def __bestks_bin(self, single_series, y):
df_input = _Basic.basic_prepare(y, self.good, self.bad)
self.kwargs['df_'] = pd.DataFrame(y)
self.kwargs['response'] = y.name
part_, single_series, special = CutMethods.cut_method_flow(
single_series, **self.kwargs)
uncheck_len = 2 if self.strict_monotonicity else 3 # 严格单调性
arrays = []
for group in part_:
if len(group) < 2: # 用于special存在的情况
arrays.append(group)
elif len(group) <= uncheck_len:
if _Basic.check_proportion(single_series, group, self.kwargs):
arrays.append(group)
else:
try: # 只检测了单调性，可以增加检测分组的数量,增加个参数每个组至少0.05
if _Basic.check_proportion(single_series, group,
self.kwargs):
tmp_woe = _Basic.get_tmp_woe(df_input, single_series,
group, y.name,
self.kwargs)
if _Basic.check_monotonic(tmp_woe, self.kwargs):
arrays.append(group)
except KeyError as error:
logging.error(error)
ivs = 0
df_last = None
cut_last = []
if not arrays:
return False
for array in arrays:
if special[0]: # 将特殊点加进去
if special[1] == 0.01:
array[0] = special[0] + special[1]
array.append(special[0])
array.sort()
df_input_tmp = copy.deepcopy(df_input)
out = pd.cut(single_series, array,
include_lowest=True) # 只留下符合单调的切分后重新切分得到结果
out = out.cat.add_categories([self.fill_value
]).fillna(self.fill_value)
out = out.cat.remove_unused_categories()
df_input_tmp[single_series.name] = out
df_input_tmp = _Basic.get_pivot_table(df_input_tmp, y.name,
single_series.name)
df_input_tmp = _Basic.add_basic_info_to_df(df_input_tmp)
df_input_tmp = _Basic.add_woe_iv_to_df(df_input_tmp)
# 上面都是重复之前的操作
df_output = _Basic.add_ks_to_df(df_input_tmp)
iv_sum = df_output["IV"].sum()
if ivs < iv_sum:
cut_last = array
ivs = iv_sum
df_last = copy.deepcopy(df_output)
df_last.sort_index(inplace=True)
return cut_last, df_last
def single_bin(self, single_series, y):
"""当一列的类别<=12种的时候，直接对这些数据进行分组
:return: "single",df
"""
df_input = _Basic.basic_prepare(y, self.good, self.bad)
df_input[single_series.name] = single_series
df_input = _Basic.get_pivot_table(df_input, y.name, single_series.name)
df_input = _Basic.add_basic_info_to_df(df_input)
df_input = _Basic.add_woe_iv_to_df(df_input)
df_input = _Basic.add_ks_to_df(df_input)
df_input = df_input.sort_index()
return 'single', df_input
def get_sample_bestks(df_, test):
kwargs["cut_method"] = "bestks"
print "*****************************{0}**************************************".format(
kwargs["cut_method"])
part_, test = _CutMethods.cut_method_flow(test,
kwargs,
response="code",
df_=df_)
# groups为切割的组合，后面进行排列组合计算iv的值
df_input = _Basic.basic_prepare(df_["code"], kwargs["good"], kwargs["bad"])
uncheck_len = 4
if kwargs["strict_monotonicity"]: # 严格单调性
uncheck_len = 3
arrays = []
for group in part_:
if len(group) <= uncheck_len:
if _Basic.check_proportion(test, group, kwargs):
arrays.append(group)
else:
try: # 只检测了单调性，可以增加检测分组的数量,增加个参数每个组至少0.05
if _Basic.check_proportion(test, group, kwargs):
tmp_woe = _Basic.get_tmp_woe(df_input, test, group, "code",
kwargs)
if _Basic.check_monotonic(tmp_woe, kwargs):
arrays.append(group)
except KeyError as error:
logging.error(error)
# 筛选出符合单调的要求和分布要求的,再筛选出iv最大的一个
ivs = 0
df_last = None
for array in arrays:
df_input_tmp = copy.deepcopy(df_input)
out = pd.cut(test, array, include_lowest=True) # 只留下符合单调的切分后重新切分得到结果
out = out.cat.add_categories([kwargs["fill_value"]
]).fillna(kwargs["fill_value"])
out = out.cat.remove_unused_categories()
df_input_tmp["test"] = out
df_input_tmp = _Basic.get_pivot_table(df_input_tmp, "code", "test")
df_input_tmp = _Basic.add_basic_info_to_df(df_input_tmp)
df_input_tmp = _Basic.add_woe_iv_to_df(df_input_tmp)
