def test_Insert_Node(self):
bst = BST()
tNode1 = Node(21)
tNode2 = Node(20)
treeInsert(bst, tNode1)
treeInsert(bst, tNode2)
self.assertEqual(2, len(bst))
searched = treeSearch(bst.root, 20)
self.assertIsNotNone(searched)
def _insert(self, value, currentNode):
if value <= currentNode.value:
if currentNode.hasLeftChild():
self._insert(value, currentNode.left)
else:
currentNode.left = Node(value, parent=currentNode)
else:
if currentNode.hasRightChild():
self._insert(value, currentNode.right)
else:
currentNode.right = Node(value, parent=currentNode)
def add_node(self, node, key):
if node.key == key:
return
if node.key > key:
if node.left is None:
node.left = Node(key)
else:
self.add_node(node.left, key)
else:
if node.right is None:
node.right = Node(key)
else:
self.add_node(node.right, key)
def test_Delete_Node(self):
bst = BST()
treeInsert(bst, Node(21))
treeInsert(bst, Node(20))
treeInsert(bst, Node(35))
treeInsert(bst, Node(64))
treeInsert(bst, Node(32))
self.assertEqual(5, len(bst))
searched = treeSearch(bst.root, 20)
self.assertIsNotNone(searched)
treeDelete(bst, searched)
searchAgain = treeSearch(bst.root, 20)
self.assertIsNone(searchAgain)
def randomForest(data, attributes, numAttributes, numDataPoints, numTrees, holdoutSet):
trees = []
uncategorizedPoints = 0
numeric = isNumeric(data[0])
for _ in range(numTrees):
randomData, randomAttributes = selectRandomData(data, attributes, numAttributes, numDataPoints)
currentTree = Node('Root', None)
buildTree(randomData, randomAttributes, currentTree, THRESHOLD)
trees.append(currentTree)
bestClassifs = []
for index, dataPoint in enumerate(holdoutSet):
if numeric:
classifs = [classifyPointNum(tree, dataPoint) for tree in trees]
else:
classifs = [classifyPointCat(tree, dataPoint) for tree in trees]
classifs = list(filter(lambda x: x is not None, classifs))
if len(classifs) == 0:
uncategorizedPoints += 1
continue
mostFreqClassif = max(classifs, key=classifs.count)
bestClassifs.append((dataPoint['id'], mostFreqClassif))
return bestClassifs, uncategorizedPoints
class BST:
def __init__(self, val):
self.root = Node(val)
def setRoot(self, val):
self.root = Node(val)
def getRoot(self):
return self.root.get()
def insert(self, val):
self.root.insert(val)
def search(self, val):
return self.root.search(val)
def insert(self, key):
if self.root is None:
self.root = Node(key)
self.count += 1
else:
self.add_node(self.root, key)
self.count += 1
def __init__(self, level, threshold, num_label, file):
self.__level = level
self.__threshold = threshold
data = xlrd.open_workbook(file)
sheet = data.sheets()
# read file in to input table
for col in range(0, sheet[0].ncols - num_label):
att = []
for row in range(0, sheet[0].nrows):
att.append(sheet[0].cell(row, col).value)
self.__table.append(att)
# read file in to label table
for col in range(sheet[0].ncols - num_label, sheet[0].ncols):
att = []
for row in range(0, sheet[0].nrows):
att.append(sheet[0].cell(row, col).value)
self.__label_table.append(att)
for att in range(0, 1):
self.__label.clear()
t = copy.copy(self.__table)
t.append(self.__label_table[att])
self.__label = self.__classifiedAtt__(len(t) - 1, t)
root = Node(None, None, None)
self.__generateTree__(0, root, t)
self.__tree.append(root)
print("tree is create " + str(att))
return
def main():
data = csvParser.parse(sys.argv[1])
attributes = list(data[0].keys())
root = Node('Root', None)
build(data, attributes, root, 0.01)
print(root.name)
for child in root.children:
print(child.name)
def main():
# CHANGED TO READ FROM SPECIFIC FILE NOT ARGV[1]
data = csvParser.parse("trunk/tree03-100-words.csv")
attributes = list(data[0].keys())
root = Node('Root', None)
build(data, attributes, root, 0.01)
s = etree.tostring(outputXML(root), pretty_print=True, encoding='unicode')
print(s)
def add_to_tree(self, node, key):
if self.root is None:
self.root = Node(key)
self.count += 1
else:
if node.key == key:
return
if node.key > key:
if node.left is None:
node.left = Node(key)
else:
self.add_to_tree(node.left, key)
else:
if node.right is None:
node.right = Node(key)
else:
self.add_to_tree(node.right, key)
self.count += 1
def from_json(self, filename):
if self.trained:
print(
"This tree has already been trained. This procedure will purge the trained rules."
