def main():
trainSetFilename, testSetFilename, m = getArgs()
trainData = arffParser.parse(trainSetFilename)
testData = arffParser.parse(testSetFilename)
data = trainData['data']
attr = trainData['attributes']
targetIndex = len(trainData['attributes']) - 1
tree = DecisionTree(data, attr, targetIndex, m)
if tree.root == None:
return
tree.printTree()
print('<Predictions for the Test Set Instances>')
predictedData = []
for row in testData['data']:
predictedClass = tree.classify(row, testData['attributes'])
predictedRow = row[-1:] + [predictedClass]
predictedData.append(predictedRow)
numCorrect = 0
for i, row in zip(list(range(1, len(predictedData) + 1)), predictedData):
print('{0}: Actual: {1} Predicted: {2}'.format(i, row[0], row[1]))
if row[0] == row[1]:
numCorrect += 1
print('Number of correctly classified: ' + str(numCorrect),
'Total number of test instances: ' + str(len(predictedData)))
def main():
# Check if the number of command line arguments is correct
if len(sys.argv) < 6:
print(
"-- python --|-- main.py --|-- L --|-- K --|-- training_set.csv --|-- validation_set.csv --|-- test_set.csv --|"
)
sys.exit(1)
# The program takes two integer L and K as input to prune the decision tree
L = int(sys.argv[1])
K = int(sys.argv[2])
# Get the file path of the training data, validation data and test data
dataDir = './data1/'
training_set = dataDir + sys.argv[3]
validation_set = dataDir + sys.argv[4]
test_set = dataDir + sys.argv[5]
# Build a decision tree on training data
decisionTree = DecisionTree(training_set)
#############################################
# decisionTree.exportTree('tree.txt')
# print decisionTree
#############################################
# Create a validator using test data to calculate the prediction accuracy of a given decision tree
validator = Validator(test_set)
# Calculate the prediction accuracy of the original decision tree on test data
validator.calculateAccuracy(decisionTree.root)
# Display the prediction accuracy before pruning
print("\nA decision tree is fully grown to fit the training data.")
validator.displayAccuracy()
# Post pruning the decision tree
print("\nPost prunning", '.' * 30)
print("L =", L, ", K =", K, ", the pruned decision tree is:\n")
# Prune the original decision tree using L, K and validation data as inputs
decisionTree.pruneTree(L, K, validation_set)
##############################################
# decisionTree.exportTree('pruned_tree.txt')
##############################################
# print the decision tree to standard output
print(decisionTree) # Override the __str__ method in DecisionTree class
# Calculate the prediction accuracy of the pruned decision tree on test data
validator.calculateAccuracy(decisionTree.root)
# Display the prediction accuracy after pruning
validator.displayAccuracy()
def train(self, data, labels):
self.data = data
self.labels = labels
for i in range(self.num_trees):
sample_index = np.random.choice(self.data.shape[0],
self.num_sample,
replace=True)
train_data = self.data[sample_index, :]
train_labels = self.labels[sample_index]
tree = DecisionTree(self.max_depth, self.num_feature)
tree.train(train_data, train_labels)
self.trees.append(tree)
def test_all_file(self):
options = {
'df': pd.read_csv("benchmark.csv", sep=';'),
'label_column': "Joga"
}
tr = DecisionTree()
model = tr.train(options)
for _, row in options['df'].iterrows():
target_label = row["Joga"]
predicted = model.predict(row.drop("Joga"))
self.assertEqual(target_label, predicted)
def test_benchmark(self):
options = {
'df': pd.read_csv("benchmark.csv", sep=';'),
'label_column': "Joga"
}
tr = DecisionTree()
model = tr.train(options)
inf_data = pd.Series(
["Ensolarado", "Quente", "Normal", "Verdadeiro"],
