mltreelib 0.0.2

A real tree based ML package

mltree

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 Dataset
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()

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	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 = Dataset(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')])