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Machine Learning library written in Python and NumPy.

Project description

pykitml logo

pykitml (Python Kit for Machine Learning)

Machine Learning library written in Python and NumPy.


python3 -m pip install pykitml



  • Linear Regression
  • Logistic Regression
  • Support Vector Machine
  • Neural Network
  • Nearest Neighbor
  • Decision Tree
  • Random Forest
  • Naive Bayes
  • K-Means Clustering
  • Principle Component Analysis

Benchmark (Intel i5-6400, 4 cores @ 3.3GHz)

Model Dataset Dataset Size Time
Logistic regression, 1500 epochs, 10 examples/batch Adult 392106x13 < 1 sec
784x100x10 Network, 1200 epochs, 50 examples/batch MNIST 60000x784 35 sec
SVM, 1000 epochs, 20 examples/batch MNIST 10000x784 39 sec
Decision Tree, 6 max-depth, 83 nodes Adult 392106x13 1 min 51 sec
Random forest, 9 max-depth, 100 trees Adult 392106x13 1 hour 35 min

Demo (MNIST)


import os.path

import numpy as np
import pykitml as pk
from pykitml.datasets import mnist

# Download dataset
if(not os.path.exists('mnist.pkl')): mnist.get()

# Load dataset
training_data, training_targets, testing_data, testing_targets = mnist.load()

# Create a new neural network
digit_classifier = pk.NeuralNetwork([784, 100, 10])

# Train it
    optimizer=pk.Adam(learning_rate=0.012, decay_rate=0.95), 

# Save it, 'digit_classifier_network.pkl')

# Show performance
accuracy = digit_classifier.accuracy(training_data, training_targets)
print('Train Accuracy:', accuracy)        
accuracy = digit_classifier.accuracy(testing_data, testing_targets)
print('Test Accuracy:', accuracy)

# Plot performance graph

# Show confusion matrix
digit_classifier.confusion_matrix(training_data, training_targets)

Trying the model

import random

import numpy as np
import matplotlib.pyplot as plt
import pykitml as pk
from pykitml.datasets import mnist

# Load dataset
inputs_train, outputs_train, inputs_test, outputs_test = mnist.load()

# Use only first 10000
inputs_train = inputs_train[:10000]
outputs_train = outputs_train[:10000]

# Load the trained network
svm_mnist_classifier = pk.load('svm_mnist_classifier.pkl')

# Pick a random example from testing data
index = random.randint(0, 9000)

# Show the test data and the label
plt.imshow(inputs_train[index].reshape(28, 28))
print('Label: ', outputs_train[index])

# Transform the input
input_data = pk.gaussian_kernel(inputs_train[index], inputs_train)

# Show prediction
model_output = svm_mnist_classifier.get_output_onehot()
print('Predicted: ', model_output)

Performance Graph

Performance Graph

Confusion Matrix

Confusion Matrix

Project details

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