MLFeatureSelection 0.0.1.7

geenral select features based on selected algorithm

Features Selection
=====================================

This code is for general features selection based on certain machine learning algorithm and evaluation methos

How to run (see demo.py)
------------------------------------------------

The demo is based on the IJCAI-2018 data moning competitions

- Import library from FeatureSelection.py and also other necessary library

.. code-block:: python
import MLFeaturesSelection as FS
from sklearn.metrics import log_loss
import lightgbm as lgbm
import pandas as pd
import numpy as np


- Generate for dataset

.. code-block:: python

def prepareData():
df = pd.read_csv('IJCAI-2018/data/train/trainb.csv')
df = df[~pd.isnull(df.is_trade)]
item_category_list_unique = list(np.unique(df.item_category_list))
df.item_category_list.replace(item_category_list_unique, list(np.arange(len(item_category_list_unique))), inplace=True)
return df


- Define your loss function

.. code-block:: python

def modelscore(y_test, y_pred):
return log_loss(y_test, y_pred)


- Define the way to validate

.. code-block:: python

def validation(X,y,clf,lossfunction):
totaltest = 0
for D in [24]:
T = (X.day != D)
X_train, X_test = X[T], X[~T]
X_train, X_test = X_train, X_test
y_train, y_test = y[T], y[~T]
clf.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric='logloss', verbose=False,early_stopping_rounds=200) #the train method must match your selected algorithm
totaltest += lossfunction(y_test, clf.predict_proba(X_test)[:,1])
totaltest /= 2.0
return totaltest


- Define the cross method (required when *Cross = True*)

.. code-block:: python

def add(x,y):
return x + y

def substract(x,y):
return x - y

def times(x,y):
return x * y

def divide(x,y):
return (x + 0.001)/(y + 0.001)

CrossMethod = {'+':add,
'-':substract,
'*':times,
'/':divide,}


- Initial the seacher with customized procedure (sequence + random + cross)

.. code:: python

sf = FS.Select(Sequence = False, Random = True, Cross = False) #select the way you want to process searching


- Import loss function

.. code:: python

sf.ImportDF(prepareData(),label = 'is_trade')


- Import cross method (required when *Cross = True*)

.. code:: python

sf.ImportCrossMethod(CrossMethod)


- Define non-trainable features

.. code:: python

sf.NonTrainableFeatures = ['used','instance_id', 'item_property_list', 'context_id', 'context_timestamp', 'predict_category_property', 'is_trade']


- Define initial features' combination

.. code:: python

sf.InitialFeatures(['item_category_list', 'item_price_level','item_sales_level','item_collected_level', 'item_pv_level'])


- Define algorithm

.. code:: python

sf.clf = lgbm.LGBMClassifier(random_state=1, num_leaves = 6, n_estimators=5000, max_depth=3, learning_rate = 0.05, n_jobs=8)


- Define log file name

.. code:: python

sf.logfile = 'record.log'


- Run with self-define validate method

.. code:: python

sf.run(validation)


- This code take a while to run, you can stop it any time and restart by replace the best features combination in temp sf.InitialFeatures()


This features selection method achieved
------------------------------------------------------------------------------

- **1st** in Rong360

- https://github.com/duxuhao/rong360-season2

- **12nd** in IJCAI-2018 1st round


Algorithm details
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.. image:: (https://github.com/duxuhao/Feature-Selection/blob/master/Procedure.png)