def compute_gradient(data_instances, fore_gradient, fit_intercept):
"""
Compute hetero-regression gradient
Parameters
----------
data_instances: DTable, input data
fore_gradient: DTable, fore_gradient
fit_intercept: bool, if model has intercept or not
Returns
----------
DTable
the hetero regression model's gradient
"""
feat_join_grad = data_instances.join(fore_gradient, lambda d, g:
(d.features, g))
is_sparse = data_overview.is_sparse_data(data_instances)
f = functools.partial(__compute_partition_gradient,
fit_intercept=fit_intercept,
is_sparse=is_sparse)
gradient_partition = feat_join_grad.applyPartitions(f)
gradient_partition = gradient_partition.reduce(lambda x, y: x + y)
gradient = gradient_partition / data_instances.count()
return gradient
def query_quantile_point(self, data_instances, cols, query_points):
# self.cols = cols
# self._init_cols(data_instances)
is_sparse = data_overview.is_sparse_data(data_instances)
if self.summary_dict is None:
f = functools.partial(self.approxi_quantile,
cols_dict=self.bin_inner_param.bin_cols_map,
params=self.params,
header=self.header,
abnormal_list=self.abnormal_list,
is_sparse=is_sparse)
summary_dict = data_instances.mapPartitions(f)
summary_dict = summary_dict.reduce(self.merge_summary_dict)
self.summary_dict = summary_dict
else:
summary_dict = self.summary_dict
if isinstance(query_points, (int, float)):
query_dict = {}
for col_name in cols:
query_dict[col_name] = query_points
elif isinstance(query_points, dict):
query_dict = query_points
else:
raise ValueError(
"query_points has wrong type, should be a float, int or dict")
result = {}
for col_name, query_point in query_dict.items():
summary = summary_dict[col_name]
result[col_name] = summary.query(query_point)
return result
def fit_split_points(self, data_instances):
"""
Apply the binning method
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
}
"""
header = data_overview.get_header(data_instances)
self._default_setting(header)
# self._init_cols(data_instances)
percent_value = 1.0 / self.bin_num
# calculate the split points
percentile_rate = [i * percent_value for i in range(1, self.bin_num)]
percentile_rate.append(1.0)
is_sparse = data_overview.is_sparse_data(data_instances)
# self._fit_split_point_deprecate(data_instances, is_sparse, percentile_rate)
self._fit_split_point(data_instances, is_sparse, percentile_rate)
self.fit_category_features(data_instances)
return self.bin_results.all_split_points
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 fit_split_points(self, data_instances):
"""
Apply the binning method
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
"""
header = data_overview.get_header(data_instances)
self._default_setting(header)
# self._init_cols(data_instances)
percent_value = 1.0 / self.bin_num
# calculate the split points
percentile_rate = [i * percent_value for i in range(1, self.bin_num)]
percentile_rate.append(1.0)
is_sparse = data_overview.is_sparse_data(data_instances)
if self.summary_dict is None:
f = functools.partial(self.approxi_quantile,
params=self.params,
abnormal_list=self.abnormal_list,
cols_dict=self.bin_inner_param.bin_cols_map,
header=self.header,
is_sparse=is_sparse)
summary_dict = data_instances.mapPartitions(f)
summary_dict = summary_dict.reduce(self.merge_summary_dict)
if is_sparse:
total_count = data_instances.count()
for _, summary_obj in summary_dict.items():
summary_obj.set_total_count(total_count)
self.summary_dict = summary_dict
else:
summary_dict = self.summary_dict
# split_points = {}
for col_name, summary in summary_dict.items():
split_point = []
for percen_rate in percentile_rate:
s_p = summary.query(percen_rate)
if s_p not in split_point:
split_point.append(s_p)
self.bin_results.put_col_split_points(col_name, split_point)
self.fit_category_features(data_instances)
return self.bin_results.all_split_points
def convert_feature_to_bin(self, data_instances, transform_cols_idx=-1, split_points=None):
self._init_cols(data_instances)
if transform_cols_idx is None:
return data_instances, None, None
if transform_cols_idx == -1:
transform_cols_idx = self.cols_index
else:
assert isinstance(transform_cols_idx, (list, tuple))
LOGGER.debug('In convert_feature_to_bin, transform_cols_idx: {}, col_index: {}, cols: {}'.format(
