except Exception as e:
print("缺失严重, 插值未定义:", e)
return input_data
data_pollution_IDW = get_IDW(data_pollution)
# 空间全局: 迭代函数法,缺失特征作为y，其他特征作为x
data_pollution_Iterative = IterativeImputer(
max_iter=10).fit_transform(data_pollution)
data_pollution_Iterative = pd.DataFrame(data_pollution_Iterative)
# 对结果的0值取np.nan
data_pollution_KNN.replace(0, np.nan, inplace=True)
data_pollution_ewm.replace(0, np.nan, inplace=True)
data_pollution_IDW.replace(0, np.nan, inplace=True)
data_pollution_Iterative.replace(0, np.nan, inplace=True)
# 合并相同方法的结果
data_pollution_KNN = data_pollution_KNN.set_index(data_pollution.index)
data_pollution_KNN.columns = data_pollution.columns
# data_pollution_KNN["日期合并用"] = data_pollution_KNN.index
data_pollution_ewm = data_pollution_ewm.set_index(data_pollution.index)
data_pollution_ewm.columns = data_pollution.columns
# data_pollution_ewm["日期合并用"] = data_pollution_ewm.index
data_pollution_IDW = data_pollution_IDW.set_index(data_pollution.index)
data_pollution_IDW.columns = data_pollution.columns
# data_pollution_IDW["日期合并用"] = data_pollution_IDW.index
data_pollution_Iterative = data_pollution_Iterative.set_index(
data_pollution.index)
data_pollution_Iterative.columns = data_pollution.columns
# data_pollution_Iterative["日期合并用"] = data_pollution_Iterative.index
if 'add' in CCCOLT:
del data_Terra_Iterative[CCCOLT]
data_Terra_Iterative = pd.DataFrame(data_Terra_Iterative)
data_Terra_Iterative = data_Terra_Iterative.set_index(data_Terra.index)
data_Terra_Iterative.columns = ['NDVI_0']
# data_Terra_Iterative["日期合并用"] = data_Terra_Iterative.index
# 对结果的0值取np.nan
data_Aqua_KNN.replace(0, np.nan, inplace=True)
data_Terra_KNN.replace(0, np.nan, inplace=True)
data_Aqua_ewm.replace(0, np.nan, inplace=True)
data_Terra_ewm.replace(0, np.nan, inplace=True)
data_Aqua_IDW.replace(0, np.nan, inplace=True)
data_Terra_IDW.replace(0, np.nan, inplace=True)
data_Aqua_Iterative.replace(0, np.nan, inplace=True)
data_Terra_Iterative.replace(0, np.nan, inplace=True)
"""
data_KNN = pd.merge(data_Terra_KNN,data_Aqua_KNN,how='right',on='日期合并用')
data_ewm = pd.merge(data_Terra_ewm,data_Aqua_ewm,how='right',on='日期合并用')
data_IDW = pd.merge(data_Terra_IDW,data_Aqua_IDW,how='right',on='日期合并用')
data_Iterative = pd.merge(data_Terra_Iterative,data_Aqua_Iterative,how='right',on='日期合并用')
"""
data_KNN = pd.concat([data_Terra_KNN, data_Aqua_KNN], axis=1, sort=True)
data_ewm = pd.concat([data_Terra_ewm, data_Aqua_ewm], axis=1, sort=True)
data_IDW = pd.concat([data_Terra_IDW, data_Aqua_IDW], axis=1, sort=True)
data_Iterative = pd.concat([data_Terra_Iterative, data_Aqua_Iterative],
axis=1,
sort=True)
# 合并不同方法下的A/T为一个文件
ignore_na=False,
adjust=True).mean() # 参数设置不同
# 空间局部: IDW
data_input_IDW = get_IDW(data_input)
# 空间全局: 迭代函数法,缺失特征作为y，其他特征作为x
data_input_Iterative = IterativeImputer(
max_iter=10).fit_transform(data_input)
data_input_Iterative = pd.DataFrame(data_input_Iterative)
# 对结果的0值取np.nan
data_input_KNN.replace(0, np.nan, inplace=True)
data_input_ewm.replace(0, np.nan, inplace=True)
data_input_IDW.replace(0, np.nan, inplace=True)
data_input_Iterative.replace(0, np.nan, inplace=True)
# 合并相同方法的结果
data_input_KNN = data_input_KNN.set_index(data_input.index)
data_input_KNN.columns = data_input.columns
data_input_KNN["日期合并用"] = data_input_KNN.index
data_input_ewm = data_input_ewm.set_index(data_input.index)
data_input_ewm.columns = data_input.columns
data_input_ewm["日期合并用"] = data_input_ewm.index
data_input_IDW = data_input_IDW.set_index(data_input.index)
data_input_IDW.columns = data_input.columns
data_input_IDW["日期合并用"] = data_input_IDW.index
data_input_Iterative = data_input_Iterative.set_index(data_input.index)
data_input_Iterative.columns = data_input.columns
data_input_Iterative["日期合并用"] = data_input_Iterative.index
