

def __init__(self, uniform_variables=None, uniform_label=1, n_neighbors=50, efficiency_steps=20, n_estimators=40, base_estimator=DecisionTreeClassifier(max_depth=1), bagging=True, train_variables=None, algorithm='SAMME', smoothing=None, ipc_profile=None, random_state=None):
    "uBoost classifier, am algorithm of boosting targeted to obtain\n        flat efficiency in signal along some variables. See [1] for details.\n\n        Parameters\n        ----------\n        uniform_variables: list of strings, names of variables,\n            along which flatness is desired\n\n        uniform_label: int, (default=1)\n            tha label of class for which uniformity is desired\n\n        n_neighbours: int, (default=50) the number of neighbours,\n            which are used to compute local efficiency\n\n        n_estimators : integer, optional (default=50)\n            The maximum number of estimators at which boosting is terminated.\n            In case of perfect fit, the learning procedure is stopped early.\n\n        efficiency_steps: integer, optional (default=20),\n            How many uBoostBDTs should be trained\n            (each with its own target_efficiency)\n\n        base_estimator : object, optional (default=DecisionTreeClassifier)\n            The base estimator from which the boosted ensemble is built.\n            Support for sample weighting is required,\n            as well as proper `classes_` and `n_classes_` attributes.\n\n        bagging: float or bool (default=True), bagging usually\n            speeds up the convergence and prevents overfitting\n            (see http://en.wikipedia.org/wiki/Bootstrap_aggregating)\n            if True, usual bootstrap aggregating is used\n            (sampling with replacement at each iteration, size=len(X))\n            if float, used sampling with replacement, the size of generated\n               set is bagging * len(X)\n            if False, usual boosting is used\n\n        train_variables: list of strings,\n            names of variables used in fit/predict.\n            if None, all the variables are used (including uniform_variables)\n\n        smoothing: float, default=None, used to smooth computing of\n            local efficiencies, 0.0 corresponds to usual uBoost,\n\n        random_state : int, RandomState instance or None, (default=None)\n            optional\n            If int, random_state is the seed used by\n                the random number generator;\n            If RandomState instance, random_state is\n                the random number generator;\n            If None, the random number generator is the RandomState\n            instance used by `np.random`.\n\n        parallel_profile: profile (name of cluster) in IPython\n            to parallelize computations\n\n        Reference\n        ----------\n        .. [1] Justin Stevens, Mike Williams 'uBoost: A boosting method\n            for producing uniform\n            selection efficiencies from multivariate classifiers'\n        "
    self.uniform_variables = uniform_variables
    self.uniform_label = uniform_label
    self.knn = n_neighbors
    self.efficiency_steps = efficiency_steps
    self.random_state = random_state
    self.n_estimators = n_estimators
    self.base_estimator = base_estimator
    self.bagging = bagging
    self.train_variables = train_variables
    self.smoothing = smoothing
    self.ipc_profile = ipc_profile
    self.algorithm = algorithm

dataset/ETHPy150Open anaderi/lhcb_trigger_ml/hep_ml/uboost.py uBoostClassifier.__init__/original