transform_cols_idx, self.cols_index, self.cols
))
for col in transform_cols_idx:
if col not in self.cols_index:
raise RuntimeError("Binning Transform cols: {} should be fit before transform".format(col))
transform_cols_idx = list(map(int, transform_cols_idx))
if split_points is None:
split_points = self.split_points
is_sparse = data_overview.is_sparse_data(data_instances)
LOGGER.debug("In convert_feature_to_bin, split_points: {}, header: {}, transform_cols_idx: {}".format(
split_points, self.header, transform_cols_idx
))
if is_sparse:
f = functools.partial(self._convert_sparse_data,
transform_cols_idx=transform_cols_idx,
split_points_dict=split_points,
header=self.header,
abnormal_list=self.abnormal_list
)
new_data = data_instances.mapValues(f)
else:
f = functools.partial(self._convert_dense_data,
transform_cols_idx=transform_cols_idx,
split_points_dict=split_points,
header=self.header,
abnormal_list=self.abnormal_list)
new_data = data_instances.mapValues(f)
new_data.schema = {"header": self.header}
bin_sparse = self.get_sparse_bin(transform_cols_idx, split_points)
split_points_result = []
for idx, col_name in enumerate(self.header):
if col_name not in self.split_points:
continue
s_ps = self.split_points[col_name]
s_ps = np.array(s_ps)
split_points_result.append(s_ps)
split_points_result = np.array(split_points_result)
assert len(split_points_result) == len(self.split_points)
LOGGER.debug("Original split_points: {}, changed split_point: {}".format(self.split_points, split_points_result))
LOGGER.debug("In convert_feature_to_bin, new_data: {}, split_point_result: {}, bin_sparse: {}".format(
new_data, split_points_result, bin_sparse
))
return new_data, split_points_result, bin_sparse
def init_bucket(self, data_instances):
header = data_overview.get_header(data_instances)
self._default_setting(header)
init_bucket_param = copy.deepcopy(self.params)
init_bucket_param.bin_num = self.optimal_param.init_bin_nums
if self.optimal_param.init_bucket_method == consts.QUANTILE:
init_binning_obj = QuantileBinningTool(param_obj=init_bucket_param,
allow_duplicate=False)
else:
init_binning_obj = BucketBinning(params=init_bucket_param)
init_binning_obj.set_bin_inner_param(self.bin_inner_param)
init_split_points = init_binning_obj.fit_split_points(data_instances)
is_sparse = data_overview.is_sparse_data(data_instances)
bucket_dict = dict()
for col_name, sps in init_split_points.items():
bucket_list = []
for idx, sp in enumerate(sps):
bucket = bucket_info.Bucket(idx,
self.adjustment_factor,
right_bound=sp)
if idx == 0:
bucket.left_bound = -math.inf
bucket.set_left_neighbor(None)
else:
bucket.left_bound = sps[idx - 1]
bucket.event_total = self.event_total
bucket.non_event_total = self.non_event_total
bucket_list.append(bucket)
bucket_list[-1].set_right_neighbor(None)
bucket_dict[col_name] = bucket_list
# LOGGER.debug(f"col_name: {col_name}, length of sps: {len(sps)}, "
# f"length of list: {len(bucket_list)}")
convert_func = functools.partial(
self.convert_data_to_bucket,
split_points=init_split_points,
headers=self.header,
bucket_dict=copy.deepcopy(bucket_dict),
is_sparse=is_sparse,
get_bin_num_func=self.get_bin_num)
bucket_table = data_instances.mapReducePartitions(
convert_func, self.merge_bucket_list)
# bucket_table = dict(bucket_table.collect())
# for k, v in bucket_table.items():
# LOGGER.debug(f"[feature] {k}, length of list: {len(v)}")
# LOGGER.debug("bucket_table: {}, length: {}".format(type(bucket_table), len(bucket_table)))
# bucket_table = [(k, v) for k, v in bucket_table.items()]
# LOGGER.debug("bucket_table: {}, length: {}".format(type(bucket_table), len(bucket_table)))
# bucket_table = session.parallelize(bucket_table, include_key=True, partition=data_instances.partitions)
return bucket_table
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 compute_gradient(self,
data_instances,
fore_gradient,
fit_intercept,
need_average=True):
"""
Compute hetero-regression gradient
Parameters
----------
data_instances: Table, input data
fore_gradient: Table, fore_gradient
fit_intercept: bool, if model has intercept or not
need_average: bool, gradient needs to be averaged or not
Returns
----------
Table
the hetero regression model's gradient
"""
