def ada_boost_experiment():
examples = ID3.data_parsing(FILE_PATH_TRAIN, numeric_cols)
# hypothesis = AdaBoost.ada_boost(examples, 5, numeric_cols, missing_identifier)
# print(hypothesis)
iterations = 100
hypothesis = AdaBoost.ada_boost(examples, iterations, numeric_cols,
missing_identifier)
ada_results_train = AdaBoost.test_ada_boost_hypothesis(
hypothesis, FILE_PATH_TRAIN, numeric_cols, missing_identifier)
ada_results_test = AdaBoost.test_ada_boost_hypothesis(
hypothesis, FILE_PATH_TEST, numeric_cols, missing_identifier)
# for t in range(iterations):
# print("AdaBoost Training Set - t:", t, "results:", ada_results_train[t],
# "{0:.2%}".format(1-ada_results_train[t][0]/ada_results_train[t][1]))
# for t in range(iterations):
# print("AdaBoost Testing Set - t:", t, "results:", ada_results_test[t],
# "{0:.2%}".format(1-ada_results_test[t][0]/ada_results_test[t][1]))
# for t in range(iterations):
# tree_results = ID3.test_tree(hypothesis[t][0],FILE_PATH_TRAIN, numeric_cols, missing_identifier)
# print("Decision Tree Training Set - t:", t, "results:", tree_results,
# "{0:.2%}".format(1 - tree_results[0] / tree_results[1]))
# for t in range(iterations):
# tree_results = ID3.test_tree(hypothesis[t][0],FILE_PATH_TEST, numeric_cols, missing_identifier)
# print("Decision Tree Test Set - t:", t, "results:", tree_results,
# "{0:.2%}".format(1 - tree_results[0] / tree_results[1]))
ada_train = []
ada_test = []
dec_train = []
dec_test = []
for t in range(iterations):
ada_train.append(1 - ada_results_train[t][0] / ada_results_train[t][1])
ada_test.append(1 - ada_results_test[t][0] / ada_results_test[t][1])
tree_results = ID3.test_tree(hypothesis[t][0], FILE_PATH_TRAIN,
numeric_cols, missing_identifier)
dec_train.append(1 - tree_results[0] / tree_results[1])
tree_results = ID3.test_tree(hypothesis[t][0], FILE_PATH_TEST,
numeric_cols, missing_identifier)
dec_test.append(1 - tree_results[0] / tree_results[1])
ada_graph = [
tuple([ada_train, "AdaBoost Train"]),
tuple([ada_test, "AdaBoost Test"])
]
GraphUtility.graph(ada_graph, "AdaBoost Data", "Iterations", "Error")
tree_graph = [
tuple([dec_train, "Tree Train"]),
tuple([dec_test, "Tree Test"])
]
GraphUtility.graph(tree_graph, "Decision Tree Data", "Iterations", "Error")
def credit_experiment():
file_path = "/home/john/PycharmProjects/u1201441_Private_Repository/CS6350_Files/HW2/credit/default of credit card clients.csv"
numeric_cols = [0, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]
missing_identifier = None
training_data = []
test_data = []
data = ID3.data_parsing(file_path, numeric_cols)
LABEL_INDEX = len(data[0]) - 2
for instance in data:
if instance[LABEL_INDEX] == '1':
instance[LABEL_INDEX] = "yes"
else:
instance[LABEL_INDEX] = "no"
test_indices = random.sample(range(len(data)), len(data))
for i in test_indices:
if i < 6000:
test_data.append(data[i])
else:
training_data.append(data[i])
iterations = 100
decision_tree = ID3.build_decision_tree(
training_data,
max_depth=-1,
info_gain_type=1,
numeric_cols=numeric_cols,
missing_identifier=missing_identifier)
adaboost = AdaBoost.ada_boost(training_data,
iterations=iterations,
numeric_cols=numeric_cols,
missing_identifier=missing_identifier)
bagged_tree = BaggedTrees.bagged_trees(
training_data,
iterations=iterations,
sample_size=100,
numeric_cols=numeric_cols,
missing_identifier=missing_identifier)
forest = RandomForest.random_forest(training_data,
iterations=iterations,
sample_size=100,
numeric_cols=numeric_cols,
missing_identifier=missing_identifier,
feature_size=4)
# Decision Tree results
tree_results = ID3.test_tree(decision_tree, training_data, numeric_cols,
missing_identifier)
tree_train = 1 - tree_results[0] / tree_results[1]
tree_results = ID3.test_tree(decision_tree, test_data, numeric_cols,
missing_identifier)
tree_test = 1 - tree_results[0] / tree_results[1]
tree_train_ln = []
tree_test_ln = []
for t in range(iterations):
tree_train_ln.append(tree_train)
tree_test_ln.append(tree_test)
# AdaBoost results
ada_results_train = AdaBoost.test_ada_boost_hypothesis(
adaboost, training_data, numeric_cols, missing_identifier)
ada_results_test = AdaBoost.test_ada_boost_hypothesis(
adaboost, test_data, numeric_cols, missing_identifier)
ada_train = []
ada_test = []
for t in range(iterations):
ada_train.append(1 - ada_results_train[t][0] / ada_results_train[t][1])
ada_test.append(1 - ada_results_test[t][0] / ada_results_test[t][1])
ada_graph = [
tuple([ada_train, "AdaBoost Train"]),
tuple([ada_test, "AdaBoost Test"]),
tuple([tree_train_ln, "Tree Train"]),
tuple([tree_test_ln, "Tree Test"])
]
GraphUtility.graph(ada_graph, "AdaBoost Data", "Iterations", "Error")
# Bagging results
results_train = BaggedTrees.test_bagged_tree_hypothesis(
bagged_tree, training_data, numeric_cols, missing_identifier)
results_test = BaggedTrees.test_bagged_tree_hypothesis(
bagged_tree, test_data, numeric_cols, missing_identifier)
# Charts
bag_train = []
bag_test = []
for t in range(iterations):
bag_train.append(1 - results_train[t][0] / results_train[t][1])
bag_test.append(1 - results_test[t][0] / results_test[t][1])
bag_graph = [
tuple([bag_train, "Bagging Train"]),
tuple([bag_test, "Bagging Test"]),
tuple([tree_train_ln, "Tree Train"]),
tuple([tree_test_ln, "Tree Test"])
]
GraphUtility.graph(bag_graph, "Bagged Tree Data", "Num Trees", "Error")
# Forest Results
results_train = RandomForest.test_random_forest_hypothesis(
forest, training_data, numeric_cols, missing_identifier)
results_test = RandomForest.test_random_forest_hypothesis(
forest, test_data, numeric_cols, missing_identifier)
# Charts
forest_train = []
forest_test = []
for t in range(iterations):
forest_train.append(1 - results_train[t][0] / results_train[t][1])
forest_test.append(1 - results_test[t][0] / results_test[t][1])
forest_graph = [
tuple([forest_train, "Forest Train - " + str(2) + " features"]),
tuple([forest_test, "Forest Test - " + str(2) + " features"]),
tuple([tree_train_ln, "Tree Train"]),
tuple([tree_test_ln, "Tree Test"])
]
GraphUtility.graph(forest_graph, "Random Forest Data", "Num Trees",
"Error")
