title: SVM: Different scores between grid search results and mine, an entire class absence and wrong prediction. Why?
body: <p>I have 155 images and 8 classes
  Provided that the features are not scaled in range [0-1].</p>
  
  <p>grid search-cross validation suggest me  linear kernel and C = 1000 with this scores:</p>
  
  <pre><code>         precision    recall  f1-score   support
  
        1       0.54      0.88      0.67         8
        2       0.73      1.00      0.84         8
        3       1.00      1.00      1.00         6
        4       0.75      0.50      0.60        12
        5       0.83      0.83      0.83         6
        6       0.92      0.65      0.76        17
        7       0.71      0.42      0.53        12
        8       0.60      1.00      0.75         9
  
  avg / total       0.77      0.73      0.72        78 
  </code></pre>
  
  <p>but when i try linear kernel and C=1000 i obtain:</p>
  
  <pre><code>         precision    recall  f1-score   support
  
        1       0.00      0.00      0.00         0
        2       1.00      0.70      0.82        10
        3       1.00      1.00      1.00        13
        4       0.73      0.58      0.65        19
        5       1.00      0.95      0.97        19
        6       0.96      0.88      0.92        25
        7       0.82      0.67      0.73        27
        8       0.70      1.00      0.82        16
  
  avg / total       0.88      0.81      0.84       129
  
  
  Confusion matrix:
  [[ 0  0  0  0  0  0  0  0]
  [ 0  7  0  0  0  0  3  0]
  [ 0  0 13  0  0  0  0  0]
  [ 2  0  0 11  0  1  0  5]
  [ 0  0  0  1 18  0  0  0]
  [ 0  0  0  0  0 22  1  2]
  [ 6  0  0  3  0  0 18  0]
  [ 0  0  0  0  0  0  0 16]]
  </code></pre>
  
  <p>Why class 1 have all zeros?</p>
  
  <p>I saw also that with rbf kernel i have best results, but always zeros in first class:</p>
  
  <pre><code>         precision    recall  f1-score   support
  
        1       0.00      0.00      0.00         0
        2       1.00      1.00      1.00        10
        3       1.00      1.00      1.00        13
        4       0.94      0.89      0.92        19
        5       1.00      0.95      0.97        19
        6       0.93      1.00      0.96        25
        7       1.00      0.78      0.88        27
        8       1.00      1.00      1.00        16
  
  avg / total       0.98      0.93      0.95       129
  
  
  Confusion matrix:
  [[ 0  0  0  0  0  0  0  0]
   [ 0 10  0  0  0  0  0  0]
   [ 0  0 13  0  0  0  0  0]
   [ 1  0  0 17  0  1  0  0]
   [ 0  0  0  1 18  0  0  0]
   [ 0  0  0  0  0 25  0  0]
   [ 5  0  0  0  0  1 21  0]
   [ 0  0  0  0  0  0  0 16]]
  </code></pre>
  
  <p>at the end when i try to predict some same images of the training set</p>
  
  <pre><code>print(clf.predict(fv))
  </code></pre>
  
  <p>where fv is an image feature vector: </p>
  
  <pre><code>[0.16666666666628771, 5.169878828456423e-26, 2.3475644278196356e-21, 1.0, 1.0000000000027285]
  </code></pre>
  
  <p>and assigns to the feature vector a wrong class! (i.e. image owns 4 class but predict() outcome is 5 class)</p>
  
  <p><strong>RE-UPDATE</strong></p>
  
  <p>image set: <a href="https://docs.google.com/file/d/0ByS6Z5WRz-h2V3RkejFkb21Fb0E/edit?usp=sharing" rel="nofollow">https://docs.google.com/file/d/0ByS6Z5WRz-h2V3RkejFkb21Fb0E/edit?usp=sharing</a></p>
  
  <p>features imaage set: <a href="https://docs.google.com/file/d/0ByS6Z5WRz-h2YlhuUmFBaElXVEE/edit?usp=sharing" rel="nofollow">https://docs.google.com/file/d/0ByS6Z5WRz-h2YlhuUmFBaElXVEE/edit?usp=sharing</a></p>
  
  <p>FULL CODE:</p>
  
  <pre><code>import os
  import glob
  import numpy as np
  from numpy import array
  import cv2
  
  target = [      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,
            2,2,2,2,3,3,3,
            3,3,3,3,3,3,3,
            3,3,3,4,4,4,4,
            4,4,4,4,4,4,4,
            4,4,4,4,4,4,4,        
            4,5,5,5,5,5,5,
            5,5,5,5,5,5,5,
            5,5,5,5,5,5,6,
            6,6,6,6,6,6,6,
            6,6,6,6,6,6,6,
            6,6,6,6,6,6,6,
            6,6,6,7,7,7,7,               
            7,7,7,7,7,7,7,
            7,7,7,7,7,7,7,
            7,7,7,7,7,7,7,                  
            7,7,8,8,8,8,8,
            8,8,8,8,8,8,8,
            8,8,8,8]
  