# feature_num = data_overview.get_features_shape(data_instances)
# data_count = data_instances.count()
is_sparse = data_overview.is_sparse_data(data_instances)
LOGGER.debug("Use apply partitions")
feat_join_grad = data_instances.join(fore_gradient, lambda d, g:
(d.features, g))
f = functools.partial(self.__apply_cal_gradient,
fixed_point_encoder=self.fixed_point_encoder,
is_sparse=is_sparse)
gradient_sum = feat_join_grad.applyPartitions(f)
gradient_sum = gradient_sum.reduce(lambda x, y: x + y)
if fit_intercept:
# bias_grad = np.sum(fore_gradient)
bias_grad = fore_gradient.reduce(lambda x, y: x + y)
gradient_sum = np.append(gradient_sum, bias_grad)
if need_average:
gradient = gradient_sum / data_instances.count()
else:
gradient = gradient_sum
"""
else:
LOGGER.debug(f"Original_method")
feat_join_grad = data_instances.join(fore_gradient,
lambda d, g: (d.features, g))
f = functools.partial(self.__compute_partition_gradient,
fit_intercept=fit_intercept,
is_sparse=is_sparse)
gradient_partition = feat_join_grad.applyPartitions(f)
gradient_partition = gradient_partition.reduce(lambda x, y: x + y)
gradient = gradient_partition / data_count
"""
return gradient
def woe_transformer(data_instances, bin_inner_param, multi_class_bin_res: MultiClassBinResult,
abnormal_list=None):
if abnormal_list is None:
abnormal_list = []
bin_res = multi_class_bin_res.bin_results[0]
transform_cols_idx = bin_inner_param.transform_bin_indexes
split_points_dict = bin_res.all_split_points
is_sparse = data_overview.is_sparse_data(data_instances)
def convert(instances):
if is_sparse:
all_data = instances.features.get_all_data()
indice = []
sparse_value = []
data_shape = instances.features.get_shape()
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)
col_results = bin_res.all_cols_results.get(col_name)
woe_value = col_results.woe_array[bin_num]
sparse_value.append(woe_value)
else:
indice.append(col_idx)
sparse_value.append(col_value)
sparse_vector = SparseVector(indice, sparse_value, data_shape)
instances.features = sparse_vector
else:
features = instances.features
assert isinstance(features, np.ndarray)
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)
col_results = bin_res.all_cols_results.get(col_name)
woe_value = col_results.woe_array[bin_num]
features[col_idx] = woe_value
instances.features = features
return instances
return data_instances.mapValues(convert)
def _static_sums(self):
"""
Statics sum, sum_square, max_value, min_value,
so that variance is available.
"""
is_sparse = data_overview.is_sparse_data(self.data_instances)
partition_cal = functools.partial(self.static_in_partition,
cols_index=self.cols_index,
summary_statistics=copy.deepcopy(self.summary_statistics),
is_sparse=is_sparse)
self.summary_statistics = self.data_instances.applyPartitions(partition_cal). \
reduce(lambda x, y: self.copy_merge(x, y))
# self.summary_statistics = summary_statistic_dict.reduce(self.aggregate_statics)
self.finish_fit_statics = True
def fit_split_points(self, data_instances):
"""
Apply the binning method
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
"""
header = data_overview.get_header(data_instances)
self._default_setting(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
def check_containing_missing_value(data_instances):
is_sparse = data_overview.is_sparse_data(data_instances)
def _sparse_check(instance):
result = set()
sparse_data = instance.features.get_all_data()
for col_idx, col_value in sparse_data:
if np.isnan(col_value):
result.add(col_idx)
return result
if is_sparse:
has_missing_value = data_instances.mapValues(_sparse_check).reduce(
lambda a, b: a.union(b)
)
else:
has_missing_value = data_instances.mapValues(lambda x: x.features).reduce(operator.add)
has_missing_value = {idx for idx, value in enumerate(has_missing_value) if np.isnan(value)}
return has_missing_value
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 fit_summary(self, data_instances, is_sparse=None):
if is_sparse is None:
is_sparse = data_overview.is_sparse_data(data_instances)
LOGGER.debug(f"is_sparse: {is_sparse}")
f = functools.partial(self.feature_summary,
params=self.params,
abnormal_list=self.abnormal_list,
cols_dict=self.bin_inner_param.bin_cols_map,
header=self.header,
is_sparse=is_sparse)
summary_dict_table = data_instances.mapReducePartitions(
f, self.copy_merge)
