def build_tree(data, varnames):
# >>>> YOUR CODE GOES HERE <<<<
total = len(data)
var_len = len(varnames) - 1
# base case
if len(data) == 0:
return
if len(data) == 1:
return node.Leaf(varnames, data[0][var_len])
if var_len == 0:
return node.Leaf(varnames, )
# do the counting to calculate info gain
py, pxi_list, py_pxi_list = count_collect(data, varnames)
# calculate all info gain (checked)
gain_list = [0] * var_len
for i in range(var_len):
gain_list[i] = infogain(py_pxi_list[i], pxi_list[i], py, total)
print("gain list:", gain_list)
# find max info gain
max_gain_pos = split_on_variable(var_len, gain_list)
print("max_gain_pos:", max_gain_pos)
# partition
data_l, data_r = partition(data, max_gain_pos)
print(data_l)
varnames.pop(max_gain_pos)
rt = node.Split(varnames, max_gain_pos, build_tree(data_l, varnames),
build_tree(data_r, varnames))
# recursive call
return rt
def build_tree(data, varnames): py, total = collect_count (data) guess = float (py) / total if guess == 1 or (len(varnames) == 1 and guess > 0.5): return node.Leaf(varnames, 1) elif guess == 0 or (len(varnames) == 1 and guess <= 0.5): return node.Leaf(varnames, 0) gain, index = find_split(data, varnames, py, total) if gain == 0: if guess > 0.5: return node.Leaf(varnames, 1) else: return node.Leaf(varnames, 0) left = [] right = [] for i in range(total): if data[i][index] == 0: l = data[i] left.append(l) else: l = data[i] right.append(l) return node.Split(varnames, index, build_tree(left, varnames), build_tree(right, varnames))
def build_tree(data, varnames, visited):
guess = get_most_label(data) #most common label in data
#base cases
#base case 1 and 2 : all examples 0
if check_pure_example(data, 0):
#print (data)
#print("all zero")
return node.Leaf(varnames, 0)
elif check_pure_example(data, 1):
#print("all one")
return node.Leaf(varnames, 1)
#base case 2: no more attributes
#elif check_all_visited(visited):
# return node.Leaf(varnames, guess)
else:
#get best atrribute
(att_index, threshold) = calculate_entropy(data, varnames, visited)
#check if threshold then stop
if threshold:
#print("threshold")
return node.Leaf(varnames, guess)
att_val = varnames[att_index]
#set the attribute as used
visited[att_index] = True
#divide the data for att_val os 0 and 1
no = get_data_subset(data, 0, att_index)
yes = get_data_subset(data, 1, att_index)
left = build_tree(no, varnames, visited)
right = build_tree(yes, varnames, visited)
return node.Split(varnames, att_val, left, right)
def build_tree(data, varnames): localdata = data[:] localVarNames = varnames[:] XPosYPos = count_var(localdata) totalPosY = count_Y(localdata) total = len(localdata) listofVarGain = [0]*(len(localVarNames)-1) ind = 0 for (XVal, YVal) in XPosYPos: #super inefficient maybe change this if ind != len(XPosYPos)-1: listofVarGain[ind] = infogain(YVal,XVal, totalPosY, total) ind += 1 indextoremove = listofVarGain.index(max(listofVarGain)) if max(listofVarGain) == 0: if YVal >= (totalPosY-YVal): return node.Split(localVarNames, indextoremove, node.Leaf(localVarNames, 1), node.Leaf(localVarNames, 0)) else: return node.Split(localVarNames, indextoremove, node.Leaf(localVarNames, 0), node.Leaf(localVarNames, 1)) #split data leftData = [] rightData = [] countRight = 0 countLeft = 0 newVarNames = localVarNames[:] for datarow in localdata: if datarow[indextoremove] == 0: countLeft += datarow[-1] leftData.append(datarow) else: countRight += datarow[-1] rightData.append(datarow) if countRight == 0 and countLeft == len(leftData): return node.Split(localVarNames, indextoremove, node.Leaf(localVarNames, 1), node.Leaf(localVarNames, 0)) elif countRight == len(rightData) and