# -*- coding: utf-8 -*-
'''
MAT-Tools: Python Framework for Multiple Aspect Trajectory Data Mining
The present package offers a tool, to support the user in the task of data analysis of multiple aspect trajectories. It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods.
Copyright (C) 2022, MIT license (this portion of code is subject to licensing from source project distribution)
Created on Dec, 2021
Copyright (C) 2022, License GPL Version 3 or superior (this portion of code is subject to licensing from source project distribution)
Authors:
- Tarlis Portela
'''
# --------------------------------------------------------------------------------
import time
import pandas as pd
import numpy as np
from numpy import argmax
from tqdm.auto import tqdm
import itertools
# --------------------------------------------------------------------------------
import graphviz
from sklearn.tree import DecisionTreeClassifier, export_graphviz
# --------------------------------------------------------------------------------
from matclassification.methods.core import SimilarityClassifier
[docs]
class TDT(SimilarityClassifier):
"""
A similarity-based classifier using Decision Trees.
This class extends the SimilarityClassifier to implement a similarity-based
classifier utilizing Decision Trees. It enables classification of trajectory
data by learning decision boundaries in feature space.
Parameters:
-----------
save_results (bool):
Flag indicating whether to save results (default is False).
n_jobs : int, optional (default=-1)
The number of parallel jobs to run for computation. -1 means using all processors.
verbose : int, optional (default=2)
Verbosity level. Higher values enable more detailed output during training and model creation.
random_state : int, optional (default=42)
Random seed used for reproducibility.
filterwarnings : str, optional (default='ignore')
Controls the filter for output warnings.
Methods:
--------
fit(X, y):
Fit the SVC model to the training data.
predict(X):
Predict class labels for the provided data.
predict_proba(X):
Predict class probabilities for the provided data.
evaluate(X_test, y_test):
Evaluate the model performance on the test data.
tree():
Generates and returns a graphical representation of the decision tree.
"""
def __init__(self,
# TODO: Params here
save_results=False,
n_jobs=1,
verbose=0,
random_state=42,
filterwarnings='ignore'):
super().__init__('TDT', save_results=save_results, n_jobs=n_jobs, verbose=verbose, random_state=random_state, filterwarnings=filterwarnings)
# self.add_config(k=k)
# self.grid_search(k, weights)
[docs]
def create(self, config):
# k = config[0]
# w = config[1]
# Initializing Model
return DecisionTreeClassifier()
[docs]
def tree(self):
# X_train = self.X_train
#y_train = self.le.inverse_transform( argmax(self.y_train, axis = 1) )
features = list(map(lambda i: 't'+str(i), self.y_train))
if self.model == None:
model = self.create()
model.fit(self.X_train, y_train)
else:
model = self.model
# DOT data
dot_data = export_graphviz(model, out_file=None,
feature_names=features,
# class_names=classes,
filled=True)
# Draw graph
graph = graphviz.Source(dot_data, format="png")
return graph