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Make necessary imports





In [1]:



    


import sys
sys.path.insert(0, '../')
import pandas as pd
import numpy as np
np.set_printoptions(precision=3, linewidth=200, suppress=True)
from library.datasets.cifar10 import CIFAR10
from library.plot_tools import plot_tools
from sklearn.model_selection import KFold, train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.model_selection import cross_val_score, learning_curve
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.externals import joblib
import time
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import sklearn.metrics as skm
import matplotlib.pyplot as plt
from library.utils import file_utils
from scipy.misc import toimage
%matplotlib inline



    


















    






None

















In [2]:



    


from sklearn import svm
from library.hog.hog import HOG

Step 0: Parameters for the experiment





In [3]:



    


exp_no = 102
file_no = 6
one_hot = False
data_source = 'Website'
train_validate_split = 0.2
train_validate_split_data = None
num_images_required = 0.3
scale_method = 'StandardScaler'
output_directory = '../logs/cifar10/' + str(file_no).zfill(2) + '_svm_hog_cross_val/' + 'exp_no_' + str(exp_no).zfill(3) + '/'



    











In [4]:



    


total_time = 0
svm_max_iter = 10000
svm_cs = 500
search_method = 'grid'
exp_jobs = 10
num_folds = 10



    











In [5]:



    


# param_grid = [
#   {'C': [1, 10, 100, 1000], 'kernel': ['linear']},
#   {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},
#  ]
param_grid = [
    {'C': [1, 10, 100, 1000], 'gamma': [0.1, 0.01, 0.001, 0.0001], 'kernel': ['rbf']},
    {'C': [1, 10], 'gamma': [0.01, 0.001], 'kernel': ['poly']},
    {'C': [1, 10], 'gamma': [0.01, 0.001], 'kernel': ['sigmoid']},
]
param_name = 'exp_' + str(exp_no).zfill(3)

Step 1: Load CIFAR 10 HOG dataset amd visualize CIFAR 10 dataset





In [6]:



    


block_size = (8,8)
cell_size = (4,4)
nbins = 9



    











In [7]:



    


start = time.time()
cifar10 = CIFAR10(one_hot_encode=one_hot, num_images=num_images_required, image_mode='grey',
                  train_validate_split=train_validate_split_data, endian='little')
cifar10.load_data(train=True, test=True, data_directory='./datasets/cifar10/')
end = time.time()
print('[ Step 0] Dataset loaded in %5.6f ms' %((end-start)*1000))
print('Dataset size: ' + str(cifar10.train.data.shape))
num_train_images = cifar10.train.data.shape[0]
total_time += (end-start)



    


















    






Loading CIFAR 10 Dataset
Downloading and extracting CIFAR 10 file
MD5sum of the file: ./datasets/cifar10/cifar-10.tar.gz is verified
Loading 15000 train images
Loading CIFAR 10 Training Dataset
Reading unpicked data file: ./datasets/cifar10/cifar-10-batches/data_batch_1
Reading unpicked data file: ./datasets/cifar10/cifar-10-batches/data_batch_2
Reading unpicked data file: ./datasets/cifar10/cifar-10-batches/data_batch_3
Reading unpicked data file: ./datasets/cifar10/cifar-10-batches/data_batch_4
Reading unpicked data file: ./datasets/cifar10/cifar-10-batches/data_batch_5
Loading 10000 test images
Loading CIFAR 10 Test Dataset
Unpickling test file: ./datasets/cifar10/cifar-10-batches/test_batch
Reading unpicked test file: ./datasets/cifar10/cifar-10-batches/test_batch
Loaded CIFAR 10 Dataset in 1.7394 seconds
[ Step 0] Dataset loaded in 1740.109205 ms
Dataset size: (15000, 3072)

















In [8]:



    


cifar10.plot_sample(plot_data=True, plot_test=True, fig_size=(7, 7))



    


















    






Plotting CIFAR 10 Train Dataset










    

















    






Plotting CIFAR 10 Test Dataset










    
























In [9]:



    


cifar10.plot_images(cifar10.train.images[:50, :], cifar10.train.class_names[:50], 
                    nrows=5, ncols=10, fig_size=(20,50), fontsize=35, convert=False)



    


















    

















    
Out[9]:








True

Step 1.1: Make CIFAR 10 HOG Train dataset





In [10]:



    


start = time.time()
data_hog = []
feature_size = 0
print('Block size     : ' + str(block_size))
print('Cell size      : ' + str(cell_size))
print('Number of bins : ' + str(nbins))
hog = HOG(block_size=block_size, cell_size=cell_size, nbins=nbins)
print('Generating HOG features for %d data images' %cifar10.train.images.shape[0])
for fig_num in range(cifar10.train.images.shape[0]):
    img = cifar10.train.images[fig_num, :]
    gradients = hog.make_hog_gradients(img.astype('uint8'))
    data_hog.append(gradients.flatten())
    feature_size = gradients.size
data_hog = np.array(data_hog)
print('HOG Features for data: ' + str(data_hog.shape))
end = time.time()
print('Generated HOG for train images in %.6f ms' %((end-start)*1000))



