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logreg.py
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logreg.py
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import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss, recall_score, precision_score, accuracy_score, f1_score
from load_data import load_full
from sklearn.cross_validation import StratifiedShuffleSplit
def check_vb(datanm, samples_per_class, Cs, num_classes, num_iter = 100):
data, labels = load_full(datanm, samples_per_class)
slo = StratifiedShuffleSplit(labels, n_iter=num_iter, test_size=0.5, train_size=0.5, random_state=None)
ans = np.zeros((len(Cs), samples_per_class/2, 2))
for train_index, test_index in slo:
train_data = [data[train_index, :], labels[train_index]]
valid_data = [data[test_index , :], labels[test_index ]]
for l in xrange(samples_per_class/2):
ind_train = []
ind_valid = []
for k in xrange(num_classes):
ind_train = ind_train + np.where(train_data[1] == k)[0].tolist()[:l+1]
ind_valid = ind_valid + np.where(valid_data[1] == k)[0].tolist()[:l+1]
ctrain_data = [ train_data[0][ind_train], train_data[1][ind_train] ]
cvalid_data = [ valid_data[0][ind_valid], valid_data[1][ind_valid] ]
for i, C in enumerate(Cs):
clf = LogisticRegression(C =C , penalty='l2', multi_class = 'ovr',
tol=0.001, n_jobs = -1 , verbose = 0)#, solver = 'newton-cg')
clf.fit(ctrain_data[0], ctrain_data[1])
out_train = clf.predict_proba(ctrain_data[0])
out_valid = clf.predict_proba(cvalid_data[0])
ans[i, l, 0] += log_loss(ctrain_data[1], out_train)
ans[i, l, 1] += log_loss(cvalid_data[1], out_valid)
ans /= num_iter
np.savez("logreg_bv", ans= ans, Cs = Cs, num_iter = num_iter, num_classes = num_classes, samples_per_class = samples_per_class)
return ans
def check_lambda(datanm, samples_per_class, Cs, num_classes, num_iter=100, save_filename=None):
data, labels = load_full(datanm, samples_per_class)
slo = StratifiedShuffleSplit(labels, n_iter=num_iter, test_size=0.3, train_size=0.7, random_state=None)
ans = np.zeros((len(Cs), 2))
for train_index, test_index in slo:
train_data = [data[train_index, :], labels[train_index]]
valid_data = [data[test_index , :], labels[test_index ]]
for i, C in enumerate(Cs):
clf = LogisticRegression(C =C, penalty='l2', multi_class = 'ovr',
tol=0.001, n_jobs = -1, verbose = 0)#, solver = 'newton-cg')
clf.fit(train_data[0], train_data[1])
out_train = clf.predict_proba(train_data[0])
out_valid = clf.predict_proba(valid_data[0])
ans[i, 0] += log_loss(train_data[1], out_train)
ans[i, 1] += log_loss(valid_data[1], out_valid)
ans[:, :] /= num_iter
if save_filename is not None:
np.savez(save_filename, ans= ans, Cs = Cs, num_iter = num_iter, num_classes = num_classes, samples_per_class = samples_per_class)
return ans
def main_func(datanm, samples_per_class, C, num_classes, num_iter = 100):
data, labels = load_full(datanm, samples_per_class)
slo = StratifiedShuffleSplit(labels, n_iter=num_iter, test_size=0.3, train_size=0.7, random_state=None)
recall = np.zeros((num_classes+1, 2))
precision = np.zeros((num_classes+1, 2))
f1 = np.zeros((num_classes+1, 2))
accuracy = np.zeros((2))
logloss = np.zeros((2))
for train_index, test_index in slo:
train_data = [data[train_index, :], labels[train_index]]
valid_data = [data[test_index , :], labels[test_index ]]
clf = LogisticRegression(C =C, penalty='l2', multi_class = 'ovr',
tol=0.001, n_jobs = -1, verbose = 0)#, solver = 'newton-cg')
clf.fit(train_data[0], train_data[1])
out_train = clf.predict_proba(train_data[0])
out_valid = clf.predict_proba(valid_data[0])
logloss[0] += log_loss(train_data[1], out_train)
logloss[1] += log_loss(valid_data[1], out_valid)
out_train = clf.predict(train_data[0])
out_valid = clf.predict(valid_data[0])
accuracy[0] += accuracy_score(train_data[1], out_train)
accuracy[1] += accuracy_score(valid_data[1], out_valid)
precision[:-1, 0] += precision_score(train_data[1], out_train, average = None)
precision[-1, 0] += precision_score(train_data[1], out_train, average = 'macro')
precision[:-1, 1] += precision_score(valid_data[1], out_valid, average = None)
precision[-1, 1] += precision_score(valid_data[1], out_valid, average = 'macro')
recall[:-1, 0] += recall_score(train_data[1], out_train, average = None)
recall[-1, 0] += recall_score(train_data[1], out_train, average = 'macro')
recall[:-1, 1] += recall_score(valid_data[1], out_valid, average = None)
recall[-1, 1] += recall_score(valid_data[1], out_valid, average = 'macro')
f1[:-1, 0] += f1_score(train_data[1], out_train, average = None)
f1[-1, 0] += f1_score(train_data[1], out_train, average = 'macro')
f1[:-1, 1] += f1_score(valid_data[1], out_valid, average = None)
f1[-1, 1] += f1_score(valid_data[1], out_valid, average = 'macro')
f1 /= num_iter
recall /= num_iter
precision /= num_iter
logloss /= num_iter
accuracy /= num_iter
np.savez("logreg_final", accuracy = accuracy, recall = recall, f1 = f1,
precision = precision, logloss = logloss, C = C,
num_iter = num_iter, num_classes = num_classes,
samples_per_class = samples_per_class)
return [accuracy, recall, f1, precision, logloss]
if __name__ == '__main__':
datanm = '../data/a.npz'
#Cs = [100., 50., 10., 5., 1., 0.75, 0.5, 0.25, 0.01]
Cs = [100., 50., 10., 5., 1., 0.80, 0.75, 0.6, 0.65, 0.5, 0.25, 0.01] #=> 0.80
Cb = 0.8
a = check_lambda(datanm, samples_per_class = 20, Cs = Cs, num_classes = 36, num_iter = 100)
a = check_vb(datanm, samples_per_class = 20, Cs = [Cb], num_classes = 36, num_iter = 100)
l = main_func(datanm, samples_per_class = 20, C = Cb, num_classes = 36, num_iter = 100)