  
  
  features = [ [0.26912666717306399, 0.012738398606387012, 0.011347858467581035, 0.1896938013442868, 2.444553429782046]
  ,
  [0.36793086934925351, 0.034364344308391102, 0.019054536791551006, 0.0076875387476751395, 3.03091214703604]
  ,
  [0.36793086934925351, 0.034364344308391102, 0.019054536791551006, 0.0076875387476751395, 3.03091214703604]
  ,
  [0.30406240228443038, 0.047100329090555518, 0.0049653458889261448, 0.0004618404341300081, 5.987025009738751]
  ,
  [0.36660353297714748, 0.034256126367653919, 0.01892501331178556, 0.007723901183105499, 3.0392760101225234]
  ,
  [0.26708884220978957, 0.012126741224471632, 0.0063753119877062942, 0.0005937801528983894, 2.403113171408598]
  ,
  [0.27070254516425241, 0.01293684867974746, 0.01159661796151442, 0.008380724334031727, 2.4492688425144986]
  ,
  [0.27076540467770038, 0.012502407901054009, 0.011180048331833999, 0.0007116977225672878, 2.4068989750876266]
  ,
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  ,
  [0.22374814412737717, 0.0095258889624651646, 0.0040833924467236719, 0.1884906960716747, 2.5474055920602514]
  ,
  [0.23860556210266026, 0.0067860933136106557, 0.0052050705189953389, 0.01498751040799334, 2.0545849084769694]
  ,
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  ,
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  ,
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  ,
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  ,
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  ,
  [0.97303451800669749, 0.075165987107118692, 0.23350656471824954, 0.04989418850724402, 1.7845923298199189]
  ,
  [0.32292438991638828, 0.0078312712861588109, 0.018256154769458615, 0.05861489639723726, 1.754975905310628]
  ,
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  ,
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  ,
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  ,
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  ,
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  ,
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  ,
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  ,
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  ,
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  ,
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  ,
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  ,
  [0.61892415483059771, 0.1855733578950316, 0.024118739298890277, 0.00010742003920831431, 5.579333799263049]
  ,
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  ,
  [0.64410628333823972, 0.20178318336365086, 0.039546324622261202, 8.006565383614564e-05, 5.609490756132282]
  ,
  [0.6214309265075304, 0.18779664186718673, 0.061337975720487534, 0.006350402281839464, 5.608301926807521]
  ,
  [0.20135445416653119, 0.0070220507238874311, 0.0027092098815647042, 0.4125833006664053, 2.4256545571324732]
  ,
  [0.20123494853445922, 0.0069845347246147793, 0.0027020357704780201, 0.4106724003127443, 2.420576584506546]
  ,
  [0.2015816556223165, 0.0070631416111702362, 0.0025149608542164329, 0.4106073986851143, 2.4300340608128606]
  ,
  [0.70115857527896985, 0.35625759453714789, 0.028386898853323388, 0.001234186979327368, 12.446918085552586]
  ,
  [0.68366020888533297, 0.2387861974848598, 0.04047049559400958, 0.0725675987982436, 6.011803834536788]
  ,
  [0.70115857527896985, 0.35625759453714789, 0.028386898853323388, 0.001234186979327368, 12.446918085552586]
  ,
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  ,
  [0.72219309919241148, 0.37567368174335658, 0.029371875736917675, 0.48066298342541436, 12.21840343375]
  ,
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  [0.53892887245870857, 0.12723100136939183, 0.047871070696486759, 0.0003630422944273008, 4.914680204854179]
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  ,
  [0.53876191407701801, 0.12675358533640296, 0.048092146277654686, 0.0003630422944273008, 4.896575690597256]
  ,
  [0.64579700029686937, 0.053345962571719745, 0.047671705312373282, 0.00021581957483543757, 2.1135534993967275]
  ,
  [0.52907834506993823, 0.11839951044942501, 0.046693278117526091, 0.001802451333813987, 4.720197357775248]
  ,
  [0.62431811267333093, 0.16822847351832676, 0.078460359627903944, 0.0002954864445593558, 4.830349593161275]
  ,
  [0.52957671831590236, 0.1206620716356978, 0.044424337085019652, 0.00036251586006887804, 4.812745400588476]
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  [0.64778861076667615, 0.011264454903514588, 0.26034582337509793, 0.00017355085039916696, 1.3918887090929497]
  ,
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               ]
  
  
  
  
  ############################ PREDICTION TEST 1 IMAGE ################
  print("TRY IMAGE")
  import numpy as np
  from sklearn import svm, metrics
  X = features
  y = target
  from sklearn.svm import SVC
  C = 1000.0
  clf = svm.SVC(kernel='rbf', C=C).fit(X, y)
  #svm.SVC(kernel='linear', C=C).fit(X, y) #SVC()
  #clf.fit(X, y)
  print("predizione")
  
  #fv is class 8 but show me 5
  fv = [0.16666666666628771, 5.169878828456423e-26, 2.584939414228212e-22, 1.0, 1.0000000000027285]
  print(fv)
  print(clf.predict([fv]))
  
  
  
  
  ############### METRICS ##########
  
  
  # We learn the digits on the first half of the digits
  
  
  # Now predict the value of the digit on the second half:
  import matplotlib.pyplot as plt
  
  expected = y[26:]
  predicted = clf.predict(X[26:])
  print("expected")
  print(len(expected))
  print("predicted")
  print(len(predicted))
  
  print "Classification report for classifier %s:\n%s\n" % (
      clf, metrics.classification_report(expected, predicted))
  print "Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted)
  </code></pre>