    


















    






Block size     : (8, 8)
Cell size      : (4, 4)
Number of bins : 9
Generating HOG features for 15000 data images
HOG Features for data: (15000, 576)
Generated HOG for train images in 7309.341669 ms

Step 1.2: Make CIFAR 10 HOG Test dataset





In [11]:



    


start = time.time()
test_hog = []
feature_size = 0
print('Generating HOG features for %d test images' %cifar10.test.images.shape[0])
for fig_num in range(cifar10.test.images.shape[0]):
    img = cifar10.test.images[fig_num, :]
    gradients = hog.make_hog_gradients(img.astype('uint8'))
    test_hog.append(gradients.flatten())
    feature_size = gradients.size
test_hog = np.array(test_hog)
print('HOG Features for test: ' + str(test_hog.shape))
end = time.time()
print('Generated HOG for test images in %.6f ms' %((end-start)*1000))



    


















    






Generating HOG features for 10000 test images
HOG Features for test: (10000, 576)
Generated HOG for test images in 4695.998669 ms

Step 2: Preprocess data





In [12]:



    


start = time.time()
if scale_method == 'StandardScaler':
    ss = StandardScaler()
elif scale_method == 'MinMaxScaler':
    ss = MinMaxScaler()
else:
    ss = StandardScaler()
data_images = ss.fit_transform(data_hog)
test_images = ss.fit_transform(test_hog)
end = time.time()
print('[ Step 1] Dataset transformations done in %5.6f ms' %((end-start)*1000))
print('Training the classifier on %d images' % num_train_images)
print('Dataset size: ' + str(data_images.shape))
total_time += (end-start)



    


















    






[ Step 1] Dataset transformations done in 204.220533 ms
Training the classifier on 15000 images
Dataset size: (15000, 576)

Step 3: Parameters for estimating best model in SVM





In [13]:



    


print('Parameters to search for')
print('\n'.join([str(param) for param in param_grid])); print()



    


















    






Parameters to search for
{'C': [1, 10, 100, 1000], 'gamma': [0.1, 0.01, 0.001, 0.0001], 'kernel': ['rbf']}
{'C': [1, 10], 'gamma': [0.01, 0.001], 'kernel': ['poly']}
{'C': [1, 10], 'gamma': [0.01, 0.001], 'kernel': ['sigmoid']}


















In [14]:



    


scores = []
scores_mean = []
scores_std = []

Step 3.1: Run a search method for best parameters





In [15]:



    


svm_clf = svm.SVC(random_state=0, max_iter=svm_max_iter, cache_size=svm_cs, verbose=True)
print(svm_clf)



    


















    






SVC(C=1.0, cache_size=500, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=10000, probability=False, random_state=0, shrinking=True,
  tol=0.001, verbose=True)

















In [16]:



    


start = time.time()
if search_method == 'grid':
    print('Applying GridSearchCV')
    estimator = GridSearchCV(svm_clf, param_grid, cv=num_folds, scoring='accuracy', verbose=3, n_jobs=exp_jobs)
elif serach_method == 'random':
    print('Applying RandomizedSearchCV')
    estimator = RandomizedSearchCV(svm_clf, param_grid, cv=num_folds, scoring='accuracy', n_iter=10, 
                              random_state=0, verbose=3, n_jobs=exp_jobs)
else:
    print('Applying GridSearchCV')
    estimator = GridSearchCV(svm_clf, param_grid, cv=num_folds, scoring='accuracy', verbose=3, n_jobs=exp_jobs)
print(estimator)
estimator_result = estimator.fit(data_images, cifar10.train.class_labels)
end = time.time()
total_time += (end-start)
print('Total Time taken for cross validation and finding best parameters: %.4f ms' %((end-start)*1000))



    


















    






Applying GridSearchCV
GridSearchCV(cv=10, error_score='raise',
       estimator=SVC(C=1.0, cache_size=500, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=10000, probability=False, random_state=0, shrinking=True,
  tol=0.001, verbose=True),
       fit_params={}, iid=True, n_jobs=10,
       param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.1, 0.01, 0.001, 0.0001], 'kernel': ['rbf']}, {'C': [1, 10], 'gamma': [0.01, 0.001], 'kernel': ['poly']}, {'C': [1, 10], 'gamma': [0.01, 0.001], 'kernel': ['sigmoid']}],
       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,
       scoring='accuracy', verbose=3)
Fitting 10 folds for each of 24 candidates, totalling 240 fits
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.103264, total= 6.4min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.103654, total= 6.4min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102735, total= 6.3min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.103654, total= 6.4min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.103540, total= 6.4min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.103792, total= 6.4min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102735, total= 6.4min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.103471, total= 6.4min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102941, total= 6.4min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102872, total= 6.5min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.360133, total= 6.1min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.342648, total= 6.1min
[CV] C=1, gamma=0.001, kernel=rbf ....................................