countLeft == 0: return node.Split(localVarNames, indextoremove, node.Leaf(localVarNames, 0), node.Leaf(localVarNames, 1)) elif countRight == 0: return node.Split(localVarNames, indextoremove, (build_tree(leftData, newVarNames)), node.Leaf(localVarNames, 0)) elif countRight == len(rightData): return node.Split(localVarNames, indextoremove, (build_tree(leftData, newVarNames)), node.Leaf(localVarNames, 1)) elif countLeft == 0: return node.Split(localVarNames, indextoremove, node.Leaf(localVarNames, 0), (build_tree(rightData, newVarNames))) elif countLeft == len(leftData): return node.Split(localVarNames, indextoremove, node.Leaf(localVarNames, 1), (build_tree(rightData, newVarNames))) else: return node.Split(localVarNames, indextoremove, (build_tree(leftData, newVarNames)), (build_tree(rightData, newVarNames)))
def build_tree(data, varnames):
total = len(data)
py = getpy(data)
if py == 0:
return node.Leaf(varnames, 0)
elif py == total:
return node.Leaf(varnames, 1)
else:
(positivedata, negativedata) = splitdata(data, varnames)
index = findbestindex(data, varnames)
if index == -1:
return node.Leaf(varnames, 0)
return node.Split(varnames, index, build_tree(negativedata, varnames),
build_tree(positivedata, varnames))
return None
def build_tree(data, ingredients, labels):
# >>>> YOUR CODE GOES HERE <<<<
# global CUR_ITER
guess = [0] * (len(labels))
for item in data:
index = item[len(item) - 1]
# print index
# print "Old guess[index] ", guess[index]
guess[index] = guess[index] + 1
# print "New guess[index] ", guess[index]
print guess
g = 0
gIndex = 0
oneLabel = 0
for item in guess:
if item > 0:
oneLabel += 1
if item > g:
g = item
gIndex = guess.index(item)
moreFeatures = False
for i in ingredients:
if i != "":
moreFeatures = True
if oneLabel <= 1:
print "Only one label", gIndex
return node.Leaf(ingredients, gIndex)
elif not moreFeatures:
return node.Leaf(ingredients, gIndex)
# elif CUR_ITER == MAX_ITER:
# return node.Leaf(ingredients, g)
else:
# # CUR_ITER += 1
# print "Finding best var"
(value, index) = bestVar(data, ingredients, labels)
if value <= 0:
return node.Leaf(ingredients, gIndex)
(no, yes) = partition(data, index)
var = list(ingredients)
var[index] = ""
left = build_tree(no, var, labels)
right = build_tree(yes, var, labels)
return node.Split(ingredients, index, left, right)
def build_tree(data, varnames, used_attributes=[]):
if len(data[0]) == len(used_attributes):
return node.Leaf(varnames, find_bigger(data))
else:
split_on = highest_info_gain(data, used_attributes)
used_attributes.append(split_on)
new_used = copy.deepcopy(used_attributes)
#print "Split on(",split_on,",\t",varnames[split_on],")"
left, right = partition_data(data, split_on)
l_pure, l_val = check_if_pure(left)
r_pure, r_val = check_if_pure(right)
if l_pure and r_pure:
return node.Split(varnames, \
split_on, \
node.Leaf(varnames, \
l_val),
node.Leaf(varnames, \
r_val))
elif l_pure:
return node.Split(varnames, \
split_on, \
node.Leaf(varnames, \
l_val),
build_tree(right, \
varnames, \
copy.deepcopy(used_attributes)))
elif r_pure:
return node.Split(varnames, \
split_on, \
build_tree(left, \
varnames, \
copy.deepcopy(used_attributes)), \
node.Leaf(varnames, \
r_val))
else:
return node.Split(varnames,\
split_on,
build_tree(left,\
varnames,\
copy.deepcopy(used_attributes)),\
build_tree(right,\
varnames,\
copy.deepcopy(used_attributes)))
def build_tree(data, varnames, depth):
#print "Current Depth:", depth
if len(data) == 0:
print "BAD SPLIT"
return
# compute the max gain
split_index = compute_max_gain(data, varnames)
# Base cases
if split_index == -1:
#print "LEAF CASE"
#print data
#print "\n"