# summary_dict = dict(summary_dict.collect())
if is_sparse:
total_count = data_instances.count()
summary_dict_table = summary_dict_table.mapValues(
lambda x: x.set_total_count(total_count))
return summary_dict_table
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 empty_column_detection(data_instance):
contains_empty_columns = False
lost_feat = []
is_sparse = data_overview.is_sparse_data(data_instance)
if is_sparse:
raise ValueError(
'sparse format empty column detection is not supported for now')
map_func = functools.partial(column_gathering, )
map_rs = data_instance.applyPartitions(map_func)
reduce_rs = map_rs.reduce(merge_column_sets)
# transform col index to col name
reduce_rs = np.array(data_instance.schema['header'])[list(reduce_rs)]
reduce_rs = set(reduce_rs)
if reduce_rs != set(data_instance.schema['header']):
lost_feat = list(
set(data_instance.schema['header']).difference(reduce_rs))
contains_empty_columns = True
if contains_empty_columns:
raise ValueError('column(s) {} contain(s) no values'.format(lost_feat))
def init_bucket(self, data_instances):
header = data_overview.get_header(data_instances)
self._default_setting(header)
init_bucket_param = copy.deepcopy(self.params)
init_bucket_param.bin_num = self.optimal_param.init_bin_nums
if self.optimal_param.init_bucket_method == consts.QUANTILE:
init_binning_obj = QuantileBinningTool(param_obj=init_bucket_param, allow_duplicate=False)
else:
init_binning_obj = BucketBinning(params=init_bucket_param)
init_binning_obj.set_bin_inner_param(self.bin_inner_param)
init_split_points = init_binning_obj.fit_split_points(data_instances)
is_sparse = data_overview.is_sparse_data(data_instances)
bucket_dict = dict()
for col_name, sps in init_split_points.items():
# bucket_list = [bucket_info.Bucket(idx, self.adjustment_factor, right_bound=sp)
# for idx, sp in enumerate(sps)]
bucket_list = []
for idx, sp in enumerate(sps):
bucket = bucket_info.Bucket(idx, self.adjustment_factor, right_bound=sp)
if idx == 0:
bucket.left_bound = -math.inf
bucket.set_left_neighbor(None)
else:
bucket.left_bound = sps[idx - 1]
bucket.event_total = self.event_total
bucket.non_event_total = self.non_event_total
bucket_list.append(bucket)
bucket_list[-1].set_right_neighbor(None)
bucket_dict[col_name] = bucket_list
LOGGER.debug(f"col_name: {col_name}, length of sps: {len(sps)}, "
f"length of list: {len(bucket_list)}")
# bucket_table = data_instances.mapPartitions2(convert_func)
# bucket_table = bucket_table.reduce(self.merge_bucket_list, key_func=lambda key: key[1])
from fate_arch.common.versions import get_eggroll_version
version = get_eggroll_version()
if version.startswith('2.0'):
convert_func = functools.partial(self.convert_data_to_bucket_old,
split_points=init_split_points,
headers=self.header,
bucket_dict=copy.deepcopy(bucket_dict),
is_sparse=is_sparse,
get_bin_num_func=self.get_bin_num)
summary_dict = data_instances.mapPartitions(convert_func, use_previous_behavior=False)
# summary_dict = summary_dict.reduce(self.copy_merge, key_func=lambda key: key[1])
from federatedml.util.reduce_by_key import reduce
bucket_table = reduce(summary_dict, self.merge_bucket_list, key_func=lambda key: key[1])
elif version.startswith('2.2'):
convert_func = functools.partial(self.convert_data_to_bucket,
split_points=init_split_points,
headers=self.header,
bucket_dict=copy.deepcopy(bucket_dict),
is_sparse=is_sparse,
get_bin_num_func=self.get_bin_num)
bucket_table = data_instances.mapReducePartitions(convert_func, self.merge_bucket_list)
bucket_table = dict(bucket_table.collect())
else:
raise RuntimeError(f"Cannot recognized eggroll version: {version}")
for k, v in bucket_table.items():
LOGGER.debug(f"[feature] {k}, length of list: {len(v)}")
LOGGER.debug("bucket_table: {}, length: {}".format(type(bucket_table), len(bucket_table)))
bucket_table = [(k, v) for k, v in bucket_table.items()]
LOGGER.debug("bucket_table: {}, length: {}".format(type(bucket_table), len(bucket_table)))
bucket_table = session.parallelize(bucket_table, include_key=True, partition=data_instances.partitions)
return bucket_table
def _find_kth_mode(self, data_instances, k):
"""
Find 1/k mode. If there is a mode that number of which is larger than 1/k of total nums, return this mode and
its percentage. If there is not, return None, None.
