def rotationforest(number):
print("Rotation Forest estimators=%d" % (number))
rotation = RotationForestClassifier(n_estimators=number)
rotation.fit(X_train, y_train)
y_pred = rotation.predict(X_test)
print(confusion_matrix(y_test, y_pred))
print(accuracy_score(y_test, y_pred))
def test_rotation_forest(self):
""" Smoke test for rotation forest """
X, y = classification_data()
xt, xv, yt, yv = train_test_split(X, y, test_size=0.3, random_state=77)
clf = RotationForestClassifier(random_state=1234)
clf.fit(xt, yt)
proba = clf.predict_proba(xv)
assert proba.shape[0] == xv.shape[0]
assert np.all(proba <= 1)
assert np.all(proba >= 0)
yhat = clf.predict(xv)
assert yhat.shape[0] == xv.shape[0]
assert np.unique(yhat).tolist() == [0, 1]
def cross_val(x_train, y_train):
skf = StratifiedKFold(n_splits=10)
model = RotationForestClassifier(n_estimators=100,
random_state=47,
verbose=4,
n_jobs=-2)
accuracy = []
mcc = []
precision = []
roc_auc = []
Sensitivity = []
Specificity = []
score = []
f1 = []
for x in range(10):
for train_index, test_index in skf.split(x_train, y_train):
X_train, X_test = x_train[train_index], x_train[test_index]
Y_train, Y_test = y_train[train_index], y_train[test_index]
model.fit(X_train, Y_train)
y_predict = model.predict(X_test)
score.append(model.score(X_test, Y_test))
accuracy.append(accuracy_score(Y_test, y_predict))
mcc.append(matthews_corrcoef(Y_test, y_predict))
precision.append(precision_score(Y_test, y_predict))
f1.append(f1_score(Y_test, y_predict))
roc_auc.append(roc_auc_score(Y_test, y_predict))
Sensitivity.append(sensitivity(Y_test, y_predict))
Specificity.append(specificity(Y_test, y_predict))
with open('./data/rotation_forest_knn_human100.pkl', 'wb') as f:
pickle.dump(model, f)
print("****************************************")
print("Accuracy: ", np.mean(accuracy))
print("MCC: ", np.mean(mcc))
print("Precision: ", np.mean(precision))
print("Roc auc score: ", np.mean(roc_auc))
print("F1 score: {}\n".format(np.mean(f1)))
print("Sensitivity: ", np.mean(Sensitivity))
print("Specifity: ", np.mean(Specificity))
def svm(x_train, y_train, x_test, y_test):
model = RotationForestClassifier(n_estimators=100,
random_state=47,
verbose=4)
# model = SVC(C=1.0, kernel='poly', gamma=1.0, random_state=47, verbose=True)
model.fit(x_train, y_train)
with open('./data/svm-knn.pkl', 'wb') as f:
pickle.dump(model, f)
y_predict = model.predict(x_test)
print("****************************************")
print("Accuracy: ", accuracy_score(y_test, y_predict))
print("MCC: ", matthews_corrcoef(y_test, y_predict))
print("Precision: ", precision_score(y_test, y_predict))
print("ROC auc score: ", roc_auc_score(y_test, y_predict))
print("AUC def: ", auc(y_test, y_predict))
print("F1 score: ", f1_score(y_test, y_predict))
print("Sensitivity: ", sensitivity(y_test, y_predict))
print("Specifity: ", specificity(y_test, y_predict))
def robust_cross_val(x_train, y_train, x_test, y_test, folds):
skf = StratifiedKFold(n_splits=folds, random_state=47)
model = RotationForestClassifier(n_estimators=100,
random_state=47,
verbose=4,
n_jobs=-2)
accuracy = []
mcc = []
precision = []
roc_auc = []
Sensitivity = []
Specificity = []
auc_score = []
f1 = []
score = []
for x in range(10):
for train_index, test_index in skf.split(x_train, y_train):
X_train, X_test = x_train[train_index], x_train[test_index]
Y_train, Y_test = y_train[train_index], y_train[test_index]
model.fit(X_train, Y_train)
y_predict = model.predict(X_test)
score.append(model.score(X_test, Y_test))
accuracy.append(accuracy_score(Y_test, y_predict))
mcc.append(matthews_corrcoef(Y_test, y_predict))
precision.append(precision_score(Y_test, y_predict))
roc_auc.append(roc_auc_score(Y_test, y_predict))
auc_score.append(auc(Y_test, y_predict))
f1.append(f1_score(Y_test, y_predict))
Sensitivity.append(sensitivity(Y_test, y_predict))
Specificity.append(specificity(Y_test, y_predict))
with open('../data/rotation_forest_human.pkl', 'wb') as f:
pickle.dump(model, f)
res = "{} folds\n".format(folds)
res += "******************** Cross Validation Score ********************\n"
res += "Accuracy: {}\n".format(np.mean(accuracy))
res += "MCC: {}\n".format(np.mean(mcc))
res += "Precision: {}\n".format(np.mean(precision))
res += "Roc AUC score: {}\n".format(np.mean(roc_auc))
res += "AUC score: {}\n".format(np.mean(auc_score))
res += "F1 score: {}\n".format(np.mean(f1))
res += "Sensitivity: {}\n".format(np.mean(Sensitivity))
res += "Specifity: {}\n".format(np.mean(Specificity))
y_test_predict = model.predict(x_test)
res += "\n******************** Independent Test Score ********************\n"
res += "Accuracy: {}\n".format(accuracy_score(y_test, y_test_predict))
res += "MCC: {}\n".format(matthews_corrcoef(y_test, y_test_predict))
res += "Precision: {}\n".format(precision_score(y_test, y_test_predict))
res += "Roc AUC score: {}\n".format(roc_auc_score(y_test, y_test_predict))
res += "AUC score: {}\n".format(auc(y_test, y_test_predict))
res += "F1 score: {}\n".format(f1_score(y_test, y_test_predict))
res += "Sensitivity: {}\n".format(sensitivity(y_test, y_test_predict))
res += "Specifity: {}\n\n\n".format(specificity(y_test, y_test_predict))
with open('../data/rotation_forest_human_result.txt', 'a') as f:
f.write(res)
lgbm.fit(trainingData, trainingLabel)
lgbm_result = lgbm.predict(testingData)
f1_weighted_lightgbm_save.append(
f1_score(testingLabel, lgbm_result, average='weighted'))
f1_macro_lightgbm_save.append(
f1_score(testingLabel, lgbm_result, average='macro'))
f1_micro_lightgbm_save.append(
f1_score(testingLabel, lgbm_result, average='micro'))
rot_forest = RotationForestClassifier(
n_estimators=n_estimators,
max_depth=max_depth,
max_features=max_features,
random_state=0)
rot_forest.fit(trainingData, trainingLabel)
rot_forest_result = rot_forest.predict(testingData)
f1_weighted_rotation_forest_save.append(
f1_score(testingLabel,
rot_forest_result,
average='weighted'))
f1_macro_rotation_forest_save.append(
f1_score(testingLabel, rot_forest_result, average='macro'))
f1_micro_rotation_forest_save.append(
f1_score(testingLabel, rot_forest_result, average='micro'))
svm_classifier = SVC(kernel='rbf')
svm_classifier.fit(trainingData, trainingLabel)
svm_result = svm_classifier.predict(testingData)
f1_weighted_svm_save.append(
f1_score(testingLabel, svm_result, average='weighted'))
f1_macro_svm_save.append(