General features selection based on certain machine learning algorithm and evaluation methods
- sequence_selection
from MLFeatureSelection import sequence_selection
from sklearn.linear_model import LogisticRegression
sf = sequence_selection.Select(Sequence = True, Random = True, Cross = False)
sf.ImportDF(df,label = 'Label') #import dataframe and label
sf.ImportLossFunction(lossfunction, direction = 'ascend') #import loss function handle and optimize direction, 'ascend' for AUC, ACC, 'descend' for logloss etc.
sf.InitialNonTrainableFeatures(notusable) #those features that is not trainable in the dataframe, user_id, string, etc
sf.InitialFeatures(initialfeatures) #initial initialfeatures as list
sf.GenerateCol() #generate features for selection
sf.SetFeatureEachRound(50, False) # set number of feature each round, and set how the features are selected from all features (True: sample selection, False: select chunk by chunk)
sf.clf = LogisticRegression() #set the selected algorithm, can be any algorithm
sf.SetLogFile('record.log') #log file
sf.run(validate) #run with validation function, validate is the function handle of the validation function, return best features combination- importance_selection
from MLFeatureSelection import importance_selection
import xgboost as xgb
sf = importance_selection.Select()
sf.ImportDF(df,label = 'Label') #import dataframe and label
sf.ImportLossFunction(lossfunction, direction = 'ascend') #import loss function and optimize direction
sf.InitialFeatures() #initial features, input
sf.SelectRemoveMode(batch = 2)
sf.clf = xgb.XGBClassifier()
sf.SetLogFile('record.log') #log file
sf.run(validate) #run with validation function, return best features combination- coherence_selection
from MLFeatureSelection import coherence_selection
import xgboost as xgb
sf = coherence_selection.Select()
sf.ImportDF(df,label = 'Label') #import dataframe and label
sf.ImportLossFunction(lossfunction, direction = 'ascend') #import loss function and optimize direction
sf.InitialFeatures() #initial features, input
sf.SelectRemoveMode(batch = 2)
sf.clf = xgb.XGBClassifier()
sf.SetLogFile('record.log') #log file
sf.run(validate) #run with validation function, return best features combination- log reader
from MLFeatureSelection.tools import readlog
logfile = 'record.log'
logscore = 0.5 #any score in the logfile
features_combination = readlog(logfile, logscore)- complete dataset when there is cross-term features
from MLFeatureSelection.tools import readlog, filldf
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/y
def sq(x,y):
return x ** 2
CrossMethod = {'+':add,
'-':substract,
'*':times,
'/':divide,
} # set your own cross method
df = pd.read_csv('XXX')
logfile = 'record.log'
logscore = 0.5 #any score in the logfile
features_combination = readlog(logfile, logscore)
df = filldf(df, features_combination, CrossMethod)- format of validate and lossfunction
define your own:
validate: validation method in function , ie k-fold, last time section valdate, random sampling validation, etc
lossfunction: model performance evaluation method, ie logloss, auc, accuracy, etc
def validate(X, y, features, clf, lossfunction):
"""define your own validation function with 5 parameters
input as X, y, features, clf, lossfunction
clf is set by SetClassifier()
lossfunction is import earlier
features will be generate automatically
function return score and trained classfier
"""
clf.fit(X[features],y)
y_pred = clf.predict(X[features])
score = lossfuntion(y_pred,y)
return score, clf
def lossfunction(y_pred, y_test):
"""define your own loss function with y_pred and y_test
return score
"""
return np.mean(y_pred == y_test)Multiple processing can be set in validate function when you are doing N-fold.
More examples are added in example folder include: