The general idea
How these scripts work?

The general idea

The general idea behind ensemble learning is that instead of a specific algorithm for classification, we have a meta classifier that takes advantage of couple of classic classifier. In this project we have these 4:

Adaboosted decision trees
Random forrest
Bagged MLPs
Some sort of bootstraping + Soft voting

It’s worth mentioning that every hard coded parameter is retrieved from previous projects and tests.

How these scripts work?

Loading data

Needed modules and global variables

from os.path import exists, join
import numpy as np


seprator = "\t"
dataset_folder = "lfw"
train_file = "pairsDevTrain.txt"
test_file = "pairsDevTest.txt"

Parsing the arrays in dataset

def parse_array(stringed_array):
    #parse the stringed array
    #return the array
    result = []
    for string in stringed_array:
        if "[" in string:
            string = string[2:]
        if "]" in string:
            string = string[:-2]
        for token in string.split():
            result.append(float(token))
    return np.array(result)

Extracting arrays for parsing

We need to figure out path to each txt file in dataset in order to load them as a string and then parse them and load them into memory using previous function.

def extract_array_from_line(path, line):
    #Name _ Pic.No1 _ Pic.No2
    #extract array from Pics and return them
    try:
        name, pic1, pic2 = line.split(seprator)
        pic1 = int(pic1)
        pic2 = int(pic2)
        pic1 = f'{name}_{pic1:04d}.txt'
        pic2 = f'{name}_{pic2:04d}.txt'

        final_path = join(path, name)
        final_pic1 = join(final_path, pic1)
        final_pic2 = join(final_path, pic2)
        parsed_pic1 = open(final_pic1, 'r').readlines()
        parsed_pic2 = open(final_pic2, 'r').readlines()


        parsed_pic1 = parse_array(parsed_pic1)
        parsed_pic2 = parse_array(parsed_pic2)
        return np.array([parsed_pic1, parsed_pic2])
    except Exception:
        name1, pic1, name2, pic2 = line.split(seprator)
        pic1 = int(pic1)
        pic2 = int(pic2)
        pic1 = f'{name1}_{pic1:04d}.txt'
        pic2 = f'{name2}_{pic2:04d}.txt'

        final_path1 = join(path, name1)
        final_path2 = join(path, name2)

        final_pic1 = join(final_path1, pic1)
        final_pic2 = join(final_path2, pic2)

        parsed_pic1 = open(final_pic1, 'r').readlines()
        parsed_pic2 = open(final_pic2, 'r').readlines()


        parsed_pic1 = parse_array(parsed_pic1)
        parsed_pic2 = parse_array(parsed_pic2)
        return np.array([parsed_pic1, parsed_pic2])

Loading the dataset

Now we use all the functions above to load our dataset.

def load(path):
    #check if the file exists
    # if not, return None
    # if yes, load the data
    # return the data
    if not exists(path):
        return None

    data_path = join(path, dataset_folder)
    train_path = join(path, train_file)
    test_path = join(path, test_file)
    train_data_plus = []
    test_data_plus = []
    train_data_negative = []
    test_data_negative = []
    train_path_handle = open(train_path, 'r')
    test_path_handle = open(test_path, 'r')

    for i, line in enumerate(train_path_handle.readlines()):
        if i == 0:
            count = int(line)
            continue
        if i <= count:
            train_data_plus.append(extract_array_from_line(data_path, line))
        else:
            train_data_negative.append(extract_array_from_line(data_path, line))

    for i, line in enumerate(test_path_handle.readlines()):
        if i == 0:
            count = int(line)
            continue
        if i <= count:
            test_data_plus.append(extract_array_from_line(data_path, line))
        else:
            test_data_negative.append(extract_array_from_line(data_path, line))

    return np.array(train_data_plus), np.array(train_data_negative), np.array(test_data_plus), np.array(test_data_negative)

Adaboosted Decision Trees

Sklearn’s implementation of all meta classifiers is used in this project.

