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 mltreelibHow to use
Create a sample data
import numpy as np
import pandas as pd
from mltreelib.data import Dataset
from mltreelib.tree import Treen_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')])
