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Variable selection for NIR spectral analysis(regression and classification) based on WRC, VIP, SFS, and SPA

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variable_selection

Variable selection for NIR spectral analysis(regression and classification) based on WRC, VIP, SFS, and SPA

WRC(Weighted Regression Coefficient)

Based on PLS coefficient for variables and standard deviation of variables, the score of variables is constructed for finding the indices of removeable variables.

VIP(Variable Importance in Projection)

Based on VIP score, the indices of removeable variables are found.

SFS(Sequential Feature Selection)

Through sequential calculation of loss error during one-by-one removal for every variables, the indices of removeal variables are found for revealing min loss error.

SPA(Successive Projections Algorithm)

For chained variables from QR projection process, the best variables set is found, and then some of removeable variables are found based on WRC.

Usage

Regression

$ python3 variable_selection_regression.py
samle.feature_names
 ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
sample.data
 (442, 10)
sample.target
 (442,)

(python:143670): Gtk-WARNING **: 00:13:55.367: Theme parsing error: gtk.css:5597:11: Not using units is deprecated. Assuming 'px'.

(python:143670): Gtk-WARNING **: 00:13:55.367: Theme parsing error: gtk.css:5597:14: '0' is not a valid color name

(python:143670): Gtk-WARNING **: 00:13:55.367: Theme parsing error: gtk.css:5831:14: '202020' is not a valid color name

(python:143670): Gtk-WARNING **: 00:13:55.367: Theme parsing error: gtk.css:5846:20: Junk at end of value for border-width

(python:143670): Gtk-WARNING **: 00:13:55.367: Theme parsing error: gtk.css:5871:20: Junk at end of value for border-width

(python:143670): Gtk-WARNING **: 00:13:55.367: Theme parsing error: gtk.css:5898:20: Junk at end of value for border-width
### getRemovalIndicesByFixedScoreXStd ###
Removeable Variable's Indices:
 [9 0 6 4 5]
Optimal Variable's Indices:
 {1, 2, 3, 7, 8}
### getRemovalIndicesByUpdatingScoreXStd ###
Removeable Variable's Indices:
 []
Optimal Variable's Indices:
 {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
### getRemovalIndicesByFixedVariableImportanceInProjection ###
Removeable Variable's Indices:
 [0 1 4 5]
Optimal Variable's Indices:
 {2, 3, 6, 7, 8, 9}
### getRemovalIndicesByUpdatingVariableImportanceInProjection ###
Removeable Variable's Indices:
 [0 1 4 5]
Optimal Variable's Indices:
 {2, 3, 6, 7, 8, 9}
### getRemovalIndicesBySequentialSearch ###
Removeable Variable's Indices:
 [7 6 0]
Optimal Variable's Indices:
 {1, 2, 3, 4, 5, 8, 9}
### getIndicesBySuccessiveProjectionAlgorithm ###
Optimal Variable's Indices:
 [2 8 3 6 4 1 9]
Removeable Variable's Indices:
 {0, 5, 7}

plot

Classification

$ python3 variable_selection_classification.py
sample.feature_names
 ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']
sample.data
 [[1.423e+01 1.710e+00 2.430e+00 ... 1.040e+00 3.920e+00 1.065e+03]
 [1.320e+01 1.780e+00 2.140e+00 ... 1.050e+00 3.400e+00 1.050e+03]
 [1.316e+01 2.360e+00 2.670e+00 ... 1.030e+00 3.170e+00 1.185e+03]
 ...
 [1.327e+01 4.280e+00 2.260e+00 ... 5.900e-01 1.560e+00 8.350e+02]
 [1.317e+01 2.590e+00 2.370e+00 ... 6.000e-01 1.620e+00 8.400e+02]
 [1.413e+01 4.100e+00 2.740e+00 ... 6.100e-01 1.600e+00 5.600e+02]]
sample.target
 [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 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
one-hot style
 [[1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
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 [1 0 0]
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 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 1 0]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
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 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]]
X:
 [[ 1.51861254 -0.5622498   0.23205254 ...  0.36217728  1.84791957
   1.01300893]
 [ 0.24628963 -0.49941338 -0.82799632 ...  0.40605066  1.1134493
   0.96524152]
 [ 0.19687903  0.02123125  1.10933436 ...  0.31830389  0.78858745
   1.39514818]
 ...
 [ 0.33275817  1.74474449 -0.38935541 ... -1.61212515 -1.48544548
   0.28057537]
 [ 0.20923168  0.22769377  0.01273209 ... -1.56825176 -1.40069891
   0.29649784]
 [ 1.39508604  1.58316512  1.36520822 ... -1.52437837 -1.42894777
  -0.59516041]]
Y:
 [[1 0 0]
 [1 0 0]
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 [0 0 1]
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 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]]

(python:144166): Gtk-WARNING **: 00:17:07.889: Theme parsing error: gtk.css:5597:11: Not using units is deprecated. Assuming 'px'.

(python:144166): Gtk-WARNING **: 00:17:07.889: Theme parsing error: gtk.css:5597:14: '0' is not a valid color name

(python:144166): Gtk-WARNING **: 00:17:07.889: Theme parsing error: gtk.css:5831:14: '202020' is not a valid color name

(python:144166): Gtk-WARNING **: 00:17:07.889: Theme parsing error: gtk.css:5846:20: Junk at end of value for border-width

(python:144166): Gtk-WARNING **: 00:17:07.889: Theme parsing error: gtk.css:5871:20: Junk at end of value for border-width

(python:144166): Gtk-WARNING **: 00:17:07.889: Theme parsing error: gtk.css:5898:20: Junk at end of value for border-width
### getRemovalIndicesByFixedScoreXStd ###
Removeable Variable's Indices:
 [4 5 7 8]
Optimal Variable's Indices:
 {0, 1, 2, 3, 6, 9, 10, 11, 12}
### getRemovalIndicesByUpdatingScoreXStd ###
Removeable Variable's Indices:
 [4 5 7 8]
Optimal Variable's Indices:
 {0, 1, 2, 3, 6, 9, 10, 11, 12}
### getRemovalIndicesByFixedVariableImportanceInProjection ###
Removeable Variable's Indices:
 [4]
Optimal Variable's Indices:
 {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}
### getRemovalIndicesByUpdatingVariableImportanceInProjection ###
Removeable Variable's Indices:
 [4]
Optimal Variable's Indices:
 {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}
### getRemovalIndicesBySequentialSearch ###
Removeable Variable's Indices:
 [ 8 10  5  4]
Optimal Variable's Indices:
 {0, 1, 2, 3, 6, 7, 9, 11, 12}
### getIndicesBySuccessiveProjectionAlgorithm ###
Optimal Variable's Indices:
 [ 6 12  9  0  2  3 11 10  1  7]
Removeable Variable's Indices:
 {8, 4, 5}

plot

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Variable selection for NIR spectral analysis(regression and classification) based on WRC, VIP, SFS, and SPA

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mlr spectroscopy nir pls plsda

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