def basicconn(skf,X,y):
total_score = 0
for train_index, test_index in skf:
#print("TRAIN:", train_index, "TEST:", test_index)
# Feature selection
#selectf = SelectFpr().fit(X[train_index],y[train_index])
#selectf = SelectKBest(f_classif, k=750).fit(X[train_index],y[train_index])
#tmp_x = selectf.transform(X[train_index])
# Train
#clf = RandomForestClassifier(n_estimators=20)
#clf = clf.fit(tmp_x, y[train_index])
#clf.feature_importances_
# SVM
#clf = svm.LinearSVC()
#clf = svm.SVC()
#clf.fit(tmp_x, y[train_index])
clf = plib.classif(X[train_index], y[train_index])
#clf.support_vec()
# Test
#pred = clf.predict(selectf.transform(X[test_index]))
pred = clf.predict(X[test_index])
print "Target : ", y[test_index]
print "Prediction : ", pred
matchs = np.equal(pred, y[test_index])
score = np.divide(np.sum(matchs), np.float64(matchs.size))
total_score = score + total_score
return np.divide(total_score, skf.n_folds)
def basicconn(skf, X, y):
total_score = 0
for train_index, test_index in skf:
#print("TRAIN:", train_index, "TEST:", test_index)
# Feature selection
#selectf = SelectFpr().fit(X[train_index],y[train_index])
#selectf = SelectKBest(f_classif, k=750).fit(X[train_index],y[train_index])
#tmp_x = selectf.transform(X[train_index])
# Train
#clf = RandomForestClassifier(n_estimators=20)
#clf = clf.fit(tmp_x, y[train_index])
#clf.feature_importances_
# SVM
#clf = svm.LinearSVC()
#clf = svm.SVC()
#clf.fit(tmp_x, y[train_index])
clf = plib.classif(X[train_index], y[train_index])
#clf.support_vec()
# Test
#pred = clf.predict(selectf.transform(X[test_index]))
pred = clf.predict(X[test_index])
print "Target : ", y[test_index]
print "Prediction : ", pred
matchs = np.equal(pred, y[test_index])
score = np.divide(np.sum(matchs), np.float64(matchs.size))
total_score = score + total_score
return np.divide(total_score, skf.n_folds)
def splitconn(skf,X,y):
total_score = 0
for train_index, test_index in skf:
# Train
clf1 = plib.classif(X[train_index, 0:2475:1], y[train_index])
clf2 = plib.classif(X[train_index, 2475:4950:1], y[train_index])
pred1 = clf1.decision_function(X[train_index, 0:2475:1])
pred2 = clf2.decision_function(X[train_index, 2475:4950:1])
clf3 = svm.SVC()
y[train_index].shape
np.array([pred1, pred2])
clf3.fit(np.array([pred1, pred2]).transpose(), y[train_index])
#clf3 = plib.classif(np.matrix([pred1,pred2]).transpose(),y[train_index])
# Test
pred1 = clf1.decision_function(X[test_index, 0:2475:1])
pred2 = clf2.decision_function(X[test_index, 2475:4950:1])
predfinal = clf3.predict(np.matrix([pred1, pred2]).transpose())
print "Target : ", y[test_index]
print "Prediction : ", predfinal
matchs = np.equal(predfinal, y[test_index])
score = np.divide(np.sum(matchs), np.float64(matchs.size))
total_score = score + total_score
return np.divide(total_score, skf.n_folds)
def splitconn(skf, X, y):
total_score = 0
for train_index, test_index in skf:
# Train
clf1 = plib.classif(X[train_index, 0:2475:1], y[train_index])
clf2 = plib.classif(X[train_index, 2475:4950:1], y[train_index])
pred1 = clf1.decision_function(X[train_index, 0:2475:1])
pred2 = clf2.decision_function(X[train_index, 2475:4950:1])
clf3 = svm.SVC()
y[train_index].shape
np.array([pred1, pred2])
clf3.fit(np.array([pred1, pred2]).transpose(), y[train_index])
#clf3 = plib.classif(np.matrix([pred1,pred2]).transpose(),y[train_index])
# Test
pred1 = clf1.decision_function(X[test_index, 0:2475:1])
pred2 = clf2.decision_function(X[test_index, 2475:4950:1])
predfinal = clf3.predict(np.matrix([pred1, pred2]).transpose())
print "Target : ", y[test_index]
print "Prediction : ", predfinal
matchs = np.equal(predfinal, y[test_index])
score = np.divide(np.sum(matchs), np.float64(matchs.size))
total_score = score + total_score
return np.divide(total_score, skf.n_folds)
def multisplit(skf, X, y, stepsize=1000):
total_score = 0
for train_index, test_index in skf:
wl = []
pred1 = np.matrix([])
# Training
for x in range(0, len(X[0]), stepsize):
clf1 = plib.classif(X[train_index, x:x + stepsize], y[train_index])
tmp_p = np.matrix(
clf1.decision_function(X[train_index, x:x + stepsize]))
if pred1.size == 0:
pred1 = tmp_p
else:
pred1 = np.concatenate((pred1, tmp_p), axis=1)
wl.append(clf1)
#selectf = SelectKBest(f_classif, k=5).fit(pred1, y[train_index])
selectf = SelectFpr().fit(pred1, y[train_index])
clf3 = AdaBoostClassifier(n_estimators=100)
#clf3 = svm.SVC(class_weight='auto')
#clf3 = RandomForestClassifier(n_estimators=20)
clf3.fit(selectf.transform(pred1), y[train_index])
# Testing
predtest = np.matrix([])
k = 0
for x in range(0, len(X[0]), stepsize):
tmp_p = np.matrix(wl[k].decision_function(X[test_index,
x:x + stepsize]))
if predtest.size == 0:
predtest = tmp_p
else:
predtest = np.concatenate((predtest, tmp_p), axis=1)
k += 1
# Final prediction
predfinal = clf3.predict(selectf.transform(predtest))
print "Target : ", y[test_index]
print "Prediction : ", predfinal
matchs = np.equal(predfinal, y[test_index])
score = np.divide(np.sum(matchs), np.float64(matchs.size))
total_score = score + total_score
return np.divide(total_score, skf.n_folds)
def multisplit(skf,X,y,stepsize=1000):
total_score = 0
for train_index, test_index in skf:
wl = []
pred1 = np.matrix([])
# Training
for x in range(0, len(X[0]), stepsize):
clf1 = plib.classif(X[train_index, x:x + stepsize], y[train_index])
tmp_p = np.matrix(clf1.decision_function(X[train_index, x:x + stepsize]))
if pred1.size == 0:
pred1 = tmp_p
else:
pred1 = np.concatenate((pred1, tmp_p), axis=1)
wl.append(clf1)
#selectf = SelectKBest(f_classif, k=5).fit(pred1, y[train_index])
selectf = SelectFpr().fit(pred1, y[train_index])
clf3 = AdaBoostClassifier(n_estimators=100)
#clf3 = svm.SVC(class_weight='auto')
#clf3 = RandomForestClassifier(n_estimators=20)
clf3.fit(selectf.transform(pred1), y[train_index])
# Testing
predtest = np.matrix([])
k = 0
for x in range(0, len(X[0]), stepsize):
tmp_p = np.matrix(wl[k].decision_function(X[test_index, x:x + stepsize]))
if predtest.size == 0:
predtest = tmp_p
else:
predtest = np.concatenate((predtest, tmp_p), axis=1)
k += 1
# Final prediction
predfinal = clf3.predict(selectf.transform(predtest))
print "Target : ", y[test_index]
print "Prediction : ", predfinal
matchs = np.equal(predfinal, y[test_index])
score = np.divide(np.sum(matchs), np.float64(matchs.size))
total_score = score + total_score
return np.divide(total_score, skf.n_folds)