Needed modules and global variables

from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

train_data = None
train_label = None
test_data = None
test_label = None

Initializing global variables

The way I’ve modeled the data for this meta classifier, forms a vector from concatenating vector of each image, In other words imagine we have extracted parts of people’s DNA and we want to know whether they are related or not, First we form a table containing DNA parts of each of those people’s DNA in each row and we want our decision tree to figure out existence of any blood relationship. We are doing the samething here, each vector being image’s DNA and their concatenation being each row. We transform the data into our desired shape and then use this function to load them.

def init_vars(_train_data, _train_label, _test_data, _test_label):
    #Initializez global variables
    global train_data
    global train_label
    global test_data
    global test_label
    train_data = _train_data
    train_label = _train_label
    test_data = _test_data
    test_label = _test_label

Initializing the meta classifier

First we need to construct a decision tree classifier with our desired parameters and then passing that to our meta classifier and at the end train the meta classifier and the measure its performance. That’s what we do for all of meta classifiers in this project.

def get_populated_dtc(max_depth=8, min_samples_split=2, min_samples_leaf=4, min_impurity_decrease=0.0):
    #Return a DecisionTreeClassifier object
    dtc = DecisionTreeClassifier(max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_impurity_decrease=min_impurity_decrease)
    return dtc

def adaboosted_dt():
    ab_dt = AdaBoostClassifier(
        get_populated_dtc(), n_estimators=100
    )

    return ab_dt.fit(train_data, train_label)

def test_accuracy(trained_model):
    test1 = accuracy_score(test_label, trained_model.predict(test_data))
    train1 = accuracy_score(train_label, trained_model.predict(train_data))
    return test1, train1

I’ll talk about its results and performance at the end of this document.

Random Forrest

Needed modules and global variables

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import numpy as np

train_data = None
train_label = None
test_data = None
test_label = None

Initializing global variables

Here I’ve experimented with a different way of modeling data, I’ve imagined that each vector is not just an array of features but it actually represents and actual vector in a 512 dimension and their differences and their distance can mean something, So this time I create a vector of size 513 which its first 512 elements are absolute value of two vectors and its last element is their distance.

def preprocess_data(d):
    #Calculate vector distance and absolute value of their difference
    res1 = []
    res2 = []
    for data in d:
        res = np.subtract(data[0], data[1])
        res1.append(np.abs(res))
        res2.append(np.linalg.norm(res))
    res = [np.append(x, y) for x, y in zip(res1, res2)]
    return np.array(res)

def init_vars(_train_data, _train_label, _test_data, _test_label):
    #Initializez global variables
    global train_data
    global train_label
    global test_data
    global test_label
    train_data = _train_data.reshape(len(_train_data), 2, -1)
    train_label = _train_label
    test_data = _test_data.reshape(len(_test_data), 2, -1)
    test_label = _test_label
    train_data = preprocess_data(train_data)
    test_data = preprocess_data(test_data)

Initializing the meta classifier

def test_accuracy(trained_model):
    test1 = accuracy_score(test_label, trained_model.predict(test_data))
    train1 = accuracy_score(train_label, trained_model.predict(train_data))
    return test1, train1

def handle_random_forrest():
    rfc = RandomForestClassifier(max_depth=8, min_samples_split=2, min_samples_leaf=4, min_impurity_decrease=0.0)
    return rfc.fit(train_data, train_label)

Bagged MLPs

Needed modules and global variables

from sklearn.ensemble import BaggingClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, adjusted_rand_score

train_data = None
train_label = None
test_data = None
test_label = None

Initializing global variables

Here I’ve used the same modeling as what I used in Adaboosted Decision Trees.

def init_vars(_train_data, _train_label, _test_data, _test_label):
    #Initializez global variables
    global train_data
    global train_label
    global test_data
    global test_label
    train_data = _train_data
    train_label = _train_label
    test_data = _test_data
    test_label = _test_label

