Accelerated Agglomerative Learning Algorithm for GFMM

This example shows how to use the GFMM classifier using an accelerated agglomerative learning algorithm (AGGLO-2)

Note that the numerical features in training and testing datasets must be in the range of [0, 1] because the AGGLO-2-GFMM classifiers require features in the unit cube.

1. Execute directly from the python file

%matplotlib notebook

import os
import warnings
warnings.filterwarnings('ignore')

Get the path to the this jupyter notebook file

this_notebook_dir = os.path.dirname(os.path.abspath("__file__"))
this_notebook_dir

'C:\\hyperbox-brain\\docs\\tutorials'

Get the home folder of the Hyperbox-Brain project

from pathlib import Path
project_dir = Path(this_notebook_dir).parent.parent
project_dir

WindowsPath('C:/hyperbox-brain')

Create the path to the Python file containing the implementation of the GFMM classifier using the accelerated agglomerative learning algorithm

accel_agglo_file_path = os.path.join(project_dir, Path("hbbrain/numerical_data/batch_learner/accel_agglo_gfmm.py"))
accel_agglo_file_path

'C:\\hyperbox-brain\\hbbrain\\numerical_data\\batch_learner\\accel_agglo_gfmm.py'

Run the found file by showing the execution directions

!python "{accel_agglo_file_path}" -h

usage: accel_agglo_gfmm.py [-h] -training_file TRAINING_FILE -testing_file
                           TESTING_FILE [--theta THETA] [--gamma GAMMA]
                           [--min_simil MIN_SIMIL]
                           [--simil_measure {mid,long,short}]
                           [--asimil_type {min,max}] [--is_draw IS_DRAW]

The description of parameters

required arguments:
  -training_file TRAINING_FILE
                        A required argument for the path to training data file
                        (including file name)
  -testing_file TESTING_FILE
                        A required argument for the path to testing data file
                        (including file name)

optional arguments:
  --theta THETA         Maximum hyperbox size (in the range of (0, 1])
                        (default: 0.5)
  --gamma GAMMA         A sensitivity parameter describing the speed of
                        decreasing of the membership function in each
                        dimension (larger than 0) (default: 1)
  --min_simil MIN_SIMIL
                        Mimimum similarity value so that two hyperboxes can be
                        merged (in the range of [0, 1])(default: 0.5)
  --simil_measure {mid,long,short}
                        Type of similarity measure (default: mid)
  --asimil_type {min,max}
                        Type of handling asymmetric similarity matrix
                        (default: max)
  --is_draw IS_DRAW     Show the existing hyperboxes during the training
                        process on the screen (default: False)

Create the path to training and testing datasets stored in the dataset folder

training_data_file = os.path.join(project_dir, Path("dataset/syn_num_train.csv"))
training_data_file

'C:\\hyperbox-brain\\dataset\\syn_num_train.csv'

testing_data_file = os.path.join(project_dir, Path("dataset/syn_num_test.csv"))
testing_data_file

'C:\\hyperbox-brain\\dataset\\syn_num_test.csv'

Run a demo program

!python "{accel_agglo_file_path}" -training_file "{training_data_file}" -testing_file "{testing_data_file}" --theta 0.1 --min_simil 0.5 --simil_measure "short" --asimil_type "max" --gamma 1

Number of hyperboxes = 61
Testing accuracy (using a probability measure for samples on the boundary) =  85.00%
Testing accuracy (using a Manhattan distance for samples on the boundary) =  85.00%

2. Using the GFMM classifier with the accelerated agglomerative learning algorithm through its init, fit, and predict functions

from hbbrain.numerical_data.batch_learner.accel_agglo_gfmm import AccelAgglomerativeLearningGFMM
import pandas as pd

Create training and testing data sets

df_train = pd.read_csv(training_data_file, header=None)
df_test = pd.read_csv(testing_data_file, header=None)

Xy_train = df_train.to_numpy()
Xy_test = df_test.to_numpy()

Xtr = Xy_train[:, :-1]
ytr = Xy_train[:, -1]

