def load_model(self, model_dict):
model_param = list(model_dict.get('model').values())[0].get(MODEL_PARAM_NAME)
model_meta = list(model_dict.get('model').values())[0].get(MODEL_META_NAME)
self.bin_inner_param = BinInnerParam()
assert isinstance(model_meta, feature_binning_meta_pb2.FeatureBinningMeta)
assert isinstance(model_param, feature_binning_param_pb2.FeatureBinningParam)
self.header = list(model_param.header)
self.bin_inner_param.set_header(self.header)
self.bin_inner_param.add_transform_bin_indexes(list(model_meta.transform_param.transform_cols))
self.bin_inner_param.add_bin_names(list(model_meta.cols))
self.transform_type = model_meta.transform_param.transform_type
bin_method = str(model_meta.method)
if bin_method == consts.QUANTILE:
self.binning_obj = QuantileBinning(params=model_meta)
else:
self.binning_obj = BucketBinning(params=model_meta)
self.binning_obj.set_role_party(self.role, self.component_properties.local_partyid)
self.binning_obj.set_bin_inner_param(self.bin_inner_param)
self.binning_obj.bin_results.reconstruct(model_param.binning_result)
self.host_results = []
for host_pb in model_param.host_results:
host_bin_obj = BaseBinning()
host_bin_obj.bin_results.reconstruct(host_pb)
self.host_results.append(host_bin_obj)
def _setup_bin_inner_param(self, data_instances, params):
if self.bin_inner_param is not None:
return
self.bin_inner_param = BinInnerParam()
header = get_header(data_instances)
LOGGER.debug("_setup_bin_inner_param, get header length: {}".format(
len(self.header)))
self.schema = data_instances.schema
self.bin_inner_param.set_header(header)
if params.bin_indexes == -1:
self.bin_inner_param.set_bin_all()
else:
self.bin_inner_param.add_bin_indexes(params.bin_indexes)
self.bin_inner_param.add_bin_names(params.bin_names)
self.bin_inner_param.add_category_indexes(params.category_indexes)
self.bin_inner_param.add_category_names(params.category_names)
if params.transform_param.transform_cols == -1:
self.bin_inner_param.set_transform_all()
else:
self.bin_inner_param.add_transform_bin_indexes(
params.transform_param.transform_cols)
self.bin_inner_param.add_transform_bin_names(
params.transform_param.transform_names)
self.set_bin_inner_param(self.bin_inner_param)
def __init__(self):
super(BaseHeteroFeatureBinning, self).__init__()
self.transfer_variable = HeteroFeatureBinningTransferVariable()
self.binning_obj = None
self.header = None
self.schema = None
self.host_results = []
self.transform_type = None
self.model_param = FeatureBinningParam()
self.bin_inner_param = BinInnerParam()
def _default_setting(self, header):
if self.bin_inner_param is not None:
return
bin_inner_param = BinInnerParam()
bin_inner_param.set_header(header)
bin_inner_param.set_bin_all()
bin_inner_param.set_transform_all()
self.set_bin_inner_param(bin_inner_param)
def __init__(self):
super(BaseFeatureBinning, self).__init__()
self.transfer_variable = HeteroFeatureBinningTransferVariable()
self.binning_obj: BaseBinning = None
self.header = None
self.header_anonymous = None
self.schema = None
self.host_results = []
self.transform_type = None
self.model_param = FeatureBinningParam()
self.bin_inner_param = BinInnerParam()
self.bin_result = MultiClassBinResult(labels=[0, 1])
self.has_missing_value = False
self.labels = []
def _default_setting(self, header):
if self.bin_inner_param is not None:
return
self.bin_inner_param = BinInnerParam()
self.bin_inner_param.set_header(header)
if self.params.bin_indexes == -1:
self.bin_inner_param.set_bin_all()
else:
self.bin_inner_param.add_bin_indexes(self.params.bin_indexes)
self.bin_inner_param.add_bin_names(self.params.bin_names)
self.bin_inner_param.add_category_indexes(self.params.category_indexes)
self.bin_inner_param.add_category_names(self.params.category_names)
if self.params.transform_param.transform_cols == -1:
self.bin_inner_param.set_transform_all()
else:
self.bin_inner_param.add_transform_bin_indexes(self.params.transform_param.transform_cols)
self.bin_inner_param.add_transform_bin_names(self.params.transform_param.transform_names)
class BaseBinning(object):
"""
This is use for discrete data so that can transform data or use information for feature selection.
"""
def __init__(self, params=None, abnormal_list=None, labels=None):
self.bin_inner_param: BinInnerParam = None
self.is_multi_class = False
self.bin_results = SplitPointsResult()
if params is None:
return
self.params = params
self.bin_num = params.bin_num
if abnormal_list is None:
self.abnormal_list = []
else:
self.abnormal_list = abnormal_list
self.split_points = None
@property
def header(self):
return self.bin_inner_param.header
# @property
# def split_points(self):
# return self.bin_results.all_split_points
def _default_setting(self, header):
if self.bin_inner_param is not None:
return
self.bin_inner_param = BinInnerParam()
self.bin_inner_param.set_header(header)
if self.params.bin_indexes == -1:
self.bin_inner_param.set_bin_all()
else:
self.bin_inner_param.add_bin_indexes(self.params.bin_indexes)
self.bin_inner_param.add_bin_names(self.params.bin_names)
self.bin_inner_param.add_category_indexes(self.params.category_indexes)
self.bin_inner_param.add_category_names(self.params.category_names)
if self.params.transform_param.transform_cols == -1:
self.bin_inner_param.set_transform_all()
else:
self.bin_inner_param.add_transform_bin_indexes(
self.params.transform_param.transform_cols)
self.bin_inner_param.add_transform_bin_names(
self.params.transform_param.transform_names)
def fit_split_points(self, data_instances):
"""
Get split points
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
"""
pass
def fit_category_features(self, data_instances):
is_sparse = data_overview.is_sparse_data(data_instances)
if len(self.bin_inner_param.category_indexes) > 0:
statics_obj = data_overview.DataStatistics()
category_col_values = statics_obj.static_all_values(
data_instances, self.bin_inner_param.category_indexes,
is_sparse)
for col_name, split_points in zip(
self.bin_inner_param.category_names, category_col_values):
self.bin_results.put_col_split_points(col_name, split_points)
def set_bin_inner_param(self, bin_inner_param):
self.bin_inner_param = bin_inner_param
def transform(self, data_instances, transform_type):
# self._init_cols(data_instances)
for col_name in self.bin_inner_param.transform_bin_names:
if col_name not in self.header:
raise ValueError(
"Transform col_name: {} is not existed".format(col_name))
if transform_type == 'bin_num':
data_instances, _, _ = self.convert_feature_to_bin(data_instances)
elif transform_type == 'woe':
data_instances = self.convert_feature_to_woe(data_instances)
return data_instances
@staticmethod
def get_data_bin(data_instances, split_points, bin_cols_map):
"""
Apply the binning method
Parameters
----------
data_instances : Table
The input data
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
Returns
-------
data_bin_table : Table.
Each element represent for the corresponding bin number this feature belongs to.
e.g. it could be:
[{'x1': 1, 'x2': 5, 'x3': 2}
...