# choose whichever result is more common
pos, neg = collect_counts(data)
#print "pos:", pos, "neg:", neg
if pos > neg:
return node.Leaf(varnames, 1)
else:
return node.Leaf(varnames, 0)
# split the data at max_index attribute
l_data, r_data = split_data(data, split_index)
# make new node split
# left child - buildtree on left split
# right child - buildtree on right split
var = varnames[split_index]
#print "SPLIT CASE:", var
#print "\n"
#print "***Recursing L_tree***"
#print l_data
L_tree = build_tree(l_data, varnames, depth + 1)
#print "***L_tree returned, depth=", depth
#print "***Recursing R_tree***"
#print r_data
R_tree = build_tree(r_data, varnames, depth + 1)
#print "***R_tree returned, depth=", depth
return node.Split(varnames, split_index, L_tree, R_tree)
def build_tree(data, varnames):
# >>>> YOUR CODE GOES HERE <<<<
# For now, always return a leaf predicting "1":
for i in range(len(varnames) - 1):
counts = var_counts(data, i)
if counts[2] == counts[3]:
return node.Leaf(varnames, 1)
elif counts[2] == 0:
return node.Leaf(varnames, 0)
else:
best_split = split_on(data, varnames)
if best_split == None:
return node.Leaf(varnames,1)
left_split, right_split = partition(data, varnames, best_split)
best_node = node.Split(varnames, best_split, build_tree(left_split, varnames), build_tree(right_split, varnames))
return best_node
def build_tree(data, varnames):
# >>>> YOUR CODE GOES HERE <<<<
# For now, always return a leaf predicting "1":
for i in range(len(varnames) - 1):
#counts = count(data, i)
var_count = 0
var_count_label = 0
label = 0
total = 0
for item in data:
total += 1
if item[i] == 1:
var_count += 1
if item[-1] == 1:
label += 1
if item[i] == 1 and item[-1] == 1:
var_count_label += 1
if label == total:
return node.Leaf(varnames, 1)
elif label == 0:
return node.Leaf(varnames, 0)
else:
gain = 0
best_gain = None
for i in range(len(varnames) - 1):
counts = count(data, i)
temp = infogain(counts[0], counts[1], counts[2], counts[3])
if temp > gain:
gain = temp
best_gain = i
best_split = best_gain
if best_split == None:
return node.Leaf(varnames,1)
left_split, right_split = partition(data, varnames, best_split)
best_node = node.Split(varnames, best_split, build_tree(left_split, varnames), build_tree(right_split, varnames))
return best_node
def build_tree_helper(used, data, varnames, l, curr_ent, max_class):
# Everything is the same, return the Class of the first data point as a Leaf
if curr_ent == 0:
return node.Leaf(varnames, data[0][l])
# We can assume this is non-zero since handled by wrapper function
feat, ig = split_on_variable(used, data)
zeroes, ones = partition(feat, data)
# There are no more valid features, - we are done (all splits were bad) - return most common
if len(used) == l or ig == 0:
return node.Leaf(varnames, max_class)
# This split fully explains the Classes of the data
if ig == curr_ent:
if data[0][feat] == data[0][l]:
return node.Split(varnames, feat, node.Leaf(varnames, 0),
node.Leaf(varnames, 1))
else:
return node.Split(varnames, feat, node.Leaf(varnames, 1),
node.Leaf(varnames, 0))
z_ent = my_entropy(l, zeroes)
o_ent = my_entropy(l, ones)
# True copy of python list
u = used[:]
u.append(feat)
# Recursive case
return node.Split(
varnames, feat,
build_tree_helper(u, zeroes, varnames, l, z_ent, max_class),
build_tree_helper(u, ones, varnames, l, o_ent, max_class))
def build_tree(data, varnames):
best_candidate = maxgain(data)
# Base Case: if all our data has the same classification then we can create a leaf node to classify our root->leaf path
if best_candidate == -1:
for entry in data:
if entry[-1] == 1:
return node.Leaf(varnames, 1)
return node.Leaf(varnames, 0)