)
self.__untrain()
with open(filename, "r") as jsonfile:
tree_dict = json.load(jsonfile)
#load basics
try:
self.depth_limit = tree_dict["depth"]
self.dimensions = tree_dict["dimensions"]
levels = tree_dict["levels"]
except KeyError:
print("Invalid JSON format")
return False
#first run, just create Node instances so that you can reference them in output/parents
for i in xrange(len(levels)):
key = "lvl%d" % i
level = levels[key]
if len(level) > 0:
self.nodes.append([])
for r in range(len(level)):
n = Node(level=i)
self.nodes[i].extend([n])
for key, level in levels.iteritems():
i = int(key[-1])
if len(level) == 0:
continue
for n, node in enumerate(level):
mynode = self.nodes[i][n]
mynode.entropy = node["entropy"]
if node["terminal"]:
mynode.outcome = [node["outcome"]]
else:
mynode.feature = node["feature"]
mynode.threshold = node["threshold"]
left_outcome = self.nodes[i +
1][node["outcome"][0]["index"]]
right_outcome = self.nodes[i +
1][node["outcome"][1]["index"]]
mynode.outcome = [left_outcome, right_outcome]
mynode.terminal = False
if i > 0:
parent = self.nodes[i - 1][node["parent_index"]]
mynode.parent = parent
self.trained = True
return self
class Tree:
def __init__(self, rootVal):
self.root = Node(rootVal)
def addChild(self, newNode):
kek = self.root.addChild(newNode)
# print(kek)
return kek
def levelPrint(self):
self.root.x = 0
self.root.y = 0
self.root.z = 0
level_counter = 0
self.root.printCoords()
currentLevel = [self.root]
nextLevel = []
while len(currentLevel) > 0:
level_counter += 1
vals = []
for child in currentLevel:
vals += [child.getValue()]
num_of_childs = len(child.getChildren())
child_count = 0
for child_1 in child.getChildren():
degrees = getDegrees(child_count, num_of_childs)
child_1.setZ(level_counter)
child_1.setX(child.x, degrees)
child_1.setY(child.y, degrees)
child_1.printCoords()
pEdeges(child, child_1)
child_count += 1
nextLevel += [child_1]
print(vals)
currentLevel = nextLevel
nextLevel = []
def build(data, attributes, tree, threshold):
if isUniform(d['Class'] for d in data): # All class labels are the same
tree.setName(data[0]['Class'])
elif len(attributes) == 0: # No more attributes
tree.setName(mostFrequentCategory(data))
else:
bestAttribute = selectSplittingAttributeN(attributes, data, threshold)
if not bestAttribute: # No best attribute to split on
tree.setName(mostFrequentCategory(data))
else:
valToSplit = bestAttribute[1] # -1 if attribute is categorical
bestAttribute = bestAttribute[0]
tree.setName(bestAttribute)
if valToSplit > 0: # attribute is continuous
# Split data on valToSplit
splits = splitOnVal(data, bestAttribute, valToSplit)
# Recursive call on data <= split val
nodeLT = Node(None, "<= {}".format(valToSplit))
tree.addChild(nodeLT)
build(splits[0], attributes, nodeLT, threshold)
# Recursive call on data > split val
nodeGT = Node(None, "> {}".format(valToSplit))
tree.addChild(nodeGT)
build(splits[1], attributes, nodeGT, threshold)
else: # attribute is categorical
attributeDict = groupByAttribute(data, bestAttribute)
for attributeName in attributeDict.keys():
newData = attributeDict[attributeName]
if len(newData) > 0:
newAttributes = list(attributes)
newAttributes.remove(bestAttribute)
childNode = Node(None, attributeName)
tree.addChild(childNode)
build(newData, newAttributes, childNode, threshold)
def main():
shrooms = r"E:\Documents\CSC466\Lab 3\466-lab-3.git\trunk\agaricus-lepiota.data.csv"
iris = r"E:\Documents\CSC466\Lab 3\466-lab-3.git\trunk\iris.data"
d = parsing.parseData(shrooms)
data = d[0]
attributes = d[1]
root = Node('Root', None)