Xtest = Xy_test[:, :-1]
ytest = Xy_test[:, -1]

Initializing parameters

theta = 0.1
min_simil = 0.5
simil_measure = 'short'
asimil_type = 'max'
gamma = 1
is_draw = True

Training

accel_agglo_gfmm_clf = AccelAgglomerativeLearningGFMM(theta=theta, min_simil=min_simil, simil_measure=simil_measure, asimil_type=asimil_type, gamma=gamma, is_draw=is_draw)
accel_agglo_gfmm_clf.fit(Xtr, ytr)

AccelAgglomerativeLearningGFMM(is_draw=True, simil_measure='short', theta=0.1)

The code below shows how to display decision boundaries among classes if input data are 2-dimensional

accel_agglo_gfmm_clf.draw_hyperbox_and_boundary("The trained GFMM classifier and its decision boundaries")

print("Number of existing hyperboxes = %d"%(accel_agglo_gfmm_clf.get_n_hyperboxes()))

Number of existing hyperboxes = 61

print("Training time = %f (s)"%accel_agglo_gfmm_clf.elapsed_training_time)

Training time = 3.239400 (s)

Prediction

from sklearn.metrics import accuracy_score
from hbbrain.constants import MANHATTAN_DIS

y_pred = accel_agglo_gfmm_clf.predict(Xtest)
acc = accuracy_score(ytest, y_pred)
print(f'Accuracy (using a probability measure for samples on the boundary) = {acc * 100: .2f}%')

Accuracy (using a probability measure for samples on the boundary) =  85.00%

y_pred = accel_agglo_gfmm_clf.predict(Xtest, MANHATTAN_DIS)
acc = accuracy_score(ytest, y_pred)
print(f'Accuracy (using a Manhattan distance for samples on the boundary) = {acc * 100: .2f}%')

Accuracy (using a Manhattan distance for samples on the boundary) =  85.00%

Explaining the predicted result for the input sample by showing membership values and hyperboxes for each class

sample_need_explain = 10
y_pred_input_0, mem_val_classes, min_points_classes, max_points_classes = accel_agglo_gfmm_clf.get_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain])

print("Predicted class for sample X = [%f, %f] is %d and real class is %d" % (Xtest[sample_need_explain, 0], Xtest[sample_need_explain, 1], y_pred_input_0, ytest[sample_need_explain]))

Predicted class for sample X = [0.571640, 0.233700] is 2 and real class is 2

print("Membership values:")
for key, val in mem_val_classes.items():
    print("Class %d has the maximum membership value = %f" % (key, val))

for key in min_points_classes:
    print("Class %d has the representative hyperbox: V = %s and W = %s" % (key, min_points_classes[key], max_points_classes[key]))

Membership values:
Class 1 has the maximum membership value = 0.870180
Class 2 has the maximum membership value = 0.961410
Class 1 has the representative hyperbox: V = [0.66562 0.36352] and W = [0.66562 0.36352]
Class 2 has the representative hyperbox: V = [0.57285 0.27229] and W = [0.61106 0.28476]

Show input sample and hyperboxes belonging to each class. In 2D, we can show rectangles or use parallel coordinates

Using rectangles to show explanations

accel_agglo_gfmm_clf.show_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain], min_points_classes, max_points_classes, y_pred_input_0, "2D")

Using parallel coordinates. This mode best fits for any dimensions

# Create a parallel coordinates graph
accel_agglo_gfmm_clf.show_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain], min_points_classes, max_points_classes, y_pred_input_0, file_path="par_cord/accel_agglo_gfmm_par_cord.html")

# Load parallel coordinates to display on the notebook
from IPython.display import IFrame
# We load the parallel coordinates from GitHub here for demostration in readthedocs
# On the local notebook, we only need to load from the graph storing at 'par_cord/accel_agglo_gfmm_par_cord.html'
IFrame('https://uts-caslab.github.io/hyperbox-brain/docs/tutorials/par_cord/accel_agglo_gfmm_par_cord.html', width=820, height=520)