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
In [3]:
total_time = 0
file_no = 5
exp_no = 102
output_directory = '../logs/cifar10/' + str(file_no).zfill(2) + '_svm_raw_cross_val/' + 'exp_no_' + str(exp_no).zfill(3) + '/'
In [4]:
data_source = 'Website'
search_method = 'grid'
train_validate_split_data=None
one_hot=False
In [5]:
svm_max_iter = 10000
svm_cs = 500
exp_jobs = 10
num_images_required = 0.3
num_folds = 10
In [6]:
# param_grid = [
# {'C': [1, 10, 100, 1000], 'kernel': ['linear']},
# {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001, 0.00001], '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)
In [7]:
start = time.time()
cifar10 = CIFAR10(one_hot_encode=one_hot, num_images=num_images_required,
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 1.9943 seconds
[ Step 0] Dataset loaded in 1994.961977 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.data[:50, :], cifar10.train.class_names[:50],
nrows=5, ncols=10, fig_size=(20,50), fontsize=35, convert=True)
Out[9]:
True
In [10]:
print('Training images')
print(cifar10.train.data[:5])
if one_hot is True:
print('Training labels')
print(cifar10.train.one_hot_labels[:5])
print('Training classes')
print(cifar10.train.class_labels[:5])
print('Testing images')
print(cifar10.test.data[:5])
if one_hot is True:
print('Testing labels')
print(cifar10.test.one_hot_labels[:5])
print('Testing classes')
print(cifar10.test.class_labels[:5])
print('[ Step 0.1] Working with only %d images' %num_train_images)
Training images
[[ 59 43 50 ..., 140 84 72]
[154 126 105 ..., 139 142 144]
[255 253 253 ..., 83 83 84]
[ 28 37 38 ..., 28 37 46]
[170 168 177 ..., 82 78 80]]
Training classes
[6 9 9 4 1]
Testing images
[[158 159 165 ..., 124 129 110]
[235 231 232 ..., 178 191 199]
[158 158 139 ..., 8 3 7]
[155 167 176 ..., 50 52 50]
[ 65 70 48 ..., 136 146 117]]
Testing classes
[3 8 8 0 6]
[ Step 0.1] Working with only 15000 images
In [11]:
start = time.time()
ss = StandardScaler()
data_images = ss.fit_transform(cifar10.train.data)
test_images = ss.fit_transform(cifar10.test.data)
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(cifar10.train.data.shape))
total_time += (end-start)
/net/voxel03/misc/me/praneethas/Softwares/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.
warnings.warn(msg, _DataConversionWarning)
[ Step 1] Dataset transformations done in 970.286846 ms
Training the classifier on 15000 images
Dataset size: (15000, 3072)
In [12]:
print('Parameters to serach for')
print('\n'.join([str(param) for param in param_grid])); print()
Parameters to serach 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 [13]:
scores = []
scores_mean = []
scores_std = []
In [14]:
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 [15]:
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 search_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=0.102941, total=24.7min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102735, total=24.8min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102735, total=24.8min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102872, total=24.8min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.103127, total=24.8min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102804, total=24.7min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102990, total=24.6min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102872, total=24.6min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102598, total=24.6min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ....... C=1, gamma=0.1, kernel=rbf, score=0.102990, total=24.7min
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.111628, total=23.2min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.109406, total=24.8min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[Parallel(n_jobs=10)]: Done 12 tasks | elapsed: 73.9min
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.110220, total=24.5min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.112150, total=24.6min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.110220, total=24.6min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.107119, total=24.6min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.112957, total=24.7min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.105615, total=24.7min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.108594, total=24.7min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ...... C=1, gamma=0.01, kernel=rbf, score=0.110073, total=24.7min
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.471761, total=20.4min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.477273, total=21.7min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.479626, total=21.8min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.475968, total=21.9min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.462258, total=21.9min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.449767, total=21.4min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.477682, total=21.6min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.470314, total=21.8min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.480987, total=21.6min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] ..... C=1, gamma=0.001, kernel=rbf, score=0.493688, total=21.8min
[CV] C=1, gamma=0.0001, kernel=rbf ...................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.438538, total=13.6min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.460307, total=14.5min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.433534, total=14.3min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.457582, total=14.5min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.460614, total=14.3min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.454485, total=14.3min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.455214, total=14.5min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.447632, total=14.3min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.437126, total=14.4min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] .... C=1, gamma=0.0001, kernel=rbf, score=0.458361, total=14.4min
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102990, total=23.2min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102990, total=23.9min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102804, total=24.8min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102872, total=24.6min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102872, total=24.8min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102598, total=24.8min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102941, total=24.8min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.103127, total=24.6min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102735, total=24.6min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ...... C=10, gamma=0.1, kernel=rbf, score=0.102735, total=24.6min
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.118272, total=25.5min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.107928, total=25.3min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.110073, total=25.3min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.112742, total=25.5min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.115615, total=25.0min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.113560, total=25.5min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.114896, total=25.4min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.117490, total=25.6min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.108957, total=25.5min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] ..... C=10, gamma=0.01, kernel=rbf, score=0.113107, total=25.4min
