sztuczna_inteligencja_2023_smieciarka/machine_learning/decisionTree.py

import os
from trainingData import TrainingData
from sklearn import tree
import joblib
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import numpy as np

def _read_training_data() -> TrainingData:
    attributes = []
    classes = []
    location = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
    file = open(os.path.join(location, 'training_data.csv'))
    lines = file.readlines()[1:]
    file.close()
    for line in lines:
        actual_row = line.replace('\n', '')
        values = actual_row.split(',')
        line_attributes = values[:-1]
        line_class = values[-1]
        attributes.append(line_attributes)
        classes.append(line_class)
    return TrainingData(attributes, classes)


trainning_data = _read_training_data()

X = trainning_data.attributes
Y = trainning_data.classes

le_shape = LabelEncoder()
le_flexibility = LabelEncoder()
le_color = LabelEncoder()

le_shape.fit([x[0] for x in X])
le_flexibility.fit([x[3] for x in X])
le_color.fit([x[4] for x in X])

X_encoded = np.array([
    [le_shape.transform([x[0]])[0], x[1], x[2], le_flexibility.transform([x[3]])[0], le_color.transform([x[4]])[0]]
    for x in X
])

encoder = OneHotEncoder(categories='auto', sparse=False)
X_encoded = encoder.fit_transform(X_encoded)

model = tree.DecisionTreeClassifier()
model.fit(X_encoded, Y)

joblib.dump(model, 'model.pkl')