    






[Parallel(n_jobs=10)]: Done  12 tasks      | elapsed: 17.9min










    






[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.349101, total= 6.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.376744, total= 6.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.348232, total= 6.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.373582, total= 6.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.358478, total= 6.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.363393, total= 6.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.384102, total= 6.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.366979, total= 6.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.574751, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.595349, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.572285, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.574850, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.596526, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.575050, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.596128, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.575818, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.591061, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.572193, total= 3.1min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.521595, total= 3.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.510963, total= 3.7min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.507651, total= 3.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.514343, total= 3.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.486342, total= 3.7min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.528705, total= 3.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.501002, total= 3.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.529726, total= 3.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.525017, total= 3.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.502674, total= 3.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.104319, total= 6.3min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.104319, total= 6.4min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102735, total= 6.3min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.103264, total= 6.3min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102735, total= 6.3min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.105544, total= 6.3min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102872, total= 6.3min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.104139, total= 6.4min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.103792, total= 6.5min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102941, total= 6.3min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.405316, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.379747, total= 6.1min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.372588, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.378738, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.368245, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.400934, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.410822, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.391856, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.396390, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.388110, total= 6.2min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.593355, total= 3.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.591362, total= 3.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.585724, total= 3.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.599600, total= 3.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.612150, total= 3.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.594145, total= 3.8min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.607071, total= 3.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.615230, total= 3.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.582888, total= 3.6min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.587842, total= 3.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.530233, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.545515, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.508994, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.534930, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.527018, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.555704, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.552737, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.527054, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.549098, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.540107, total= 2.7min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.104319, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.103792, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.104319, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.103264, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102735, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102735, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102872, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102941, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.104139, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.105544, total= 6.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.405316, total= 6.2min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.378738, total= 6.1min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.368245, total= 6.1min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.379747, total= 6.2min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.372588, total= 6.2min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.400934, total= 6.2min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.388110, total= 6.1min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.391856, total= 6.2min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.410822, total= 6.2min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.396390, total= 6.2min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.590033, total= 3.8min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.590033, total= 3.8min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.594810, total= 3.7min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.602931, total= 3.7min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.585057, total= 3.8min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.603736, total= 3.8min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.614152, total= 3.7min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.618570, total= 3.7min
[CV] C=100, gamma=0.0001, kernel=rbf .................................










    






[Parallel(n_jobs=10)]: Done 108 tasks      | elapsed: 83.3min










    






[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.583556, total= 3.7min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.588510, total= 3.8min
[CV] C=100, gamma=0.0001, kernel=rbf .................................










    






/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)










    






[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.523588, total= 2.6min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.528277, total= 2.6min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.530233, total= 2.7min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.524317, total= 2.6min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.525684, total= 2.6min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.537742, total= 2.6min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.534356, total= 2.6min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.543391, total= 2.7min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.533422, total= 2.6min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.534402, total= 2.7min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.104319, total= 6.3min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.104319, total= 6.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.103264, total= 6.3min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.103792, total= 6.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102735, total= 6.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102735, total= 6.3min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.104139, total= 6.3min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.105544, total= 6.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102941, total= 6.3min
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102872, total= 6.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.405316, total= 6.2min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.372588, total= 6.1min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.378738, total= 6.2min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.379747, total= 6.2min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.368245, total= 6.1min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.400934, total= 6.2min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.391856, total= 6.2min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.410822, total= 6.1min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.388110, total= 6.2min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.396390, total= 6.2min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.594810, total= 3.7min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.590033, total= 3.7min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.590033, total= 3.7min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.602931, total= 3.7min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.614152, total= 3.6min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.603736, total= 3.7min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.585057, total= 3.7min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.618570, total= 3.7min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.583556, total= 3.6min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.588510, total= 3.7min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................










    






/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)










    






[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.541639, total= 2.5min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.522924, total= 2.6min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.517631, total= 2.6min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.522924, total= 2.6min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.497665, total= 2.5min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.517011, total= 2.6min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.536716, total= 2.6min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.540414, total= 2.6min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.522378, total= 2.6min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.518717, total= 2.6min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.572757, total= 5.2min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.600000, total= 5.2min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.595603, total= 5.2min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.579508, total= 5.3min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.577051, total= 5.2min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.591061, total= 5.2min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.584502, total= 5.2min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.594522, total= 5.3min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.590788, total= 5.3min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.581551, total= 5.3min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.338870, total= 5.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.362791, total= 5.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.353764, total= 5.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.345309, total= 5.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.340227, total= 5.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.377585, total= 5.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.381842, total= 5.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.350033, total= 5.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.343353, total= 5.1min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.354947, total= 5.1min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.572757, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.600000, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.579508, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.595603, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.577051, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.591061, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.590788, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.594522, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.584502, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.581551, total= 5.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.532890, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.576080, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.534356, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.542914, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.562292, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.562375, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.554740, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.559786, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.553774, total= 4.9min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.550134, total= 4.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.239203, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.277076, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.253165, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.267465, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.269513, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.241494, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.261015, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.261857, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.253173, total= 2.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.258690, total= 2.1min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.514950, total= 2.5min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.519601, total= 2.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.486342, total= 2.5min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.514305, total= 2.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.511674, total= 2.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.521681, total= 2.6min
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.530040, total= 2.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.537074, total= 2.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.512358, total= 2.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.516711, total= 2.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.235216, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.261477, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.274419, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.247835, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.266845, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.239493, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.255674, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.256513, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.254679, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.256513, total= 2.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................










