from unittest import TestCase
from sklearn.ensemble import RandomForestClassifier
from app.keras import logistic_regression
class TestLogisticRegression(TestCase):
def test_log_reg(self):
# Arrange
# Act
logistic_regression.main()
from deslib.des.knora_e import KNORAE
# Train a pool of 10 classifiers
pool_classifiers = RandomForestClassifier(n_estimators=10)
pool_classifiers.fit(X_train, y_train)
# Initialize the DES model
knorae = KNORAE(pool_classifiers)
# Preprocess the Dynamic Selection dataset (DSEL)
knorae.fit(X_dsel, y_dsel)
# Predict new examples:
knorae.predict(X_test)
val_error = mean_squared_error(y_val, y_pred)
print("Validation MSE:", val_error)
if xgboost is not None: # not shown in the book
xgb_reg.fit(X_train, y_train,
eval_set=[(X_val, y_val)], early_stopping_rounds=2)
y_pred = xgb_reg.predict(X_val)
val_error = mean_squared_error(y_val, y_pred)
print("Validation MSE:", val_error)
# Stacking
# data set
X, y = make_moons(n_samples=500, noise=0.30, random_state=42)
X_train, X_dsel, y_train, y_dsel = train_test_split(X, y, test_size=0.5, random_state=42)
# Train a pool of 10 classifiers
pool_classifiers = RandomForestClassifier(n_estimators=10)
pool_classifiers.fit(X_train, y_train)
# Initialize the DES model
knorae = KNORAE(pool_classifiers)
# Preprocess the Dynamic Selection dataset (DSEL)
knorae.fit(X_dsel, y_dsel)
# Predict new examples:
y_pred_des = knorae.predict(X_test)
print('des accuracy score: ', accuracy_score(y_test, y_pred_des)) # 0.984 much better than bagging and pasting
print('des RMSE: ', np.sqrt(mean_squared_error(y_test, y_pred_des))) # 0.12649110640673517
#------------------- META DES TRAINING -------------------------------------
train_time_mdes_start = time.time()
meta = METADES(pool_classifiers)
meta.fit(X_dsel, y_dsel)
train_time_mdes_end = time.time()
#------------------- KONARAU TRAINING --------------------------------------
train_time_knu_start = time.time()
knu = KNORAU(pool_classifiers)
knu.fit(X_dsel, y_dsel)
train_time_knu_end = time.time()
#---------------------- Test KNE Pharse --------------------------
test_time_kne_start = time.time()
y_kne_pred = kne.predict(X_test)
accuracy_kne = accuracy_score(Y_test, y_kne_pred)
# print('accuracy = ', accuracy_kne)
micro_f1_kne = f1_score(Y_test - 1, y_kne_pred - 1, average='micro')
# print('micro_f1 =', micro_f1_kne)
macro_f1_kne = f1_score(Y_test - 1, y_kne_pred - 1, average='macro')
# print('macro_f1 =', macro_f1_kne)
test_time_kne_end = time.time()
#---------------------- Test META DES Pharse --------------------------
test_time_mdes_start = time.time()
y_mdes_pred = meta.predict(X_test)
for rep in range(1,6):
skf = StratifiedKFold(n_splits=4)
for train_index, test_index in skf.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
neigh = KNeighborsClassifier(n_neighbors=k_neigh)
neigh.fit(X_train, y_train)
X_train, X_dsel, y_train, y_dsel = train_test_split(X_train, y_train, test_size=0.66)
pool_classifiers = es.fit(X_train, y_train).estimators_
knorau = KNORAE(pool_classifiers)
knorau.fit(X_dsel, y_dsel)
y_pred = []
for instance in X_test:
#use_des = select_classifier(threshold, k_neigh, X_dsel, y_dsel, instance)
use_des = True
if(use_des):
result = knorau.predict([instance])
y_pred.append(result[0])
else:
result = neigh.predict([instance])
y_pred.append(result[0])
for name, metric in zip(['accuracy','roc_auc','gmean','f1'], [accuracy_score, roc_auc_score, gmean, f1_score]):
results[name].append(metric(y_test, y_pred))
print_results(results)