Initializing the meta classifier

def get_populated_mlpc():
    #Return a MLPClassifier object
    mlpc = MLPClassifier()
    mlpc.set_params(hidden_layer_sizes=(200, 80, 20), alpha=0.032, random_state=1, solver="adam", activation="relu", max_iter=500)
    return mlpc

def bagged_mlp():
    bg_mlpc = BaggingClassifier(base_estimator= get_populated_mlpc(), n_estimators=20, random_state=1, n_jobs=12)
    return bg_mlpc.fit(train_data, train_label)

def test_accuracy(trained_model):
    test1 = accuracy_score(test_label, trained_model.predict(test_data))
    train1 = accuracy_score(train_label, trained_model.predict(train_data))
    return test1, train1

Some sort of bootstraping + Soft voting

Here I’ve taken advantage of three different classifiers:

I loop over them and in each iteration train them on a random subset of dataset and modify an array of weights based on their performance and at the very end I pass them to a VotingClassifier and train that meta classifier on the whole dataset.

Needed modules and global variables

from sklearn.neural_network import MLPClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier

from sklearn.metrics import accuracy_score
import numpy as np

train_data = None
train_label = None
test_data = None
test_label = None

Initializing global variables

Here I’ve used same modeling as RandomForrest.

def preprocess_data(d):
    #Calculate vector distance and absolute value of their difference
    res1 = []
    res2 = []
    for data in d:
        res = np.subtract(data[0], data[1])
        res1.append(np.abs(res))
        res2.append(np.linalg.norm(res))
    res = [np.append(x, y) for x, y in zip(res1, res2)]
    return np.array(res)

def init_vars(_train_data, _train_label, _test_data, _test_label):
    #Initializez global variables
    global train_data
    global train_label
    global test_data
    global test_label
    train_data = _train_data.reshape(len(_train_data), 2, -1)
    train_label = _train_label
    test_data = _test_data.reshape(len(_test_data), 2, -1)
    test_label = _test_label
    train_data = preprocess_data(train_data)
    test_data = preprocess_data(test_data)

The pipeline

last_acc[0]: weights[0] += 0.05 elif mlp_acc > last_acc[1]: weights[1] += 0.05 elif dt_acc > last_acc[2]: weights[2] += 0.05 last_acc = [svm_acc, mlp_acc, dt_acc] if debug: print("**SVM: {}, MLP: {}, DT: {}**".format(svm_acc, mlp_acc, dt_acc)) svm_acc = svm_acc * weights[0] mlp_acc = mlp_acc * weights[1] dt_acc = dt_acc * weights[2] if debug: print("##SVM: {}, MLP: {}, DT: {}##".format(svm_acc, mlp_acc, dt_acc)) if svm_acc > mlp_acc and svm_acc > dt_acc: weights[0] += 0.1 elif mlp_acc > svm_acc and mlp_acc > dt_acc: weights[1] += 0.1 elif dt_acc > svm_acc and dt_acc > mlp_acc: weights[2] += 0.1 vt_svm_mlp_dt = VotingClassifier(estimators=[ ('svm', svm), ('mlp', mlp), ('dt', dt)], voting='soft', weights=weights, n_jobs=12) res = vt_svm_mlp_dt.fit(train_data, train_label) if debug: print("Final accuracy: {}".format(test_accuracy(res)[0])) return res">

def get_random_samples(number_of_samples=3):
    #Return a list of random samples from dataset
    res = []
    for _ in range(number_of_samples):
        indexes = np.random.choice(len(train_data), size=(int(len(train_data) / number_of_samples)), replace=False)
        res.append(indexes)
    return res

def test_accuracy_pipeline(trained_model):
    test1 = accuracy_score(test_label, trained_model.predict(test_data))
    return test1

def test_accuracy(trained_model):
    test1 = accuracy_score(test_label, trained_model.predict(test_data))
    train1 = accuracy_score(train_label, trained_model.predict(train_data))
    return test1, train1