    






/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)










    






[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.465116, total= 2.0min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.471058, total= 2.0min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.463123, total= 2.0min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.452365, total= 2.0min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.473649, total= 2.0min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.471648, total= 2.0min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.489987, total= 2.0min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.482966, total= 2.0min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.471610, total= 2.1min
[LibSVM][CV]  C=10, gamma=0.001, kernel=sigmoid, score=0.472594, total= 2.1min










    






[Parallel(n_jobs=10)]: Done 240 out of 240 | elapsed: 162.8min finished










    






[LibSVM]Total Time taken for cross validation and finding best parameters: 9906009.2649 ms










    






/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
  % self.max_iter, ConvergenceWarning)

















In [17]:



    


print('\n'.join('{}: {}'.format(k, v) for k, v in estimator.cv_results_.items())); print()
print('Scores for each set of parameters')
print('\n'.join([str(param) for param in estimator.grid_scores_])); print()
print('Best score')
print(estimator.best_score_); print()
print('Parameters corresponding to best score')
print(estimator.best_params_); print()



    


















    






split0_test_score: [ 0.104  0.377  0.575  0.511  0.104  0.405  0.593  0.53   0.104  0.405  0.59   0.524  0.104  0.405  0.59   0.523  0.573  0.339  0.573  0.533  0.239  0.515  0.235  0.465]
split1_test_score: [ 0.104  0.36   0.595  0.522  0.104  0.379  0.591  0.546  0.104  0.379  0.59   0.53   0.104  0.379  0.59   0.523  0.6    0.363  0.6    0.576  0.277  0.52   0.274  0.463]
split2_test_score: [ 0.104  0.343  0.575  0.508  0.104  0.373  0.594  0.535  0.104  0.373  0.595  0.528  0.104  0.373  0.595  0.518  0.58   0.345  0.58   0.543  0.267  0.514  0.261  0.471]
split3_test_score: [ 0.103  0.349  0.572  0.486  0.103  0.38   0.6    0.509  0.103  0.38   0.603  0.524  0.103  0.38   0.603  0.542  0.596  0.354  0.596  0.562  0.253  0.486  0.248  0.452]
split4_test_score: [ 0.103  0.348  0.575  0.514  0.103  0.368  0.586  0.527  0.103  0.368  0.585  0.526  0.103  0.368  0.585  0.498  0.577  0.34   0.577  0.534  0.27   0.512  0.267  0.474]
split5_test_score: [ 0.103  0.374  0.591  0.525  0.103  0.401  0.607  0.556  0.103  0.401  0.604  0.534  0.103  0.401  0.604  0.517  0.591  0.378  0.591  0.562  0.241  0.522  0.239  0.472]
split6_test_score: [ 0.103  0.358  0.596  0.529  0.104  0.392  0.612  0.553  0.104  0.392  0.614  0.543  0.104  0.392  0.614  0.537  0.591  0.382  0.591  0.555  0.261  0.53   0.256  0.49 ]
split7_test_score: [ 0.103  0.384  0.597  0.53   0.103  0.411  0.615  0.549  0.103  0.411  0.619  0.538  0.103  0.411  0.619  0.54   0.595  0.35   0.595  0.56   0.262  0.537  0.257  0.483]
split8_test_score: [ 0.104  0.363  0.576  0.501  0.106  0.388  0.588  0.527  0.106  0.388  0.589  0.534  0.106  0.388  0.589  0.522  0.585  0.343  0.585  0.554  0.253  0.512  0.257  0.472]
split9_test_score: [ 0.103  0.367  0.572  0.503  0.103  0.396  0.583  0.54   0.103  0.396  0.584  0.533  0.103  0.396  0.584  0.519  0.582  0.355  0.582  0.55   0.259  0.517  0.255  0.473]
mean_test_score: [ 0.103  0.362  0.582  0.513  0.104  0.389  0.597  0.537  0.104  0.389  0.597  0.532  0.104  0.389  0.597  0.524  0.587  0.355  0.587  0.553  0.258  0.516  0.255  0.471]
std_test_score: [ 0.     0.013  0.01   0.013  0.001  0.014  0.011  0.014  0.001  0.014  0.012  0.006  0.001  0.014  0.012  0.012  0.009  0.014  0.009  0.013  0.011  0.013  0.011  0.01 ]
rank_test_score: [24 17  6 12 21 14  3  8 21 14  1  9 21 14  1 10  4 18  4  7 19 11 20 13]
split0_train_score: [ 1.     1.     0.767  0.552  1.     1.     0.999  0.658  1.     1.     1.     0.861  1.     1.     1.     1.     1.     0.545  1.     0.968  0.226  0.565  0.235  0.515]
split1_train_score: [ 1.     0.999  0.77   0.551  1.     1.     0.999  0.658  1.     1.     1.     0.858  1.     1.     1.     1.     1.     0.536  1.     0.968  0.219  0.569  0.231  0.517]
