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alt_dataset #1

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s474137 wants to merge 7 commits from alt_dataset into tree_increase
pull from: alt_dataset
merge into: s474137:tree_increase
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3 changed files with 308 additions and 17 deletions
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2
.gitignore vendored
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@ -57,3 +57,5 @@ docs/_build/
Pipfile Pipfile
Pipfile.lock Pipfile.lock
decision_tree
decision_tree.pdf

33
TreeConcept.py
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@ -1,26 +1,26 @@
from sklearn import tree from sklearn import tree
import pandas as pd #for manipulating the csv data
import numpy as np
import graphviz import graphviz
import os import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/' os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/'
# Define the training dataset with 8 attributes and corresponding labels # Define the training dataset with 8 attributes and corresponding labels
training_data = [
[0, 0, 0, 1, 0, 1, 1, 0, 'A'], #train_data_m = pd.read_csv("dataset.csv") #importing the dataset from the disk
[1, 0, 0, 0, 1, 1, 1, 1, 'A'], train_data_m=np.genfromtxt("dataset.csv", delimiter=",",skip_header=1);
[0, 1, 0, 1, 0, 1, 1, 1, 'B'], #print(train_data_m)
[1, 0, 0, 1, 1, 0, 1, 0, 'B'], # print(train_data_m) #viewing some row of the dataset
[1, 1, 1, 0, 1, 0, 0, 1, 'B'],
[0, 0, 0, 0, 1, 1, 1, 0, 'A'],
[0, 0, 0, 1, 0, 0, 0, 0, 'B'],
[1, 1, 0, 1, 1, 1, 0, 1, 'A'],
[0, 0, 0, 0, 0, 0, 1, 1, 'B'],
[1, 0, 1, 0, 0, 1, 0, 0, 'B']
]
# Separate the attributes and labels # Separate the attributes and labels
X_train = [data[:-1] for data in training_data] #X_train = [data[:-1] for data in training_data]
y_train = [data[-1] for data in training_data] #y_train = [data[-1] for data in training_data]
X_train = [data[:-1] for data in train_data_m]
y_train = [data[-1] for data in train_data_m]
#X_train = pd.get_dummies(data[:-1] for data in train_data_m)
#print(X_train)
#print(y_train)
# Create the decision tree classifier using the ID3 algorithm # Create the decision tree classifier using the ID3 algorithm
clf = tree.DecisionTreeClassifier(criterion='entropy') clf = tree.DecisionTreeClassifier(criterion='entropy')
@ -28,11 +28,12 @@ clf = tree.DecisionTreeClassifier(criterion='entropy')
clf.fit(X_train, y_train) clf.fit(X_train, y_train)
# Visualize the trained decision tree # Visualize the trained decision tree
dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Attr1', 'Attr2', 'Attr3', 'Attr4', 'Attr5', 'Attr6', 'Attr7', 'Attr8'], class_names=['A', 'B'], filled=True) dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Attr1', 'Attr2', 'Attr3', 'Attr4', 'Attr5', 'Attr6', 'Attr7'], class_names=['YES', 'NO'], filled=True)
graph = graphviz.Source(dot_data) graph = graphviz.Source(dot_data)
graph.render("decision_tree") # Save the visualization as a PDF file graph.render("decision_tree") # Save the visualization as a PDF file
# Test the decision tree with a new example # Test the decision tree with a new example
new_example = [1, 0, 0, 1, 1, 0, 0, 1] # Example with 8 attributes #new_example = [1, 0, 0, 1, 1, 0, 0, 1] # Example with 8 attributes
new_example = [2, 0, 0, 1, 1 ,2, 1] # Example with 8 attributes
predicted_label = clf.predict([new_example]) predicted_label = clf.predict([new_example])
print("Predicted Label:", predicted_label[0]) print("Predicted Label:", predicted_label[0])

288
dataset.csv Normal file
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@ -0,0 +1,288 @@
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