# 上面都是重复之前的操作
df_output = _Basic.add_ks_to_df(df_input_tmp)
iv_sum = df_output["IV"].sum()
# print df_output
if ivs < iv_sum:
ivs = iv_sum
df_last = copy.deepcopy(df_output)
print df_last
print "*****************************{0}**************************************".format(
kwargs["cut_method"])
def __general_bin(self, single_series, y):
array = []
df_input = _Basic.basic_prepare(y, self.good, self.bad)
part_, single_series, special = CutMethods.cut_method_flow(
single_series, **self.kwargs)
uncheck_len = 2 if self.strict_monotonicity else 3 # 严格单调性
for group in part_:
if len(group) < 2: # 用于special存在的情况
array.append(group)
elif len(group) <= uncheck_len:
if _Basic.check_proportion(single_series, group, self.kwargs):
array.append(group)
else:
try: # 只检测了单调性，可以增加检测分组的数量,增加个参数每个组至少0.05
if _Basic.check_proportion(single_series, group,
self.kwargs):
tmp_woe = _Basic.get_tmp_woe(df_input, single_series,
group, y.name,
self.kwargs)
if _Basic.check_monotonic(tmp_woe, self.kwargs):
array.append(group)
except KeyError as error:
logging.error(error)
if not array:
return False
cut_points = array[-1]
if special[0]: # 将特殊点加进去
if special[1] == 0.01:
cut_points[0] = special[0] + special[1]
cut_points.append(special[0])
cut_points.sort()
out = pd.cut(single_series, cut_points,
include_lowest=True) # 只留下符合单调的切分后重新切分得到结果
out = out.cat.add_categories([self.fill_value]).fillna(self.fill_value)
out = out.cat.remove_unused_categories()
df_input[single_series.name] = out
df_input = _Basic.get_pivot_table(df_input, y.name, single_series.name)
df_input = _Basic.add_basic_info_to_df(df_input)
df_input = _Basic.add_woe_iv_to_df(df_input)
# 上面都是重复之前的操作
df_output = _Basic.add_ks_to_df(df_input)
df_output.sort_index(inplace=True)
return cut_points, df_output
def get_sample(df_, test):
kwargs["cut_method"] = "quantile"
print "*****************************{0}**************************************".format(
kwargs["cut_method"])
df_input = _Basic.basic_prepare(df_["code"], kwargs["good"], kwargs["bad"])
array = []
part_, test = _CutMethods.cut_method_flow(test, kwargs)
# part_各个分位数的值,test原始数据
uncheck_len = 4
if kwargs["strict_monotonicity"]: # 严格单调性
uncheck_len = 3
for group in part_:
if len(group) <= uncheck_len:
if _Basic.check_proportion(test, group, kwargs):
array.append(group)
else:
try: # 只检测了单调性，可以增加检测分组的数量,增加个参数每个组至少0.05
if _Basic.check_proportion(test, group, kwargs):
tmp_woe = _Basic.get_tmp_woe(df_input, test, group, "code",
kwargs)
if _Basic.check_monotonic(tmp_woe, kwargs):
array.append(group)
# else:
# break
# else: # 不满足单调舍弃
# break
except KeyError as error:
logging.error(error)
print(array)
out = pd.cut(test, array[-1], include_lowest=True) # 只留下符合单调的切分后重新切分得到结果
out = out.cat.add_categories([kwargs["fill_value"]
]).fillna(kwargs["fill_value"])
out = out.cat.remove_unused_categories()
df_input["test"] = out
df_input = _Basic.get_pivot_table(df_input, "code", "test")
df_input = _Basic.add_basic_info_to_df(df_input)
df_input = _Basic.add_woe_iv_to_df(df_input)
# 上面都是重复之前的操作
df_output = _Basic.add_ks_to_df(df_input)
print df_output
print "*****************************{0}**************************************".format(
kwargs["cut_method"])
def get_sample():
df_ = pd.DataFrame()
df_["code"] = np.zeros(1000)
df_["code"][:300] = 1
test = pd.Series(np.random.rand(1000), name="test")
test[270:350] = np.NaN
df_input = _Basic.basic_prepare(df_["code"], kwargs["good"], kwargs["bad"])
test = test.round(3)
array = []
part_, test = _CutMethods.cut_method_flow(test, kwargs)
