Decision Tree
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"""
Implementation of a basic regression decision tree.
Input data set: The input data set must be 1-dimensional with continuous labels.
Output: The decision tree maps a real number input to a real number output.
"""
import numpy as np
class DecisionTree:
def __init__(self, depth=5, min_leaf_size=5):
self.depth = depth
self.decision_boundary = 0
self.left = None
self.right = None
self.min_leaf_size = min_leaf_size
self.prediction = None
def mean_squared_error(self, labels, prediction):
"""
mean_squared_error:
@param labels: a one-dimensional numpy array
@param prediction: a floating point value
return value: mean_squared_error calculates the error if prediction is used to
estimate the labels
>>> tester = DecisionTree()
>>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
>>> test_prediction = float(6)
>>> tester.mean_squared_error(test_labels, test_prediction) == (
... TestDecisionTree.helper_mean_squared_error_test(test_labels,
... test_prediction))
True
>>> test_labels = np.array([1,2,3])
>>> test_prediction = float(2)
>>> tester.mean_squared_error(test_labels, test_prediction) == (
... TestDecisionTree.helper_mean_squared_error_test(test_labels,
... test_prediction))
True
"""
if labels.ndim != 1:
print("Error: Input labels must be one dimensional")
return np.mean((labels - prediction) ** 2)
def train(self, x, y):
"""
train:
@param x: a one-dimensional numpy array
@param y: a one-dimensional numpy array.
The contents of y are the labels for the corresponding X values
train() does not have a return value
Examples:
1. Try to train when x & y are of same length & 1 dimensions (No errors)
>>> dt = DecisionTree()
>>> dt.train(np.array([10,20,30,40,50]),np.array([0,0,0,1,1]))
2. Try to train when x is 2 dimensions
>>> dt = DecisionTree()
>>> dt.train(np.array([[1,2,3,4,5],[1,2,3,4,5]]),np.array([0,0,0,1,1]))
Traceback (most recent call last):
...
ValueError: Input data set must be one-dimensional
3. Try to train when x and y are not of the same length
>>> dt = DecisionTree()
>>> dt.train(np.array([1,2,3,4,5]),np.array([[0,0,0,1,1],[0,0,0,1,1]]))
Traceback (most recent call last):
...
ValueError: x and y have different lengths
4. Try to train when x & y are of the same length but different dimensions
>>> dt = DecisionTree()
>>> dt.train(np.array([1,2,3,4,5]),np.array([[1],[2],[3],[4],[5]]))
Traceback (most recent call last):
...
ValueError: Data set labels must be one-dimensional
This section is to check that the inputs conform to our dimensionality
constraints
"""
if x.ndim != 1:
raise ValueError("Input data set must be one-dimensional")
if len(x) != len(y):
raise ValueError("x and y have different lengths")
if y.ndim != 1:
raise ValueError("Data set labels must be one-dimensional")
if len(x) < 2 * self.min_leaf_size:
self.prediction = np.mean(y)
return
if self.depth == 1:
self.prediction = np.mean(y)
return
best_split = 0
min_error = self.mean_squared_error(x, np.mean(y)) * 2
"""
loop over all possible splits for the decision tree. find the best split.
if no split exists that is less than 2 * error for the entire array
then the data set is not split and the average for the entire array is used as
the predictor
"""
for i in range(len(x)):
if len(x[:i]) < self.min_leaf_size:
continue
elif len(x[i:]) < self.min_leaf_size:
continue
else:
error_left = self.mean_squared_error(x[:i], np.mean(y[:i]))
error_right = self.mean_squared_error(x[i:], np.mean(y[i:]))
error = error_left + error_right
if error < min_error:
best_split = i
min_error = error
if best_split != 0:
left_x = x[:best_split]
left_y = y[:best_split]
right_x = x[best_split:]
right_y = y[best_split:]
self.decision_boundary = x[best_split]
self.left = DecisionTree(
depth=self.depth - 1, min_leaf_size=self.min_leaf_size
)
self.right = DecisionTree(
depth=self.depth - 1, min_leaf_size=self.min_leaf_size
)
self.left.train(left_x, left_y)
self.right.train(right_x, right_y)
else:
self.prediction = np.mean(y)
return
def predict(self, x):
"""
predict:
@param x: a floating point value to predict the label of
the prediction function works by recursively calling the predict function
of the appropriate subtrees based on the tree's decision boundary
"""
if self.prediction is not None:
return self.prediction
elif self.left or self.right is not None:
if x >= self.decision_boundary:
return self.right.predict(x)
else:
return self.left.predict(x)
else:
print("Error: Decision tree not yet trained")
return None
class TestDecisionTree:
"""Decision Tres test class"""
@staticmethod
def helper_mean_squared_error_test(labels, prediction):
"""
helper_mean_squared_error_test:
@param labels: a one dimensional numpy array
@param prediction: a floating point value
return value: helper_mean_squared_error_test calculates the mean squared error
"""
squared_error_sum = float(0)
for label in labels:
squared_error_sum += (label - prediction) ** 2
return float(squared_error_sum / labels.size)
def main():
"""
In this demonstration we're generating a sample data set from the sin function in
numpy. We then train a decision tree on the data set and use the decision tree to
predict the label of 10 different test values. Then the mean squared error over
this test is displayed.
