Poisson- Valueerror:操作数无法与形状一起播放

发布于 2025-01-25 05:06:57 字数 1371 浏览 0 评论 0原文

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()

如果你对这篇内容有疑问,欢迎到本站社区发帖提问 参与讨论,获取更多帮助,或者扫码二维码加入 Web 技术交流群。

扫码二维码加入Web技术交流群

发布评论

需要 登录 才能够评论, 你可以免费 注册 一个本站的账号。
列表为空,暂无数据
我们使用 Cookies 和其他技术来定制您的体验包括您的登录状态等。通过阅读我们的 隐私政策 了解更多相关信息。 单击 接受 或继续使用网站,即表示您同意使用 Cookies 和您的相关数据。
原文