def learn_with_purity(purity): # training decision tree with specific purity
dtree = DecisionTree(x_train, y_train, max_depth=n_attr, purity=purity)
dtree.fit()
# train_accuracy = dtree.accuracy(x_train, y_train)
# test_accuracy = dtree.accuracy(x_test, y_test)
test_preds = dtree.predict(x_test)
return test_preds
def train(x, y):
tree.train(x, y)
if enable_prune[0]:
tree.prune(train_data[:, 0:6], train_data[:, 6], min_gain=0.001)
trainer = LabelTrainer(data=train_data[:, 0:6],
data_labels=train_data[:, 6],
labels=(0, 1),
parameters={
"max_depth": (6, 7, 8, 9),
"min_samples_split": (2, 10, 20),
"min_samples_leaf": (1, 5, 10),
"prune": [False, True]
},
config_function=configure,
train_function=train,
predict_function=tree.predict)
trainer.auto_train(maximize="f1",
validation_split_times=1,
print_info=True)
trainer.config_model()
trainer.train_model()
tree_visualizer = DecisionTreeVisualizer(tree=tree)
dot_data = tree_visualizer.visualize()
tree_visualizer.print()
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_png("output/visualize.png")
labels = tree.predict(test_data[:, 0:6])
utils.save_predict(labels, test_out_path)
# %%
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)
