mltreelib : Machine Learnign with Tree Based Library

This package evovled from the attempt to make right kind of Decision Tress which was ideated by many people like Hastie, Tibshirani, Friedman, Quilan, Loh, Chaudhari.

Install

pip install mltreelib

How to use

Create a sample data

import numpy as np
import pandas as pd
from mltreelib.data import Data
from mltreelib.tree import Tree

n_size = 1000
rnd = np.random.RandomState(1234)
dummy_data = pd.DataFrame({
    'numericfull':rnd.randint(1,500,size=n_size),
    'unitint':rnd.randint(1,25,size=n_size),
    'floatfull':rnd.random_sample(size=n_size),
    'floatsmall':np.round(rnd.random_sample(size=n_size)+rnd.randint(1,25,size=n_size),2),
    'categoryobj':rnd.choice(['a','b','c','d'],size=n_size),
    'stringobj':rnd.choice(["{:c}".format(k) for k in range(97, 123)],size=n_size)})
    
dummy_data.head().to_markdown()

|    |   numericfull |   unitint |   floatfull |   floatsmall | categoryobj   | stringobj   |
|---:|--------------:|----------:|------------:|-------------:|:--------------|:------------|
|  0 |           304 |        18 |    0.908959 |         8.56 | a             | c           |
|  1 |           212 |        24 |    0.348582 |        14.35 | a             | g           |
|  2 |           295 |        15 |    0.392977 |        21.98 | a             | y           |
|  3 |            54 |        20 |    0.720856 |         5.33 | a             | q           |
|  4 |           205 |        21 |    0.897588 |        23.03 | c             | k           |

Create a Dataset

dataset = Data(df=dummy_data)
print(dataset)
print('Pandas Data Frame        : ',np.round(dummy_data.memory_usage(deep=True).sum()*1e-6,2),'MB')
print('Dataset Structured Array : ',np.round(dataset.data.nbytes*1e-6/ 1024 * 1024,2),'MB')
dataset.data[:5]

Dataset(df=Shape((1000, 6), reduce_datatype=True, encode_category=None, add_intercept=False, na_treatment=allow, copy=False, digits=None, n_category=None, split_ratio=None)
Pandas Data Frame        :  0.15 MB
Dataset Structured Array :  0.03 MB

array([(304, 18, 0.9089594 ,  8.56, 'a', 'c'),
       (212, 24, 0.34858167, 14.35, 'a', 'g'),
       (295, 15, 0.39297667, 21.98, 'a', 'y'),
       ( 54, 20, 0.7208556 ,  5.33, 'a', 'q'),
       (205, 21, 0.89758754, 23.03, 'c', 'k')],
      dtype=[('numericfull', '<u2'), ('unitint', 'u1'), ('floatfull', '<f4'), ('floatsmall', '<f4'), ('categoryobj', 'O'), ('stringobj', 'O')])