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Feat u re e x traction D IME N SION AL ITY R E D U C TION IN P YTH ON Jeroen Boe y e Machine Learning Engineer , Faktion Feat u re selection DIMENSIONALITY REDUCTION IN PYTHON Feat u re selection Feat u re e x traction DIMENSIONALITY REDUCTION

  1. Feat u re e x traction D IME N SION AL ITY R E D U C TION IN P YTH ON Jeroen Boe y e Machine Learning Engineer , Faktion

  2. Feat u re selection DIMENSIONALITY REDUCTION IN PYTHON

  3. Feat u re selection Feat u re e x traction DIMENSIONALITY REDUCTION IN PYTHON

  4. Feat u re generation - BMI df_body['BMI'] = df_body['Weight kg'] / df_body['Height m'] ** 2 DIMENSIONALITY REDUCTION IN PYTHON

  5. Feat u re generation - BMI df_body['BMI'] = df_body['Weight kg'] / df_body['Height m'] ** 2 Weight kg Height m BMI 81.5 1.776 25.84 72.6 1.702 25.06 92.9 1.735 30.86 DIMENSIONALITY REDUCTION IN PYTHON

  6. Feat u re generation - BMI df_body.drop(['Weight kg', 'Height m'], axis=1) BMI 25.84 25.06 30.86 DIMENSIONALITY REDUCTION IN PYTHON

  7. Feat u re generation - a v erages le � leg mm right leg mm 882 885 870 869 901 900 leg_df['leg mm'] = leg_df[['right leg mm', 'left leg mm']].mean(axis=1) DIMENSIONALITY REDUCTION IN PYTHON

  8. Feat u re generation - a v erages leg_df.drop(['right leg mm', 'left leg mm'], axis=1) leg mm 883.5 869.5 900.5 DIMENSIONALITY REDUCTION IN PYTHON

  9. Cost of taking the a v erage DIMENSIONALITY REDUCTION IN PYTHON

  10. Cost of taking the a v erage DIMENSIONALITY REDUCTION IN PYTHON

  11. Cost of taking the a v erage DIMENSIONALITY REDUCTION IN PYTHON

  12. Cost of taking the a v erage DIMENSIONALITY REDUCTION IN PYTHON

  13. Intro to PCA sns.scatterplot(data=df, x='handlength', y='footlength') DIMENSIONALITY REDUCTION IN PYTHON

  14. Intro to PCA scaler = StandardScaler() df_std = pd.DataFrame(scaler.fit_transform(df), columns = df.columns) DIMENSIONALITY REDUCTION IN PYTHON

  15. Intro to PCA scaler = StandardScaler() df_std = pd.DataFrame(scaler.fit_transform(df), columns = df.columns) DIMENSIONALITY REDUCTION IN PYTHON

  16. Intro to PCA scaler = StandardScaler() df_std = pd.DataFrame(scaler.fit_transform(df), columns = df.columns) DIMENSIONALITY REDUCTION IN PYTHON

  17. Intro to PCA scaler = StandardScaler() df_std = pd.DataFrame(scaler.fit_transform(df), columns = df.columns) DIMENSIONALITY REDUCTION IN PYTHON

  18. Let ' s practice ! D IME N SION AL ITY R E D U C TION IN P YTH ON

  19. Principal component anal y sis D IME N SION AL ITY R E D U C TION IN P YTH ON Jeroen Boe y e Machine Learning Engineer , Faktion

  20. PCA concept DIMENSIONALITY REDUCTION IN PYTHON

  21. PCA concept DIMENSIONALITY REDUCTION IN PYTHON

  22. PCA concept DIMENSIONALITY REDUCTION IN PYTHON

  23. Calc u lating the principal components from sklearn.preprocessing import StandardScaler scaler = StandardScaler() std_df = scaler.fit_transform(df) from sklearn.decomposition import PCA pca = PCA() print(pca.fit_transform(std_df)) [[-0.08320426 -0.12242952] [ 0.31478004 0.57048158] ... [-0.5609523 0.13713944] [-0.0448304 -0.37898246]] DIMENSIONALITY REDUCTION IN PYTHON

  24. PCA remo v es correlation DIMENSIONALITY REDUCTION IN PYTHON

  25. Principal component e x plained v ariance ratio from sklearn.decomposition import PCA pca = PCA() pca.fit(std_df) print(pca.explained_variance_ratio_) array([0.90, 0.10]) DIMENSIONALITY REDUCTION IN PYTHON

  26. PCA for dimensionalit y red u ction DIMENSIONALITY REDUCTION IN PYTHON

  27. PCA for dimensionalit y red u ction print(pca.explained_variance_ratio_) array([0.9997, 0.0003]) DIMENSIONALITY REDUCTION IN PYTHON

  28. PCA for dimensionalit y red u ction pca = PCA() pca.fit(ansur_std_df) print(pca.explained_variance_ratio_) array([0.44, 0.18, 0.04, 0.03, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , ... 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]) DIMENSIONALITY REDUCTION IN PYTHON

  29. PCA for dimensionalit y red u ction pca = PCA() pca.fit(ansur_std_df) print(pca.explained_variance_ratio_.cumsum()) array([0.44, 0.62, 0.66, 0.69, 0.72, 0.74, 0.76, 0.77, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.87, 0.88, 0.89, 0.89, 0.9 , 0.9 , 0.91, 0.92, 0.92, 0.92, 0.93, 0.93, 0.94, 0.94, 0.94, 0.95, ... 0.99, 0.99, 0.99, 0.99, 0.99, 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ]) DIMENSIONALITY REDUCTION IN PYTHON