]
"""
# self._init_cols(data_instances)
is_sparse = data_overview.is_sparse_data(data_instances)
header = data_instances.schema.get('header')
f = functools.partial(BaseBinning.bin_data,
split_points=split_points,
cols_dict=bin_cols_map,
header=header,
is_sparse=is_sparse)
data_bin_dict = data_instances.mapValues(f)
return data_bin_dict
def convert_feature_to_woe(self, data_instances):
is_sparse = data_overview.is_sparse_data(data_instances)
schema = data_instances.schema
if is_sparse:
f = functools.partial(self._convert_sparse_data,
bin_inner_param=self.bin_inner_param,
bin_results=self.bin_results,
abnormal_list=self.abnormal_list,
convert_type='woe')
new_data = data_instances.mapValues(f)
else:
f = functools.partial(self._convert_dense_data,
bin_inner_param=self.bin_inner_param,
bin_results=self.bin_results,
abnormal_list=self.abnormal_list,
convert_type='woe')
new_data = data_instances.mapValues(f)
new_data.schema = schema
return new_data
def convert_feature_to_bin(self, data_instances, split_points=None):
is_sparse = data_overview.is_sparse_data(data_instances)
schema = data_instances.schema
if split_points is None:
split_points = self.bin_results.all_split_points
else:
for col_name, sp in split_points.items():
self.bin_results.put_col_split_points(col_name, sp)
if is_sparse:
f = functools.partial(self._convert_sparse_data,
bin_inner_param=self.bin_inner_param,
bin_results=self.bin_results,
abnormal_list=self.abnormal_list,
convert_type='bin_num')
new_data = data_instances.mapValues(f)
else:
f = functools.partial(self._convert_dense_data,
bin_inner_param=self.bin_inner_param,
bin_results=self.bin_results,
abnormal_list=self.abnormal_list,
convert_type='bin_num')
new_data = data_instances.mapValues(f)
new_data.schema = schema
header = get_header(data_instances)
bin_sparse = self.get_sparse_bin(
self.bin_inner_param.transform_bin_indexes, split_points, header)
split_points_result = self.bin_results.get_split_points_array(
self.bin_inner_param.transform_bin_names)
return new_data, split_points_result, bin_sparse
def _setup_bin_inner_param(self, data_instances, params):
if self.bin_inner_param is not None:
return
self.bin_inner_param = BinInnerParam()
header = get_header(data_instances)
LOGGER.debug("_setup_bin_inner_param, get header length: {}".format(
len(self.header)))
self.schema = data_instances.schema
self.bin_inner_param.set_header(header)
if params.bin_indexes == -1:
self.bin_inner_param.set_bin_all()
else:
self.bin_inner_param.add_bin_indexes(params.bin_indexes)
self.bin_inner_param.add_bin_names(params.bin_names)
self.bin_inner_param.add_category_indexes(params.category_indexes)
self.bin_inner_param.add_category_names(params.category_names)
if params.transform_param.transform_cols == -1:
self.bin_inner_param.set_transform_all()
else:
self.bin_inner_param.add_transform_bin_indexes(
params.transform_param.transform_cols)
self.bin_inner_param.add_transform_bin_names(
params.transform_param.transform_names)
self.set_bin_inner_param(self.bin_inner_param)
@staticmethod
def _convert_sparse_data(instances,
bin_inner_param: BinInnerParam,
bin_results: SplitPointsResult,
abnormal_list: list,
convert_type: str = 'bin_num'):
instances = copy.deepcopy(instances)
all_data = instances.features.get_all_data()
data_shape = instances.features.get_shape()
indice = []
sparse_value = []
transform_cols_idx = bin_inner_param.transform_bin_indexes
split_points_dict = bin_results.all_split_points
for col_idx, col_value in all_data:
if col_idx in transform_cols_idx:
if col_value in abnormal_list:
indice.append(col_idx)
sparse_value.append(col_value)
continue
# Maybe it is because missing value add in sparse value, but
col_name = bin_inner_param.header[col_idx]
split_points = split_points_dict[col_name]
bin_num = BaseBinning.get_bin_num(col_value, split_points)
indice.append(col_idx)
if convert_type == 'bin_num':
sparse_value.append(bin_num)
else:
sparse_value.append(col_value)
else:
indice.append(col_idx)
sparse_value.append(col_value)
sparse_vector = SparseVector(indice, sparse_value, data_shape)
instances.features = sparse_vector
return instances
@staticmethod
def get_sparse_bin(transform_cols_idx, split_points_dict, header):
"""
Get which bins the 0 located at for each column.
Returns
-------
Dict of sparse bin num
{0: 2, 1: 3, 2:5 ... }
"""
result = {}
for col_idx in transform_cols_idx:
col_name = header[col_idx]
split_points = split_points_dict[col_name]
sparse_bin_num = BaseBinning.get_bin_num(0, split_points)
result[col_idx] = sparse_bin_num
return result
@staticmethod
def _convert_dense_data(instances,
bin_inner_param: BinInnerParam,
bin_results: SplitPointsResult,
abnormal_list: list,
convert_type: str = 'bin_num'):
instances = copy.deepcopy(instances)
features = instances.features
transform_cols_idx = bin_inner_param.transform_bin_indexes
split_points_dict = bin_results.all_split_points
transform_cols_idx_set = set(transform_cols_idx)
for col_idx, col_value in enumerate(features):
if col_idx in transform_cols_idx_set:
if col_value in abnormal_list:
features[col_idx] = col_value
continue
col_name = bin_inner_param.header[col_idx]
split_points = split_points_dict[col_name]
bin_num = BaseBinning.get_bin_num(col_value, split_points)
if convert_type == 'bin_num':
features[col_idx] = bin_num
else:
features[col_idx] = col_value
instances.features = features
return instances
@staticmethod
def convert_bin_counts_table(result_counts, idx):
"""
Given event count information calculate iv information
Parameters
----------
result_counts: table.
It is like:
('x1': [[label_0_count, label_1_count, ...], [label_0_count, label_1_count, ...] ... ],
'x2': [[label_0_count, label_1_count, ...], [label_0_count, label_1_count, ...] ... ],
...
)
idx: int
Returns
-------
('x1': [[event_count, non_event_count], [event_count, non_event_count] ... ],
'x2': [[event_count, non_event_count], [event_count, non_event_count] ... ],
...
)
"""
def _convert(list_counts):
res = []
for c_array in list_counts:
event_count = c_array[idx]
non_event_count = np.sum(c_array) - event_count
res.append([event_count, non_event_count])
return res
return result_counts.mapValues(_convert)
@staticmethod
def fill_sparse_result(col_name, static_nums, sparse_bin_points,
label_counts):
"""
Parameters
----------
static_nums : list.
It is like:
[[event_count, total_num], [event_count, total_num] ... ]
sparse_bin_points : dict
Dict of sparse bin num
{"x1": 2, "x2": 3, "x3": 5 ... }
label_counts: np.array
eg. [100, 200, ...]
Returns
-------
The format is same as result_counts.
"""
curt_all = functools.reduce(lambda x, y: x + y, static_nums)
sparse_bin = sparse_bin_points.get(col_name)
static_nums[sparse_bin] = label_counts - curt_all
return col_name, static_nums
@staticmethod
def bin_data(instance, split_points, cols_dict, header, is_sparse):
"""
Apply the binning method
Parameters
----------
instance : Table
The input data
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
cols_dict: dict
Record key, value pairs where key is cols' name, and value is cols' index.
header: list
header of Table
is_sparse: bool
Specify whether it is sparse data or not
Returns
-------
result_bin_dict : dict.
Each element represent for the corresponding bin number this feature belongs to.
e.g. it could be:
[{1: 1, 2: 5, 3: 2}
...
] # Each number represent for the bin number it belongs to.