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.475748, total=24.9min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.502990, total=24.8min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.490340, total=24.7min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.486974, total=24.5min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.461078, total=24.9min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.485314, total=24.9min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.473649, total=25.0min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.494330, total=25.1min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.468938, total=25.0min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] .... C=10, gamma=0.001, kernel=rbf, score=0.488636, total=25.1min
[CV] C=10, gamma=0.0001, kernel=rbf ..................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.477076, total=13.2min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.505648, total=13.3min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.489319, total=13.4min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.485638, total=13.5min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.515656, total=13.3min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.458250, total=13.4min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.478377, total=13.6min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.482989, total=13.5min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.495321, total=13.5min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ... C=10, gamma=0.0001, kernel=rbf, score=0.478986, total=13.5min
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102990, total=23.3min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102735, total=24.9min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.103127, total=25.0min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102872, total=24.7min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102804, total=24.8min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102872, total=24.9min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102941, total=24.8min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102990, total=24.7min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102598, total=24.8min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] ..... C=100, gamma=0.1, kernel=rbf, score=0.102735, total=24.8min
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.118272, total=24.5min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.113107, total=24.7min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.107928, total=24.7min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.115615, total=24.9min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.112742, total=24.9min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.113560, total=24.8min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.117490, total=24.8min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.110073, total=24.9min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.114896, total=24.8min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] .... C=100, gamma=0.01, kernel=rbf, score=0.108957, total=24.8min
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.475083, total=23.1min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.461743, total=23.3min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.484646, total=23.3min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.501661, total=23.5min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.473649, total=23.3min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.491006, total=23.6min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.494330, total=23.5min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.470274, total=23.4min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[Parallel(n_jobs=10)]: Done 108 tasks | elapsed: 384.0min
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.487968, total=23.3min
[CV] C=100, gamma=0.0001, kernel=rbf .................................
[LibSVM][CV] ... C=100, gamma=0.001, kernel=rbf, score=0.486974, total=23.5min
[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=0.475748, total=12.9min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.467110, total=12.9min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.472389, total=12.9min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.469646, total=13.0min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.487675, total=13.0min
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.453636, total=13.0min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.458250, total=12.9min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.468938, total=13.0min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.476636, total=13.1min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .. C=100, gamma=0.0001, kernel=rbf, score=0.460561, total=13.0min
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102990, total=24.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102990, total=24.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.103127, total=24.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102735, total=24.4min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102598, total=24.5min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102735, total=24.6min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102872, total=24.6min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102804, total=24.6min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102941, total=24.6min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] .... C=1000, gamma=0.1, kernel=rbf, score=0.102872, total=24.7min
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.118272, total=24.4min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.115615, total=24.4min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.113107, total=24.4min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.107928, total=24.5min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.112742, total=24.6min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.117490, total=24.6min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.110073, total=24.6min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.113560, total=24.6min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.114896, total=24.7min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] ... C=1000, gamma=0.01, kernel=rbf, score=0.108957, total=24.7min
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.475083, total=23.1min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.501661, total=23.2min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.461743, total=23.2min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.491006, total=23.2min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.473649, total=23.3min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.494330, total=23.4min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.486974, total=23.3min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.484646, total=23.4min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.470274, total=23.4min
[CV] C=1000, gamma=0.0001, kernel=rbf ................................