index=["Tempo", "Temperatura", "Umidade", "Ventoso"],
name="InferenceData")
self.assertEqual(model.predict(inf_data), 'Sim')
def testSimpleCase(self):
tree = DecisionTree()
tree.fit(simpleData)
for datum in simpleData:
self.assertEqual(datum[-1], tree.predict(datum))
tree.print()
def fit(self, dataset):
self.forest = []
for i in range(self.num_trees):
# Generate a random subset of the dataset to train the tree on
subset = [
dataset[random.randrange(0, len(dataset))]
for a in range(self.num_samples)
]
self.forest.append(DecisionTree(self.max_depth).fit(subset))
return self
def train(self, options):
"""
train a random forest, using n_trees decision trees
options['df']: pandas dataframe
options['n_trees']: number of trees
options['label_column']: label column to be predicted
options['bootstrap_size']: the size of the bootstrap, entries not used in the bootstrap will be ignored
"""
num_trees = options['n_trees']
df = options['df']
bootstrap_size = options['bootstrap_size']
tree_options = {
'label_column': options['label_column']
}
for i in range(num_trees):
tree_options['df'] = get_bootstrap(df, bootstrap_size)
new_tree = DecisionTree()
self.ensemble.append(new_tree.train(tree_options))
return self
cat_data_test = np.array(cat_data_test).T
cat_data_test = np.array(cat_data_test, dtype='float')
# zip categorical and non-categorical data together
train_data = np.concatenate((cat_data, non_cat_data), axis=1)
train_label = data[:, -1].astype(int)
validation_data = train_data[:200, :]
validation_label = train_label[:200]
train_data = train_data[:, :]
train_label = train_label[:]
test_data = np.concatenate((cat_data_test, non_cat_data_test), axis=1)
# decision tree
tree = DecisionTree(5, train_data.shape[0])
tree.train(train_data, train_label)
res = tree.predict(validation_data)
score = 0
for i in range(len(res)):
if res[i] == validation_label[i]:
score += 1
score /= len(res)
print(score)
# random forest
forest = RandomForest(100,5,train_data.shape[0],6)
forest.train(train_data, train_label)
res = forest.predict(validation_data)
label2id = {'loss': 0, 'draw': 1, 'win': 2}
p2f = {'b': 0, 'x': 1, 'o': 2}
for line in fin:
line = line.strip().split(',')
label = label2id[line[-1]]
feature = np.array([p2f[p] for p in line[:-1]])
data.append((feature, label))
fin.close()
return data
data = read('connect-4.data')
x = np.array([d[0] for d in data])
y = np.array([d[1] for d in data])
#y = label_binarize(y, classes=list(range(3)))
kf = KFold(5, True)
all_f1 = []
for train_index, test_index in kf.split(x):
x_train, y_train, x_test, y_test = x[train_index], y[train_index], x[
test_index], y[test_index]
#x_train, x_test, y_train, y_test = train_test_split(x, y)
#print('training')
#model = OneVsRestClassifier(SVC(kernel='rbf'))
model = DecisionTree()
model.fit(x_train, y_train)
#print('testing')
y_pred = model.predict(x_test)
all_f1.append(f1_score(y_test, y_pred, average='macro'))
print(sum(all_f1) / 5)
__author__ = 'zephyrYin'
from decisionTree import DecisionTree
dT = DecisionTree('data/featnames.csv', 'data/trainfeat.csv', 'data/trainlabs.csv', 'data/testfeat.csv', 'data/testlabs.csv', 0.01)
#dT = DecisionTree('data/weatherFeatureName.csv', 'data/weatherTrainFeature.csv', 'data/weatherTrainLabel.csv', 'data/weatherTrainFeature.csv', 'data/weatherTrainLabel.csv', 1)
dT.buildTree()
dT.predictTestSet()
result = dT.evaluate(dT.contrastResult[0], dT.contrastResult[1])
print(result)
print(str(dT.nodeCnt) + ' nodes')
