = minmax.fit_transform(X) 

# new data frame with the new scaled data. 
X = pd.DataFrame(np_scaled, columns = cols)

from sklearn.model_selection import train_test_split 

X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    test_size=0.3, 
                                                    random_state=111) 
X_train.shape, X_test.shape, y_train.shape, y_test.shape

from sklearn.metrics import accuracy_score 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.tree import DecisionTreeClassifier 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.linear_model import LogisticRegression 
import catboost as cb 
from xgboost import XGBClassifier 

rf = RandomForestClassifier(n_estimators=1000, max_depth=10, random_state=0) 
cbc = cb.CatBoostClassifier(verbose=0, eval_metric='Accuracy', loss_function='MultiClass') 
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05) 

for clf in (rf, cbc, xgb): 
    clf.fit(X_train, y_train) 
    preds = clf.predict(X_test) 
    print(clf.__class__.__name__,accuracy_score(y_test, preds))

import tensorflow.keras as keras 
from tensorflow.keras import Sequential 
from tensorflow.keras.layers import *

model = Sequential() 

model.add(Flatten(input_shape=(58,))) 
model.add(Dense(256, activation='relu')) 
model.add(BatchNormalization()) 
model.add(Dense(128, activation='relu')) 
model.add(Dropout(0.3)) 
model.add(Dense(10, activation='softmax')) 
model.summary()

# compile the model 
adam = keras.optimizers.Adam(lr=1e-4) 
model.compile(optimizer=adam, 
            loss="sparse_categorical_crossentropy", 
            metrics=["accuracy"]) 

hist = model.fit(X_train, y_train, 
                validation_data = (X_test,y_test), 
                epochs = 100, 
                batch_size = 32)

test_error, test_accuracy = model.evaluate(X_test, y_test, verbose=1) 
print(f"Test accuracy: {test_accuracy}")

fig, axs = plt.subplots(2,figsize=(10,10)) 

# accuracy 
axs[0].plot(hist.history["accuracy"], label="train") 
axs[0].plot(hist.history["val_accuracy"], label="test")  
axs[0].set_ylabel("Accuracy") 
axs[0].legend() 
axs[0].set_title("Accuracy") 

# Error 
axs[1].plot(hist.history["loss"], label="train") 
axs[1].plot(hist.history["val_loss"], label="test")  
axs[1].set_ylabel("Error") 
axs[1].legend() 
axs[1].set_title("Error") 

plt.show()