import pandas as pd # Read in track metadata with genre labels tracks = pd.read_csv("datasets/fma-rock-vs-hiphop.csv") # Read in track metrics with the features echonest_metrics = pd.read_json("datasets/echonest-metrics.json", precise_float=True) # Merge the relevant columns of tracks and echonest_metrics echo_tracks = echonest_metrics.merge(tracks[['track_id', 'genre_top']], on='track_id') # Inspect the resultant dataframe echo_tracks.info()

echo_tracks.head()

# Create a correlation matrix corr_metrics = echo_tracks.corr() corr_metrics.style.background_gradient()

# Import train_test_split function and Decision tree classifier # ... YOUR CODE ... from sklearn.model_selection import train_test_split # Create features #features = echo_tracks.loc[:, echo_tracks.columns != ['track_id', 'genre_top']] features = echo_tracks.drop(["track_id", "genre_top"], axis=1).values #print(features) # Create labels labels = echo_tracks['genre_top'].values #print(labels) # Split our data train_features, test_features, train_labels, test_labels = train_test_split( features, labels, random_state=10)

# Import the StandardScaler from sklearn.preprocessing import StandardScaler # Scale the features and set the values to a new variable scaler = StandardScaler() # Scale train_features and test_features scaled_train_features = scaler.fit_transform(train_features) scaled_test_features = scaler.transform(test_features)

# This is just to make plots appear in the notebook %matplotlib inline # Import our plotting module, and PCA class #... YOUR CODE ... import matplotlib.pyplot as plt from sklearn.decomposition import PCA # Get our explained variance ratios from PCA using all features pca = PCA() pca.fit(scaled_train_features) exp_variance = pca.explained_variance_ratio_ # plot the explained variance using a barplot fig, ax = plt.subplots() ax.bar(range(pca.n_components_), exp_variance) ax.set_xlabel('Principal Component #')

# Import numpy import numpy as np # Calculate the cumulative explained variance cum_exp_variance = np.cumsum(exp_variance) # Plot the cumulative explained variance and draw a dashed line at 0.85. fig, ax = plt.subplots() ax.plot(cum_exp_variance) ax.axhline(y=0.85, linestyle='--')

# Perform PCA with the chosen number of components and project data onto components pca = PCA(n_components=6, random_state=10) # Fit and transform the scaled training features using pca train_pca = pca.fit_transform(scaled_train_features) # Fit and transform the scaled test features using pca test_pca = pca.transform(scaled_test_features)

# Import Decision tree classifier # ... YOUR CODE ... from sklearn.tree import DecisionTreeClassifier # Train our decision tree tree = DecisionTreeClassifier(random_state=10) tree.fit(train_pca, train_labels) # Predict the labels for the test data pred_labels_tree = tree.predict(test_pca)

# Import LogisticRegression from sklearn.linear_model import LogisticRegression # Train our logistic regression and predict labels for the test set logreg = LogisticRegression(random_state=10) logreg.fit(train_pca, train_labels) pred_labels_logit = logreg.predict(test_pca) # Create the classification report for both models from sklearn.metrics import classification_report class_rep_tree = classification_report(test_labels, pred_labels_tree) class_rep_log = classification_report(test_labels, pred_labels_logit) print("Decision Tree: \n", class_rep_tree) print("Logistic Regression: \n", class_rep_log)

# Subset only the hip-hop tracks, and then only the rock tracks hop_only = echo_tracks.loc[echo_tracks['genre_top'] == 'Hip-Hop'] rock_only = echo_tracks.loc[echo_tracks['genre_top'] == 'Rock'] # sample the rocks songs to be the same number as there are hip-hop songs rock_only = rock_only.sample(hop_only.shape[0], random_state=10) # concatenate the dataframes rock_only and hop_only rock_hop_bal = pd.concat([rock_only, hop_only]) # The features, labels, and pca projection are created for the balanced dataframe features = rock_hop_bal.drop(['genre_top', 'track_id'], axis=1) labels = rock_hop_bal['genre_top'] # Redefine the train and test set with the pca_projection from the balanced data train_features, test_features, train_labels, test_labels = train_test_split(features, labels, random_state=10) train_pca = pca.fit_transform(scaler.fit_transform(train_features)) test_pca = pca.transform(scaler.transform(test_features))

# Train our decision tree on the balanced data tree = DecisionTreeClassifier(random_state=10) tree.fit(train_pca, train_labels) pred_labels_tree = tree.predict(test_pca) # Train our logistic regression on the balanced data logreg = LogisticRegression(random_state=10) logreg.fit(train_pca, train_labels) pred_labels_logit = logreg.predict(test_pca) # Compare the models print("Decision Tree: \n", classification_report(test_labels, pred_labels_tree)) print("Logistic Regression: \n", classification_report(test_labels, pred_labels_logit))

from sklearn.model_selection import KFold, cross_val_score from sklearn.pipeline import Pipeline tree_pipe = Pipeline([("scaler", StandardScaler()), ("pca", PCA(n_components=6)), ("tree", DecisionTreeClassifier(random_state=10))]) logreg_pipe = Pipeline([("scaler", StandardScaler()), ("pca", PCA(n_components=6)), ("logreg", LogisticRegression(random_state=10))]) # Set up our K-fold cross-validation kf = KFold(10) # Train our models using KFold cv tree_score = cross_val_score(tree_pipe, features, labels, cv=kf) logit_score = cross_val_score(logreg_pipe, features, labels, cv=kf) # Print the mean of each array o scores print("Decision Tree:", np.mean(tree_score), "Logistic Regression:", np.mean(logit_score))