# dT.readTrainFeatures()
# wholeCnt = len(dT.testFeatures)
# posCnt = dT.countPositive(dT.testFeatures)
# print(wholeCnt)
# print(posCnt)
# print(posCnt/float(wholeCnt))
# dT.readFeatureNames()
# dT.readTrainFeatures()
# dT.readTestFeatures()
#
# for testFea in dT.testFeatures:
# for i in range(len(testFea)-1):
# if testFea[i] not in dT.featureValue[i]:
# print i
# print(str(testFea[i]) + ' not in ' + str(dT.featureValue[i]))
#
cat_data_test = np.array(cat_data_test, dtype='int')
# zip categorical and non-categorical data together
train_data = np.concatenate((cat_data, non_cat_data), axis=1)
train_label = data[:, -1].astype(int)
validation_data = train_data[:6000, :]
validation_label = train_label[:6000]
train_data = train_data[6000:,:]
train_label = train_label[6000:]
test_data = np.concatenate((cat_data_test,non_cat_data_test), axis=1)
# plot accuracy
accuracy = []
for i in range(40):
tree = DecisionTree(i,105)
tree.train(train_data,train_label)
res = tree.predict(validation_data)
score = 0
for i in range(len(res)):
if res[i] == validation_label[i]:
score += 1
score /= len(res)
accuracy.append(score)
plt.plot(accuracy)
plt.xlabel('depth')
plt.ylabel('accuracy')
plt.title('Accuracy vs. Decision Tree Depth')
plt.savefig('p6.png')
plt.show()
def testComplexCase(self):
tree = DecisionTree()
tree.fit(complexData)
for datum in complexData:
self.assertEqual(datum[-1], tree.predict(datum))
# Overcast = YES
self.assertEqual(
Label.YES,
tree.predict(
[Outlook.Overcast, Temperature.Hot, Humidity.High, Wind.Weak]))
self.assertEqual(
Label.YES,
tree.predict([
Outlook.Overcast, Temperature.Cool, Humidity.Normal,
Wind.Strong
]))
# Sunny + Normal = YES
self.assertEqual(
Label.YES,
tree.predict([
Outlook.Sunny, Temperature.Cool, Humidity.Normal, Wind.Strong
]))
self.assertEqual(
Label.YES,
tree.predict(
[Outlook.Sunny, Temperature.Hot, Humidity.Normal, Wind.Weak]))
# Sunny + High = NO
self.assertEqual(
Label.NO,
tree.predict(
[Outlook.Sunny, Temperature.Cool, Humidity.High, Wind.Strong]))
self.assertEqual(
Label.NO,
tree.predict(
[Outlook.Sunny, Temperature.Hot, Humidity.High, Wind.Weak]))
# Rain + Weak = Yes
self.assertEqual(
Label.YES,
tree.predict(
[Outlook.Rain, Temperature.Cool, Humidity.Normal, Wind.Weak]))
self.assertEqual(
Label.YES,
tree.predict(
[Outlook.Rain, Temperature.Hot, Humidity.High, Wind.Weak]))
# Rain + Strong = No
self.assertEqual(
Label.NO,
tree.predict(
[Outlook.Rain, Temperature.Cool, Humidity.Normal,
Wind.Strong]))
self.assertEqual(
Label.NO,
tree.predict(
[Outlook.Rain, Temperature.Hot, Humidity.High, Wind.Strong]))
tree.print()
def run(self):
dt = DecisionTree(self.pathname, self.G)
dt.dtree()
f_score = f1_score(y_test, pred, pos_label='grapefruit')
# print('Confusion matrix:\n', cm)
# print('Accuracy: {}\nPrecision: {}\nRecall: {}\nF1_score: {}'.format(
# acc, precision, recall, f_score))
return acc, precision, recall, f_score
# load data
df = pd.read_csv("citrus.csv")
# split columns
X = df.drop("name", axis=1)
y = df["name"].values
# initialize classifiers
clf1 = DecisionTree()
clf2 = KNeighborsClassifier(n_neighbors=7)
clf3 = SVC(kernel='rbf', probability=True)
clf4 = GaussianNB()
test_cases = [[clf1], [clf2], [clf3], [clf4], [clf1, clf2, clf3, clf4],
[clf2, clf3, clf4]]
test_number = 1
for classifiers in test_cases:
accuracy = []
precision = []
recall = []
f_score = []
def test(): X = [[1, 2, 0, 1, 0], [0, 1, 1, 0, 1], [1, 0, 0, 0, 1], [2, 1, 1, 0, 1], [1, 1, 0, 1, 1]] y = ['yes','yes','no','no','no'] decision_tree = DecisionTree(mode = 'C4.5') decision_tree.fit(X,y) res = decision_tree.predict(X) print res model_name = 'test.dt' decision_tree.saveModel(model_name) new_tree = DecisionTree() new_tree.loadModel(model_name) print new_tree.predict(X)
import matplotlib.pyplot as plt
import numpy as np
from loadData import LoadData
from decisionTree import Node, DecisionTree, Evaluate
from inspection import Inspection