[LibSVM][CV] .. C=1000, gamma=0.001, kernel=rbf, score=0.487968, total=23.5min
[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=0.470432, total=12.8min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.471761, total=12.9min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.470393, total=12.8min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.487675, total=12.8min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.469646, total=13.0min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.450967, total=12.9min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.479973, total=12.9min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.460922, total=13.0min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.443554, total=12.9min
[CV] C=1, gamma=0.01, kernel=poly ....................................
[LibSVM][CV] . C=1000, gamma=0.0001, kernel=rbf, score=0.456551, total=12.9min
[CV] C=1, gamma=0.01, kernel=poly ....................................
/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=1, gamma=0.01, kernel=poly, score=0.387375, total=20.0min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.399202, total=19.0min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.411960, total=20.1min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.411059, total=20.0min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.405604, total=20.2min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.409486, total=20.1min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.387850, total=20.3min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.400267, total=20.9min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.378758, total=20.3min
[CV] C=1, gamma=0.001, kernel=poly ...................................
[LibSVM][CV] ..... C=1, gamma=0.01, kernel=poly, score=0.401070, total=20.3min
[CV] C=1, gamma=0.001, kernel=poly ...................................
/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=1, gamma=0.001, kernel=poly, score=0.411960, total=21.2min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.409847, total=21.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.423920, total=21.4min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.418388, total=21.3min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.403204, total=21.2min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.415610, total=21.5min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.406146, total=21.3min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.421614, total=22.0min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.418838, total=22.1min
[CV] C=10, gamma=0.01, kernel=poly ...................................
[LibSVM][CV] .... C=1, gamma=0.001, kernel=poly, score=0.409759, total=21.6min
[CV] C=10, gamma=0.01, kernel=poly ...................................
/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=0.01, kernel=poly, score=0.399202, total=19.2min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.387375, total=20.2min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.411059, total=20.1min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.411960, total=20.3min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.405604, total=20.4min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.387850, total=20.2min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.400267, total=21.0min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.409486, total=20.2min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.378758, total=20.3min
[CV] C=10, gamma=0.001, kernel=poly ..................................
[LibSVM][CV] .... C=10, gamma=0.01, kernel=poly, score=0.401070, total=20.3min
[CV] C=10, gamma=0.001, kernel=poly ..................................
/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=poly, score=0.418388, total=20.6min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.394544, total=19.7min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.392691, total=20.2min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.415947, total=20.8min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.406938, total=20.5min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.381175, total=20.5min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.407605, total=21.1min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.390782, total=20.5min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.408150, total=20.7min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] ... C=10, gamma=0.001, kernel=poly, score=0.399064, total=20.3min
[CV] C=1, gamma=0.01, kernel=sigmoid .................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.149502, total=12.1min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.141905, total=13.4min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.141528, total=12.8min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.137425, total=13.0min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.142382, total=13.2min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.136758, total=13.0min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.123498, total=12.9min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.153641, total=12.8min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.151738, total=13.0min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] .. C=1, gamma=0.01, kernel=sigmoid, score=0.131597, total=13.4min
[CV] C=1, gamma=0.001, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.164784, total=10.8min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.165444, total=11.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.163891, total=11.7min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.168771, total=11.5min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.176314, total=11.5min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.162108, total=11.7min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.178357, total=11.6min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.176353, total=11.5min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.174232, total=11.7min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=1, gamma=0.001, kernel=sigmoid, score=0.176471, total=11.7min
[CV] C=10, gamma=0.01, kernel=sigmoid ................................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.147508, total=12.0min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.141239, total=13.3min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.145515, total=12.6min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.142095, total=13.0min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.128753, total=13.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.152973, total=12.7min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.151070, total=12.7min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.122830, total=12.9min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.146374, total=13.1min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] . C=10, gamma=0.01, kernel=sigmoid, score=0.126253, total=13.3min
[CV] C=10, gamma=0.001, kernel=sigmoid ...............................
