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Tree - Dataset

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Mirrowel 2023-05-25 22:14:09 +02:00
parent 75be644015
commit 0c34087424
3 changed files with 308 additions and 17 deletions
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4
.gitignore vendored
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@ -56,4 +56,6 @@ docs/_build/
# Pipfiles
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
import pandas as pd #for manipulating the csv data
import numpy as np
import graphviz
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/'
# Define the training dataset with 8 attributes and corresponding labels
training_data = [
[0, 0, 0, 1, 0, 1, 1, 0, 'A'],
[1, 0, 0, 0, 1, 1, 1, 1, 'A'],
[0, 1, 0, 1, 0, 1, 1, 1, 'B'],
[1, 0, 0, 1, 1, 0, 1, 0, 'B'],
[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']
]
#train_data_m = pd.read_csv("dataset.csv") #importing the dataset from the disk
train_data_m=np.genfromtxt("dataset.csv", delimiter=",",skip_header=1);
#print(train_data_m)
# print(train_data_m) #viewing some row of the dataset
# Separate the attributes and labels
X_train = [data[:-1] for data in training_data]
y_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]
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
clf = tree.DecisionTreeClassifier(criterion='entropy')
@ -28,11 +28,12 @@ clf = tree.DecisionTreeClassifier(criterion='entropy')
clf.fit(X_train, y_train)
# 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.render("decision_tree") # Save the visualization as a PDF file
# 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])
print("Predicted Label:", predicted_label[0])

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