split2_train_score: [ 1.     0.999  0.769  0.551  1.     1.     0.999  0.657  1.     1.     1.     0.859  1.     1.     1.     1.     1.     0.532  1.     0.966  0.221  0.567  0.231  0.516]
split3_train_score: [ 1.     1.     0.769  0.554  1.     1.     0.999  0.663  1.     1.     1.     0.862  1.     1.     1.     1.     1.     0.538  1.     0.968  0.228  0.57   0.235  0.516]
split4_train_score: [ 1.     0.999  0.772  0.552  1.     1.     0.999  0.659  1.     1.     1.     0.859  1.     1.     1.     1.     1.     0.533  1.     0.968  0.229  0.568  0.232  0.514]
split5_train_score: [ 1.     0.999  0.77   0.548  1.     1.     0.998  0.657  1.     1.     1.     0.858  1.     1.     1.     1.     1.     0.537  1.     0.969  0.224  0.565  0.229  0.51 ]
split6_train_score: [ 1.     0.999  0.77   0.548  1.     1.     0.999  0.657  1.     1.     1.     0.858  1.     1.     1.     1.     1.     0.54   1.     0.969  0.219  0.565  0.23   0.51 ]
split7_train_score: [ 1.     0.999  0.769  0.549  1.     1.     0.998  0.657  1.     1.     1.     0.86   1.     1.     1.     1.     1.     0.545  1.     0.968  0.224  0.565  0.232  0.511]
split8_train_score: [ 1.     0.999  0.772  0.551  1.     1.     0.999  0.658  1.     1.     1.     0.861  1.     1.     1.     1.     1.     0.531  1.     0.967  0.226  0.57   0.236  0.51 ]
split9_train_score: [ 1.     0.999  0.77   0.553  1.     1.     0.998  0.661  1.     1.     1.     0.858  1.     1.     1.     1.     1.     0.545  1.     0.968  0.229  0.569  0.236  0.514]
mean_train_score: [ 1.     0.999  0.77   0.551  1.     1.     0.999  0.659  1.     1.     1.     0.86   1.     1.     1.     1.     1.     0.538  1.     0.968  0.225  0.567  0.233  0.513]
std_train_score: [ 0.     0.     0.001  0.002  0.     0.     0.     0.002  0.     0.     0.     0.001  0.     0.     0.     0.     0.     0.005  0.     0.001  0.004  0.002  0.003  0.003]
mean_fit_time: [ 365.786  352.27   169.464  200.78   362.827  353.361  206.219  146.419  361.246  351.991  209.416  143.449  363.126  351.857  205.54   139.308  299.313  285.786  291.279  274.148  117.424  139.784
  112.192  109.929]
std_fit_time: [ 2.802  1.401  0.955  1.215  2.348  1.891  1.798  0.672  0.895  1.671  2.512  1.664  0.982  1.32   1.999  0.722  1.803  1.547  1.49   1.669  0.949  0.846  0.882  0.823]
mean_score_time: [ 17.912  17.999  16.075  17.182  17.914  18.2    16.39   15.426  18.059  18.224  16.47   15.038  17.945  18.114  15.221  14.439  14.488  15.205  14.486  14.515  13.043  13.643  12.792  12.158]
std_score_time: [ 0.199  0.176  0.21   0.12   0.484  0.351  0.21   0.091  0.227  0.326  0.218  0.082  0.443  0.298  0.178  0.238  0.114  0.063  0.092  0.075  0.13   0.101  0.125  0.061]
param_C: [1 1 1 1 10 10 10 10 100 100 100 100 1000 1000 1000 1000 1 1 10 10 1 1 10 10]
param_gamma: [0.1 0.01 0.001 0.0001 0.1 0.01 0.001 0.0001 0.1 0.01 0.001 0.0001 0.1 0.01 0.001 0.0001 0.01 0.001 0.01 0.001 0.01 0.001 0.01 0.001]
param_kernel: ['rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'poly' 'poly' 'poly' 'poly' 'sigmoid' 'sigmoid' 'sigmoid' 'sigmoid']
params: ({'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}, {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}, {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}, {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}, {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}, {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}, {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}, {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}, {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}, {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}, {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}, {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}, {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}, {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}, {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}, {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}, {'C': 1, 'gamma': 0.01, 'kernel': 'poly'}, {'C': 1, 'gamma': 0.001, 'kernel': 'poly'}, {'C': 10, 'gamma': 0.01, 'kernel': 'poly'}, {'C': 10, 'gamma': 0.001, 'kernel': 'poly'}, {'C': 1, 'gamma': 0.01, 'kernel': 'sigmoid'}, {'C': 1, 'gamma': 0.001, 'kernel': 'sigmoid'}, {'C': 10, 'gamma': 0.01, 'kernel': 'sigmoid'}, {'C': 10, 'gamma': 0.001, 'kernel': 'sigmoid'})