if __name__ == '__main__':
train_input = '../handout/education_train.tsv'
test_input = '../handout/education_test.tsv'
train_output = '../result/education_train.labels'
test_output = '../result/education_test.labels'
ld = LoadData()
dataset = ld.load_data(train_input)
dt = DecisionTree(ld)
tr_err = []
te_err = []
x_arr = []
print(ld.head)
for i in range(len(ld.head)):
root = dt.construct(dataset, i)
# dt.traverse(root)
dt.classify(ld.load_data(train_input), root, train_output)
dt.classify(ld.load_data(test_input), root, test_output)
with open(train_output, 'r') as f:
predcol = f.read().splitlines()
realcol = np.loadtxt(train_input,
dtype=str,
delimiter='\t',
skiprows=1)[:, -1]
from decisionTree import DecisionTree
from decisionTreePlot import DecisionTreePlot
if __name__ == "__main__":
trainingSetFile = open('SampleSets/training_set.csv')
trainingSetData = trainingSetFile.readlines()
trainingSetFile.close()
decisionTree = DecisionTree(trainingSetData)
#Algorithm Type - Example
decisionTree.C45()
#decisionTree.ID3()
#decisionTree.ID3K(2)
#decisionTree.SID3()
#decisionTree.LSID3(3)
#decisionTree.LSID3PathSample(2)
#decisionTree.LSID3MC(1, 0.1)
#decisionTree.BLSID3(1)
#decisionTree.BLSID3PathSample(1)
#decisionTree.LSID3Sequenced(2)
#decisionTree.IIDT(10, 0.5)
print("****Tree Data BEFORE Pruning****")
print("Tree Size - Number of Nodes:", decisionTree.size())
print("Number of Leafs:", decisionTree.getNumLeafs())
print("Tree Depth:", decisionTree.getTreeDepth())
testSetFile = open('SampleSets/test_set.csv')
testSetData = testSetFile.readlines()
testSetFile.close()
print("Prediction:", str(decisionTree.predict(testSetData) * 100) + "%")
#decisionTreePlot = DecisionTreePlot()
#decisionTreePlot.createDecisionTreePlot(decisionTree)
from fileIO import FileIO
from preprocess import Preprocessing
from decisionTree import DecisionTree
if __name__ == '__main__':
filename = 'house-votes-84.data.txt'
fileio = FileIO()
data = fileio.read_csv(filename)
preprocessing = Preprocessing()
preprocessing.assume_missing_values(data)
for percent in range(3, 8):
training_data, testing_data = preprocessing.split_into_training_and_testing(data, percent/float(10))
attributes_number = len(training_data[0]) - 1
decision_tree = DecisionTree()
root_node = decision_tree.build(training_data)
# decision_tree.print()
# print("Classification: ")
accuracy = 0
for row in testing_data:
classified = decision_tree.classify(row, decision_tree.root)
classified.calc_percentages(len(testing_data))
if classified.republicans_percent > 50.0 and row[0] == 'republican' or (
classified.democrats_percent > 50.0 and row[0] == 'democrat'):
accuracy += 1
accuracy = accuracy / float(len(testing_data))
print("Accuracy using training data", percent/float(10)*100, "% is: ", accuracy)
std_dev = 0
for a in accuracies:
std_dev += ((a - average_accuracy)**2)
std_dev = math.sqrt(1 / len(accuracies) * std_dev)
print("Accuracy: {}, Std dev: {}".format(average_accuracy, std_dev))
return (average_accuracy, std_dev)
"""
Main program. Uses decisionTree.py as a support program to classify data
"""
values = [list(a) for a in skset.data]
targets = [int(a) for a in skset.target]
dataset = [Data(values[i], targets[i]) for i in range(len(values))]
# print([str(a) for a in dataset])
# dataset = d.processFile('data/digitsModified.txt')
if algorithm == Algorithm.DECISION_TREE:
d = DecisionTree(max_depth=max_depth)
# tree = d.fit(datase)
check_accuracy(dt=d, dataset=dataset, num_repeats=10)
elif algorithm == Algorithm.RANDOM_FOREST:
forest = Forest(max_depth=max_depth,
num_trees=num_trees,
num_samples=num_samples)
# f = forest.fit(dataset)
check_accuracy(dt=forest, dataset=dataset, num_repeats=10)