"""
x = np.arange(-1.0, 1.0, 0.005)
y = np.sin(x)
tree = DecisionTree(depth=10, min_leaf_size=10)
tree.train(x, y)
rng = np.random.default_rng()
test_cases = (rng.random(10) * 2) - 1
predictions = np.array([tree.predict(x) for x in test_cases])
avg_error = np.mean((predictions - test_cases) ** 2)
print("Test values: " + str(test_cases))
print("Predictions: " + str(predictions))
print("Average error: " + str(avg_error))
if __name__ == "__main__":
main()
import doctest
doctest.testmod(name="mean_squarred_error", verbose=True)
Acerca de este algoritmo
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
# In[ ]:
class D_TREE :
def fit(self,Xin):
#fitting the values
self.X=Xin #training_dataset_
self.my_tree=self.tree(Xin) #calls tree() function to create a tree based on the dataset provided
def label_count(self,t):
#count the unique labels
count = {} #a dictionary that will store the no of times every label has occurred
for i in range(len(t)):
lbl = t[i][-1] #The last field or column in t actually contains the labels
if lbl not in count:
count[lbl] = 0 #If the label is not present previously,initialize it with zero
count[lbl]+=1 #Everytime a particular label is encountered its count is increased by 1
return count
class Question :
#stores the question and matches the question
def __init__(self,col,value):
self.col = col #The column to which the question belongs to
self.question = value #the particualr cell in the column which is treated as question
def is_digit_or_char(self,n):
#checks whether a particular value is a number or not
return isinstance(n,int) or isinstance(n,float)
def check(self,row):
value=row[self.col] #the value to be tested with the question
if(self.is_digit_or_char(self.question)):
return value>=self.question #if the value is numeric in nature check whether it is greater or equal to question
else :
return value==self.question #if the value is a character or string check whether it is equal to the question or not
def gini(self,t):
#Calculates the gini score
label = np.unique(t) #No of unique labels
impurity = 1
for i in range(len(label)):
impurity -= (np.sum(t[:,-1]==label[i])/t.shape[0])**2 #formula for calculating impurity based on probability
return impurity
def information_gain(self,l,r,current_uncertainity):
#Information gain is calculated
p = len (l) / float ( len(l) + len(r) )
return current_uncertainity - p*self.gini(l) - (1-p)*self.gini(r)
def best_split(self,t):
#Selects the best question and split based on the gini score
maxm=0
best_question = None
tr_row=[]
fl_row=[]
for i in range(t.shape[1]-1):
y=np.unique(t[:,i]) #no of unique labels in a particular column
m=y.shape[0] #no of examples
for j in range(m):
question = self.Question(i,y[j]) #each unique label is considered a question one at a time
tr_row , fl_row = self.split(t,question)#splits the rows based on the question
if(len(fl_row)==0 or len(tr_row)==0):
continue #if any of the branch has zero rows,the question is skipped
info_gain= self.information_gain(tr_row,fl_row,self.gini(t)) #information gain is calculated
if(info_gain>maxm):
"""best question
with maximum informaion
gain is selected"""
maxm = info_gain
best_question = question
return maxm,best_question
def split(self,t,question)
#Splits the dataset based on the best question
tr_row=[]
fl_row=[]
for k in range(t.shape[0]):
"""checks every row of the dataset
with the queston & if it matches,
it is appended to the true rows
else to the false rows"""
if question.check(t[k]):
tr_row=np.append(tr_row,t[k])
else:
fl_row=np.append(fl_row,t[k])
tr_row = np.reshape(tr_row,(len(tr_row)//t.shape[1],t.shape[1])) #just reshapes the one-d matrix into a readable 2d matrix
