def main():
sf = FS.Select(Sequence = False, Random = True, Cross = False) #select the way you want to process searching
sf.ImportDF(prepareData(),label = 'is_trade')
sf.ImportLossFunction(modelscore,direction = 'descend')
sf.ImportCrossMethod(CrossMethod)
sf.NonTrainableFeatures = ['used','instance_id', 'item_property_list', 'context_id', 'context_timestamp', 'predict_category_property', 'is_trade']
sf.InitialFeatures(['item_category_list', 'item_price_level','item_sales_level','item_collected_level', 'item_pv_level','day'])
sf.clf = lgbm.LGBMClassifier(random_state=1, num_leaves = 6, n_estimators=5000, max_depth=3, learning_rate = 0.05, n_jobs=8)
sf.logfile = 'record.log'
sf.run(validation)
def main():
sf = FS.Select(Sequence=True, Random=True, Cross=True)
sf.ImportDF(prepareData(), label='Survived')
sf.ImportLossFunction(modelscore, direction='ascend')
sf.ImportCrossMethod(CrossMethod)
sf.NonTrainableFeatures = ['Survived']
sf.InitialFeatures([])
sf.PotentialAdd = ['Pclass']
# sf.clf = lgbm.LGBMClassifier(random_state=1, num_leaves = 6, n_estimators=5000, max_depth=3, learning_rate = 0.05, n_jobs=1)
sf.clf = LogisticRegression()
sf.logfile = 'record.log'
sf.run(validation)
def main():
PotentialAdd = ['min_query_time_gap_after', 'hour', 'shop_score_delivery', 'min_query_time_gap_before_user_item', 'shop_id_smooth_query_rate',
'min_query_time_gap_before', 'shop_score_description', 'item_sales_level', 'shop_query_count', 'user_star_level', 'user_age_level', 'item_sales_query_rate',
'item_query_count', 'shop_score_service', 'shop_review_positive_rate', 'item_price_level', 'min_query_time_gap_after_user_item']
'''
PotentialAdd = []
'''
sf = FS.Select(Sequence = True, Random = True, Cross = False, PotentialAdd = PotentialAdd) #select the way you want to process searching
sf.ImportDF(prepareData(),label = 'is_trade')
sf.ImportLossFunction(modelscore,direction = 'descend')
sf.ImportCrossMethod(CrossMethod)
sf.NonTrainableFeatures = ['instance_id', 'item_id', 'item_brand_id', 'item_city_id', 'user_id', 'context_id', 'shop_id', 'item_category_0', 'time',
'context_timestamp', 'item_property_list', 'predict_category_property',
'item_category_list', 'is_trade', 'day', ]
sf.InitialFeatures(['item_price_level', 'item_sales_level', 'item_collected_level', 'min_query_time_gap_after', 'min_query_time_gap_before_user_item',
'min_query_time_gap_after_user_item', 'hour', 'item_category_1', 'shop_score_service', 'user_age_level', 'user_star_level', 'context_page_id', 'min_query_time_gap_before',
'shop_query_count', 'item_sales_count'])
#sf.InitialFeatures(['item_price_level','item_sales_level','item_collected_level', 'item_pv_level'])
sf.clf = lgbm.LGBMClassifier(random_state=1, num_leaves = 6, n_estimators=5000, max_depth=3, learning_rate = 0.05, n_jobs=8)
sf.logfile = 'record.log'
sf.run(validation)