"""
result_bin_nums = {}
if is_sparse:
sparse_data = instance.features.get_all_data()
for col_idx, col_value in sparse_data:
col_name = header[col_idx]
if col_name in cols_dict:
col_split_points = split_points[col_name]
col_bin_num = BaseBinning.get_bin_num(
col_value, col_split_points)
result_bin_nums[col_name] = col_bin_num
return result_bin_nums
# For dense data
for col_name, col_index in cols_dict.items():
col_split_points = split_points[col_name]
value = instance.features[col_index]
col_bin_num = BaseBinning.get_bin_num(value, col_split_points)
result_bin_nums[col_name] = col_bin_num
return result_bin_nums
@staticmethod
def get_bin_num(value, split_points):
if np.isnan(value):
return len(split_points)
sp = split_points[:-1]
col_bin_num = bisect.bisect_left(sp, value)
# col_bin_num = bisect.bisect_left(split_points, value)
return col_bin_num
@staticmethod
def add_label_in_partition_bak(data_bin_with_table, sparse_bin_points):
"""
Add all label, so that become convenient to calculate woe and iv
Parameters
----------
data_bin_with_table : Table
The input data, the Table is like:
(id, {'x1': 1, 'x2': 5, 'x3': 2}, y)
sparse_bin_points: dict
Dict of sparse bin num
{0: 2, 1: 3, 2:5 ... }
Returns
-------
result_sum: the result Table. It is like:
{'x1': [[event_count, total_num], [event_count, total_num] ... ],
'x2': [[event_count, total_num], [event_count, total_num] ... ],
...
}
"""
result_sum = {}
for _, datas in data_bin_with_table:
bin_idx_dict = datas[0]
y = datas[1]
# y = y_combo[0]
# inverse_y = y_combo[1]
for col_name, bin_idx in bin_idx_dict.items():
result_sum.setdefault(col_name, [])
col_sum = result_sum[col_name]
while bin_idx >= len(col_sum):
col_sum.append([0, 0])
if bin_idx == sparse_bin_points[col_name]:
continue
label_sum = col_sum[bin_idx]
label_sum[0] = label_sum[0] + y
label_sum[1] = label_sum[1] + 1
col_sum[bin_idx] = label_sum
result_sum[col_name] = col_sum
return list(result_sum.items())
class BaseBinning(object):
"""
This is use for discrete data so that can transform data or use information for feature selection.
"""
def __init__(self, params=None, abnormal_list=None):
self.bin_inner_param: BinInnerParam = None
self.bin_results = BinResults()
if params is None:
return
self.params = params
self.bin_num = params.bin_num
self.event_total = None
self.non_event_total = None
if abnormal_list is None:
self.abnormal_list = []
else:
self.abnormal_list = abnormal_list
def set_role_party(self, role, party_id):
self.bin_results.set_role_party(role, party_id)
@property
def header(self):
return self.bin_inner_param.header
@property
def split_points(self):
return self.bin_results.all_split_points
def _default_setting(self, header):
if self.bin_inner_param is not None:
return
bin_inner_param = BinInnerParam()
bin_inner_param.set_header(header)
bin_inner_param.set_bin_all()
bin_inner_param.set_transform_all()
self.set_bin_inner_param(bin_inner_param)
def fit_split_points(self, data_instances):
"""
Get split points
Parameters
----------
data_instances : DTable
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
"""
pass
def fit_category_features(self, data_instances):
is_sparse = data_overview.is_sparse_data(data_instances)
if len(self.bin_inner_param.category_indexes) > 0:
statics_obj = data_overview.DataStatistics()
category_col_values = statics_obj.static_all_values(
data_instances, self.bin_inner_param.category_indexes,
is_sparse)
for col_name, split_points in zip(
self.bin_inner_param.category_names, category_col_values):
self.bin_results.put_col_split_points(col_name, split_points)
def set_bin_inner_param(self, bin_inner_param):
self.bin_inner_param = bin_inner_param
def transform(self, data_instances, transform_type):
# self._init_cols(data_instances)
for col_name in self.bin_inner_param.transform_bin_names:
if col_name not in self.header:
raise ValueError(
"Transform col_name: {} is not existed".format(col_name))
if transform_type == 'bin_num':
data_instances, _, _ = self.convert_feature_to_bin(data_instances)
elif transform_type == 'woe':
data_instances = self.convert_feature_to_woe(data_instances)
return data_instances
def get_data_bin(self, data_instances, split_points=None):
"""
Apply the binning method
Parameters
----------
data_instances : DTable
The input data
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
Returns
-------
data_bin_table : DTable.
Each element represent for the corresponding bin number this feature belongs to.
e.g. it could be:
[{'x1': 1, 'x2': 5, 'x3': 2}
...
]
"""
# self._init_cols(data_instances)
is_sparse = data_overview.is_sparse_data(data_instances)
if split_points is None:
split_points = self.fit_split_points(data_instances)
f = functools.partial(self.bin_data,
split_points=split_points,
cols_dict=self.bin_inner_param.bin_cols_map,
header=self.header,
is_sparse=is_sparse)
data_bin_dict = data_instances.mapValues(f)
return data_bin_dict
def convert_feature_to_woe(self, data_instances):
is_sparse = data_overview.is_sparse_data(data_instances)
schema = data_instances.schema
if is_sparse:
f = functools.partial(self._convert_sparse_data,
bin_inner_param=self.bin_inner_param,
bin_results=self.bin_results,
abnormal_list=self.abnormal_list,
convert_type='woe')
new_data = data_instances.mapValues(f)
else:
f = functools.partial(self._convert_dense_data,
bin_inner_param=self.bin_inner_param,
bin_results=self.bin_results,
abnormal_list=self.abnormal_list,
convert_type='woe')
new_data = data_instances.mapValues(f)
new_data.schema = schema
return new_data
def convert_feature_to_bin(self, data_instances, split_points=None):
is_sparse = data_overview.is_sparse_data(data_instances)
schema = data_instances.schema
if split_points is None:
split_points = self.bin_results.all_split_points
else:
for col_name, sp in split_points.items():
self.bin_results.put_col_split_points(col_name, sp)
if is_sparse:
f = functools.partial(self._convert_sparse_data,
bin_inner_param=self.bin_inner_param,
bin_results=self.bin_results,
abnormal_list=self.abnormal_list,
convert_type='bin_num')
new_data = data_instances.mapValues(f)
else:
f = functools.partial(self._convert_dense_data,
bin_inner_param=self.bin_inner_param,
bin_results=self.bin_results,
abnormal_list=self.abnormal_list,
convert_type='bin_num')
new_data = data_instances.mapValues(f)
new_data.schema = schema
bin_sparse = self.get_sparse_bin(
self.bin_inner_param.transform_bin_indexes, split_points)
split_points_result = self.bin_results.get_split_points_array(
self.bin_inner_param.transform_bin_names)
return new_data, split_points_result, bin_sparse
def _setup_bin_inner_param(self, data_instances, params):
if self.bin_inner_param is not None:
return
self.bin_inner_param = BinInnerParam()
header = get_header(data_instances)
LOGGER.debug("_setup_bin_inner_param, get header length: {}".format(
len(self.header)))
self.schema = data_instances.schema
self.bin_inner_param.set_header(header)
if params.bin_indexes == -1:
self.bin_inner_param.set_bin_all()
else:
self.bin_inner_param.add_bin_indexes(params.bin_indexes)
self.bin_inner_param.add_bin_names(params.bin_names)
self.bin_inner_param.add_category_indexes(params.category_indexes)
self.bin_inner_param.add_category_names(params.category_names)
if params.transform_param.transform_cols == -1:
self.bin_inner_param.set_transform_all()
else:
self.bin_inner_param.add_transform_bin_indexes(
params.transform_param.transform_cols)
self.bin_inner_param.add_transform_bin_names(
params.transform_param.transform_names)
self.set_bin_inner_param(self.bin_inner_param)
@staticmethod
def _convert_sparse_data(instances,
bin_inner_param: BinInnerParam,
bin_results: BinResults,
abnormal_list: list,
convert_type: str = 'bin_num'):
instances = copy.deepcopy(instances)
all_data = instances.features.get_all_data()
data_shape = instances.features.get_shape()
indice = []
sparse_value = []
transform_cols_idx = bin_inner_param.transform_bin_indexes
split_points_dict = bin_results.all_split_points
for col_idx, col_value in all_data:
if col_idx in transform_cols_idx:
if col_value in abnormal_list:
indice.append(col_idx)
sparse_value.append(col_value)
continue
# Maybe it is because missing value add in sparse value, but
col_name = bin_inner_param.header[col_idx]
split_points = split_points_dict[col_name]
bin_num = BaseBinning.get_bin_num(col_value, split_points)
indice.append(col_idx)
if convert_type == 'bin_num':
sparse_value.append(bin_num)
elif convert_type == 'woe':
col_results = bin_results.all_cols_results.get(col_name)
woe_value = col_results.woe_array[bin_num]
sparse_value.append(woe_value)
else:
sparse_value.append(col_value)
else:
indice.append(col_idx)
sparse_value.append(col_value)
sparse_vector = SparseVector(indice, sparse_value, data_shape)
instances.features = sparse_vector
return instances
def get_sparse_bin(self, transform_cols_idx, split_points_dict):
"""
Get which bins the 0 located at for each column.