fl_row = np.reshape(fl_row,(len(fl_row)//t.shape[1],t.shape[1])) #just reshapes the one-d matrix into a readable 2d matrix
return tr_row,fl_row
class Decision_Node:
#Stores the different question,true branch and false branch for all parts of the tree
def __init__(self,question,true_branch,false_branch):
self.question = question
self.true_branch = true_branch
self.false_branch = false_branch
class Leaf:
#the terminal of a tree is the leaf
def __init__(self,t):
self.predictions = D_TREE().label_count(t)
def tree(self,t):
"""the most important part of the entire algorithm
this is where the tree is constructed from the root
to the leaves"""
gain,question = self.best_split(t) #best question with maximum gain is selected
if(gain==0):
return self.Leaf(t) #no gain indicates that leaf is reached
"""if the control has reached this far,it means
there is useful gain and teh datset can be subdivided
or branched into true rows and false rows"""
true_rows , false_rows = self.split(t,question)
true_node = self.tree(true_rows) #A recursion is carried out till all the true rows are found out
false_node= self.tree(false_rows) #after true rows,the false rows are assigned to the node in a reverse fashion
return self.Decision_Node(question,true_node,false_node)
def check_testing_data(self,test,node):
#checks the testing data by recursively calling itself
if isinstance(node,self.Leaf):
return node.predictions #when the leaf is reached prediction is made
"""a row is made to travel in the tree,till it reaches a leaf,
it is checked with all decision nodes, and accordingly
it travels along true branch or false branch,till
it reaches a leaf"""
if(node.question.check(test)):
return self.check_testing_data(test,node.true_branch)
else:
return self.check_testing_data(test,node.false_branch)
def print_leaf(self,LEAF):
#prints a leaf
p={}
for i in LEAF.keys():
p[i] = str(100*LEAF[i]/float(sum(LEAF.values()))) + "%"
print(p)
def pred(self,X_test):
#predicts values for test data
y_pred=[0]*X_test.shape[0]
for i in range(X_test.shape[0]):
"""when a row reaches a particular leaf
it is assigned the label which
appears maximum in the leaf"""
r= self.check_testing_data(X_test[i],self.my_tree) #deals with one row at a time
y_pred[i] = max(r.keys(), key=(lambda k: r[k]))
return y_pred
def accuracy(self,y_test,y_pred):
#Calculate the accuracy of the model
return np.mean(y_test==y_pred)*100
#Importing libraries
import numpy as np
#Importing data set
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
X=data['data']
Y=data['target']
Y=np.reshape(Y,(Y.shape[0],1))
X=X[:,:5]
X=np.append(X,Y,axis=1)#Separating training set and test set
sz = len(X)//4
X_test=X[:sz,:]
X_train=X[sz:,:] #X_train already contains Y_train as its last column so no need to define it separately
Y_test=X_test[:,-1]
n=X.shape[1]#Training the algorithm
train=D_TREE()
train.fit(X_train)#Predictions on test data
y_pred=train.pred(X_test)#A few predictions
for i in range(10):
train.print_leaf(train.check_testing_data(X_test[i],train.my_tree)){1.0: '100.0%'}
{0.0: '100.0%'}
{0.0: '100.0%'}
{1.0: '100.0%'}
{0.0: '100.0%'}
{1.0: '100.0%'}
{0.0: '100.0%'}
{0.0: '100.0%'}
{0.0: '100.0%'}
{1.0: '100.0%'}
print("Accuracy of my model : ",train.accuracy(Y_test,y_pred),"%")Accuracy of my model : 80.28169014084507 %
#calculating accuracy using sklearn model
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train[:,0:n-1], Y_train)
y_pred=clf.predict(X_test[:,:n-1])
error=(y_pred==Y_test.flatten())
print("accuracy of sklearn model = ",np.mean(error)*100,"%")accuracy of sklearn model = 80.28169014084507 %