# Recursive Step: split our data into two lists: (1) positive classification (2) negative classifcation. Then
# build the left and right subtree by calling the 'build_tree' function again.
else:
positive_data = []
negative_data = []
for entry in data:
if entry[best_candidate] == 1:
positive_data.append(entry)
else:
negative_data.append(entry)
# Trees are by defintion recursive, so after splitting data we can continue with the process of building our tree.
# autograder checks left -> zero and right -> one
left_node = build_tree(negative_data, varnames)
right_node = build_tree(positive_data, varnames)
return node.Split(varnames, best_candidate, left_node, right_node)
def build_tree(data, varnames):
# Check if data is empty
if len(data) == 0:
return node.Leaf(varnames, 0)
# Check if leaf node,
attribute = len(data[0]) - 1
pc, tc, tp, t = collect_counts(data, attribute)
# Check if all values are 0 or 1
if tc[0] == 0:
return node.Leaf(varnames, 1)
if tc[1] == 0:
return node.Leaf(varnames, 0)
# Find best feature to split data on
max_gain = best_split(data, varnames)
#print(varnames[max_gain])
# Split data based on best splitter
newData = split(data, max_gain)
left = newData[0]
right = newData[1]
# Update data/varnames
# Change variable name in varnames to notify that that feature has already been used
varnames[max_gain] == "USED"
# Build left and right subtrees
left_subtree = build_tree(left, varnames)
right_subtree = build_tree(right, varnames)
# Build tree
root = node.Split(varnames, max_gain, left_subtree, right_subtree)
# Return tree
return root
def build_tree(data, varnames):
#py_pxi, pxi, py, total
py_pxi = 0
pxi = 0
py = 0
total = len(data)
for i in data:
if i[len(data[0]) - 1] == 1:
py += 1
guess = py / (total * 1.0)
if guess == 1:
return node.Leaf(varnames, 1)
elif guess == 0:
return node.Leaf(varnames, 0)
if len(varnames) == 1:
if guess > 0.5:
return node.Leaf(varnames, 1)
else:
return node.Leaf(varnames, 0)
gain = 0
for i in range(len(varnames) - 1):
for j in data:
if j[i] == 1:
pxi += 1
if j[i] == 1 and j[-1] == 1:
py_pxi += 1
if infogain(py_pxi, pxi, py, total) > gain:
gain = infogain(py_pxi, pxi, py, total)
index = i
py_pxi = 0
pxi = 0
if gain == 0:
if guess > 0.5:
return node.Leaf(varnames, 1)
else:
return node.Leaf(varnames, 0)
# divide the data
data0 = []
data1 = []
for i in range(len(data)):
if data[i][index] == 0:
list = data[i]
data0.append(list)
else:
list = data[i]
data1.append(list)
return node.Split(varnames, index, build_tree(data0, varnames),
build_tree(data1, varnames))
def build_tree_helper(used, data, varnames, var_len, curr_ent):
if curr_ent == 0:
return node.Leaf(varnames, data[0][l])
feat, ig = split_on_variable(used, data)
zeros, ones = partition(feat, data)
if len(used) == var_len or ig == 0:
aff = count_class(var_len, data)
neg = len(data) - aff
if aff > neg:
return node.Leaf(varnames, 1)
else:
return node.Leaf(varnames, 0)
if ig == curr_ent:
if len(zeros) == 0:
return node.Leaf(varnames, ones[0][var_len])
elif len(ones) == 0:
return node.Leaf(varnames, zeros[0][var_len])
else:
if data[0][feat] == data[0][var_len]:
return node.Split()
def build_tree(data, varnames):
# >>>> YOUR CODE GOES HERE <<<<
# For now, always return a leaf predicting "1":
attr = []
