Machine learning Basics - decision tree-12

Decision tree Decision Tree

Decision tree - Example

from sklearn.feature_extraction import DictVectorizer
from sklearn import tree
from sklearn import preprocessing
import csv

#  Read in the data 
Dtree = open(r'AllElectronics.csv', 'r')
reader = csv.reader(Dtree)

#  Get the first row of data 
headers = reader.__next__()
print(headers)

#  Define two lists 
featureList = []
labelList = []

# 
for row in reader:
    #  hold label Deposit in list
    labelList.append(row[-1])
    rowDict = {
    }
    for i in range(1, len(row)-1):
        # Build a data dictionary 
        rowDict[headers[i]] = row[i]
    #  Store the data dictionary in list
    featureList.append(rowDict)

print(featureList)

#  Convert data into 01 Express 
vec = DictVectorizer()
x_data = vec.fit_transform(featureList).toarray()
print("x_data: " + str(x_data))

#  Print attribute name 
print(vec.get_feature_names())

#  Print labels 
print("labelList: " + str(labelList))

#  Convert labels into 01 Express 
lb = preprocessing.LabelBinarizer()
y_data = lb.fit_transform(labelList)
print("y_data: " + str(y_data))

#  Create a decision tree model 
model = tree.DecisionTreeClassifier(criterion='entropy')
#  Input data to build model 
model.fit(x_data, y_data)

#  test 
x_test = x_data[0]
print("x_test: " + str(x_test))

predict = model.predict(x_test.reshape(1,-1))
print("predict: " + str(predict))

Decision tree -CART

from sklearn import tree
import numpy as np

#  Load data 
data = np.genfromtxt("cart.csv", delimiter=",")
x_data = data[1:,1:-1]
y_data = data[1:,-1]

#  Create a decision tree model 
model = tree.DecisionTreeClassifier()
#  Input data to build model 
model.fit(x_data, y_data)

#  Export decision tree 
import graphviz # http://www.graphviz.org/

dot_data = tree.export_graphviz(model, 
                                out_file = None, 
                                feature_names = ['house_yes','house_no','single','married','divorced','income'],
                                class_names = ['no','yes'],
                                filled = True,
                                rounded = True,
                                special_characters = True)
graph = graphviz.Source(dot_data)
graph.render('cart')

Decision tree - Linear dichotomy

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report
from sklearn import tree

#  Load data 
data = np.genfromtxt("LR-testSet.csv", delimiter=",")
x_data = data[:,:-1]
y_data = data[:,-1]

plt.scatter(x_data[:,0],x_data[:,1],c=y_data) 
plt.show()

#  Create a decision tree model 
model = tree.DecisionTreeClassifier()
#  Input data to build model 
model.fit(x_data, y_data)

#  Export decision tree 
import graphviz # http://www.graphviz.org/

dot_data = tree.export_graphviz(model, 
                                out_file = None, 
                                feature_names = ['x','y'],
                                class_names = ['label0','label1'],
                                filled = True,
                                rounded = True,
                                special_characters = True)
graph = graphviz.Source(dot_data)

#  Get the range of data values 
x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1

#  Generate grid matrix 
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))

z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel And flatten similar , Multidimensional data to one dimension .flatten Will not change the original data ,ravel Will change the original data 
z = z.reshape(xx.shape)
#  Contour map 
cs = plt.contourf(xx, yy, z)
#  Sample scatter plot 
plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
plt.show()

predictions = model.predict(x_data)
print(classification_report(predictions,y_data))

Decision tree - Nonlinear dichotomy

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report
from sklearn import tree
from sklearn.model_selection import train_test_split

#  Load data 
data = np.genfromtxt("LR-testSet2.txt", delimiter=",")
x_data = data[:,:-1]
y_data = data[:,-1]
    
plt.scatter(x_data[:,0],x_data[:,1],c=y_data) 
plt.show()

# Split data 
x_train,x_test,y_train,y_test = train_test_split(x_data, y_data) 

#  Create a decision tree model 
# max_depth, Depth of tree 
# min_samples_split  Minimum number of samples required for internal node subdivision 
model = tree.DecisionTreeClassifier(max_depth=7,min_samples_split=4)
#  Input data to build model 
model.fit(x_train, y_train)

#  Export decision tree 
import graphviz # http://www.graphviz.org/

dot_data = tree.export_graphviz(model, 
                                out_file = None, 
                                feature_names = ['x','y'],
                                class_names = ['label0','label1'],
                                filled = True,
                                rounded = True,
                                special_characters = True)
graph = graphviz.Source(dot_data)

#  Get the range of data values 
x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1

#  Generate grid matrix 
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))

z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel And flatten similar , Multidimensional data to one dimension .flatten Will not change the original data ,ravel Will change the original data 
z = z.reshape(xx.shape)
#  Contour map 
cs = plt.contourf(xx, yy, z)
#  Sample scatter plot 
plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
plt.show()

predictions = model.predict(x_train)
print(classification_report(predictions,y_train))

predictions = model.predict(x_test)
print(classification_report(predictions,y_test))

Back to the tree

import numpy as np
import matplotlib.pyplot as plt
from sklearn import tree

#  Load data 
data = np.genfromtxt("data.csv", delimiter=",")
x_data = data[:,0,np.newaxis]
y_data = data[:,1,np.newaxis]
plt.scatter(x_data,y_data)
plt.show()

model = tree.DecisionTreeRegressor(max_depth=5)
model.fit(x_data, y_data)

x_test = np.linspace(20,80,100)
x_test = x_test[:,np.newaxis]

#  drawing 
plt.plot(x_data, y_data, 'b.')
plt.plot(x_test, model.predict(x_test), 'r')
plt.show()

#  Export decision tree 
import graphviz # http://www.graphviz.org/

dot_data = tree.export_graphviz(model, 
                                out_file = None, 
                                feature_names = ['x','y'],
                                class_names = ['label0','label1'],
                                filled = True,
                                rounded = True,
                                special_characters = True)
graph = graphviz.Source(dot_data)

Back to the tree - Forecast house prices

from sklearn import tree
from sklearn.datasets.california_housing import fetch_california_housing
from sklearn.model_selection import train_test_split

housing = fetch_california_housing()
print(housing.DESCR)

housing.data.shape

housing.data[0]

housing.target[0]

x_data = housing.data
y_data = housing.target
x_train,x_test,y_train,y_test = train_test_split(x_data, y_data)

model = tree.DecisionTreeRegressor()
model.fit(x_train, y_train)