Poisson- Valueerror:操作数无法与形状一起播放
使用从statsmodels.api
导入的泊松回归为sm。
我正在做类似的事情,但是另一个数据集。
https://timeseriesreason.com/contents/contents/poisson-regresson-regression-model/ 我的模型:
y_train.shape = (52, 52)
X_train.shape = (52, 503)
运行时会出现错误:
poisson_training_results = sm.GLM(y_train, X_train, family = sm.families.Poisson()).fit()
“ valueerror:操作数无法与形状(52,1,52)(52,503)一起广播”
它看起来只是随机添加'1' y_train形状。
有人知道为什么这样做吗?
编辑:代码。
mask = np.random.rand(len(final_delta_df)) < 0.8
df_train = final_delta_df[mask]
df_test = final_delta_df[~mask]
print('Training data set length = '+str(len(df_train)))
print('Testing data set length = '+str(len(df_test)))
训练数据集长度= 52 测试数据集长度= 12
expr = """deaths_sum ~ positive_auc + vax_pop + hospital_beds_avg + diabetes_prevalence + aged_70_older + stringency_avg + Avg_temp + Avg_UV_index + Avg_water_vapour + Delta_prop"""
y_train, X_train = dmatrices(expr, df_train, return_type = 'dataframe')
y_test, X_test = dmatrices(expr, df_test, return_type = 'dataframe')
poisson_training_results = sm.GLM(y_train, X_train, family = sm.families.Poisson()).fit()
Using the Poisson regression importing from statsmodels.api
as sm.
I am doing something similar to this, but a different dataset.
https://timeseriesreasoning.com/contents/poisson-regression-model/
In my model:
y_train.shape = (52, 52)
X_train.shape = (52, 503)
I get an error when I run:
poisson_training_results = sm.GLM(y_train, X_train, family = sm.families.Poisson()).fit()
"ValueError: operands could not be broadcast together with shapes (52,1,52) (52,503)"
It just looks like it randomly adds '1' to y_train shape.
Does anyone know why it is doing this?
Edit: CODE.
mask = np.random.rand(len(final_delta_df)) < 0.8
df_train = final_delta_df[mask]
df_test = final_delta_df[~mask]
print('Training data set length = '+str(len(df_train)))
print('Testing data set length = '+str(len(df_test)))
Training data set length=52
Testing data set length=12
expr = """deaths_sum ~ positive_auc + vax_pop + hospital_beds_avg + diabetes_prevalence + aged_70_older + stringency_avg + Avg_temp + Avg_UV_index + Avg_water_vapour + Delta_prop"""
y_train, X_train = dmatrices(expr, df_train, return_type = 'dataframe')
y_test, X_test = dmatrices(expr, df_test, return_type = 'dataframe')
poisson_training_results = sm.GLM(y_train, X_train, family = sm.families.Poisson()).fit()
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