val = []
for index, entry in enumerate(varnames):
attr.append(entry)
val.append([0])
val[index].append(0)
val[index].append(0)
val[index].append(0)
for line in data:
for index, x in enumerate(line):
if ( x == 1):
if ( line[len(line) - 1] == 1 ):
val[index][0] += 1
else:
val[index][1] += 1
else:
if ( line[len(line) - 1] == 1):
val[index][2] += 1
else:
val[index][3] += 1
#select new node
current = 0.0
for index, x in enumerate(attr):
if ( x != '-' ):
check = info_Gain(pEnt, val[index][0], val[index][1], val[index][2], val[index][3])
if (check > current):
current = check
use = index
#create node
parent = Node(attr[use])
attr[use] = '-'
recur_Tree(parent, attr[], val[])
return node.Leaf(varnames, 1)
def build_tree(data, varnames):
bestChosenAttribute = -1
alreadyConsidered = []
leftToBeConsidered = []
global_varnames = globals().get('varnames')
#print("varnames")
#print(varnames)
#For the first time varnames is ['A', 'B', 'C', 'D', '[A_and_B]_or_[C_and_D]']
#for the next recursions: varnames is leftToBeConsidered
(x, value) = checkAllSame(data, varnames)
if x == len(data):
#print("Leaf 1")
return node.Leaf(global_varnames, value)
if (len(varnames) == 0):
#print("Leaf 2")
return node.Leaf(global_varnames, value)
if len(data[0]) == len(varnames):
j = 0
for j in range(len(varnames)):
leftToBeConsidered.insert(j, j)
alreadyConsidered.append(
len(varnames) - 1) #For the first time: alreadyConsidered is [4]
#print("first time")
else:
leftToBeConsidered.extend(varnames)
j = 0
for j in range(0, leftToBeConsidered[len(varnames) - 1]):
if j not in leftToBeConsidered:
alreadyConsidered.append(
j
) #For the next recursions: alreadyConsidered is [0]--[0, 2]--[0]--[0, 1]--[0, 1, 2]
#print("I am recursing")
#print("alreadyConsidered")
stri = ''
for row in alreadyConsidered:
stri = stri + ',' + global_varnames[row]
#print(stri)
if len(leftToBeConsidered) == 1:
#print("Leaf 3")
return node.Leaf(global_varnames, value)
(bestChosenAttribute, maxGain) = selectMaxGainAttribute(
data, varnames,
alreadyConsidered) # bestChosenAttribute: 0 -- 2--3--1--2--3
if bestChosenAttribute in leftToBeConsidered:
leftToBeConsidered.remove(
bestChosenAttribute
) #leftToBeConsidered: [1, 2, 3, 4]--[1, 3, 4]--[1, 4]--[2, 3, 4]--[3, 4]--[4]
#print("bestChosenAttribute")
#print(global_varnames[bestChosenAttribute])
#print("Gain")
#print(maxGain)
#print("leftToBeConsidered")
stri = ''
for row in leftToBeConsidered:
stri = stri + ',' + global_varnames[row]
#print(stri)
if len(data) == 0:
return node.Leaf(global_varnames, value)
negativeDataSet = negData(data, bestChosenAttribute)
positiveDataSet = posData(data, bestChosenAttribute)
if maxGain == 0.0 and bestChosenAttribute >= 0:
#print("Leaf 4")
if data[0][-1] == 0:
return node.Split(
globals().get('varnames'), bestChosenAttribute,
node.Leaf(global_varnames, negativeDataSet[0][-1]),
build_tree(positiveDataSet, leftToBeConsidered))
else:
return node.Split(
globals().get('varnames'), bestChosenAttribute,
build_tree(negativeDataSet, leftToBeConsidered),
node.Leaf(global_varnames, positiveDataSet[0][-1]))
elif maxGain > 0:
return node.Split(
globals().get('varnames'), bestChosenAttribute,
build_tree(negativeDataSet, leftToBeConsidered),
build_tree(positiveDataSet, leftToBeConsidered)