Returns
-------
Dict of sparse bin num
{0: 2, 1: 3, 2:5 ... }
"""
result = {}
for col_idx in transform_cols_idx:
col_name = self.header[col_idx]
split_points = split_points_dict[col_name]
sparse_bin_num = self.get_bin_num(0, split_points)
result[col_idx] = sparse_bin_num
return result
@staticmethod
def _convert_dense_data(instances,
bin_inner_param: BinInnerParam,
bin_results: BinResults,
abnormal_list: list,
convert_type: str = 'bin_num'):
instances = copy.deepcopy(instances)
features = instances.features
transform_cols_idx = bin_inner_param.transform_bin_indexes
split_points_dict = bin_results.all_split_points
transform_cols_idx_set = set(transform_cols_idx)
for col_idx, col_value in enumerate(features):
if col_idx in transform_cols_idx_set:
if col_value in abnormal_list:
features[col_idx] = col_value
continue
col_name = bin_inner_param.header[col_idx]
split_points = split_points_dict[col_name]
bin_num = BaseBinning.get_bin_num(col_value, split_points)
if convert_type == 'bin_num':
features[col_idx] = bin_num
elif convert_type == 'woe':
col_results = bin_results.all_cols_results.get(col_name)
woe_value = col_results.woe_array[bin_num]
features[col_idx] = woe_value
else:
features[col_idx] = col_value
instances.features = features
return instances
def cal_local_iv(self,
data_instances,
label_counts,
split_points=None,
label_table=None):
"""
Calculate iv attributes
Parameters
----------
data_instances : DTable
The input data
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
label_table : DTable
id with labels
Returns
-------
Dict of IVAttributes object
"""
# self._init_cols(data_instances)
if split_points is None and self.split_points is None:
split_points = self.fit_split_points(data_instances)
elif split_points is None:
split_points = self.split_points
data_bin_table = self.get_data_bin(data_instances, split_points)
sparse_bin_points = self.get_sparse_bin(
self.bin_inner_param.bin_indexes, self.split_points)
sparse_bin_points = {
self.bin_inner_param.header[k]: v
for k, v in sparse_bin_points.items()
}
if label_table is None:
label_table = data_instances.mapValues(lambda x: x.label)
# event_count_table = label_table.mapValues(lambda x: (x, 1 - x))
data_bin_with_label = data_bin_table.join(label_table, lambda x, y:
(x, y))
f = functools.partial(self.add_label_in_partition,
sparse_bin_points=sparse_bin_points)
result_counts = data_bin_with_label.mapReducePartitions(
f, self.aggregate_partition_label)
def cal_zeros(bin_results):
for b in bin_results:
b[1] = b[1] - b[0]
return bin_results
result_counts = result_counts.mapValues(cal_zeros)
f = functools.partial(self.fill_sparse_result,
sparse_bin_points=sparse_bin_points,
label_counts=label_counts)
result_counts = result_counts.map(f)
self.cal_iv_woe(result_counts, self.params.adjustment_factor)
@staticmethod
def fill_sparse_result(col_name, static_nums, sparse_bin_points,
label_counts):
"""
Parameters
----------
static_nums : list.
It is like:
[[event_count, total_num], [event_count, total_num] ... ]
sparse_bin_points : dict
Dict of sparse bin num
{"x1": 2, "x2": 3, "x3": 5 ... }
label_counts: dict
eg. {0: 100, 1: 200}
Returns
-------
The format is same as result_counts.
"""
curt_all = functools.reduce(lambda x, y: (x[0] + y[0], x[1] + y[1]),
static_nums)
# LOGGER.debug(f"In fill_sparse_result, curt_all: {curt_all}, label_count: {label_counts}")
sparse_bin = sparse_bin_points.get(col_name)
static_nums[sparse_bin] = [
label_counts[1] - curt_all[0], label_counts[0] - curt_all[1]
]
return col_name, static_nums
@staticmethod
def bin_data(instance, split_points, cols_dict, header, is_sparse):
"""
Apply the binning method
Parameters
----------
instance : DTable
The input data
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
cols_dict: dict
Record key, value pairs where key is cols' name, and value is cols' index.
is_sparse: bool
Specify whether it is sparse data or not
Returns
-------
result_bin_dict : dict.
Each element represent for the corresponding bin number this feature belongs to.
e.g. it could be:
[{1: 1, 2: 5, 3: 2}
...
] # Each number represent for the bin number it belongs to.
"""
result_bin_nums = {}
if is_sparse:
sparse_data = instance.features.get_all_data()
for col_idx, col_value in sparse_data:
col_name = header[col_idx]
if col_name in cols_dict:
col_split_points = split_points[col_name]
col_bin_num = BaseBinning.get_bin_num(
col_value, col_split_points)
result_bin_nums[col_name] = col_bin_num
return result_bin_nums
# For dense data
for col_name, col_index in cols_dict.items():
col_split_points = split_points[col_name]
value = instance.features[col_index]
col_bin_num = BaseBinning.get_bin_num(value, col_split_points)
result_bin_nums[col_name] = col_bin_num
return result_bin_nums
@staticmethod
def get_bin_num(value, split_points):
sp = split_points[:-1]
col_bin_num = bisect.bisect_left(sp, value)
# col_bin_num = bisect.bisect_left(split_points, value)
return col_bin_num
@staticmethod
def woe_1d(data_event_count, adjustment_factor):
"""
Given event and non-event count in one column, calculate its woe value.
