PyDojoML 0.4.0

A General Purpose Machine Learning Library for Python

PyDojoML

A General Purpose Machine Learning Library for Python

A quick taste of PyDojoML

How to install

You can easily install it with pip.
Copy-paste this in your terminal and run it.

pip install pydojoml

Good job, now it's time we rock-and-roll!

Simple Linear Regression example:

import numpy as np
from dojo.linear import LinearRegression

# Let's create some data to fit the model to.
X = np.random.randn(100_000, 100)
y = X @ np.random.rand(100)

# Building the model.
linear_reg = LinearRegression()

# Fitting the model is as easy as a call of a method.
linear_reg.fit(X, y)

# Now lets predict.
prediction = linear_reg.predict(X[:20, :])

Dojo's ingredients

Linear Models

• Linear Regression
• LASSO
• Ridge
• Logistic Regression

Deep Neural Networks

• Layers:
  • Dense
  • Activation

Activation functions

• Linear
• Sigmoid
• Softmax
• TanH
• ReLU
• Leaky ReLU

Losses

• Squared Error
• Cross Entropy

Optimizers

• Stochastic/Batch/Mini-batch Gradient Descent
• Momentum
• RMSprop
• Adam

Regularizer

• L1
• L2

Tree Models

• Classification And Regression Trees (CARTs)
• Extra-Trees

Support Vector Machines

• C-SVM
• Epsilon-SVM
• Nu-SVMs

Bayes

• Naive Bayes algorithm

Ensemble Learning

• AdaBoost
• Model Stacking

Clustering

• Hierarchical Clustering
• K-Means algorithm

Anomaly detection

• Univariate and Multivariate Gaussian Distribution

Dimensionality Reduction Techniques

• Principal Component Analysis
• Linear Discriminant Analysis

Preprocessing

• Encoders:
  • Label Encoder
  • OneHot Encoder
• Scalers:
  • Normalizer

Various metrics

• classification
• regression
• clustering

Model evaluation utils

• Train-Test splits
• K-Fold Cross Validation

Plotting

• Decision Boundary plotter

Data Preprocessing utils

• encoders
• normalizers
• scalers

Natural Language Processing utils

• TF-IDF

Recommender Systems

• Content Based
• Collaborative Filtering