def batch():
print("Tesing the performance of LogisticRegression(batch)...")
# Train model
clf = LogisticRegression()
clf.fit(X=X_train, y=y_train, lr=0.05, epochs=200)
# Model evaluation
model_evaluation(clf, X_test, y_test)
def batch():
print("Tesing the performance of LogisticRegression(batch)...")
# Train model
clf = LogisticRegression()
clf.fit(data=data_train, label=label_train, learning_rate=0.1, epochs=1000)
# Model evaluation
model_evaluation(clf, data_test, label_test)
print(clf)
def stochastic():
print("Tesing the performance of LogisticRegression(stochastic)...")
# Train model
clf = LogisticRegression()
clf.fit(data=data_train, label=label_train, learning_rate=0.01, epochs=100,
method="stochastic", sample_rate=0.8)
# Model evaluation
model_evaluation(clf, data_test, label_test)
print(clf)
def stochastic():
print("Tesing the performance of LogisticRegression(stochastic)...")
# Train model
clf = LogisticRegression()
clf.fit(X=X_train, y=y_train, lr=0.01, epochs=100,
method="stochastic", sample_rate=0.8)
# Model evaluation
model_evaluation(clf, X_test, y_test)
print(clf)
def main():
print("Tesing the performance of RandomForest...")
# Load data
X, y = load_breast_cancer()
# Split data randomly, train set rate 70%
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=40)
# Train model
rf = RandomForest()
rf.fit(X_train, y_train, n_samples=300, max_depth=3, n_estimators=20)
# Model evaluation
model_evaluation(rf, X_test, y_test)
def main():
print("Tesing the performance of KNN classifier...")
# Load data
X, y = load_breast_cancer()
X = min_max_scale(X)
# Split data randomly, train set rate 70%
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=20)
# Train model
clf = KNeighborsClassifier()
clf.fit(X_train, y_train, k_neighbors=21)
# Model evaluation
model_evaluation(clf, X_test, y_test)
def main():
print("Tesing the performance of DecisionTree...")
# Load data
X, y = load_breast_cancer()
# Split data randomly, train set rate 70%
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
# Train model
clf = DecisionTree()
clf.fit(X_train, y_train, max_depth=3)
# Show rules
clf.rules
# Model evaluation
model_evaluation(clf, X_test, y_test)
def main():
"""Tesing the performance of DecisionTree
"""
print("Tesing the performance of DecisionTree...")
# Load data
data, label = load_breast_cancer()
# Split data randomly, train set rate 70%
data_train, data_test, label_train, label_test = train_test_split(
data, label, random_state=10)
# Train model
clf = DecisionTree()
clf.fit(data_train, label_train, max_depth=4)
# Show rules
print(clf)
# Model evaluation
model_evaluation(clf, data_test, label_test)
def main():
print("Tesing the performance of GBDT classifier...")
# Load data
X, y = load_breast_cancer()
# Split data randomly, train set rate 70%
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=20)
# Train model
clf = GradientBoostingClassifier()
clf.fit(X_train,
y_train,
n_estimators=2,
lr=0.8,
max_depth=3,
min_samples_split=2)
# Model evaluation
model_evaluation(clf, X_test, y_test)
def main():
"""Tesing the performance of RandomForest...
"""
print("Tesing the performance of RandomForest...")
# Load data
data, label = load_breast_cancer()
# Split data randomly, train set rate 70%
data_train, data_test, label_train, label_test = train_test_split(
data, label, random_state=40)
# Train model
clf = RandomForest()
clf.fit(data_train,
label_train,
n_estimators=50,
max_depth=5,
random_state=10)
# Model evaluation
model_evaluation(clf, data_test, label_test)
def main():
"""Tesing the performance of GBDT classifier"""
print("Tesing the performance of GBDT classifier...")
# Load data
data, label = load_breast_cancer()
# Split data randomly, train set rate 70%
data_train, data_test, label_train, label_test = train_test_split(
data, label, random_state=20)
# Train model
clf = GradientBoostingClassifier()
clf.fit(data_train,
label_train,
n_estimators=2,
learning_rate=0.8,
max_depth=3,
min_samples_split=2)
# Model evaluation
model_evaluation(clf, data_test, label_test)