Parameters
----------
data_event_count : list
[(event_sum, non-event_sum), (same sum in second_bin), (in third bin) ...]
adjustment_factor : float
The adjustment factor when calculating WOE
Returns
-------
IVAttributes : object
Stored information that related iv and woe value
"""
event_total = 0
non_event_total = 0
for event_sum, non_event_sum in data_event_count:
event_total += event_sum
non_event_total += non_event_sum
if event_total == 0:
# raise ValueError("NO event label in target data")
event_total = 1
if non_event_total == 0:
# raise ValueError("NO non-event label in target data")
non_event_total = 1
iv = 0
event_count_array = []
non_event_count_array = []
event_rate_array = []
non_event_rate_array = []
woe_array = []
iv_array = []
for event_count, non_event_count in data_event_count:
if event_count == 0 or non_event_count == 0:
event_rate = 1.0 * (event_count +
adjustment_factor) / event_total
non_event_rate = 1.0 * (non_event_count +
adjustment_factor) / non_event_total
else:
event_rate = 1.0 * event_count / event_total
non_event_rate = 1.0 * non_event_count / non_event_total
woe_i = math.log(event_rate / non_event_rate)
event_count_array.append(event_count)
non_event_count_array.append(non_event_count)
event_rate_array.append(event_rate)
non_event_rate_array.append(non_event_rate)
woe_array.append(woe_i)
iv_i = (event_rate - non_event_rate) * woe_i
iv_array.append(iv_i)
iv += iv_i
return BinColResults(woe_array=woe_array,
iv_array=iv_array,
event_count_array=event_count_array,
non_event_count_array=non_event_count_array,
event_rate_array=event_rate_array,
non_event_rate_array=non_event_rate_array,
iv=iv)
def cal_iv_woe(self, result_counts, adjustment_factor):
"""
Given event count information calculate iv information
Parameters
----------
result_counts: dict or table.
It is like:
{'x1': [[event_count, non_event_count], [event_count, non_event_count] ... ],
'x2': [[event_count, non_event_count], [event_count, non_event_count] ... ],
...
}
adjustment_factor : float
The adjustment factor when calculating WOE
Returns
-------
Dict of IVAttributes object
{'x1': attr_obj,
'x2': attr_obj
...
}
"""
if isinstance(result_counts, dict):
for col_name, data_event_count in result_counts.items():
col_result_obj = self.woe_1d(data_event_count,
adjustment_factor)
assert isinstance(col_result_obj, BinColResults)
self.bin_results.put_col_results(col_name, col_result_obj)
else:
woe_1d = functools.partial(self.woe_1d,
adjustment_factor=adjustment_factor)
col_result_obj_dict = dict(
result_counts.mapValues(woe_1d).collect())
# col_result_obj = self.woe_1d(data_event_count, adjustment_factor)
for col_name, col_result_obj in col_result_obj_dict.items():
assert isinstance(col_result_obj, BinColResults)
self.bin_results.put_col_results(col_name, col_result_obj)
@staticmethod
def add_label_in_partition(data_bin_with_table, sparse_bin_points):
"""
Add all label, so that become convenient to calculate woe and iv
Parameters
----------
data_bin_with_table : DTable
The input data, the DTable is like:
(id, {'x1': 1, 'x2': 5, 'x3': 2}, y)
sparse_bin_points: dict
Dict of sparse bin num
{0: 2, 1: 3, 2:5 ... }
Returns
-------
result_sum: the result DTable. It is like:
{'x1': [[event_count, total_num], [event_count, total_num] ... ],
'x2': [[event_count, total_num], [event_count, total_num] ... ],
...
}
"""
result_sum = {}
for _, datas in data_bin_with_table:
bin_idx_dict = datas[0]
y = datas[1]
# y = y_combo[0]
# inverse_y = y_combo[1]
for col_name, bin_idx in bin_idx_dict.items():
result_sum.setdefault(col_name, [])
col_sum = result_sum[col_name]
while bin_idx >= len(col_sum):
col_sum.append([0, 0])
if bin_idx == sparse_bin_points[col_name]:
continue
label_sum = col_sum[bin_idx]
label_sum[0] = label_sum[0] + y
label_sum[1] = label_sum[1] + 1
col_sum[bin_idx] = label_sum
result_sum[col_name] = col_sum
return list(result_sum.items())
def shuffle_static_counts(self, statistic_counts):
"""
Shuffle bin orders, and stored orders in self.bin_results
Parameters
----------
statistic_counts : dict.
It is like:
{'x1': [[event_count, non_event_count], [event_count, non_event_count] ... ],
'x2': [[event_count, non_event_count], [event_count, non_event_count] ... ],
...
}
Returns
-------
Shuffled result
"""
result = {}
for col_name, count_sum in statistic_counts.items():
this_bin_result = self.bin_results.all_cols_results.get(
col_name, BinColResults())
shuffled_index = [x for x in range(len(count_sum))]
random.shuffle(shuffled_index)
result[col_name] = [count_sum[i] for i in shuffled_index]
this_bin_result.bin_anonymous = [
"bin_" + str(i) for i in shuffled_index
]
self.bin_results.all_cols_results[col_name] = this_bin_result
return result
@staticmethod
def aggregate_partition_label(sum1, sum2):
"""
Used in reduce function. Aggregate the result calculate from each partition.
Parameters
----------
sum1 : list.
It is like:
[[event_count, total_num], [event_count, total_num] ... ]
sum2 : list
Same as sum1
Returns
-------
Merged sum. The format is same as sum1.
"""
if sum1 is None and sum2 is None:
return None
if sum1 is None:
return sum2
if sum2 is None:
return sum1
for idx, label_sum2 in enumerate(sum2):
if idx >= len(sum1):
sum1.append(label_sum2)
else:
label_sum1 = sum1[idx]
tmp = [
label_sum1[0] + label_sum2[0],
label_sum1[1] + label_sum2[1]
]
sum1[idx] = tmp
return sum1
class BaseFeatureBinning(ModelBase):
"""
Do binning method through guest and host
"""
def __init__(self):
super(BaseFeatureBinning, self).__init__()
self.transfer_variable = HeteroFeatureBinningTransferVariable()
self.binning_obj: BaseBinning = None
self.header = None
self.header_anonymous = None
self.schema = None
self.host_results = []
self.transform_type = None
self.model_param = FeatureBinningParam()
self.bin_inner_param = BinInnerParam()
def _init_model(self, params: FeatureBinningParam):
self.model_param = params
self.transform_type = self.model_param.transform_param.transform_type
if self.model_param.method == consts.QUANTILE:
self.binning_obj = QuantileBinning(self.model_param)
elif self.model_param.method == consts.BUCKET:
self.binning_obj = BucketBinning(self.model_param)
elif self.model_param.method == consts.OPTIMAL:
if self.role == consts.HOST:
self.model_param.bin_num = self.model_param.optimal_binning_param.init_bin_nums
self.binning_obj = QuantileBinning(self.model_param)
else:
self.binning_obj = OptimalBinning(self.model_param)
else:
# self.binning_obj = QuantileBinning(self.bin_param)
raise ValueError("Binning method: {} is not supported yet".format(
