def fit(self, X, y):
self.trees = []
for _ in range(self.n_trees):
tree = Decision_Tree(min_samples_split=self.min_sample_split,
max_depth=self.max_depth,
n_feats=self.n_feats)
X_sample, y_sample = bootstrap_sample(X, y)
tree.fit(X_sample, y_sample)
self.trees.append(tree)
def decision_tree_test():
data = load_boston()
X = data.data
Y = data.target
X = X[:, [0, 2, 4, 5, 7, 11]]
dt = Decision_Tree(max_depth=5)
dt.fit(X, Y)
Y_pre = dt.predict(X[-20:])
return Y_pre
def fit(self, X, Y):
# 循环训练每一颗决策树
self.tree = []
for s_t in range(self.n_trees):
X_Sample, Y_Sample = boost_trap(X, Y)
single_tree = Decision_Tree(max_depth=self.max_depth,
classifier=True,
Loss="Gini")
single_tree.fit(X_Sample, Y_Sample)
self.tree.append(single_tree)
pass
pass
def decision_tree(self, training_data, training_labels, testing_data):
# Create and build the decision tree
tree = Decision_Tree(training_data, training_labels)
tree.print_tree_dfs()
# Test for when we encounter a new category not seen before in testing
# test = ["low", "high", "high", "high", "high", "high", "high", "potato"]
# print(f"YEET: {tree.predict(test)}")
predictions = []
for i in range(len(testing_data)):
predictions.append(tree.predict(testing_data[i]))
return predictions
from decision_tree import Decision_Tree
from sklearn.datasets import load_iris
import numpy as np
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier as DTC
from sklearn import tree
# Test w/ Iris dataset using my class
dataset = load_iris()
X, y = dataset.data, dataset.target
clf_iris = Decision_Tree(max_depth = 5)
# Test to make target class strings instead of integers
y = ["one" if val == 1 or val == 2 else "zero" for val in y]
y = np.array(y)
# Need to ordinally encode strings to integers
if "int" not in str(y.dtype):
# Reshape y array so it works w/ ordinal encoder
y = y.reshape(-1, 1)
encoder = OrdinalEncoder()
y = encoder.fit_transform(y)
y = y.astype(int)
y = y.reshape(y.size,)
clf_iris.fit(X, y)
temp1 = np.array([[3, 2, 1, .5]])
temp2 = np.array([[4, 2.9, 1.3, .2]])
temp3 = np.array([[3.8, 3, 1.4, .4]])
temp4 = np.array([[7.7, 2.8, 6.7, 2]])
from read_file import Read_File
from data_processor import Data_Processor
from svm import SVM
from decision_tree import Decision_Tree
fileName = 'data/census-income_10percentData.csv'
file_reader = Read_File(fileName)
file_reader.read()
features = file_reader.get_features()
labels = file_reader.get_labels()
data_fill = Data_Processor(features)
data_fill.fill_empty_fields()
# will perform svm task
my_svm = SVM(features, labels)
my_svm.calculate_info_gain()
my_svm.stratified_k_fold(10)
my_svm.svm()
my_svm.draw_svm()
my_tree = Decision_Tree(features, labels)
my_tree.calculate_info_gain()
for i in range(1, 14):
print "============================{}============================".format(
i)
my_tree.decision(i)
def decisionTree_class():
X, Y = make_blobs(n_samples=100, centers=10, n_features=10, random_state=5)
dt = Decision_Tree(max_depth=5, classifier=True, Loss="Gini")
dt.fit(X, Y)
Y_P = dt.predict(X)
return Y_P, Y
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from decision_tree import Decision_Tree
def accuracy(y_true, y_pred):
accuracy = np.sum(y_true == y_pred) / len(y_true)
return accuracy
data = datasets.load_breast_cancer()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1234)
clf = Decision_Tree(max_depth=10)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = accuracy(y_test, y_pred)
print("Accuracy:", acc)
{
# Choosing month of date
"date": row[0].split("/")[0],
# log10 of confirmed
"confirmed": int(math.log10(int(row[1]))),
# log10 of recovered
"recovered": int(math.log10(int(row[2]))),
# log10 of deaths
"deaths": int(math.log10(int(row[3]))),
},
discretise_target(row[4]))
examples.append(data)
# Shuffling for randomness
random.shuffle(examples)
tre = list()
tee = list()
# Split the data 80/20
split_index = (int)((80 / 100) * len(examples))
tre = examples[:split_index]
tee = examples[split_index:]
decision_tree = Decision_Tree(tre, depth, pruning)
print("DECISION TREE")
print(decision_tree)
print("MAXIMUM DEPTH REACHED")
print(decision_tree.depth_reached)
print("ACCURACY OVER TESTING")
print(decision_tree.test_accuracy(tee) * 100, "%")
