blend.py 文件源码

def models():
    params = {'n_jobs':nthread,'random_state':seed,'class_weight':None}

    # extra = ensemble.ExtraTreesClassifier(n_estimators=1000,max_features='auto',criterion= 'entropy',min_samples_split= 2, max_depth= None, min_samples_leaf= 1, **params)
    # extra1 = ensemble.ExtraTreesClassifier(n_estimators=1000,max_features=60,criterion= 'gini',min_samples_split= 4, max_depth= 40, min_samples_leaf= 2, **params)

    # rf = ensemble.RandomForestClassifier(n_estimators=1000,max_features= 'auto',criterion= 'gini',min_samples_split= 2, max_depth= None, min_samples_leaf= 1, **params)
    # rf1 = ensemble.RandomForestClassifier(n_estimators=1000,max_features=60,criterion= 'entropy',min_samples_split= 4, max_depth= 40, min_samples_leaf= 2, **params)

    # xgb_binlog = XGBClassifier(objective="binary:logistic" ,max_depth=10, learning_rate=0.01, n_estimators=5,nthread=nthread, seed=seed)
    # xgb_reglog = XGBClassifier(objective="reg:logistic", max_depth=10, learning_rate=0.01, n_estimators=5,nthread=nthread, seed=seed)
    # xgb_poi = XGBClassifier(objective="count:poisson", max_depth=10, learning_rate=0.01, n_estimators=5,nthread=nthread, seed=seed)
    # xgb_reglin = XGBClassifier(objective="reg:linear", max_depth=10, learning_rate=0.01, n_estimators=5,nthread=nthread, seed=seed)

    rf_params = {'n_estimators':850,'max_features':60,'criterion':'entropy','min_samples_split': 4,'max_depth': 40, 'min_samples_leaf': 2, 'n_jobs': -1}

    clfs = [
        # (D1, XGBRegressor(objective="reg:linear", max_depth=6, learning_rate=0.01, subsample=.8, n_estimators=2000,nthread=nthread, seed=seed)),
        (D1, XGBClassifier(objective="binary:logistic" ,max_depth=6, learning_rate=0.01, subsample=.8, n_estimators=2000,nthread=nthread, seed=seed)),
        # (D1, XGBRegressor(objective="reg:linear", max_depth=5, learning_rate=0.01, subsample=.8, n_estimators=2000,nthread=nthread, seed=seed)),
        # (D1,XGBClassifier(objective="binary:logistic", max_depth=5, learning_rate=0.01, subsample=.8, n_estimators=2000,nthread=nthread, seed=seed)),
        # (D1, XGBRegressor(objective="reg:linear", max_depth=4, learning_rate=0.01, subsample=.8, n_estimators=2000,nthread=nthread, seed=seed)),
        # (D1,XGBClassifier(objective="binary:logistic", max_depth=4, learning_rate=0.01, subsample=.8, n_estimators=2000,nthread=nthread, seed=seed)),

    ]
    for clf in clfs:
        yield clf