self.model_param.method))
LOGGER.debug("in _init_model, role: {}, local_partyid: {}".format(
self.role, self.component_properties))
self.binning_obj.set_role_party(
self.role, self.component_properties.local_partyid)
@staticmethod
def data_format_transform(row):
"""
transform data into sparse format
"""
if type(row.features).__name__ != consts.SPARSE_VECTOR:
feature_shape = row.features.shape[0]
indices = []
data = []
for i in range(feature_shape):
if np.isnan(row.features[i]):
indices.append(i)
data.append(NoneType())
elif np.abs(row.features[i]) < consts.FLOAT_ZERO:
continue
else:
indices.append(i)
data.append(row.features[i])
new_row = copy.deepcopy(row)
new_row.features = SparseVector(indices, data, feature_shape)
return new_row
else:
sparse_vec = row.features.get_sparse_vector()
replace_key = []
for key in sparse_vec:
if sparse_vec.get(key) == NoneType() or np.isnan(
sparse_vec.get(key)):
replace_key.append(key)
if len(replace_key) == 0:
return row
else:
new_row = copy.deepcopy(row)
new_sparse_vec = new_row.features.get_sparse_vector()
for key in replace_key:
new_sparse_vec[key] = NoneType()
return new_row
def _setup_bin_inner_param(self, data_instances, params):
if self.schema is not None:
return
self.header = get_header(data_instances)
LOGGER.debug("_setup_bin_inner_param, get header length: {}".format(
len(self.header)))
self.schema = data_instances.schema
self.bin_inner_param.set_header(self.header)
if params.bin_indexes == -1:
self.bin_inner_param.set_bin_all()
else:
self.bin_inner_param.add_bin_indexes(params.bin_indexes)
self.bin_inner_param.add_bin_names(params.bin_names)
self.bin_inner_param.add_category_indexes(params.category_indexes)
self.bin_inner_param.add_category_names(params.category_names)
if params.transform_param.transform_cols == -1:
self.bin_inner_param.set_transform_all()
else:
self.bin_inner_param.add_transform_bin_indexes(
params.transform_param.transform_cols)
self.bin_inner_param.add_transform_bin_names(
params.transform_param.transform_names)
self.binning_obj.set_bin_inner_param(self.bin_inner_param)
@assert_io_num_rows_equal
@assert_schema_consistent
def transform(self, data_instances):
self._setup_bin_inner_param(data_instances, self.model_param)
data_instances = self.binning_obj.transform(data_instances,
self.transform_type)
self.set_schema(data_instances)
self.data_output = data_instances
return data_instances
def _get_meta(self):
# col_list = [str(x) for x in self.cols]
transform_param = feature_binning_meta_pb2.TransformMeta(
transform_cols=self.bin_inner_param.transform_bin_indexes,
transform_type=self.model_param.transform_param.transform_type)
meta_protobuf_obj = feature_binning_meta_pb2.FeatureBinningMeta(
method=self.model_param.method,
compress_thres=self.model_param.compress_thres,
head_size=self.model_param.head_size,
error=self.model_param.error,
bin_num=self.model_param.bin_num,
cols=self.bin_inner_param.bin_names,
adjustment_factor=self.model_param.adjustment_factor,
local_only=self.model_param.local_only,
need_run=self.need_run,
transform_param=transform_param,
skip_static=self.model_param.skip_static)
return meta_protobuf_obj
def _get_param(self):
binning_result_obj = self.binning_obj.bin_results.generated_pb()
# binning_result_obj = self.bin_results.generated_pb()
host_results = [
x.bin_results.generated_pb() for x in self.host_results
]
result_obj = feature_binning_param_pb2. \
FeatureBinningParam(binning_result=binning_result_obj,
host_results=host_results,
header=self.header,
header_anonymous=self.header_anonymous,
model_name=consts.BINNING_MODEL)
return result_obj
def load_model(self, model_dict):
model_param = list(
model_dict.get('model').values())[0].get(MODEL_PARAM_NAME)
model_meta = list(
model_dict.get('model').values())[0].get(MODEL_META_NAME)
self.bin_inner_param = BinInnerParam()
assert isinstance(model_meta,
feature_binning_meta_pb2.FeatureBinningMeta)
assert isinstance(model_param,
feature_binning_param_pb2.FeatureBinningParam)
self.header = list(model_param.header)
self.bin_inner_param.set_header(self.header)
self.bin_inner_param.add_transform_bin_indexes(
list(model_meta.transform_param.transform_cols))
self.bin_inner_param.add_bin_names(list(model_meta.cols))
self.transform_type = model_meta.transform_param.transform_type
bin_method = str(model_meta.method)
if bin_method == consts.QUANTILE:
self.binning_obj = QuantileBinning(params=model_meta)
else:
self.binning_obj = BucketBinning(params=model_meta)
self.binning_obj.set_role_party(
self.role, self.component_properties.local_partyid)
self.binning_obj.set_bin_inner_param(self.bin_inner_param)
self.binning_obj.bin_results.reconstruct(model_param.binning_result)
self.host_results = []
for host_pb in model_param.host_results:
host_bin_obj = BaseBinning()
host_bin_obj.bin_results.reconstruct(host_pb)
self.host_results.append(host_bin_obj)
def export_model(self):
if self.model_output is not None:
return self.model_output
meta_obj = self._get_meta()
param_obj = self._get_param()
result = {MODEL_META_NAME: meta_obj, MODEL_PARAM_NAME: param_obj}
self.model_output = result
return result
def save_data(self):
return self.data_output
def set_schema(self, data_instance):
self.schema['header'] = self.header
data_instance.schema = self.schema
LOGGER.debug(
"After Binning, when setting schema, schema is : {}".format(
data_instance.schema))
def _abnormal_detection(self, data_instances):
"""
Make sure input data_instances is valid.
"""
abnormal_detection.empty_table_detection(data_instances)
abnormal_detection.empty_feature_detection(data_instances)
self.check_schema_content(data_instances.schema)
class BaseFeatureBinning(ModelBase):
"""
Do binning method through guest and host
"""
def __init__(self):
super(BaseFeatureBinning, self).__init__()
self.transfer_variable = HeteroFeatureBinningTransferVariable()
self.binning_obj: BaseBinning = None
self.header = None
self.header_anonymous = None
self.schema = None
self.host_results = []
self.transform_type = None
self.model_param = FeatureBinningParam()
self.bin_inner_param = BinInnerParam()
self.bin_result = MultiClassBinResult(labels=[0, 1])
self.has_missing_value = False
self.labels = []
def _init_model(self, params: FeatureBinningParam):
self.model_param = params
self.transform_type = self.model_param.transform_param.transform_type
if self.role == consts.HOST:
if self.transform_type == "woe":
raise ValueError(
"Host party do not support woe transform now.")
