def train(classifier_name, classifier_extra, label_values):
import classipy
label_values = list(label_values)
hadoopy.counter('FeatureShape', str(len(label_values[0][1])))
if classifier_name == 'svmlinear':
return classipy.SVMLinear(options={'B': '1'}).train(label_values)
elif classifier_name == 'svm':
return classipy.SVM(options={'t': '2'}).train(label_values)
elif classifier_name == 'svm_hik':
return classipy.SVMScikit(kernel=classipy.kernels.histogram_intersection).train(label_values)
elif classifier_name == 'svmlinear_autotune':
def wrapped_optimizer(*args, **kw):
for x in pyram.exponential_grid(*args, **kw):
hadoopy.counter('X-Val', 'Rounds')
yield x
b = classipy.select_parameters(classipy.SVMLinear, label_values,
{'c': (10**-2, 10**1, 10)},
wrapped_optimizer,
options={'B': '1'})[1]
print(b)
return classipy.SVMLinear(b).train(label_values)
elif classifier_name == 'plslinearsvmxval':
num_dims = label_values[0][1].size
# Set the parameters by cross-validation
#,'pls__n_components': [x for x in [1, 8, 16, 32, 64, 128, 256] if x <= num_dims]
#('pls', sklearn.pls.PLSRegression(n_components=0)),
tuned_parameters = [{'svm__C': [.001, .01, .1, 1, 10, 100]}]
p = sklearn.pipeline.Pipeline([('svm', sklearn.svm.SVC(kernel=classipy.kernels.histogram_intersection, scale_C=True))]) # was cls
#p = sklearn.grid_search.GridSearchCV(cls, tuned_parameters, score_func=sklearn.metrics.f1_score)
num_neg = 0
num_pos = 0
import random
random.shuffle(label_values)
new_label_values = []
for l, v in label_values:
if l == 1:
if num_pos < 100:
new_label_values.append((l, v))
num_pos += 1
else:
if num_neg < 100:
new_label_values.append((l, v))
num_neg += 1
import sys
sys.stderr.write('Num Neg[%d] Pos[%d]\n' % (num_neg, num_pos))
p.fit(*zip(*new_label_values)[::-1])
return p # p.best_estimator_
else:
raise ValueError('Unknown classifier [%s]' % classifier_name)
import numpy as np
import pyram
import classipy
a = [(-1, x) for x in np.random.multivariate_normal([0, 0], np.eye(2), 1000)]
a += [(1, x) for x in np.random.multivariate_normal([3, 3],
np.eye(2) * 2, 1000)]
b = classipy.select_parameters(classipy.SVM,
a, {
'c': (10**-1, 10**2, 10),
'g': (10**-1, 10**5, 10)
},
pyram.exponential_grid,
options={'t': '2'})
print(b)
import numpy as np
import pyram
import classipy
a = [(-1, x) for x in np.random.multivariate_normal([0, 0], np.eye(2), 1000)]
a += [(1, x) for x in np.random.multivariate_normal([3, 3], np.eye(2) * 2, 1000)]
b = classipy.select_parameters(classipy.SVM, a, {'c': (10**-1, 10**2, 10),
'g': (10**-1, 10**5, 10)},
pyram.exponential_grid, options={'t': '2'})
print(b)
