def learn_depths(): # training decision tree for different heights
train_acc = np.zeros(n_attr)
test_acc = np.zeros(n_attr)
for depth in range(n_attr):
dtree = DecisionTree(x_train, y_train, max_depth=depth)
dtree.fit()
train_acc[depth] = dtree.accuracy(x_train, y_train)
test_acc[depth] = dtree.accuracy(x_test, y_test)
df = pd.DataFrame({
'depth': range(1, n_attr + 1),
'Train accuracy': train_acc,
'Test accuracy': test_acc
})
# df.to_csv('res/acc.csv')
return df
def k_fold_cross_validation(x, y, k, shf=False):
if shf:
to_shf = np.column_stack((x, y))
to_shf = list(to_shf)
shuffle(to_shf)
to_shf = np.array(to_shf)
x = np.delete(to_shf, -1, axis=1)
y = to_shf[:, -1]
train_acc = np.zeros((k, n_attr))
val_acc = np.zeros((k, n_attr))
for d in range(k):
print(d, "th fold...")
x_train = np.array([row for i, row in enumerate(x) if i % k != d])
x_val = np.array([row for i, row in enumerate(x) if i % k == d])
y_train = np.array([val for i, val in enumerate(y) if i % k != d])
y_val = np.array([val for i, val in enumerate(y) if i % k == d])
for depth in range(n_attr):
dtree = DecisionTree(x_train, y_train, max_depth=depth)
dtree.fit()
# train_acc[d, depth] = dtree.accuracy(x_train, y_train)
val_acc[d, depth] = dtree.accuracy(x_val, y_val)
return val_acc
# %%
import numpy as np
from preprocess import get_train_data, get_test_data
from dtree import DecisionTree
x_train, y_train = get_train_data()
x_test, y_test = get_test_data()
decision_tree = DecisionTree(x_train, y_train, max_depth=1)
decision_tree.fit()
decision_tree.traverse()
y_hat = decision_tree.predict(x_test)
print("accuracy: ", decision_tree.accuracy(x_test, y_test))
# %%
def get_stats():
TP = np.sum(np.logical_and(y_test == 1, y_hat == 1))
FP = np.sum(np.logical_and(y_test == 0, y_hat == 1))
TN = np.sum(np.logical_and(y_test == 0, y_hat == 0))
FN = np.sum(np.logical_and(y_test == 1, y_hat == 0))
return TP, FP, TN, FN
def specificity():
TP, FP, TN, FN = get_stats()
return TN / (TN + FP)
def sensitivity():
TP, FP, TN, FN = get_stats()
return TP / (TP + FN)