  30. Let ' s practice ! D IME N SION AL ITY R E D U C TION IN P YTH ON

  31. PCA applications D IME N SION AL ITY R E D U C TION IN P YTH ON Jeroen Boe y e Machine Learning Engineer , Faktion

  32. Understanding the components print(pca.components_) array([[ 0.71, 0.71], [ -0.71, 0.71]]) PC 1 = 0.71 x Hand length + 0.71 x Foot length PC 2 = -0.71 x Hand length + 0.71 x Foot length DIMENSIONALITY REDUCTION IN PYTHON

  33. PCA for data e x ploration DIMENSIONALITY REDUCTION IN PYTHON

  34. PCA in a pipeline from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.pipeline import Pipeline pipe = Pipeline([ ('scaler', StandardScaler()), ('reducer', PCA())]) pc = pipe.fit_transform(ansur_df) print(pc[:,:2]) array([[-3.46114925, 1.5785215 ], [ 0.90860615, 2.02379935], ..., [10.7569818 , -1.40222755], [ 7.64802025, 1.07406209]]) DIMENSIONALITY REDUCTION IN PYTHON

  35. Checking the effect of categorical feat u res print(ansur_categories.head()) Branch Component Gender BMI _ class Height _ class Combat Arms Reg u lar Arm y Male O v er w eight Tall Combat S u pport Reg u lar Arm y Male O v er w eight Normal Combat S u pport Reg u lar Arm y Male O v er w eight Normal Combat Ser v ice S u pport Reg u lar Arm y Male O v er w eight Normal Combat Ser v ice S u pport Reg u lar Arm y Male O v er w eight Tall DIMENSIONALITY REDUCTION IN PYTHON

  36. Checking the effect of categorical feat u res ansur_categories['PC 1'] = pc[:,0] ansur_categories['PC 2'] = pc[:,1] sns.scatterplot(data=ansur_categories, x='PC 1', y='PC 2', hue='Height_class', alpha=0.4) DIMENSIONALITY REDUCTION IN PYTHON

  37. Checking the effect of categorical feat u res sns.scatterplot(data=ansur_categories, x='PC 1', y='PC 2', hue='Gender', alpha=0.4) DIMENSIONALITY REDUCTION IN PYTHON

  38. Checking the effect of categorical feat u res sns.scatterplot(data=ansur_categories, x='PC 1', y='PC 2', hue='BMI_class', alpha=0.4 DIMENSIONALITY REDUCTION IN PYTHON

  39. PCA in a model pipeline pipe = Pipeline([ ('scaler', StandardScaler()), ('reducer', PCA(n_components=3)), ('classifier', RandomForestClassifier())]) pipe.fit(X_train, y_train) print(pipe.steps[1]) ('reducer', PCA(copy=True, iterated_power='auto', n_components=3, random_state=None, svd_solver='auto', tol=0.0, whiten=False)) DIMENSIONALITY REDUCTION IN PYTHON

  40. PCA in a model pipeline pipe.steps[1][1].explained_variance_ratio_.cumsum() array([0.56, 0.69, 0.74]) print(pipe.score(X_test, y_test)) 0.986 DIMENSIONALITY REDUCTION IN PYTHON

  41. Let ' s practice ! D IME N SION AL ITY R E D U C TION IN P YTH ON

  42. Principal Component selection D IME N SION AL ITY R E D U C TION IN P YTH ON Jeroen Boe y e Machine Learning Engineer , Faktion

  43. Setting an e x plained v ariance threshold pipe = Pipeline([ ('scaler', StandardScaler()), ('reducer', PCA(n_components=0.9))]) # Fit the pipe to the data pipe.fit(poke_df) print(len(pipe.steps[1][1].components_)) 5 DIMENSIONALITY REDUCTION IN PYTHON

  44. An optimal n u mber of components pipe.fit(poke_df) var = pipe.steps[1][1].explained_variance_ratio_ plt.plot(var) plt.xlabel('Principal component index') plt.ylabel('Explained variance ratio') plt.show() DIMENSIONALITY REDUCTION IN PYTHON

  45. An optimal n u mber of components pipe.fit(poke_df) var = pipe.steps[1][1].explained_variance_ratio_ plt.plot(var) plt.xlabel('Principal component index') plt.ylabel('Explained variance ratio') plt.show() DIMENSIONALITY REDUCTION IN PYTHON

  46. PCA operations DIMENSIONALITY REDUCTION IN PYTHON

  47. PCA operations DIMENSIONALITY REDUCTION IN PYTHON

  48. PCA operations DIMENSIONALITY REDUCTION IN PYTHON

  49. Compressing images DIMENSIONALITY REDUCTION IN PYTHON

  50. Compressing images print(X_test.shape) (15, 2914) 62 x 47 pi x els = 2914 gra y scale v al u es print(X_train.shape) (1333, 2914) DIMENSIONALITY REDUCTION IN PYTHON

  51. Compressing images pipe = Pipeline([ ('scaler', StandardScaler()), ('reducer', PCA(n_components=290))]) pipe.fit(X_train) pc = pipe.fit_transform(X_test) print(pc.shape) (15, 290) DIMENSIONALITY REDUCTION IN PYTHON

  52. Reb u ilding images pc = pipe.transform(X_test) print(pc.shape) (15, 290) X_rebuilt = pipe.inverse_transform(pc) print(X_rebuilt.shape) (15, 2914) img_plotter(X_rebuilt) DIMENSIONALITY REDUCTION IN PYTHON

  53. Reb u ilding images DIMENSIONALITY REDUCTION IN PYTHON

  54. Let ' s practice ! D IME N SION AL ITY R E D U C TION IN P YTH ON

  55. Congrat u lations ! D IME N SION AL ITY R E D U C TION IN P YTH ON Jeroen Machine Learning Engineer , Faktion

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