Scores for each set of parameters
mean: 0.10327, std: 0.00039, params: {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}
mean: 0.36233, std: 0.01270, params: {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}
mean: 0.58240, std: 0.01025, params: {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}
mean: 0.51280, std: 0.01319, params: {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}
mean: 0.10367, std: 0.00088, params: {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}
mean: 0.38927, std: 0.01356, params: {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}
mean: 0.59693, std: 0.01065, params: {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}
mean: 0.53713, std: 0.01363, params: {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}
mean: 0.10367, std: 0.00088, params: {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}
mean: 0.38927, std: 0.01356, params: {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}
mean: 0.59713, std: 0.01156, params: {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}
mean: 0.53153, std: 0.00601, params: {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}
mean: 0.10367, std: 0.00088, params: {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}
mean: 0.38927, std: 0.01356, params: {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}
mean: 0.59713, std: 0.01156, params: {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}
mean: 0.52380, std: 0.01248, params: {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}
mean: 0.58673, std: 0.00851, params: {'C': 1, 'gamma': 0.01, 'kernel': 'poly'}
mean: 0.35487, std: 0.01423, params: {'C': 1, 'gamma': 0.001, 'kernel': 'poly'}
mean: 0.58673, std: 0.00851, params: {'C': 10, 'gamma': 0.01, 'kernel': 'poly'}
mean: 0.55293, std: 0.01272, params: {'C': 10, 'gamma': 0.001, 'kernel': 'poly'}
mean: 0.25827, std: 0.01132, params: {'C': 1, 'gamma': 0.01, 'kernel': 'sigmoid'}
mean: 0.51647, std: 0.01265, params: {'C': 1, 'gamma': 0.001, 'kernel': 'sigmoid'}
mean: 0.25487, std: 0.01117, params: {'C': 10, 'gamma': 0.01, 'kernel': 'sigmoid'}
mean: 0.47140, std: 0.00978, params: {'C': 10, 'gamma': 0.001, 'kernel': 'sigmoid'}

Best score
0.597133333333

Parameters corresponding to best score
{'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}











    






/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20
  DeprecationWarning)

Step 3.1.1 Plot error lines showing +/- std. errors of the scores





In [18]:



    


means = estimator_result.cv_results_['mean_test_score']
stds = estimator_result.cv_results_['std_test_score']
params = estimator_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))



    


















    






0.103267 (0.000390) with: {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}
0.362333 (0.012698) with: {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}
0.582400 (0.010246) with: {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}
0.512800 (0.013188) with: {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}
0.103667 (0.000875) with: {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}
0.389267 (0.013561) with: {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}
0.596933 (0.010638) with: {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}
0.537133 (0.013633) with: {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}
0.103667 (0.000875) with: {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}
0.389267 (0.013561) with: {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}
0.597133 (0.011556) with: {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}
0.531533 (0.006005) with: {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}
0.103667 (0.000875) with: {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}
0.389267 (0.013561) with: {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}
0.597133 (0.011556) with: {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}
0.523800 (0.012476) with: {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}
0.586733 (0.008513) with: {'C': 1, 'gamma': 0.01, 'kernel': 'poly'}
0.354867 (0.014229) with: {'C': 1, 'gamma': 0.001, 'kernel': 'poly'}
0.586733 (0.008513) with: {'C': 10, 'gamma': 0.01, 'kernel': 'poly'}
0.552933 (0.012734) with: {'C': 10, 'gamma': 0.001, 'kernel': 'poly'}
0.258267 (0.011333) with: {'C': 1, 'gamma': 0.01, 'kernel': 'sigmoid'}
0.516467 (0.012648) with: {'C': 1, 'gamma': 0.001, 'kernel': 'sigmoid'}
0.254867 (0.011180) with: {'C': 10, 'gamma': 0.01, 'kernel': 'sigmoid'}
0.471400 (0.009778) with: {'C': 10, 'gamma': 0.001, 'kernel': 'sigmoid'}

Step 4: Save the generated model to file





In [19]:



    


start = time.time()
file_utils.mkdir_p(output_directory)
model_output_path = output_directory + '06_' + param_name + '.pkl'
joblib.dump(estimator, model_output_path)
end = time.time()
print('[ Step 4] Write obtained model to %s in %.6f ms' %(model_output_path, ((end-start)*1000)))
total_time += (end-start)



    


















    






[ Step 4] Write obtained model to ../logs/cifar10/06_svm_hog_cross_val/exp_no_102/06_exp_102.pkl in 87.001324 ms

















In [20]:



    


scores = []
exp = []
dict_key = ['train', 'test']
for key in dict_key:
    scores_list = []
    for i in range(num_folds):
        key_name = 'split' + str(i) + '_' + key + '_score'
        scores_list.append(estimator.cv_results_[key_name].tolist())
    scores.append(scores_list)
scores = np.array(scores)
means = np.mean(np.array(scores).T, axis=1)
stds = np.std(np.array(scores).T, axis=1)



    











In [21]:



    


plot_tools.plot_variance(scores, means, stds, legend=['Training data', 'Validation data'], 
                         plot_title=['Train scores for best parameters for SVC using HOG features in CIFAR 10',
                                     'Validation scores for best parameters for SVC using HOG features in CIFAR 10'], 
                         fig_size=(800,600), 
                         plot_xlabel=['SVC Parameters', 'SVC Parameters'],
                         plot_ylabel=['Training accuracy of the model', 'Validation accuracy of the model'],
                         plot_lib='bokeh', 
                         matplotlib_style='default', bokeh_notebook=True)



    


















    







    

        
        Loading BokehJS ...
    










    




















    






/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: Plot.background_fill was deprecated in Bokeh 0.11.0 and will be removed, use Plot.background_fill_color instead.
  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: 
Supplying a user-defined data source AND iterable values to glyph methods is deprecated.

See https://github.com/bokeh/bokeh/issues/2056 for more information.

  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: 
Supplying a user-defined data source AND iterable values to glyph methods is deprecated.

See https://github.com/bokeh/bokeh/issues/2056 for more information.

  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: 
Supplying a user-defined data source AND iterable values to glyph methods is deprecated.

See https://github.com/bokeh/bokeh/issues/2056 for more information.

  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: 
Supplying a user-defined data source AND iterable values to glyph methods is deprecated.

See https://github.com/bokeh/bokeh/issues/2056 for more information.

  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: Plot.background_fill was deprecated in Bokeh 0.11.0 and will be removed, use Plot.background_fill_color instead.
  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: 
Supplying a user-defined data source AND iterable values to glyph methods is deprecated.

See https://github.com/bokeh/bokeh/issues/2056 for more information.

  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: 
Supplying a user-defined data source AND iterable values to glyph methods is deprecated.

See https://github.com/bokeh/bokeh/issues/2056 for more information.

  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: 
Supplying a user-defined data source AND iterable values to glyph methods is deprecated.

See https://github.com/bokeh/bokeh/issues/2056 for more information.

  warn(message)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: 
Supplying a user-defined data source AND iterable values to glyph methods is deprecated.

See https://github.com/bokeh/bokeh/issues/2056 for more information.

  warn(message)

Step 5: Run the predictor on test data and generate predictions





In [22]:



    


start = time.time()
prediction_numbers = estimator.predict(test_images)
prediction_classes = []
num_test_images = test_images.shape[0]
for i in range(num_test_images):
    prediction_classes.append(cifar10.classes[int(prediction_numbers[i])])
end = time.time()
print('[ Step 9] Make prediction on test dataset in %.6f ms' %((end-start)*1000))
total_time += (end-start)



    


















    






[ Step 9] Make prediction on test dataset in 76589.935064 ms

















In [23]:



    


cifar10.plot_images(cifar10.test.images[:50], cifar10.test.class_names[:50], cls_pred=prediction_classes[:50], 
                    nrows=5, ncols=10, fig_size=(20,50), fontsize=35, convert=False)



    


















    

















    
Out[23]:








True

Step 5.1 Print accuracy score of the classifier





In [24]:



    


start = time.time()
plot_tools.plot_confusion_matrix(cifar10.test.class_labels, prediction_numbers, classes=cifar10.classes,
                              normalize=True, title='Confusion matrix for test set using SVC for HOG features')
print(skm.classification_report(cifar10.test.class_labels, prediction_numbers, target_names=cifar10.classes))
test_accuracy = skm.accuracy_score(cifar10.test.class_labels, prediction_numbers, normalize=True)
print('Accuracy score on test data: ' + str(test_accuracy))
end = time.time()
total_time += (end-start)



    


















    