Parameters
----------
data_instances: DTable
Original data
k: int
"""
is_sparse = is_sparse_data(data_instances)
def find_mode_candidate(instances, select_cols):
"""
Find at most k - 1 mode candidates.
Parameters
----------
instances: Data generator
Original data
k: int
select_cols: list
Indicates columns that need to be operated.
is_sparse: bool
Whether input data format is sparse
Returns
-------
all_candidates: dict
Each key is col_index and value is a list that contains mode candidates.
"""
all_candidates = {}
for col_index in select_cols:
all_candidates[col_index] = {}
for _, instant in instances:
for col_index in select_cols:
candidate_dict = all_candidates[col_index]
if is_sparse:
feature_value = instant.features.get_data(col_index, 0)
else:
feature_value = instant.features[col_index]
if isinstance(feature_value, float):
feature_value = round(feature_value, 8)
if feature_value in candidate_dict:
candidate_dict[feature_value] += 1
elif len(candidate_dict) < k - 1:
candidate_dict[feature_value] = 1
else:
to_delete_col = []
for key in candidate_dict:
candidate_dict[key] -= 1
if candidate_dict[key] == 0:
to_delete_col.append(key)
for d_k in to_delete_col:
del candidate_dict[d_k]
for col_index, candidate_dict in all_candidates.items():
candidate_dict = {key: 0 for key, _ in candidate_dict.items()}
all_candidates[col_index] = candidate_dict
return all_candidates
def merge_mode_candidate(d1, d2):
assert len(d1) == len(d2)
for col_idx, d in d1.items():
d.update(d2[col_idx])
return d1
def merge_candidates_num(candi_1, candi_2):
assert len(candi_1) == len(candi_2)
for col_idx, candidate_dict in candi_1.items():
candi_dict_2 = candi_2[col_idx]
for feature_value, num in candi_dict_2.items():
if feature_value in candidate_dict:
candidate_dict[feature_value] += num
else:
candidate_dict[feature_value] = num
return candi_1
def static_candidates_num(instances, select_cols, all_candidates):
"""
Static number of candidates
Parameters
----------
instances: Data generator
Original data
select_cols: list
Indicates columns that need to be operated.
all_candidates: dict
Each key is col_index and value is a list that contains mode candidates.
"""
for _, instant in instances:
for col_index in select_cols:
candidate_dict = all_candidates[col_index]
if is_sparse:
feature_value = instant.features.get_data(
col_index, NoneType())
else:
feature_value = instant.features[col_index]
if isinstance(feature_value, float):
feature_value = round(feature_value, 8)
if feature_value in candidate_dict:
candidate_dict[feature_value] += 1
# mode_result = {}
# for col_index, candidate_dict in all_candidates.items():
# feature_value, nums = sorted(candidate_dict.items(), key=operator.itemgetter(1), reverse=False)[0]
# mode_result[col_index] = (feature_value, nums)
return all_candidates
find_func = functools.partial(
find_mode_candidate,
select_cols=self.selection_properties.select_col_indexes)
all_candidates = data_instances.mapPartitions(find_func).reduce(
merge_mode_candidate)
static_func = functools.partial(
static_candidates_num,
select_cols=self.selection_properties.select_col_indexes,
all_candidates=all_candidates)
mode_candidate_statics = data_instances.mapPartitions(
static_func).reduce(merge_candidates_num)
result = {}
for col_index, candidate_dict in mode_candidate_statics.items():
if len(candidate_dict) > 0:
res = sorted(candidate_dict.items(),
key=operator.itemgetter(1),
reverse=True)[0]
else:
res = None
result[col_index] = res
return result