#!/usr/bin/python
# encoding: utf-8


class model_wrapper(object):
    def __init__(self):
        pass
    def DecisionTreeClassifier(self, criterion='gini', splitter='best', max_depth=None, 
        min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, 
        random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, 
        class_weight=None, presort=False):
        '''
        criterion : 切分质量的评价准则
            string, optional (default="gini")
            The function to measure the quality of a split. Supported criteria are
            "gini" for the Gini impurity and "entropy" for the information gain.

        splitter : 指定切分原则
            string, optional (default="best")
            The strategy used to choose the split at each node. Supported
            strategies are "best" to choose the best split and "random" to choose
            the best random split.

        max_depth : int or None, optional (default=None)
            The maximum depth of the tree. If None, then nodes are expanded until
            all leaves are pure or until all leaves contain less than
            min_samples_split samples.

        min_samples_split : 每个内部节点（非叶子节点）包含的最少样本数
            int, float, optional (default=2)
            The minimum number of samples required to split an internal node:

            - If int, then consider `min_samples_split` as the minimum number.
            - If float, then `min_samples_split` is a percentage and
            `ceil(min_samples_split * n_samples)` are the minimum
            number of samples for each split.

            .. versionchanged:: 0.18
            Added float values for percentages.

        min_samples_leaf : 每个叶子节点包含的最小样本数
            int, float, optional (default=1)
            The minimum number of samples required to be at a leaf node:

            - If int, then consider `min_samples_leaf` as the minimum number.
            - If float, then `min_samples_leaf` is a percentage and
            `ceil(min_samples_leaf * n_samples)` are the minimum
            number of samples for each node.

            .. versionchanged:: 0.18
            Added float values for percentages.

        min_weight_fraction_leaf : float, optional (default=0.)
            The minimum weighted fraction of the sum total of weights (of all
            the input samples) required to be at a leaf node. Samples have
            equal weight when sample_weight is not provided.

        max_features : int, float, string or None, optional (default=None)
            The number of features to consider when looking for the best split:

                - If int, then consider `max_features` features at each split.
                - If float, then `max_features` is a percentage and
                `int(max_features * n_features)` features are considered at each
                split.
                - If "auto", then `max_features=sqrt(n_features)`.
                - If "sqrt", then `max_features=sqrt(n_features)`.
                - If "log2", then `max_features=log2(n_features)`.
                - If None, then `max_features=n_features`.

            Note: the search for a split does not stop until at least one
            valid partition of the node samples is found, even if it requires to
            effectively inspect more than ``max_features`` features.

random_state : int, RandomState instance or None, optional (default=None)
    If int, random_state is the seed used by the random number generator;
    If RandomState instance, random_state is the random number generator;
    If None, the random number generator is the RandomState instance used
    by `np.random`.

max_leaf_nodes : int or None, optional (default=None)
    Grow a tree with ``max_leaf_nodes`` in best-first fashion.
    Best nodes are defined as relative reduction in impurity.
    If None then unlimited number of leaf nodes.

min_impurity_decrease : float, optional (default=0.)
    A node will be split if this split induces a decrease of the impurity
    greater than or equal to this value.

    The weighted impurity decrease equation is the following::

        N_t / N * (impurity - N_t_R / N_t * right_impurity
                            - N_t_L / N_t * left_impurity)

    where ``N`` is the total number of samples, ``N_t`` is the number of
    samples at the current node, ``N_t_L`` is the number of samples in the
    left child, and ``N_t_R`` is the number of samples in the right child.

    ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
    if ``sample_weight`` is passed.

    .. versionadded:: 0.19

min_impurity_split : float,
    Threshold for early stopping in tree growth. A node will split
    if its impurity is above the threshold, otherwise it is a leaf.

    .. deprecated:: 0.19
       ``min_impurity_split`` has been deprecated in favor of
       ``min_impurity_decrease`` in 0.19 and will be removed in 0.21.
       Use ``min_impurity_decrease`` instead.

class_weight : dict, list of dicts, "balanced" or None, default=None
    Weights associated with classes in the form ``{class_label: weight}``.
    If not given, all classes are supposed to have weight one. For
    multi-output problems, a list of dicts can be provided in the same
    order as the columns of y.

    Note that for multioutput (including multilabel) weights should be
    defined for each class of every column in its own dict. For example,
    for four-class multilabel classification weights should be
    [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
    [{1:1}, {2:5}, {3:1}, {4:1}].

    The "balanced" mode uses the values of y to automatically adjust
    weights inversely proportional to class frequencies in the input data
    as ``n_samples / (n_classes * np.bincount(y))``

    For multi-output, the weights of each column of y will be multiplied.

    Note that these weights will be multiplied with sample_weight (passed
    through the fit method) if sample_weight is specified.