def pipeline(debug=False):
    svm = SVC(decision_function_shape="ovr", kernel="rbf", probability=True)
    mlp = MLPClassifier(hidden_layer_sizes=(40, 30, 20, 30, 40), alpha=0.032, random_state=1, solver="adam", activation="relu", max_iter=500)
    dt = DecisionTreeClassifier(max_depth=64, min_samples_split=2, min_samples_leaf=4, min_impurity_decrease=0.0)
    weights = [1 / 3, 1 / 3, 1 / 3]
    last_acc = [0, 0, 0]
    for _ in range(10):
        samples = get_random_samples()
        svm.fit(train_data[samples[0]], train_label[samples[0]])
        mlp.fit(train_data[samples[1]], train_label[samples[1]])
        dt.fit(train_data[samples[2]], train_label[samples[2]])
        svm_acc = test_accuracy_pipeline(svm)
        mlp_acc = test_accuracy_pipeline(mlp)
        dt_acc = test_accuracy_pipeline(dt)
        if svm_acc < last_acc[0]:
            weights[0] -= 0.05
        elif mlp_acc < last_acc[1]:
            weights[1] -= 0.05
        elif dt_acc < last_acc[2]:
            weights[2] -= 0.05
        if svm_acc > last_acc[0]:
            weights[0] += 0.05
        elif mlp_acc > last_acc[1]:
            weights[1] += 0.05
        elif dt_acc > last_acc[2]:
            weights[2] += 0.05
        last_acc = [svm_acc, mlp_acc, dt_acc]
        if debug:
            print("**SVM: {}, MLP: {}, DT: {}**".format(svm_acc, mlp_acc, dt_acc))
        svm_acc = svm_acc * weights[0]
        mlp_acc = mlp_acc * weights[1]
        dt_acc = dt_acc * weights[2]
        if debug:
            print("##SVM: {}, MLP: {}, DT: {}##".format(svm_acc, mlp_acc, dt_acc))
        if svm_acc > mlp_acc and svm_acc > dt_acc:
            weights[0] += 0.1
        elif mlp_acc > svm_acc and mlp_acc > dt_acc:
            weights[1] += 0.1
        elif dt_acc > svm_acc and dt_acc > mlp_acc:
            weights[2] += 0.1


    vt_svm_mlp_dt = VotingClassifier(estimators=[
        ('svm', svm), ('mlp', mlp), ('dt', dt)],
        voting='soft', weights=weights, n_jobs=12)

    res = vt_svm_mlp_dt.fit(train_data, train_label)
    if debug:
        print("Final accuracy: {}".format(test_accuracy(res)[0]))
    return res

The main script

Needed modules

import numpy as np
from utils.data_loader import load
import utils.ensemble_dt as edt
import utils.ensemble_bag_mlp as ebm
import utils.svm_mlp_dt_combo as smdt
import utils.ensemble_rf as erf
from sklearn import preprocessing
from tabulate import tabulate

Loading and manuplating the data

Here I transform loaded data into the form I explained in AdaBoostClassifier section.

train_plus, train_negative, test_plus, test_negative = load("/home/toorajtaraz/Downloads/project/")
train_data_count = train_plus.shape[0]
test_data_count = test_plus.shape[0]

train_plus = train_plus.reshape((train_data_count, -1))
train_negative = train_negative.reshape((train_data_count, -1))
test_plus = test_plus.reshape((test_data_count, -1))
test_negative = test_negative.reshape((test_data_count, -1))

train_data = []
train_label = []

test_data = []
test_label = []

for x in train_plus:
    train_data.append(x)
    train_label.append(1)

for x in train_negative:
    train_data.append(x)
    train_label.append(0)

for x in test_plus:
    test_data.append(x)
    test_label.append(1)

for x in test_negative:
    test_data.append(x)
    test_label.append(0)

test_data = np.array(test_data)
train_data = np.array(train_data)
test_label = np.array(test_label)
train_label = np.array(train_label)

