xverse
Advanced tools
xverse short for X uniVerse is collection of transformers for feature engineering and feature selection
xverse short for X uniVerse is a Python module for machine learning in the space of feature engineering, feature transformation and feature selection.
Currently, xverse package handles only binary target.
The package requires numpy, pandas, scikit-learn, scipy and statsmodels. In addition, the package is tested on Python version 3.5 and above.
To install the package, download this folder and execute:
python setup.py install
or
pip install xverse
To install the development version. you can use
pip install --upgrade git+https://github.com/Sundar0989/XuniVerse
XVerse module is fully compatible with sklearn transformers, so they can be used in pipelines or in your existing scripts. Currently, it supports only Pandas dataframes.
from xverse.transformer import MonotonicBinning
clf = MonotonicBinning()
clf.fit(X, y)
print(clf.bins)
{'age': array([19., 35., 45., 87.]),
'balance': array([-3313. , 174. , 979.33333333, 71188. ]),
'campaign': array([ 1., 3., 50.]),
'day': array([ 1., 12., 20., 31.]),
'duration': array([ 4. , 128. , 261.33333333, 3025. ]),
'pdays': array([-1.00e+00, -5.00e-01, 1.00e+00, 8.71e+02]),
'previous': array([ 0., 1., 25.])}
from xverse.transformer import WOE
clf = WOE()
clf.fit(X, y)
print(clf.woe_df.head()) #Weight of Evidence transformation dataset
+---+---------------+--------------------+-------+-------+-----------+---------------------+--------------------+---------------------+------------------------+----------------------+---------------------+
| | Variable_Name | Category | Count | Event | Non_Event | Event_Rate | Non_Event_Rate | Event_Distribution | Non_Event_Distribution | WOE | Information_Value |
+---+---------------+--------------------+-------+-------+-----------+---------------------+--------------------+---------------------+------------------------+----------------------+---------------------+
| 0 | age | (18.999, 35.0] | 1652 | 197 | 1455 | 0.11924939467312348 | 0.8807506053268765 | 0.3781190019193858 | 0.36375 | 0.038742147481056366 | 0.02469286279236605 |
+---+---------------+--------------------+-------+-------+-----------+---------------------+--------------------+---------------------+------------------------+----------------------+---------------------+
| 1 | age | (35.0, 45.0] | 1388 | 129 | 1259 | 0.09293948126801153 | 0.9070605187319885 | 0.2476007677543186 | 0.31475 | -0.2399610313340142 | 0.02469286279236605 |
+---+---------------+--------------------+-------+-------+-----------+---------------------+--------------------+---------------------+------------------------+----------------------+---------------------+
| 2 | age | (45.0, 87.0] | 1481 | 195 | 1286 | 0.13166779203241052 | 0.8683322079675895 | 0.3742802303262956 | 0.3215 | 0.15200725211484276 | 0.02469286279236605 |
+---+---------------+--------------------+-------+-------+-----------+---------------------+--------------------+---------------------+------------------------+----------------------+---------------------+
| 3 | balance | (-3313.001, 174.0] | 1512 | 133 | 1379 | 0.08796296296296297 | 0.9120370370370371 | 0.255278310940499 | 0.34475 | -0.3004651512228873 | 0.06157421302850976 |
+---+---------------+--------------------+-------+-------+-----------+---------------------+--------------------+---------------------+------------------------+----------------------+---------------------+
| 4 | balance | (174.0, 979.333] | 1502 | 163 | 1339 | 0.1085219707057257 | 0.8914780292942743 | 0.31285988483685223 | 0.33475 | -0.06762854653574929 | 0.06157421302850976 |
+---+---------------+--------------------+-------+-------+-----------+---------------------+--------------------+---------------------+------------------------+----------------------+---------------------+
print(clf.iv_df) #Information value dataset
+----+---------------+------------------------+
| | Variable_Name | Information_Value |
+----+---------------+------------------------+
| 6 | duration | 1.1606798895024775 |
+----+---------------+------------------------+
| 14 | poutcome | 0.4618899274360784 |
+----+---------------+------------------------+
| 12 | month | 0.37953277364723703 |
+----+---------------+------------------------+
| 3 | contact | 0.2477624664660033 |
+----+---------------+------------------------+
| 13 | pdays | 0.20326698063078097 |
+----+---------------+------------------------+
| 15 | previous | 0.1770811514357682 |
+----+---------------+------------------------+
| 9 | job | 0.13251854742728092 |
+----+---------------+------------------------+
| 8 | housing | 0.10655553101753026 |
+----+---------------+------------------------+
| 1 | balance | 0.06157421302850976 |
+----+---------------+------------------------+
| 10 | loan | 0.06079091829519839 |
+----+---------------+------------------------+
| 11 | marital | 0.04009032555607127 |
+----+---------------+------------------------+
| 7 | education | 0.03181211694236827 |
+----+---------------+------------------------+
| 0 | age | 0.02469286279236605 |
+----+---------------+------------------------+
| 2 | campaign | 0.019350877455830695 |
+----+---------------+------------------------+
| 4 | day | 0.0028156288525541884 |
+----+---------------+------------------------+
| 5 | default | 1.6450124824351054e-05 |
+----+---------------+------------------------+
+-------------------+-----------------------------+
| Information Value | Variable Predictiveness |
+-------------------+-----------------------------+
| Less than 0.02 | Not useful for prediction |
+-------------------+-----------------------------+
| 0.02 to 0.1 | Weak predictive Power |
+-------------------+-----------------------------+
| 0.1 to 0.3 | Medium predictive Power |
+-------------------+-----------------------------+
| 0.3 to 0.5 | Strong predictive Power |
+-------------------+-----------------------------+
| >0.5 | Suspicious Predictive Power |