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.162126, total=10.7min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.156562, total=11.6min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.159440, total=11.6min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.165665, total=11.5min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.177021, total=11.2min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.160881, total=11.6min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.174465, total=11.6min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.156771, total=11.4min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.170100, total=11.2min
[LibSVM][CV] C=10, gamma=0.001, kernel=sigmoid, score=0.175649, total=11.4min
[Parallel(n_jobs=10)]: Done 240 out of 240 | elapsed: 764.7min finished
[LibSVM]Total Time taken for cross validation and finding best parameters: 46511822.2082 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 [16]:
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: [ 0.103 0.112 0.472 0.439 0.103 0.118 0.476 0.477 0.103 0.118 0.475 0.476 0.103 0.118 0.475 0.47 0.387 0.412 0.387 0.393 0.15 0.165 0.148 0.162]
split1_test_score: [ 0.103 0.113 0.494 0.454 0.103 0.116 0.503 0.506 0.103 0.116 0.502 0.467 0.103 0.116 0.502 0.472 0.412 0.424 0.412 0.416 0.142 0.169 0.146 0.17 ]
split2_test_score: [ 0.103 0.107 0.45 0.437 0.103 0.113 0.461 0.478 0.103 0.113 0.462 0.472 0.103 0.113 0.462 0.47 0.399 0.41 0.399 0.395 0.142 0.176 0.146 0.176]
split3_test_score: [ 0.103 0.109 0.478 0.458 0.103 0.108 0.49 0.516 0.103 0.108 0.491 0.488 0.103 0.108 0.491 0.488 0.411 0.418 0.411 0.418 0.142 0.164 0.141 0.157]
split4_test_score: [ 0.103 0.109 0.481 0.46 0.103 0.113 0.494 0.483 0.103 0.113 0.494 0.47 0.103 0.113 0.494 0.47 0.406 0.422 0.406 0.407 0.137 0.165 0.129 0.159]
split5_test_score: [ 0.103 0.11 0.47 0.448 0.103 0.11 0.474 0.479 0.103 0.11 0.474 0.454 0.103 0.11 0.474 0.451 0.4 0.416 0.4 0.408 0.137 0.162 0.142 0.157]
split6_test_score: [ 0.103 0.112 0.476 0.461 0.103 0.117 0.485 0.489 0.103 0.117 0.485 0.477 0.103 0.117 0.485 0.48 0.388 0.403 0.388 0.381 0.123 0.174 0.123 0.161]
split7_test_score: [ 0.103 0.11 0.48 0.458 0.103 0.114 0.487 0.486 0.103 0.114 0.487 0.469 0.103 0.114 0.487 0.461 0.409 0.419 0.409 0.408 0.132 0.178 0.126 0.166]
split8_test_score: [ 0.103 0.11 0.462 0.434 0.103 0.115 0.469 0.458 0.103 0.115 0.47 0.458 0.103 0.115 0.47 0.444 0.379 0.406 0.379 0.391 0.154 0.176 0.153 0.177]
split9_test_score: [ 0.103 0.106 0.477 0.455 0.103 0.109 0.489 0.495 0.103 0.109 0.488 0.461 0.103 0.109 0.488 0.457 0.401 0.41 0.401 0.399 0.152 0.176 0.151 0.174]
mean_test_score: [ 0.103 0.11 0.474 0.45 0.103 0.113 0.483 0.487 0.103 0.113 0.483 0.469 0.103 0.113 0.483 0.466 0.399 0.414 0.399 0.402 0.141 0.171 0.14 0.166]
std_test_score: [ 0. 0.002 0.011 0.01 0. 0.003 0.012 0.015 0. 0.003 0.012 0.009 0. 0.003 0.012 0.013 0.011 0.006 0.011 0.011 0.009 0.006 0.01 0.007]
rank_test_score: [21 20 5 8 21 17 2 1 21 17 3 6 21 17 3 7 11 9 11 10 15 13 16 14]
split0_train_score: [ 1. 1. 0.94 0.533 1. 1. 1. 0.822 1. 1. 1. 0.997 1. 1. 1. 1. 1. 0.991 1. 0.999 0.134 0.164 0.135 0.159]
split1_train_score: [ 1. 1. 0.939 0.531 1. 1. 1. 0.818 1. 1. 1. 0.997 1. 1. 1. 1. 1. 0.991 1. 0.999 0.134 0.169 0.138 0.173]
split2_train_score: [ 1. 1. 0.94 0.53 1. 1. 1. 0.824 1. 1. 1. 0.998 1. 1. 1. 1. 0.999 0.99 0.999 0.999 0.146 0.173 0.145 0.174]
