semvec/pypi-packages huggingface dataset:
kiwiml 0.0.5
a tiny, simple, instructional machine learning library in python
Requires Python: >=3.6

# kiwi v0.0.5
A tiny, simple, instructional machine learning library in python, written by Ori Yonay.

To install:
`pip3 install kiwiml`

Current Features:
* autodiff: A simple automatic numpy-compatible multidimensional differentiator
* Machine Learning Models:
  * KNN
  * Linear Regression
  * Logistic Regression
  * Naive Bayes
  * Perceptron
  * Single Dimensional Analysis
* Utilities:
  * Accuracy score function for calculating model accuracy
  * train_test_split
  * A function to plot cost histories
  * PCA Decomposition (will be in its own dimensionality reduction class in the future)
* Error Functions:
  * Mean-Squared Error
  * Cross-Entropy Loss
* Dataset Loaders:
  * Boston dataset
  * Breast cancer dataset
  * MNIST dataset

TODO:
* Error Functions: add mean absolute deviation
* Machine Learning Models:
  * SVM
  * Decision Tree
  * Random Forest
  * K-Means Clustering
  * Neural Network Library
     * Fully-Connected layer
     * Convolutional Layer
     * Residual Layer
     * ...
* More dataset importers
* Autolearn
semisupervised 0.0.28
Python implementation of semi-supervised learning algorithm
Keywords: semi-supervised learning

SemiSupervised
==============

Describe
========

This is a Semi-supervised learning framework of Python. You can use it
for classification task in machine learning.

Install
=======

::

    pip install semisupervised

API
===

We have implemented following semi-supervised learning algorithm.

-  LabelPropagation

`reference
code <https://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html#sklearn.semi_supervised.LabelPropagation>`__

-  S3VM

`reference
code <https://github.com/d12306/Implementation-of-Transductive-SVM-Sklearn-Compatible>`__

Statement

Some of the code comes from the Internet.

Examples
--------

.. code:: python

    from __future__ import absolute_import
    import numpy as np
    from sklearn import datasets
    from sklearn import metrics
    from sklearn.model_selection import train_test_split

    # normalization
    def normalize(x):
        return (x - np.min(x))/(np.max(x) - np.min(x))

    def get_data():
        X, y = datasets.load_breast_cancer(return_X_y=True)
        X = normalize(X)
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.6, random_state = 0)
        rng = np.random.RandomState(42)
        random_unlabeled_points = rng.rand(len(X_train)) < 0.1
        y_train[random_unlabeled_points] = -1
        # 
        index, = np.where(y_train != -1)
        label_X_train = X_train[index,:]
        label_y_train = y_train[index]
        index, = np.where(y_train == -1)
        unlabel_X_train = X_train[index,:]
        unlabel_y = -1*np.ones(unlabel_X_train.shape[0]).astype(int)
        return label_X_train, label_y_train, unlabel_X_train, unlabel_y, X_test, y_test

    label_X_train, label_y_train, unlabel_X_train, unlabel_y, X_test, y_test = get_data()

    # import 
    from semisupervised import SKTSVM

    model = SKTSVM()
    model.fit(np.vstack((label_X_train, unlabel_X_train)), np.append(label_y_train, unlabel_y))
    # predict
    predict = model.predict(X_test)
    acc = metrics.accuracy_score(y_test, predict)
    # metric
    print("accuracy", acc)