dt.build(data, attributes, root, 0.1)
print("**", classifyPointCat(root, data[1000]))
"""if not len(sys.argv) == 3:
def add(self, item):
newNode = Node(item)
if self.__root is None:
self.__root = newNode
self.__items.append(self.__root)
else:
treeNode = self.__items[0]
if treeNode.getLChild() is None:
treeNode.setLChild(newNode)
self.__items.append(treeNode.getLChild())
elif treeNode.getRChild() is None:
treeNode.setRChild(newNode)
self.__items.append(treeNode.getLChild())
def test():
T = Node(17)
T.left = Node(13)
T.right = Node(26)
T.left.left = Node(14)
T.left.right = Node(5)
print("Preorder : ")
PreorderTraverse(T)
print("-------")
print("Inorder : ")
InorderTraverse(T)
print("-------")
print("Postorder : ")
PostorderTraverse(T)
print("-------")
print("Levelorder : ")
LevelorderTraverse(T)
def main():
t11 = Node(1)
t12 = Node(3)
t13 = Node(2)
t14 = Node(4)
t15 = Node(5)
t16 = Node(6)
list1 = []
list1.append(t12)
list1.append(t13)
list1.append(t14)
list2 = []
list2.append(t15)
list2.append(t16)
t11.children = list1
t12.children = list2
solution = Solution()
output = solution.postorder(t11)
print("Root Node: ", output)
def _Decision_Tree(self, tree_node):
x_data = tree_node.x_data
y_data = tree_node.y_data
feature_names = list(x_data.columns)
xy_data = pd.concat([x_data, y_data], axis=1)
label_name = list(xy_data.columns)[-1]
label_unique = xy_data[label_name].unique()
if len(label_unique) == 1:
cate = y_data.loc[0]
tree_node.category = cate
return tree_node
if len(feature_names) == 0:
cate = y_data.value_counts(ascending=False).keys()[0]
tree_node.category = cate
return tree_node
label_entr = self.label_entr(y_data)
max_gain = 0
for feature in feature_names:
info_gain = self.info_gain(label_entr, feature, label_name,
xy_data)
if info_gain > max_gain:
max_gain = info_gain
f_name = feature
if max_gain <= self.min_info_gain:
cate = y_data.value_counts(ascending=False).keys()[0]
tree_node.category = cate
return tree_node
tree_node.feature = f_name
tree_node.children = dict()
for sub_attribute in self.feature_item[tree_node.feature]:
sub_data = xy_data[xy_data[f_name] == sub_attribute]
sub_data_x = sub_data.drop(list(sub_data.columns)[-1], axis=1)
sub_data_x.drop(tree_node.feature, axis=1, inplace=True)
sub_data_y = sub_data[list(sub_data.columns)[-1]]
child_node = Node(tree_node, None, None, None, sub_data_x,
sub_data_y)
tree_node.children[sub_attribute] = Decision_Tree(child_node)
return tree_node
def build(data, attributes, tree, threshold):
if isUniform(dict['Category'] for dict in data):
tree.setName(data[0]['Category'])
elif len(attributes) == 0:
tree.setName(mostFrequentCategory(data), None)
else: # Select splitting attribute
bestAttribute = selectSplittingAttribute(data, attributes, threshold)
if not bestAttribute:
tree.setName(mostFrequentCategory(data))
else:
tree.setName(bestAttribute)
attributeDict = groupByAttribute(data, bestAttribute)
for attributeName in attributeDict.keys():
newData = attributeDict[attributeName]
if len(newData) > 0:
newAttributes = list(attributes)
newAttributes.remove(bestAttribute)
childNode = Node(None, attributeName)
tree.addChild(childNode)
build(newData, newAttributes, childNode, threshold)
def main():
"""
if not len(sys.argv) >= 2:
print("\t\tMissing arguments\n\tProper Call :\tpython C45.py <CSVFile> [<Restrictions>]")
return
dataFile = sys.argv[1]
d = parsing.parseData(dataFile)
data = d[0]
attributes = d[1]
if len(sys.argv) == 3:
restrFile = sys.argv[2]
with open(restrFile, 'r') as file:
restr = file.read().split(',')