split3_train_score: [ 1. 1. 0.94 0.532 1. 1. 1. 0.821 1. 1. 1. 0.997 1. 1. 1. 1. 0.999 0.991 0.999 0.999 0.135 0.166 0.136 0.163]
split4_train_score: [ 1. 1. 0.942 0.528 1. 1. 1. 0.821 1. 1. 1. 0.998 1. 1. 1. 1. 0.999 0.991 0.999 0.999 0.141 0.164 0.138 0.164]
split5_train_score: [ 1. 1. 0.94 0.532 1. 1. 1. 0.822 1. 1. 1. 0.997 1. 1. 1. 1. 1. 0.991 1. 1. 0.133 0.168 0.135 0.157]
split6_train_score: [ 1. 1. 0.941 0.531 1. 1. 1. 0.822 1. 1. 1. 0.997 1. 1. 1. 1. 1. 0.99 1. 0.999 0.134 0.167 0.133 0.157]
split7_train_score: [ 1. 1. 0.941 0.532 1. 1. 1. 0.823 1. 1. 1. 0.997 1. 1. 1. 1. 1. 0.991 1. 0.999 0.133 0.173 0.133 0.166]
split8_train_score: [ 1. 1. 0.941 0.534 1. 1. 1. 0.823 1. 1. 1. 0.998 1. 1. 1. 1. 1. 0.991 1. 0.999 0.137 0.177 0.138 0.176]
split9_train_score: [ 1. 1. 0.94 0.533 1. 1. 1. 0.819 1. 1. 1. 0.998 1. 1. 1. 1. 1. 0.992 1. 0.999 0.149 0.172 0.15 0.17 ]
mean_train_score: [ 1. 1. 0.94 0.532 1. 1. 1. 0.822 1. 1. 1. 0.998 1. 1. 1. 1. 1. 0.991 1. 0.999 0.138 0.169 0.138 0.166]
std_train_score: [ 0. 0. 0.001 0.002 0. 0. 0. 0.002 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.001 0. 0. 0.005 0.004 0.005 0.007]
mean_fit_time: [ 1402.499 1391.705 1217.699 783.756 1388.425 1441.93 1408.464 732.667 1400.179 1405.048 1324.612 705.038 1392.967 1394.335 1319.5 701.707 1137.791 1215.827 1142.219 1158.319
702.945 619.345 697.34 610.748]
std_fit_time: [ 4.322 26.356 23.507 14.154 28.379 9.729 10.959 7.392 27.3 5.181 7.358 3.529 5.412 6.411 6.262 3.633 26.113 18.25 24.783 21.029 22.025 15.063 21.283 15.299]
mean_score_time: [ 80.762 78.81 76.816 74.063 79.841 82.609 85.415 71.795 80.386 80.711 78.479 72.497 78.955 78.222 76.922 71.35 68.495 71.022 69.654 69.871 74.361 71.482 74.878 71.704]
std_score_time: [ 0.723 0.668 0.7 0.712 1.066 2.093 1.958 0.456 0.947 1.02 0.753 0.521 0.427 0.461 0.348 0.278 0.327 1.368 0.933 1.048 1.483 1.069 1.34 1.146]
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: 0.10287, std: 0.00015, params: {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}
mean: 0.10980, std: 0.00213, params: {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}
mean: 0.47393, std: 0.01114, params: {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}
mean: 0.45033, std: 0.00983, params: {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}
mean: 0.10287, std: 0.00015, params: {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}
mean: 0.11327, std: 0.00330, params: {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}
mean: 0.48280, std: 0.01205, params: {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}
mean: 0.48673, std: 0.01525, params: {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}
mean: 0.10287, std: 0.00015, params: {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}
mean: 0.11327, std: 0.00330, params: {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}
mean: 0.48273, std: 0.01161, params: {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}
mean: 0.46907, std: 0.00944, params: {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}
mean: 0.10287, std: 0.00015, params: {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}
mean: 0.11327, std: 0.00330, params: {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}
mean: 0.48273, std: 0.01161, params: {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}
mean: 0.46620, std: 0.01263, params: {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}
