geenral select features based on selected algorithm
Project description
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
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
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
def modelscore(y_test, y_pred):
return log_loss(y_test, y_pred)
Define the way to validate
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)
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)
sf = FS.Select(Sequence = False, Random = True, Cross = False) #select the way you want to process searching
Import loss function
sf.ImportDF(prepareData(),label = 'is_trade')
Import cross method (required when Cross = True)
sf.ImportCrossMethod(CrossMethod)
Define non-trainable features
sf.NonTrainableFeatures = ['used','instance_id', 'item_property_list', 'context_id', 'context_timestamp', 'predict_category_property', 'is_trade']
Define initial features’ combination
sf.InitialFeatures(['item_category_list', 'item_price_level','item_sales_level','item_collected_level', 'item_pv_level'])
Define algorithm
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
sf.logfile = 'record.log'
Run with self-define validate method
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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