if self.model_param.method == consts.QUANTILE:
self.binning_obj = QuantileBinning(self.model_param)
elif self.model_param.method == consts.BUCKET:
self.binning_obj = BucketBinning(self.model_param)
elif self.model_param.method == consts.OPTIMAL:
if self.role == consts.HOST:
self.model_param.bin_num = self.model_param.optimal_binning_param.init_bin_nums
self.binning_obj = QuantileBinning(self.model_param)
else:
self.binning_obj = OptimalBinning(self.model_param)
else:
raise ValueError("Binning method: {} is not supported yet".format(
self.model_param.method))
self.iv_calculator = IvCalculator(
self.model_param.adjustment_factor,
role=self.role,
party_id=self.component_properties.local_partyid)
# self.binning_obj.set_role_party(self.role, self.component_properties.local_partyid)
@staticmethod
def data_format_transform(row):
"""
transform data into sparse format
"""
if type(row.features).__name__ != consts.SPARSE_VECTOR:
feature_shape = row.features.shape[0]
indices = []
data = []
for i in range(feature_shape):
if np.isnan(row.features[i]):
indices.append(i)
data.append(NoneType())
elif np.abs(row.features[i]) < consts.FLOAT_ZERO:
continue
else:
indices.append(i)
data.append(row.features[i])
new_row = copy.deepcopy(row)
new_row.features = SparseVector(indices, data, feature_shape)
return new_row
else:
sparse_vec = row.features.get_sparse_vector()
replace_key = []
for key in sparse_vec:
if sparse_vec.get(key) == NoneType() or np.isnan(
sparse_vec.get(key)):
replace_key.append(key)
if len(replace_key) == 0:
return row
else:
new_row = copy.deepcopy(row)
new_sparse_vec = new_row.features.get_sparse_vector()
for key in replace_key:
new_sparse_vec[key] = NoneType()
return new_row
def _setup_bin_inner_param(self, data_instances, params):
if self.schema is not None:
return
self.header = get_header(data_instances)
LOGGER.debug("_setup_bin_inner_param, get header length: {}".format(
len(self.header)))
self.schema = data_instances.schema
self.bin_inner_param.set_header(self.header)
if params.bin_indexes == -1:
self.bin_inner_param.set_bin_all()
else:
self.bin_inner_param.add_bin_indexes(params.bin_indexes)
self.bin_inner_param.add_bin_names(params.bin_names)
self.bin_inner_param.add_category_indexes(params.category_indexes)
self.bin_inner_param.add_category_names(params.category_names)
if params.transform_param.transform_cols == -1:
self.bin_inner_param.set_transform_all()
else:
self.bin_inner_param.add_transform_bin_indexes(
params.transform_param.transform_cols)
self.bin_inner_param.add_transform_bin_names(
params.transform_param.transform_names)
self.binning_obj.set_bin_inner_param(self.bin_inner_param)
@assert_io_num_rows_equal
@assert_schema_consistent
def transform(self, data_instances):
self._setup_bin_inner_param(data_instances, self.model_param)
if self.transform_type != "woe":
data_instances = self.binning_obj.transform(
data_instances, self.transform_type)
elif self.role == consts.HOST:
raise ValueError(
"Woe transform is not available for host parties.")
else:
data_instances = self.iv_calculator.woe_transformer(
data_instances, self.bin_inner_param, self.bin_result)
self.set_schema(data_instances)
self.data_output = data_instances
return data_instances
def _get_meta(self):
# col_list = [str(x) for x in self.cols]
transform_param = feature_binning_meta_pb2.TransformMeta(
transform_cols=self.bin_inner_param.transform_bin_indexes,
transform_type=self.model_param.transform_param.transform_type)
meta_protobuf_obj = feature_binning_meta_pb2.FeatureBinningMeta(
method=self.model_param.method,
compress_thres=self.model_param.compress_thres,
head_size=self.model_param.head_size,
error=self.model_param.error,
bin_num=self.model_param.bin_num,
cols=self.bin_inner_param.bin_names,
adjustment_factor=self.model_param.adjustment_factor,
local_only=self.model_param.local_only,
need_run=self.need_run,
transform_param=transform_param,
skip_static=self.model_param.skip_static)
return meta_protobuf_obj
def _get_param(self):
split_points_result = self.binning_obj.bin_results.split_results
multi_class_result = self.bin_result.generated_pb_list(
split_points_result)
# LOGGER.debug(f"split_points_result: {split_points_result}")
host_multi_class_result = []
host_single_results = []
for host_res in self.host_results:
host_multi_class_result.extend(host_res.generated_pb_list())
host_single_results.append(host_res.bin_results[0].generated_pb())
has_host_result = True if len(host_multi_class_result) else False
multi_pb = feature_binning_param_pb2.MultiClassResult(
results=multi_class_result,
labels=[str(x) for x in self.labels],
host_results=host_multi_class_result,
host_party_ids=[
str(x) for x in self.component_properties.host_party_idlist
],
has_host_result=has_host_result)
result_obj = feature_binning_param_pb2. \
FeatureBinningParam(binning_result=multi_class_result[0],
host_results=host_single_results,
header=self.header,
header_anonymous=self.header_anonymous,
model_name=consts.BINNING_MODEL,
multi_class_result=multi_pb)
return result_obj
def load_model(self, model_dict):
model_param = list(
model_dict.get('model').values())[0].get(MODEL_PARAM_NAME)
model_meta = list(
model_dict.get('model').values())[0].get(MODEL_META_NAME)
self.bin_inner_param = BinInnerParam()
multi_class_result = model_param.multi_class_result
self.labels = list(multi_class_result.labels)
# if not self.labels:
# self.labels = [0, 1]
if self.labels:
self.bin_result = MultiClassBinResult.reconstruct(
list(multi_class_result.results), self.labels)
assert isinstance(model_meta,
feature_binning_meta_pb2.FeatureBinningMeta)
assert isinstance(model_param,
feature_binning_param_pb2.FeatureBinningParam)
self.header = list(model_param.header)
self.bin_inner_param.set_header(self.header)
self.bin_inner_param.add_transform_bin_indexes(
list(model_meta.transform_param.transform_cols))
self.bin_inner_param.add_bin_names(list(model_meta.cols))
self.transform_type = model_meta.transform_param.transform_type
bin_method = str(model_meta.method)
if bin_method == consts.QUANTILE:
self.binning_obj = QuantileBinning(params=model_meta)
elif bin_method == consts.OPTIMAL:
self.binning_obj = OptimalBinning(params=model_meta)
else:
self.binning_obj = BucketBinning(params=model_meta)
# self.binning_obj.set_role_party(self.role, self.component_properties.local_partyid)
self.binning_obj.set_bin_inner_param(self.bin_inner_param)
split_results = dict(model_param.binning_result.binning_result)
for col_name, sr_pb in split_results.items():
split_points = list(sr_pb.split_points)
self.binning_obj.bin_results.put_col_split_points(
col_name, split_points)
# self.binning_obj.bin_results.reconstruct(model_param.binning_result)
self.host_results = []
host_pbs = list(model_param.multi_class_result.host_results)
if len(host_pbs):
if len(self.labels) == 2:
for host_pb in host_pbs:
self.host_results.append(
MultiClassBinResult.reconstruct(host_pb, self.labels))
else:
assert len(host_pbs) % len(self.labels) == 0
i = 0
while i < len(host_pbs):
this_pbs = host_pbs[i:i + len(self.labels)]
self.host_results.append(
MultiClassBinResult.reconstruct(this_pbs, self.labels))
i += len(self.labels)
if list(model_param.header_anonymous):
self.header_anonymous = list(model_param.header_anonymous)
def export_model(self):
if self.model_output is not None:
return self.model_output
meta_obj = self._get_meta()
param_obj = self._get_param()
result = {MODEL_META_NAME: meta_obj, MODEL_PARAM_NAME: param_obj}
self.model_output = result
return result
def save_data(self):
return self.data_output
def set_schema(self, data_instance):
self.schema['header'] = self.header
data_instance.schema = self.schema
# LOGGER.debug("After Binning, when setting schema, schema is : {}".format(data_instance.schema))
def _abnormal_detection(self, data_instances):
"""
Make sure input data_instances is valid.
