TSNE_plot.py

from sklearn.manifold import TSNE

tsne = TSNE(n_components=2, perplexity=30, learning_rate=200, random_state=42)

X_embedding = tsne.fit_transform(df_signal_train)
y = np.array(df_signal_train['target'])

tsne = np.hstack((X_embedding, y.reshape(-1,1)))
tsne_to_df = pd.DataFrame(data=tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue', 2:'green'}
plt.scatter(tsne_to_df['Dimension_x'], tsne_to_df['Dimension_y'], c=tsne_to_df['Score'].apply(lambda x: colors[x]))
plt.show()

del(tsne)

ok