Confusion matrix, without normalization
[[682  25  77  24  30   9  18  11 107  17]
 [ 40 747  13  19  24   4  25   6  48  74]
 [ 85  10 495  87  90  90  70  30  32  11]
 [ 44  25 102 406  88 143  85  54  16  37]
 [ 48  13  95 107 514  46  63  77  21  16]
 [ 20  12  98 217  62 459  51  63   9   9]
 [ 25  25  61  78  53  42 677  12  18   9]
 [ 20   7  57  61  84  73  17 643  13  25]
 [ 92  74  33  25  13   8  11  19 686  39]
 [ 33  75  14  32  21  24   8  34  54 705]]
Normalized confusion matrix
[[ 0.682  0.025  0.077  0.024  0.03   0.009  0.018  0.011  0.107  0.017]
 [ 0.04   0.747  0.013  0.019  0.024  0.004  0.025  0.006  0.048  0.074]
 [ 0.085  0.01   0.495  0.087  0.09   0.09   0.07   0.03   0.032  0.011]
 [ 0.044  0.025  0.102  0.406  0.088  0.143  0.085  0.054  0.016  0.037]
 [ 0.048  0.013  0.095  0.107  0.514  0.046  0.063  0.077  0.021  0.016]
 [ 0.02   0.012  0.098  0.217  0.062  0.459  0.051  0.063  0.009  0.009]
 [ 0.025  0.025  0.061  0.078  0.053  0.042  0.677  0.012  0.018  0.009]
 [ 0.02   0.007  0.057  0.061  0.084  0.073  0.017  0.643  0.013  0.025]
 [ 0.092  0.074  0.033  0.025  0.013  0.008  0.011  0.019  0.686  0.039]
 [ 0.033  0.075  0.014  0.032  0.021  0.024  0.008  0.034  0.054  0.705]]










    

















    






             precision    recall  f1-score   support

   airplane       0.63      0.68      0.65      1000
 automobile       0.74      0.75      0.74      1000
       bird       0.47      0.49      0.48      1000
        cat       0.38      0.41      0.39      1000
       deer       0.53      0.51      0.52      1000
        dog       0.51      0.46      0.48      1000
       frog       0.66      0.68      0.67      1000
      horse       0.68      0.64      0.66      1000
       ship       0.68      0.69      0.68      1000
      truck       0.75      0.70      0.73      1000

avg / total       0.60      0.60      0.60     10000

Accuracy score on test data: 0.6014

















In [25]:



    


start = time.time()
print('Prediction done on %d images' %cifar10.test.data.shape[0])
print('Accuracy of the classifier: %.4f' %estimator.score(test_images, cifar10.test.class_labels))
end = time.time()
total_time += (end-start)



    


















    






Prediction done on 10000 images
Accuracy of the classifier: 0.6014

Step 6: Write predictions to csv file





In [26]:



    


start = time.time()
indices = np.arange(1, test_images.shape[0]+1)
predictions = np.column_stack((indices, prediction_classes))
file_utils.mkdir_p(output_directory)
output_csv_file = output_directory + '06_' + param_name + '.csv'
column_names = ['id', 'label']
predict_test_df = pd.DataFrame(data=predictions, columns=column_names)
predict_test_df.to_csv(output_csv_file, index=False)
end = time.time()
print('[ Step 6] Writing the test data to file: %s in %.6f ms' %(output_csv_file, (end-start)*1000))
total_time += (end-start)



    


















    






[ Step 6] Writing the test data to file: ../logs/cifar10/06_svm_hog_cross_val/exp_no_102/06_exp_102.csv in 12.004375 ms

Step 7: Write notebook to HTML





In [27]:



    


def output_HTML(read_file, output_file):
    from nbconvert import HTMLExporter
    import codecs
    import nbformat
    exporter = HTMLExporter()
    output_notebook = nbformat.read(read_file, as_version=4)
    print()
    output, resources = exporter.from_notebook_node(output_notebook)
    codecs.open(output_file, 'w', encoding='utf-8').write(output)



    











In [28]:



    


%%javascript
var notebook = IPython.notebook
notebook.save_notebook()



    


















    



























In [31]:



    


%%javascript
var kernel = IPython.notebook.kernel;
var thename = window.document.getElementById("notebook_name").innerHTML;
var command = "theNotebook = " + "'"+thename+"'";
kernel.execute(command);



    


















    



























In [32]:



    


current_file = './' + theNotebook + '.ipynb'
output_file = output_directory + str(file_no).zfill(2) + '_exp_no_' + str(exp_no) + '_' + theNotebook + '.html'
print('Current file: ' + str(current_file))
print('Output file: ' + str(output_file))
file_utils.mkdir_p(output_directory) 
output_HTML(current_file, output_file)



    


















    






Current file: ./06_CIFAR_10_SVM_HOG_Features_Cross_Validation.ipynb
Output file: ../logs/cifar10/06_svm_hog_cross_val/exp_no_102/06_exp_no_102_06_CIFAR_10_SVM_HOG_Features_Cross_Validation.html


















In [33]:



    


print('Code took %.6f s to run on training with %d examples' % (total_time,num_train_images))



    


















    






Code took 10061.871141 s to run on training with 15000 examples

















In [ ]:

Content source: sonapraneeth-a/object-classification

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