mean: 0.39927, std: 0.01068, params: {'C': 1, 'gamma': 0.01, 'kernel': 'poly'}
mean: 0.41393, std: 0.00647, params: {'C': 1, 'gamma': 0.001, 'kernel': 'poly'}
mean: 0.39927, std: 0.01068, params: {'C': 10, 'gamma': 0.01, 'kernel': 'poly'}
mean: 0.40153, std: 0.01125, params: {'C': 10, 'gamma': 0.001, 'kernel': 'poly'}
mean: 0.14100, std: 0.00881, params: {'C': 1, 'gamma': 0.01, 'kernel': 'sigmoid'}
mean: 0.17067, std: 0.00595, params: {'C': 1, 'gamma': 0.001, 'kernel': 'sigmoid'}
mean: 0.14047, std: 0.01016, params: {'C': 10, 'gamma': 0.01, 'kernel': 'sigmoid'}
mean: 0.16587, std: 0.00749, params: {'C': 10, 'gamma': 0.001, 'kernel': 'sigmoid'}
Best score
0.486733333333
Parameters corresponding to best score
{'C': 10, 'gamma': 0.0001, '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)
In [17]:
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))
0.102867 (0.000147) with: {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}
0.109800 (0.002135) with: {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}
0.473933 (0.011144) with: {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}
0.450333 (0.009830) with: {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}
0.102867 (0.000147) with: {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}
0.113267 (0.003298) with: {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}
0.482800 (0.012060) with: {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}
0.486733 (0.015247) with: {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}
0.102867 (0.000147) with: {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}
0.113267 (0.003298) with: {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}
0.482733 (0.011614) with: {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}
0.469067 (0.009437) with: {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}
0.102867 (0.000147) with: {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}
0.113267 (0.003298) with: {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}
0.482733 (0.011614) with: {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}
0.466200 (0.012624) with: {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}
0.399267 (0.010683) with: {'C': 1, 'gamma': 0.01, 'kernel': 'poly'}
0.413933 (0.006475) with: {'C': 1, 'gamma': 0.001, 'kernel': 'poly'}
0.399267 (0.010683) with: {'C': 10, 'gamma': 0.01, 'kernel': 'poly'}
0.401533 (0.011248) with: {'C': 10, 'gamma': 0.001, 'kernel': 'poly'}
0.141000 (0.008803) with: {'C': 1, 'gamma': 0.01, 'kernel': 'sigmoid'}
0.170667 (0.005952) with: {'C': 1, 'gamma': 0.001, 'kernel': 'sigmoid'}
0.140467 (0.010154) with: {'C': 10, 'gamma': 0.01, 'kernel': 'sigmoid'}
0.165867 (0.007492) with: {'C': 10, 'gamma': 0.001, 'kernel': 'sigmoid'}
In [18]:
start = time.time()
file_utils.mkdir_p(output_directory)
# model_output_path = model_output_directory + str(file_no).zfill(2) + '_' + 'svm_raw_features_cross_val_search_method_' \
# + search_method + '_num_train_images_' + str(num_train_images) \
# + '_cache_size_' + str(svm_cs) + '_iter_' + str(svm_max_iter) + '.pkl'
model_output_path = output_directory + '05_' + 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/cifar10/05_svm_raw_cross_val/exp_no_102/05_exp_102.pkl in 5110.032082 ms
In [19]:
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 [20]:
plot_tools.plot_variance(scores, means, stds, legend=['Training data', 'Validation data'],