uncheck_len = 4
if kwargs["strict_monotonicity"]:
uncheck_len = 3
for group in part_:
if len(group) <= uncheck_len:
array.append(group)
else:
try:
tmp_woe = __get_tmp_woe(df_input, test, group, "code")
if __check_monotonic(tmp_woe):
array.append(group)
else:
break
except KeyError as error:
logging.error(error)
out = pd.cut(test, array[-1], include_lowest=True)
out = out.cat.add_categories([kwargs["fill_value"]
]).fillna(kwargs["fill_value"])
out = out.cat.remove_unused_categories()
df_input["test"] = out
df_input = _Basic.get_pivot_table(df_input, "code", "test")
df_input = _Basic.add_basic_info_to_df(df_input)
df_input = _Basic.add_woe_iv_to_df(df_input)
df_input = _Basic.add_ks_to_df(df_input)
print df_input
def woe_bin(self, single_series, y):
df_input = _Basic.basic_prepare(y, bad=self.bad, good=self.good)
df_input[single_series.name] = single_series
df_input = _Basic.get_pivot_table(df_input,
response=y.name,
column=single_series.name)
df_input = _Basic.add_basic_info_to_df(df_input)
df_input = _Basic.add_woe_iv_to_df(df_input)
if self.woe_inf_fill == 'avg': # todo:填充woe的值，现在是平均值
avg = df_input["WOE"].replace(self.fill_items, np.nan).mean()
df_input["WOE"] = df_input["WOE"].replace(self.fill_items, avg)
df_input = _Basic.add_ks_to_df(df_input)
last_cut_points = []
choice_item_value = 0
parts1 = CutMethods.quantile_cut_flow(df_input['WOE'],
max_cut_part=self.max_cut_part,
min_cut_part=self.min_cut_part)
parts2 = CutMethods.cumsum_cut_flow(df_input['WOE'],
add_min_group=True,
max_cut_part=self.max_cut_part,
min_cut_part=self.min_cut_part)
parts = parts1 + parts2
for part in parts: # 目前是取iv最大的组，未来可以加取分组数最多的组
part = sorted(list(set(part) - {-np.inf, np.inf, np.nan}))
if len(part) <= 2:
continue
bins_ = pd.cut(df_input['WOE'], part, include_lowest=True)
df_input['bins'] = bins_.cat.codes
tmp_df = pd.pivot_table(df_input,
values=['Bad_count', 'Good_count'],
index='bins',
aggfunc=np.sum)
tmp_df = _Basic.add_basic_info_to_df(tmp_df)
tmp_df['total_percent'] = tmp_df['total'].div(
tmp_df['total'].sum())
if tmp_df['total_percent'].min(
) < self.group_min_percent: # 判断每组最小占比
continue
tmp_df = _Basic.add_woe_iv_to_df(tmp_df)
tmp_df = _Basic.add_ks_to_df(tmp_df)
if self.choice_by.lower() == "iv":
item_value = tmp_df['IV'].sum()
elif self.choice_by.lower() == 'woe':
item_value = tmp_df['WOE'].sum()
elif self.choice_by.lower() == 'ks':
item_value = tmp_df['KS'].max()
elif self.choice_by.lower() == 'len':
item_value = tmp_df.shape[0]
else:
raise ValueError('"choice_by" support ["iv","woe","ks","len"],'
'But get "{0}"'.format(self.choice_by))
if item_value > choice_item_value:
last_cut_points = part
if last_cut_points == []:
return False, False
df_input['bins'] = pd.cut(df_input['WOE'],
last_cut_points,
include_lowest=True).cat.codes
groups = {} # 分组号和每组的列表{1:["ZJ","SH"],2:["XJ","BJ"]}
for i in df_input['bins'].unique():
items_ = df_input[df_input['bins'] == i].index.tolist()
groups[i] = items_
df_ = _Basic.basic_prepare(y, bad=self.bad, good=self.good)
df_[single_series.name] = single_series
for code, items in groups.iteritems():
df_[single_series.name] = df_[single_series.name].replace(
items, code)
df_ = _Basic.get_pivot_table(df_, y.name, single_series.name)
df_ = _Basic.add_basic_info_to_df(df_)
df_ = _Basic.add_woe_iv_to_df(df_)
df_ = _Basic.add_ks_to_df(df_)
bins_ = pd.Series(['|'.join(i) for i in groups.values()],
index=[i for i in groups.keys()],
name='var_scope')
df_ = pd.concat([df_, bins_], axis=1)
return groups, df_ # ({1:["ZJ","SH"],2:["XJ","BJ"]}, df)