test = []
for j in range(i * m, i * m + m):
test.append(j)
df_train = df.drop(test)
df_test = df.iloc[test]
root = decision_tree.TreeGenerate(df_train)
# accuracy
pred_true = 0
for i in df_test.index:
label = decision_tree.Predict(root, df[df.index == i])
if label == df_test[df_test.columns[-1]][i]:
pred_true += 1
accuracy = pred_true / len(df_test.index)
accuracy_scores.append(accuracy)
# accuracy result
accuracy_sum = 0
print("accuracy: ", end="")
for i in range(k):
print("%.3f " % accuracy_scores[i], end="")
accuracy_sum += accuracy_scores[i]
print("\naverage accuracy: %.3f" % (accuracy_sum / k))
# dicision tree visualization using pydotplus.graphviz
root = decision_tree.TreeGenerate(df)
decision_tree.DrawPNG(root, "decision_tree_ID3.png")
encoding=data_file_encode) as data_file:
df = pd.read_csv(data_file)
'''
implementation of CART rely on decision_tree.py
'''
import decision_tree
# dicision tree visualization using pydotplus.graphviz
index_train = [0, 1, 2, 5, 6, 9, 13, 14, 15, 16]
df_train = df.iloc[index_train]
df_test = df.drop(index_train)
# generate a full tree
root = decision_tree.TreeGenerate(df_train)
decision_tree.DrawPNG(root, "decision_tree_full.png")
print("accuracy of full tree: %.3f" %
decision_tree.PredictAccuracy(root, df_test))
# pre-purning
root = decision_tree.PrePurn(df_train, df_test)
decision_tree.DrawPNG(root, "decision_tree_pre.png")
print("accuracy of pre-purning tree: %.3f" %
decision_tree.PredictAccuracy(root, df_test))
# # post-puring
root = decision_tree.TreeGenerate(df_train)
decision_tree.PostPurn(root, df_test)
decision_tree.DrawPNG(root, "decision_tree_post.png")
print("accuracy of post-purning tree: %.3f" %
decision_tree.PredictAccuracy(root, df_test))