attributes = restrictAttrib(attributes[:-1], restr[1:])
"""
# This is all just print testing bs
shrooms = r"E:\Documents\CSC466\Lab 3\466-lab-3.git\trunk\agaricus-lepiota.data.csv"
iris = r"E:\Documents\CSC466\Lab 3\466-lab-3.git\trunk\iris.data"
letters = r"E:\Documents\CSC466\Lab 3\466-lab-3.git\trunk\letter-recognition.data.csv"
data, attributes = parsing.parseData(letters)
print(attributes)
print("Number of records : {}\nWith {} different attributes".format(
len(data), len(attributes)))
#s = selectSplittingAttributeN(attributes, data, 0.01)
#en = entropyBinSplit(data, s[0], s[1])
#print(en)
root = Node('Root', None)
build(data, attributes, root, 0.3)
xmlOutput = etree.tostring(outputXML(root),
pretty_print=True,
encoding='unicode')
print(xmlOutput)
def BranchAndBoundAlgorithm():
queue = deque()
sortedItemList = [(index, Density(item))
for index, item in enumerate(ITEMS)]
sortedItemList = sorted(sortedItemList, key=lambda x: x[1], reverse=True)
bestNode = Node(0, 0.0, 0.0, 0.0, [])
root = Node(0, 0.0, 0.0, getUpperBoundValue(bestNode, sortedItemList), [])
queue.appendleft(root)
while len(queue) > 0:
currentNode = queue.pop()
if currentNode.bound > bestNode.value:
index = sortedItemList[currentNode.treeLevel][0]
nextCheckedItemValue = ITEMS[index].value
nextCheckedItemWeight = ITEMS[index].weight
nextAddednode = Node(currentNode.treeLevel + 1,
currentNode.value + nextCheckedItemValue,
currentNode.weight + nextCheckedItemWeight,
currentNode.bound,
currentNode.selectedItems + [index])
if nextAddednode.weight <= KNAPSACK_SIZE:
if nextAddednode.value > bestNode.value:
bestNode = nextAddednode
if nextAddednode.bound > bestNode.value:
queue.appendleft(nextAddednode)
nextNotAddedNode = Node(currentNode.treeLevel + 1,
currentNode.value, currentNode.weight,
currentNode.bound,
currentNode.selectedItems)
nextNotAddedNode.bound = getUpperBoundValue(
nextNotAddedNode, sortedItemList)
if nextNotAddedNode.bound > bestNode.value:
queue.appendleft(nextNotAddedNode)
bestSolution = [0] * len(ITEMS)
for itemBit in bestNode.selectedItems:
bestSolution[itemBit] = 1
return bestSolution, int(bestNode.value)
#if leaf node return 1 (Bcz. leaf node is present at level 1)
if root.left == None and root.right == None:
return 1
#recursively compute the levels of left and right subtree
left_subtree_levels = levels(root.left)
right_subtree_levels = levels(root.right)
#compute the overall levels of tree
total_levels = max(left_subtree_levels, right_subtree_levels) + 1
return total_levels
a = Node(1)
b = Node(2)
c = Node(3)
d = Node(4)
e = Node(5)
f = Node(6)
g = Node(7)
h = Node(8)
a.left = b
a.right = c
b.left = d
b.right = e
c.left = f
d.left = g
g.left = h
def createNode(self, val):
node = Node()
node.setVal(val)
return node
def __algorithm(self, S, labels, level=0, par_node=None, left=False, terminal_flag=False):
#calculate initial entropy
null_entropy = self.__impurity(labels)
#check if everyone is in the same class
if null_entropy <= 0. or level >= self.depth_limit or terminal_flag:
#terminate the algorithm, everyone's been classified or maximum depth has been reached
final_node = Node(parent=par_node,level=level,entropy=null_entropy)
final_node.outcome[0] = self.__bestguess(labels)
self.nodes[level].extend( [final_node] )
return final_node
else:
#go over all the features in this dataset
features = range(S.shape[1])
min_entropy = np.inf
best_split = [0,0] #this will hold feature number and threshold value for the best split
for f in features:
#try all possible splits along this feature
#return the best (lowest) entropy
