

def __init__(self, uniform_variables, target_efficiency=0.5, n_neighbors=50, bagging=True, base_estimator=DecisionTreeClassifier(max_depth=1), n_estimators=50, learning_rate=1.0, uniforming_rate=1.0, train_variables=None, smoothing=0.0, keep_debug_info=False, random_state=None, uniform_label=1, algorithm='SAMME'):
    "\n        uBoostBDT is AdaBoostClassifier, which is modified to have flat\n        efficiency of signal (class=1) along some variables.\n        Efficiency is only guaranteed at the cut,\n        corresponding to global efficiency == target_efficiency.\n\n        Can be used alone, without uBoost.\n\n        Parameters\n        ----------\n        uniform_variables: list of strings, names of variables, along which\n         flatness is desired\n\n        target_efficiency: float, the flatness is obtained at global BDT cut,\n            corresponding to global efficiency\n\n        n_neighbours: int, (default=50) the number of neighbours,\n            which are used to compute local efficiency\n\n        bagging: float or bool (default=True), bagging usually speeds up the\n            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 set\n             is bagging * len(X)\n            if False, usual boosting is used\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, as well as proper\n            `classes_` and `n_classes_` attributes.\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        learning_rate : float, optional (default=1.)\n            Learning rate shrinks the contribution of each classifier by\n            ``learning_rate``. There is a trade-off between ``learning_rate``\n            and ``n_estimators``.\n\n        uniforming_rate: float, optional (default=1.)\n            how much do we take into account the uniformity of signal,\n             there is a trade-off between uniforming_rate and the speed of\n             uniforming, zero value corresponds to plain AdaBoost\n\n        uniform_label: int, (default=1)\n            label of class on which uniformity is desired\n\n        separate_normalization: bool (default=False),\n            if True, the sum of weights of both signal and background\n            will be normalized after each iteration to 0.5\n\n        train_variables: list of strings, names of variables used in\n           fit/predict. If None, all the variables are used\n           (including uniform_variables)\n\n        smoothing: float, default=(0.), used to smooth computing of local\n           efficiencies, 0.0 corresponds to usual uBoost\n\n        random_state: int, RandomState instance or None, (default=None)\n            If int, random_state is the seed used by the\n                random number generator;\n            If RandomState instance, random_state is the\n                random number generator;\n            If None, the random number generator is the RandomState\n            instance used by `np.random`.\n\n        Attributes\n        ----------\n        `estimators_` : list of classifiers\n            The collection of fitted sub-estimators.\n\n        `estimator_weights_` : array of floats\n            Weights for each estimator in the boosted ensemble.\n\n        `estimator_errors_` : array of floats\n            Classification error for each estimator in the boosted\n            ensemble.\n\n        Reference\n        ----------\n        .. [1] Justin Stevens, Mike Williams 'uBoost: A boosting method for\n            producing uniform selection efficiencies from multivariate classifiers'\n        "
    self.base_estimator = base_estimator
    self.n_estimators = n_estimators
    self.learning_rate = learning_rate
    train_variables.uniforming_rate = uniforming_rate
    self.uniform_variables = uniform_variables
    self.target_efficiency = target_efficiency
    self.n_neighbors = n_neighbors
    self.bagging = bagging
    self.train_variables = train_variables
    self.smoothing = smoothing
    self.uniform_label = uniform_label
    self.keep_debug_info = keep_debug_info
    self.random_state = random_state
    self.algorithm = algorithm

dataset/ETHPy150Open anaderi/lhcb_trigger_ml/hep_ml/uboost.py uBoostBDT.__init__/VarMisuse@89/1393 `self`->`train_variables`