Comments (4)
Berlyli866
commented on June 18, 2024
2
thanks !!! yeah i found it in the starter notebook. thank you !
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WillianFuks
commented on June 18, 2024
1
Looks like you optimized using hmc, in this case use the code discussed in the next cell:
def get_param_index(model, name):
for i, v in enumerate(model.parameters):
if v.name == name:
return i
tf.reduce_mean(ci.model.components_by_name['SparseLinearRegression/'].params_to_weights(
ci.model_samples[get_param_index(ci.model, 'SparseLinearRegression/_global_scale_variance')],
ci.model_samples[get_param_index(ci.model, 'SparseLinearRegression/_global_scale_noncentered')],
ci.model_samples[get_param_index(ci.model, 'SparseLinearRegression/_local_scale_variances')],
ci.model_samples[get_param_index(ci.model, 'SparseLinearRegression/_local_scales_noncentered')],
ci.model_samples[get_param_index(ci.model, 'SparseLinearRegression/_weights_noncentered')],
), axis=0)from tfcausalimpact.
WillianFuks
commented on June 18, 2024
Hi @Berlyli866 ,
Yes, there is. Please refer to the getting_started.ipnb on section "2.5 Understanding Results", there's some discussion about this subject.
As a reference, here's an example of getting the average weights when the optimization technique is default variational inference:
tf.reduce_mean(ci.model.components_by_name['SparseLinearRegression/'].params_to_weights(
ci.model_samples['SparseLinearRegression/_global_scale_variance'],
ci.model_samples['SparseLinearRegression/_global_scale_noncentered'],
ci.model_samples['SparseLinearRegression/_local_scale_variances'],
ci.model_samples['SparseLinearRegression/_local_scales_noncentered'],
ci.model_samples['SparseLinearRegression/_weights_noncentered'],
), axis=0)I think TFP already offers some functions for performing this operation but couldn't update the notebook so far.
Let me know if this helps you.
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Berlyli866
commented on June 18, 2024
Hey @WillianFuks ,
i used same code and got error
ci.model.components_by_name
tf.reduce_mean(ci.model.components_by_name['SparseLinearRegression/'].params_to_weights(
ci.model_samples['SparseLinearRegression/_global_scale_variance'],
ci.model_samples['SparseLinearRegression/_global_scale_noncentered'],
ci.model_samples['SparseLinearRegression/_local_scale_variances'],
ci.model_samples['SparseLinearRegression/_local_scales_noncentered'],
ci.model_samples['SparseLinearRegression/_weights_noncentered'],
), axis=0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_12815/3091180208.py in <module>
1 tf.reduce_mean(ci.model.components_by_name['SparseLinearRegression/'].params_to_weights(
----> 2 ci.model_samples['SparseLinearRegression/_global_scale_variance'],
3 ci.model_samples['SparseLinearRegression/_global_scale_noncentered'],
4 ci.model_samples['SparseLinearRegression/_local_scale_variances'],
5 ci.model_samples['SparseLinearRegression/_local_scales_noncentered'],
TypeError: list indices must be integers or slices, not str
i checked the ci.model_samples it's a list of array instead of a dictionary. do you know how to know the order of ci.model_samples['SparseLinearRegression/_global_scale_variance'],
ci.model_samples['SparseLinearRegression/_global_scale_noncentered'],
ci.model_samples['SparseLinearRegression/_local_scale_variances'],
ci.model_samples['SparseLinearRegression/_local_scales_noncentered'],
ci.model_samples['SparseLinearRegression/_weights_noncentered'],
in the array ? the ci.model_samples total has 7 elements
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Related Issues (20)
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