FateWithShellmiaoComment/Fate/python/federatedml/feature/binning/bucket_binning.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

#
#  Copyright 2019 The FATE Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

from federatedml.feature.binning.base_binning import BaseBinning
from federatedml.statistic.statics import MultivariateStatisticalSummary
from federatedml.statistic import data_overview


class BucketBinning(BaseBinning):
    """
    For bucket binning, the length of each bin is the same which is:
    L = [max(x) - min(x)] / n

    The split points are min(x) + L * k
    where k is the index of a bin.
    """

    def fit_split_points(self, data_instances):
        """
        Apply the binning method

        Parameters
        ----------
        data_instances : Table
            The input data

        Returns
        -------
        split_points : dict.
            Each value represent for the split points for a feature. The element in each row represent for
            the corresponding split point.
            e.g.
            split_points = {'x1': [0.1, 0.2, 0.3, 0.4 ...],    # The first feature
                            'x2': [1, 2, 3, 4, ...],           # The second feature
                            ...]                         # Other features

        """
        header = data_overview.get_header(data_instances)
        anonymous_header = data_overview.get_anonymous_header(data_instances)
        self._default_setting(header, anonymous_header)

        # is_sparse = data_overview.is_sparse_data(data_instances)
        # if is_sparse:
        #     raise RuntimeError("Bucket Binning method has not supported sparse data yet.")

        # self._init_cols(data_instances)

        statistics = MultivariateStatisticalSummary(data_instances,
                                                    self.bin_inner_param.bin_indexes,
                                                    abnormal_list=self.abnormal_list)
        max_dict = statistics.get_max()
        min_dict = statistics.get_min()
        for col_name, max_value in max_dict.items():
            min_value = min_dict.get(col_name)
            split_points = []
            L = (max_value - min_value) / self.bin_num
            for k in range(self.bin_num - 1):
                s_p = min_value + (k + 1) * L
                split_points.append(s_p)
            split_points.append(max_value)
            # final_split_points[col_name] = split_point
            self.bin_results.put_col_split_points(col_name, split_points)
        self.fit_category_features(data_instances)
        return self.bin_results.all_split_points