"""
abnormal_detection.empty_table_detection(data_instances)
abnormal_detection.empty_feature_detection(data_instances)
self.check_schema_content(data_instances.schema)
def load_model(self, model_dict):
model_param = list(
model_dict.get('model').values())[0].get(MODEL_PARAM_NAME)
model_meta = list(
model_dict.get('model').values())[0].get(MODEL_META_NAME)
self.bin_inner_param = BinInnerParam()
multi_class_result = model_param.multi_class_result
self.labels = list(multi_class_result.labels)
# if not self.labels:
# self.labels = [0, 1]
if self.labels:
self.bin_result = MultiClassBinResult.reconstruct(
list(multi_class_result.results), self.labels)
assert isinstance(model_meta,
feature_binning_meta_pb2.FeatureBinningMeta)
assert isinstance(model_param,
feature_binning_param_pb2.FeatureBinningParam)
self.header = list(model_param.header)
self.bin_inner_param.set_header(self.header)
self.bin_inner_param.add_transform_bin_indexes(
list(model_meta.transform_param.transform_cols))
self.bin_inner_param.add_bin_names(list(model_meta.cols))
self.transform_type = model_meta.transform_param.transform_type
bin_method = str(model_meta.method)
if bin_method == consts.QUANTILE:
self.binning_obj = QuantileBinning(params=model_meta)
elif bin_method == consts.OPTIMAL:
self.binning_obj = OptimalBinning(params=model_meta)
else:
self.binning_obj = BucketBinning(params=model_meta)
# self.binning_obj.set_role_party(self.role, self.component_properties.local_partyid)
self.binning_obj.set_bin_inner_param(self.bin_inner_param)
split_results = dict(model_param.binning_result.binning_result)
for col_name, sr_pb in split_results.items():
split_points = list(sr_pb.split_points)
self.binning_obj.bin_results.put_col_split_points(
col_name, split_points)
# self.binning_obj.bin_results.reconstruct(model_param.binning_result)
self.host_results = []
host_pbs = list(model_param.multi_class_result.host_results)
if len(host_pbs):
if len(self.labels) == 2:
for host_pb in host_pbs:
self.host_results.append(
MultiClassBinResult.reconstruct(host_pb, self.labels))
else:
assert len(host_pbs) % len(self.labels) == 0
i = 0
while i < len(host_pbs):
this_pbs = host_pbs[i:i + len(self.labels)]
self.host_results.append(
MultiClassBinResult.reconstruct(this_pbs, self.labels))
i += len(self.labels)
if list(model_param.header_anonymous):
self.header_anonymous = list(model_param.header_anonymous)
class BaseHeteroFeatureBinning(ModelBase):
"""
Do binning method through guest and host
"""
def __init__(self):
super(BaseHeteroFeatureBinning, self).__init__()
self.transfer_variable = HeteroFeatureBinningTransferVariable()
self.binning_obj = None
self.header = None
self.schema = None
self.host_results = []
self.transform_type = None
self.model_param = FeatureBinningParam()
self.bin_inner_param = BinInnerParam()
def _init_model(self, params: FeatureBinningParam):
self.model_param = params
self.transform_type = self.model_param.transform_param.transform_type
if self.model_param.method == consts.QUANTILE:
self.binning_obj = QuantileBinning(self.model_param)
elif self.model_param.method == consts.BUCKET:
self.binning_obj = BucketBinning(self.model_param)
else:
# self.binning_obj = QuantileBinning(self.bin_param)
raise ValueError("Binning method: {} is not supported yet".format(self.model_param.method))
LOGGER.debug("in _init_model, role: {}, local_partyid: {}".format(self.role, self.component_properties))
self.binning_obj.set_role_party(self.role, self.component_properties.local_partyid)
def _setup_bin_inner_param(self, data_instances, params: FeatureBinningParam):
if self.schema is not None:
return
self.header = get_header(data_instances)
self.schema = data_instances.schema
self.bin_inner_param.set_header(self.header)
if params.bin_indexes == -1:
self.bin_inner_param.set_bin_all()
else:
self.bin_inner_param.add_bin_indexes(params.bin_indexes)
self.bin_inner_param.add_bin_names(params.bin_names)
self.bin_inner_param.add_category_indexes(params.category_indexes)
self.bin_inner_param.add_category_names(params.category_names)
if params.transform_param.transform_cols == -1:
self.bin_inner_param.set_transform_all()
else:
self.bin_inner_param.add_transform_bin_indexes(params.transform_param.transform_cols)
self.bin_inner_param.add_transform_bin_names(params.transform_param.transform_names)
self.binning_obj.set_bin_inner_param(self.bin_inner_param)
def transform(self, data_instances):
self._setup_bin_inner_param(data_instances, self.model_param)
data_instances = self.binning_obj.transform(data_instances, self.transform_type)
self.set_schema(data_instances)
self.data_output = data_instances
return data_instances
def _get_meta(self):
# col_list = [str(x) for x in self.cols]
transform_param = feature_binning_meta_pb2.TransformMeta(
transform_cols=self.bin_inner_param.transform_bin_indexes,
transform_type=self.model_param.transform_param.transform_type
)
meta_protobuf_obj = feature_binning_meta_pb2.FeatureBinningMeta(
method=self.model_param.method,
compress_thres=self.model_param.compress_thres,
head_size=self.model_param.head_size,
error=self.model_param.error,
bin_num=self.model_param.bin_num,
cols=self.bin_inner_param.bin_names,
adjustment_factor=self.model_param.adjustment_factor,
local_only=self.model_param.local_only,
need_run=self.need_run,
transform_param=transform_param
)
return meta_protobuf_obj
def _get_param(self):
binning_result_obj = self.binning_obj.bin_results.generated_pb()
host_results = [x.bin_results.generated_pb() for x in self.host_results]
result_obj = feature_binning_param_pb2.FeatureBinningParam(binning_result=binning_result_obj,
host_results=host_results,
header=self.header)
# json_result = json_format.MessageToJson(result_obj)
# LOGGER.debug("json_result: {}".format(json_result))
return result_obj
def load_model(self, model_dict):
model_param = list(model_dict.get('model').values())[0].get(MODEL_PARAM_NAME)
model_meta = list(model_dict.get('model').values())[0].get(MODEL_META_NAME)
self.bin_inner_param = BinInnerParam()
assert isinstance(model_meta, feature_binning_meta_pb2.FeatureBinningMeta)
assert isinstance(model_param, feature_binning_param_pb2.FeatureBinningParam)
self.header = list(model_param.header)
self.bin_inner_param.set_header(self.header)
self.bin_inner_param.add_transform_bin_indexes(list(model_meta.transform_param.transform_cols))
self.bin_inner_param.add_bin_names(list(model_meta.cols))
self.transform_type = model_meta.transform_param.transform_type
bin_method = str(model_meta.method)
if bin_method == consts.QUANTILE:
self.binning_obj = QuantileBinning(params=model_meta)
else:
self.binning_obj = BucketBinning(params=model_meta)
self.binning_obj.set_role_party(self.role, self.component_properties.local_partyid)
self.binning_obj.set_bin_inner_param(self.bin_inner_param)
self.binning_obj.bin_results.reconstruct(model_param.binning_result)
self.host_results = []
for host_pb in model_param.host_results:
host_bin_obj = HostBaseBinning()
host_bin_obj.bin_results.reconstruct(host_pb)
self.host_results.append(host_bin_obj)
def export_model(self):
if self.model_output is not None:
return self.model_output
meta_obj = self._get_meta()
param_obj = self._get_param()
result = {
MODEL_META_NAME: meta_obj,
MODEL_PARAM_NAME: param_obj
}
self.model_output = result
return result
def save_data(self):
return self.data_output
def set_schema(self, data_instance):
self.schema['header'] = self.header
data_instance.schema = self.schema
LOGGER.debug("After Binning, when setting schema, schema is : {}".format(data_instance.schema))
def _abnormal_detection(self, data_instances):
"""
Make sure input data_instances is valid.
"""
abnormal_detection.empty_table_detection(data_instances)
abnormal_detection.empty_feature_detection(data_instances)
