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# Untitled

a guest Dec 7th, 2019 79 Never
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1. x = df_aux['201701']#.values.reshape(-1, 1)
2. y = df_aux['n_visits']#.values.reshape(-1, 1)
3.
4. def fit_poly( degree ):
5.     p = np.polyfit( x, y, deg = degree )
6.     df_aux['fit'] = np.polyval( p, x )
7.     sns.regplot(x, y, fit_reg = False )
8.
9.     lists = sorted(zip(*[x,df_aux['fit']]))
10.     new_x, new_y = list(zip(*lists))
11.
12.     r2 = r2_score(y,df_aux['fit'])
13.     print("R squared: ", r2)
14.     plt.xlabel('n_crimes')
15.     plt.title('N_visits/N_crimes per Borough')
16.
17.     return plt.plot( new_x, new_y, color='r' )
18.
19.
20. def get_rmse( y, y_fit ):
21.     return np.sqrt( metrics.mean_squared_error( y, y_fit ) )
22.
23. train_X, test_X, train_y, test_y = train_test_split( x,
24.                                                   y,
25.                                                   test_size = 0.20,
26.                                                   random_state = 100 )
27.
28. rmse_df = pd.DataFrame( columns = ["degree", "rmse_train", "rmse_test"] )
29.
30. for i in range( 1, 15 ):
31.     p = np.polyfit( train_X, train_y, deg = i )
32.     rmse_df.loc[i-1] = [ i,
33.                       get_rmse( train_y, np.polyval( p, train_X ) ),
34.                       get_rmse( test_y, np.polyval( p, test_X ) ) ]
35.
36.
37.
38.
39.
40. plt.plot( rmse_df.degree, rmse_df.rmse_train,label='train',color = 'r' )
41.
42. plt.plot( rmse_df.degree,rmse_df.rmse_test,label='test',color = 'g' )
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