#if this entropy is smaller then current minimum, update
Sfeat = S[:,f]
split, entropy = self.__bestsplit(Sfeat, labels)
if entropy < min_entropy:
min_entropy = entropy
best_split = [f, split]
new_node = Node(feature=best_split[0], threshold=best_split[1], parent=par_node, level=level, entropy=min_entropy)
self.nodes[level].extend( [new_node] )
#split dataset
#check if S is a vector
if len(S.shape) == 1:
#S is a one-feature vector
S = S.reshape((len(S),1))
leftMask = S[:,best_split[0]] <= best_split[1]
rightMask = S[:,best_split[0]] > best_split[1]
features.remove(best_split[0])
leftLabels = labels[leftMask]
rightLabels = labels[rightMask]
# check if you shouldn't terminate here
# when the split puts all samples into left or right branch
if leftMask.all():
new_node.make_terminal(self.__bestguess(leftLabels))
return new_node
if rightMask.all():
new_node.make_terminal(self.__bestguess(rightLabels))
return new_node
if len(features) == 0:
leftS = S[leftMask,:]
rightS = S[rightMask,:]
terminal_flag = True
else:
leftS = S[leftMask,:][:,features]
rightS = S[rightMask,:][:,features]
#check if you shouldn't terminate here
if len(leftS) == 0 or leftS.shape[1] == 0:
new_node.make_terminal(self.__bestguess(rightLabels))
return new_node
if len(rightS) == 0 or rightS.shape[1] == 0:
new_node.make_terminal(self.__bestguess(leftLabels))
return new_node
#check if a level below you already exists
try:
self.nodes[level+1]
except IndexError:
self.nodes.append([])
#recursively call self again on the two children nodes
new_node.outcome[0] = self.__algorithm(leftS,leftLabels,level=level+1,par_node=new_node,terminal_flag=terminal_flag)
new_node.outcome[1] = self.__algorithm(rightS,rightLabels,level=level+1,par_node=new_node,terminal_flag=terminal_flag)
return new_node
def insert(self, val):
if self.root:
self._insert(val, self.root)
else:
self.root = Node(value=val)
self.size += 1
if root.left==None and root.right==None:
return 0
#recursively compute intermediate nodes in left and right subtree
left_subtree_non_leaf=non_leaf(root.left)
right_subtree_non_leaf=non_leaf(root.right)
#compute the total intermediates node by considering current intermediate node , left_subtree_non_leaf node and right_subtree_non_leaf node
total_non_leaf=left_subtree_non_leaf+right_subtree_non_leaf+1
return total_non_leaf
a=Node(1)
b=Node(2)
c=Node(3)
d=Node(4)
e=Node(5)
f=Node(6)
g=Node(7)
a.left=b
a.right=c
b.left=d
b.right=e
c.left=f
c.right=g
result=non_leaf(a)
print(result)
def __init__(self, min_info_gain, x_data, y_data):
self.min_info_gain = min_info_gain
self.root_node = Node(x_data=x_data, y_data=y_data)
self.feature_item = self.get_feature(x_data)
r = self.LCA(a, b, node.right)
if l and r:
print node.data
return node
if l:
return l
if r:
return r
if __name__ == '__main__':
t = BinaryTree()
t.add(Node(5))
t.add(Node(2))
t.add(Node(1))
t.add(Node(3))
t.add(Node(4))
t.add(Node(7))
t.add(Node(6))
t.add(Node(8))
t.preOrder(t.root)
print '\n======='
t.preOrderIt()
print '\n======='
t.inOrder(t.root)
print '\n======='
t.postOrder(t.root)
print '\n======='
def __init__(self, rootVal):
self.root = Node(rootVal)
if root.left.data <= root.data:
return is_bst(root.left)
else:
return False
#intermidiate node with right children only
elif root.right != None:
if root.data <= root.right.data:
return is_bst(root.right)
else:
return False
# return False
n1 = Node(1)
n2 = Node(2)
n3 = Node(3)
n4 = Node(4)
n5 = Node(5)
n6 = Node(6)
n7 = Node(7)
n4.left = n2
n4.right = n6
n2.left = n1
n2.right = n3
n6.left = n5
n6.right = n7
result = is_bst(n4)