) #for the next recursions: varnames is leftToBeConsidered
else:
#print("Leaf 5")
return node.Leaf(global_varnames, value)
def splitr(pred, varnames):
#helper function: given a prediction, creates either a 1 or 0 leaf
if pred >= 0.5:
return node.Leaf(varnames, 1)
else:
return node.Leaf(varnames, 0)
def p_t_ID(self, p):
"t : ID"
p[0] = node.Leaf('ID', p[1])
def p_t_NUM(self, p):
"t : NUM"
p[0] = node.Leaf('NUM', p[1])
def build_tree(data, varnames):
# Get class column length and count values of it
class_length = get_column(data, len(varnames) - 1)
class_pos_neg = count_values(class_length)
# Make leaf if only 0's
if class_pos_neg[0] == len(class_length):
return node.Leaf(varnames, 0)
# Make leaf if only 1's
elif class_pos_neg[1] == len(class_length):
return node.Leaf(varnames, 1)
else:
# Get column of i and class column, then compute gain and return it
returned_entropy = []
for i in range(0, len(varnames) - 1):
columns = []
columns.append(get_column(data, i))
columns.append(get_column(data, len(data[0]) - 1))
pos_and_neg = partition_data(columns)
positive_values = pos_and_neg[1]
class_values = columns[-1]
returned_entropy.append(
infogain(get_ones(positive_values), get_ones(columns[0]),
get_ones(class_values), len(columns[0])))
# Once we have a list of gains, get the highest one
best_value = split_data(returned_entropy)
# Branch the data into left and right, depending on higehst gain value in array
branch = branch_data(data, best_value[1])
# If the gain is exactly 0
if best_value[0] == 0.0:
if class_pos_neg[0] > class_pos_neg[1]:
return node.Leaf(varnames, 0)
elif class_pos_neg[0] < class_pos_neg[1]:
return node.Leaf(varnames, 1)
left = None
right = None
# There is no information gain progress
if best_value[0] <= 0.0:
# Get the columns of the branched data and count values in it
check_branch_negatives = count_values(
get_column(branch[0], best_value[1]))
check_branch_positives = count_values(
get_column(branch[1], best_value[1]))
# Get length of both original branches
length_neg = len(branch[0])
length_pos = len(branch[1])
# Make sure that neither the left, nor the right branch values are equal in length to the whole branch
# That is, the tree needs to stop branching if one of the two branches is empty
if (check_branch_negatives[0] == length_neg
or check_branch_negatives[1] == length_neg):
return node.Leaf(varnames, 1)
elif (check_branch_negatives[0] < length_neg
or check_branch_negatives[1] < length_neg):
left = build_tree(branch[0], varnames)
if (check_branch_positives[0] == length_pos
or check_branch_positives[1] == length_pos):
return node.Leaf(varnames, 1)
elif (check_branch_positives[0] < length_pos
or check_branch_positives[1] < length_pos):
right = build_tree(branch[1], varnames)
return node.Split(varnames, best_value[1], left, right)
# The gain is higher than 0, everything is good and we can keep on branching
else:
# We split the tree and recursively go through both new branches
left = build_tree(branch[0], varnames)
right = build_tree(branch[1], varnames)
return node.Split(varnames, best_value[1], left, right)
def build_tree(data, varnames):
# >>>> YOUR CODE GOES HERE <<<<
# For now, always return a leaf predicting "1":
return node.Leaf(varnames, 1)