+-------------------+-----------------------------+
clf.transform(X) #apply WOE transformation on the dataset
from xverse.ensemble import VotingSelector
clf = VotingSelector()
clf.fit(X, y)
print(clf.available_techniques)
['WOE', 'RF', 'RFE', 'ETC', 'CS', 'L_ONE']
clf.feature_importances_
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| | Variable_Name | Information_Value | Random_Forest | Recursive_Feature_Elimination | Extra_Trees | Chi_Square | L_One |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 0 | duration | 1.1606798895024775 | 0.29100016518065835 | 0.0 | 0.24336032789230097 | 62.53045588382914 | 0.0009834060765907017 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 1 | poutcome | 0.4618899274360784 | 0.05975563617541324 | 0.8149539108454378 | 0.07291945099022576 | 209.1788690088815 | 0.27884071686005385 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 2 | month | 0.37953277364723703 | 0.09472524644853274 | 0.6270707318033509 | 0.10303345973615481 | 54.81011477300214 | 0.18763733424335785 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 3 | contact | 0.2477624664660033 | 0.018358265986906014 | 0.45594899004325673 | 0.029325952072445132 | 25.357947712611868 | 0.04876094100065351 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 4 | pdays | 0.20326698063078097 | 0.04927368012222067 | 0.0 | 0.02738001362078519 | 13.808925800391403 | -0.00026932622581396677 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 5 | previous | 0.1770811514357682 | 0.02612886929056733 | 0.0 | 0.027197295919351088 | 13.019278420681164 | 0.0 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 6 | job | 0.13251854742728092 | 0.050024353325485646 | 0.5207956132479409 | 0.05775450997836301 | 13.043319831003855 | 0.11279310830899944 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 7 | housing | 0.10655553101753026 | 0.021126744587568032 | 0.28135643347861894 | 0.020830177741565564 | 28.043094016887064 | 0.0 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 8 | balance | 0.06157421302850976 | 0.0963543249575152 | 0.0 | 0.08429423739161768 | 0.03720300378031974 | -1.3553979494412002e-06 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 9 | loan | 0.06079091829519839 | 0.008783347837152861 | 0.6414812505459246 | 0.013652849211750306 | 3.4361027026756084 | 0.0 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 10 | marital | 0.04009032555607127 | 0.02648832289940045 | 0.9140684291962617 | 0.03929791951230852 | 10.889749514307464 | 0.0 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 11 | education | 0.03181211694236827 | 0.02757205345952717 | 0.21529148795958114 | 0.03980467391633981 | 4.70588768051867 | 0.0 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 12 | age | 0.02469286279236605 | 0.10164634631051869 | 0.0 | 0.08893247762137796 | 0.6818947945319156 | -0.004414426121909251 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 13 | campaign | 0.019350877455830695 | 0.04289312347011537 | 0.0 | 0.05716486374991612 | 1.8596566731099653 | -0.012650844735972498 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 14 | day | 0.0028156288525541884 | 0.083859807784465 | 0.0 | 0.09056623672332145 | 0.08687716739873641 | -0.00231307077371602 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
| 15 | default | 1.6450124824351054e-05 | 0.0020097121639531665 | 0.0 | 0.004485553922176626 | 0.007542737902818529 | 0.0 |
+----+---------------+------------------------+-----------------------+-------------------------------+----------------------+----------------------+-------------------------+
clf.feature_votes_
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| | Variable_Name | Information_Value | Random_Forest | Recursive_Feature_Elimination | Extra_Trees | Chi_Square | L_One | Votes |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 1 | poutcome | 1 | 1 | 1 | 1 | 1 | 1 | 6 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 2 | month | 1 | 1 | 1 | 1 | 1 | 1 | 6 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 6 | job | 1 | 1 | 1 | 1 | 1 | 1 | 6 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 0 | duration | 1 | 1 | 0 | 1 | 1 | 1 | 5 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 3 | contact | 1 | 0 | 1 | 0 | 1 | 1 | 4 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 4 | pdays | 1 | 1 | 0 | 0 | 1 | 0 | 3 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 7 | housing | 1 | 0 | 1 | 0 | 1 | 0 | 3 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 12 | age | 0 | 1 | 0 | 1 | 0 | 1 | 3 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 14 | day | 0 | 1 | 0 | 1 | 0 | 1 | 3 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 5 | previous | 1 | 0 | 0 | 0 | 1 | 0 | 2 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 8 | balance | 0 | 1 | 0 | 1 | 0 | 0 | 2 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 13 | campaign | 0 | 0 | 0 | 1 | 0 | 1 | 2 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 9 | loan | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 10 | marital | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 11 | education | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
| 15 | default | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+----+---------------+-------------------+---------------+-------------------------------+-------------+------------+-------+-------+
XuniVerse is under active development, if you'd like to be involved, we'd love to have you. Check out the CONTRIBUTING.md file or open an issue on the github project to get started.
https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html
https://medium.com/@sundarstyles89/variable-selection-using-python-vote-based-approach-faa42da960f0
Alessio Tamburro (https://github.com/alessiot)
FAQs
xverse short for X uniVerse is collection of transformers for feature engineering and feature selection
We found that xverse demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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