presort : bool, optional (default=False)
    Whether to presort the data to speed up the finding of best splits in
    fitting. For the default settings of a decision tree on large
    datasets, setting this to true may slow down the training process.
    When using either a smaller dataset or a restricted depth, this may
    speed up the training.
        '''
        from sklearn.tree import DecisionTreeClassifier
        return DecisionTreeClassifier(criterion, splitter, max_depth, 
            min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, 
            random_state, max_leaf_nodes, min_impurity_decrease, min_impurity_split, 
            class_weight, presort)
    def RandomForestClassifier(self,n_estimators=10, criterion='gini', 
        max_depth=None, min_samples_split=2, 
        min_samples_leaf=1, min_weight_fraction_leaf=0.0, 
        max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, 
        min_impurity_split=None, bootstrap=True, oob_score=False, 
        n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None):
        '''
        随机森林分类器
        n_estimators：决策树的数量
        criterion：string, optional (default="gini")
            The function to measure the quality of a split. Supported criteria are
            "gini" for the Gini impurity and "entropy" for the information gain.
        max_features : 每科决策树包含的特征变量的个数
            int, float, string or None, optional (default="auto")
            The number of features to consider when looking for the best split:
            - If int, then consider `max_features` features at each split.
            - If float, then `max_features` is a percentage and
            `int(max_features * n_features)` features are considered at each
            split.
            - If "auto", then `max_features=sqrt(n_features)`.
            - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
            - If "log2", then `max_features=log2(n_features)`.
            - If None, then `max_features=n_features`.
            Note: the search for a split does not stop until at least one
            valid partition of the node samples is found, even if it requires to
            effectively inspect more than ``max_features`` features.

        max_depth : integer or None, optional (default=None)
            The maximum depth of the tree. If None, then nodes are expanded until
            all leaves are pure or until all leaves contain less than
            min_samples_split samples.

        min_samples_split : int, float, optional (default=2)
            The minimum number of samples required to split an internal node:

            - If int, then consider `min_samples_split` as the minimum number.
            - If float, then `min_samples_split` is a percentage and
            `ceil(min_samples_split * n_samples)` are the minimum
            number of samples for each split.

            .. versionchanged:: 0.18
            Added float values for percentages.

        min_samples_leaf : int, float, optional (default=1)
            The minimum number of samples required to be at a leaf node:

            - If int, then consider `min_samples_leaf` as the minimum number.
            - If float, then `min_samples_leaf` is a percentage and
            `ceil(min_samples_leaf * n_samples)` are the minimum
            number of samples for each node.

            .. versionchanged:: 0.18
            Added float values for percentages.

        min_weight_fraction_leaf : float, optional (default=0.)
            The minimum weighted fraction of the sum total of weights (of all
            the input samples) required to be at a leaf node. Samples have
            equal weight when sample_weight is not provided.

        max_leaf_nodes : int or None, optional (default=None)
            Grow trees with ``max_leaf_nodes`` in best-first fashion.
            Best nodes are defined as relative reduction in impurity.
            If None then unlimited number of leaf nodes.

        min_impurity_split : float,
            Threshold for early stopping in tree growth. A node will split
            if its impurity is above the threshold, otherwise it is a leaf.

            .. deprecated:: 0.19
            ``min_impurity_split`` has been deprecated in favor of
            ``min_impurity_decrease`` in 0.19 and will be removed in 0.21.
            Use ``min_impurity_decrease`` instead.

        min_impurity_decrease : float, optional (default=0.)
            A node will be split if this split induces a decrease of the impurity
            greater than or equal to this value.

            The weighted impurity decrease equation is the following::

                N_t / N * (impurity - N_t_R / N_t * right_impurity
                                    - N_t_L / N_t * left_impurity)

            where ``N`` is the total number of samples, ``N_t`` is the number of
            samples at the current node, ``N_t_L`` is the number of samples in the
            left child, and ``N_t_R`` is the number of samples in the right child.

            ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
            if ``sample_weight`` is passed.

            .. versionadded:: 0.19

        bootstrap : boolean, optional (default=True)
            Whether bootstrap samples are used when building trees.

        oob_score : 是否使用袋外数据计算准确率
            bool (default=False)
            Whether to use out-of-bag samples to estimate the generalization accuracy.

        n_jobs : integer, optional (default=1)
            The number of jobs to run in parallel for both `fit` and `predict`.
            If -1, then the number of jobs is set to the number of cores.

        random_state : int, RandomState instance or None, optional (default=None)
            If int, random_state is the seed used by the random number generator;
            If RandomState instance, random_state is the random number generator;
            If None, the random number generator is the RandomState instance used
            by `np.random`.

        verbose : int, optional (default=0)
            Controls the verbosity of the tree building process.

        warm_start : bool, optional (default=False)
            When set to ``True``, reuse the solution of the previous call to fit
            and add more estimators to the ensemble, otherwise, just fit a whole
            new forest.

        class_weight : dict, list of dicts, "balanced",
            "balanced_subsample" or None, optional (default=None)
            Weights associated with classes in the form ``{class_label: weight}``.
            If not given, all classes are supposed to have weight one. For
            multi-output problems, a list of dicts can be provided in the same
            order as the columns of y.

            Note that for multioutput (including multilabel) weights should be
            defined for each class of every column in its own dict. For example,
            for four-class multilabel classification weights should be
            [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
            [{1:1}, {2:5}, {3:1}, {4:1}].

            The "balanced" mode uses the values of y to automatically adjust
            weights inversely proportional to class frequencies in the input data
            as ``n_samples / (n_classes * np.bincount(y))``

            The "balanced_subsample" mode is the same as "balanced" except that
            weights are computed based on the bootstrap sample for every tree
            grown.

            For multi-output, the weights of each column of y will be multiplied.

            Note that these weights will be multiplied with sample_weight (passed
            through the fit method) if sample_weight is specified.
        '''
        from sklearn.ensemble import RandomForestClassifier
        return RandomForestClassifier(n_estimators, criterion, 
            max_depth, min_samples_split, 
            min_samples_leaf, min_weight_fraction_leaf, 
            max_features, max_leaf_nodes, min_impurity_decrease, 
            min_impurity_split, bootstrap, oob_score, 
            n_jobs, random_state, verbose, warm_start, class_weight)