plot_title=['Train scores for best parameters for SVC using raw pixels in CIFAR 10',
'Validation scores for best parameters for SVC using raw pixels 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)
/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)
In [21]:
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 411213.283062 ms
In [22]:
cifar10.plot_images(cifar10.test.data[:50], cifar10.test.class_names[:50], cls_pred=prediction_classes[:50],
nrows=5, ncols=10, fig_size=(20,50), fontsize=35, convert=True)
Out[22]:
True
In [23]:
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 raw pixels')
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
[[574 33 67 21 37 15 25 31 156 41]
[ 60 607 21 36 17 20 20 27 55 137]
[ 94 25 405 74 136 60 117 51 26 12]
[ 31 30 106 371 75 148 117 50 29 43]
[ 53 21 185 59 401 43 127 78 24 9]
[ 27 19 117 193 89 352 94 62 26 21]
[ 12 17 115 111 105 32 563 20 10 15]
[ 47 29 58 70 101 73 36 512 23 51]
[102 72 21 37 22 16 10 14 652 54]
[ 52 181 17 29 14 18 39 45 78 527]]
Normalized confusion matrix
[[ 0.574 0.033 0.067 0.021 0.037 0.015 0.025 0.031 0.156 0.041]
[ 0.06 0.607 0.021 0.036 0.017 0.02 0.02 0.027 0.055 0.137]
[ 0.094 0.025 0.405 0.074 0.136 0.06 0.117 0.051 0.026 0.012]
[ 0.031 0.03 0.106 0.371 0.075 0.148 0.117 0.05 0.029 0.043]
[ 0.053 0.021 0.185 0.059 0.401 0.043 0.127 0.078 0.024 0.009]
[ 0.027 0.019 0.117 0.193 0.089 0.352 0.094 0.062 0.026 0.021]
[ 0.012 0.017 0.115 0.111 0.105 0.032 0.563 0.02 0.01 0.015]
[ 0.047 0.029 0.058 0.07 0.101 0.073 0.036 0.512 0.023 0.051]
[ 0.102 0.072 0.021 0.037 0.022 0.016 0.01 0.014 0.652 0.054]
[ 0.052 0.181 0.017 0.029 0.014 0.018 0.039 0.045 0.078 0.527]]
precision recall f1-score support
airplane 0.55 0.57 0.56 1000
automobile 0.59 0.61 0.60 1000
bird 0.36 0.41 0.38 1000
cat 0.37 0.37 0.37 1000
deer 0.40 0.40 0.40 1000
dog 0.45 0.35 0.40 1000
frog 0.49 0.56 0.52 1000
horse 0.58 0.51 0.54 1000
ship 0.60 0.65 0.63 1000
truck 0.58 0.53 0.55 1000
avg / total 0.50 0.50 0.50 10000
Accuracy score on test data: 0.4964
In [24]:
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: 0.4964
In [25]:
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 + str(file_no).zfill(2) + '_' + 'svm_raw_features_cross_val_search_method_' \
# + search_method + '_num_train_images_' + str(num_train_images) \
# + '_cache_size_' + str(svm_cs) + '_iter_' + str(svm_max_iter) + '.csv'
output_csv_file = output_directory + '05_' + 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/cifar10/05_svm_raw_cross_val/exp_no_102/05_exp_102.csv in 492.370844 ms
In [26]:
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 [27]:
%%javascript
var notebook = IPython.notebook
notebook.save_notebook()
In [30]:
%%javascript
var kernel = IPython.notebook.kernel;
var thename = window.document.getElementById("notebook_name").innerHTML;
var command = "theNotebook = " + "'"+thename+"'";
kernel.execute(command);
In [31]:
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: ./05_CIFAR_10_SVM_Raw_Features_Cross_Validation.ipynb
Output file: ../logs/cifar10/05_svm_raw_cross_val/exp_no_102/05_exp_no_102_05_CIFAR_10_SVM_Raw_Features_Cross_Validation.html
In [32]:
print('Code took %.6f s to run on training with %d examples' % (total_time,num_train_images))
Code took 47344.963610 s to run on training with 15000 examples
In [ ]:
Content source: sonapraneeth-a/object-classification
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