Preprocessing data

Sklearn Library offers a module that takes care of standardizing the data, this action improves convergence time and accuracy (at least based on what I witnessed).

scaler = preprocessing.StandardScaler().fit(train_data)
train_data = scaler.transform(train_data)

scaler = preprocessing.StandardScaler().fit(test_data)
test_data = scaler.transform(test_data)

Using what have created so far

edt.init_vars(train_data, train_label, test_data, test_label)
adaboosted_dt_acc = edt.test_accuracy(edt.adaboosted_dt())

ebm.init_vars(train_data, train_label, test_data, test_label)
bagged_mlp_acc = ebm.test_accuracy(ebm.bagged_mlp())

smdt.init_vars(train_data, train_label, test_data, test_label)
svm_mlp_dt_combo_acc = smdt.test_accuracy(smdt.pipeline(debug=True))

erf.init_vars(train_data, train_label, test_data, test_label)
random_forrest_acc = erf.test_accuracy(erf.handle_random_forrest())

Results

Overall view

TYPE	TEST_P	TRAIN_P
AdaboostedDecisionTree	0.578	1
BaggedMLP	0.766	0.997273
SVM_MLP_DT	0.844	0.963636
RandomForest	0.81	0.985909

Detailed discussion

In case of AdaboostedDecisionTree, it performed as I excepted, very well on train dataset and poorly on test dataset due to over fitting. BaggedMLPs didn’t perform very well as excepted, just about 2000 data samples are not enough for training a MLP. At first I intended to extract data from dataset folder and train my model on a larger dataset, but I figured due to our point being related to the accuracy we achieve it wouldn’t be ethical :)) RandomForest and my customish algorithm performed better than the others. During my tests I concluded that SVM with RBF kernel performs the best and the ensemble final accuracy is really close to SVM’s accuracy, but I assume it wouldn’t be the case if we had a large enough dataset, in that case MLP would be the dominant model in voting.

Computational inteligence project on faces in the wild dataset

Related tags

Overview

Table of Contents

The general idea

How these scripts work?

Loading data

Needed modules and global variables

Parsing the arrays in dataset

Extracting arrays for parsing

Loading the dataset

Adaboosted Decision Trees

Needed modules and global variables

Initializing global variables

Initializing the meta classifier

Random Forrest

Needed modules and global variables

Initializing global variables

Initializing the meta classifier

Bagged MLPs

Needed modules and global variables

Initializing global variables

Initializing the meta classifier

Some sort of bootstraping + Soft voting

Needed modules and global variables

Initializing global variables

The pipeline

The main script

Needed modules

Loading and manuplating the data

Preprocessing data

Using what have created so far

Results

Overall view

Detailed discussion

Owner

tooraj taraz

An LSTM based GAN for Human motion synthesis

Differentiable Abundance Matching With Python

[NAACL & ACL 2021] SapBERT: Self-alignment pretraining for BERT.

Source code for "Pack Together: Entity and Relation Extraction with Levitated Marker"

PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement.

Piotr - IoT firmware emulation instrumentation for training and research

PyTorch implementation of Self-supervised Contrastive Regularization for DG (SelfReg)

A PyTorch-Based Framework for Deep Learning in Computer Vision

Dynamic Token Normalization Improves Vision Transformers

An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

Few-Shot-Intent-Detection includes popular challenging intent detection datasets with/without OOS queries and state-of-the-art baselines and results.

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

CTF Challenge for CSAW Finals 2021

Implementation of light baking system for ray tracing based on Activision's UberBake

This repository provides an efficient PyTorch-based library for training deep models.

Minecraft agent to farm resources using reinforcement learning

A Survey on Deep Learning Technique for Video Segmentation

Implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning"

Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

Automatic Differentiation Multipole Moment Molecular Forcefield