Bootstrapping Final Model #716

S-Karnik · 2023-01-13T16:28:16Z

This PR will add the capability to bootstrap the final model only for estimators with final models.

…r each arg

microsoft-github-policy-service · 2023-01-13T16:28:23Z

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S-Karnik · 2023-01-13T16:29:51Z

@microsoft-github-policy-service agree company="Microsoft Research"

kbattocchi

Overall, the progress here is great. However, there are a number of small items to address.

econml/panel/dml/_dml.py

kbattocchi · 2023-01-24T16:23:41Z

econml/_ortho_learner.py

+                        return arg
+                if arg is None:
+                    return None
+                if isinstance(arg, tuple):


Why might we need to handle tuples instead of just arrays? Should the same change be made to _bootstrap.py?

More generally, it would be good to have a single implementation used in both _bootstrap.py and here rather than defining one (very similar) implementation in each; I think _bootstrap.py is the most appropriate home for such a function, but it could also go into our utilities if it seems generally useful.

The purpose of the handling of tuples is that the nuisances are passed in as tuples, which means that the individual nuisances in the tuple need to also be indexed.

econml/dml/_rlearner.py

econml/dml/dml.py

kbattocchi · 2023-01-24T16:33:15Z

econml/inference/_bootstrap.py

+        """
+        For DynamicDML only
+        Take n_bootstrap sets of samples of length n_panels among arange(n_panels) and then each sample corresponds with the chunk
+
+        """


Make this a comment rather than a docstring, and put it with the corresponding logic.

kbattocchi · 2023-01-24T16:39:04Z

econml/tests/test_bootstrap.py

+        elapsed_time = end_time - start_time
+        final_bootstrapping_time = elapsed_time
+
+        assert (all_bootstrapping_time > final_bootstrapping_time) or np.isclose(all_bootstrapping_time, final_bootstrapping_time)


For numeric results, we might tolerate near-equality rather than forcing a strict comparison, but here we really do expect the times to be significantly faster.

Suggested change

assert (all_bootstrapping_time > final_bootstrapping_time) or np.isclose(all_bootstrapping_time, final_bootstrapping_time)

assert (all_bootstrapping_time > final_bootstrapping_time)

econml/panel/dml/_dml.py

kbattocchi · 2023-01-24T18:44:15Z

econml/inference/_bootstrap.py

        def convertArg(arg, inds):
+            def convertArg_(arg, inds):
+                arr = np.asarray(arg)
+                if arr.ndim > 0:
+                    return arr[inds]
+                else:  # arg was a scalar, so we shouldn't have converted it
+                    return arg
            if arg is None:
                return None
-            arr = np.asarray(arg)
-            if arr.ndim > 0:
-                return arr[inds]
-            else:  # arg was a scalar, so we shouldn't have converted it
-                return arg
-
-        self._instances = Parallel(n_jobs=self._n_jobs, prefer='threads', verbose=self._verbose)(
-            delayed(fit)(obj,
-                         *[convertArg(arg, inds) for arg in args],
-                         **{arg: convertArg(named_args[arg], inds) for arg in named_args})
-            for obj, inds in zip(self._instances, indices)
-        )
+            if isinstance(arg, tuple):
+                converted_arg = []
+                for arg_param in arg:
+                    converted_arg.append(convertArg_(arg_param, inds))
+                return tuple(converted_arg)
+            return convertArg_(arg, inds)
+


I think it's probably worth reworking the API here a bit to pull this and some of the neighboring code out to a a new top-level function in this module (say, fit_with_subsets(clones, strata, *args, **kwargs)) that calls fit on each clone in parallel with the correctly modified arguments. This could then be used both here as well as directly within OrthoLearner for the final_only case.

kbattocchi · 2023-01-24T18:47:49Z

econml/inference/_bootstrap.py

+                for i in range(self._n_bootstrap_samples):
+                    if self._only_final:
+                        obj._set_current_cloned_ortho_learner_model_final(i)
+                    else:
+                        obj = self._instances[i]
+                    instance_results.append(f(obj, name))
+                instance_results = np.array(instance_results)
+                results = instance_results, f(self._wrapped, name)


I think it would be better to continue to use parallelism here if possible.

Based on our discussion, it seems like keeping this in _bootstrap.py and using parallel processes or threads will not be possible. In the future, we would like to make this call in _ortho_learner.py, just as we do with fit.

S-Karnik · 2023-02-03T15:18:36Z

@microsoft-github-policy-service agree company="Microsoft Research"

@microsoft-github-policy-service agree company="Microsoft Research"

Sathwik Karnik (Populus Group LLC) added 5 commits January 10, 2023 13:54

progress towards bootstrapping on only final model

c1d9e70

second iter of bootstrapping, 3 fit functions, cached_values for args

8090d94

changed the bootstrap class to actually bootstrap the full dataset fo…

b358420

…r each arg

test cases, benchmarking for bootstrapping final model only

e33a7fb

cleaned up repo for PR

bc59f4f

Sathwik Karnik (Populus Group LLC) and others added 7 commits January 13, 2023 11:33

remove .c files

98d88d5

Merge branch 'microsoft:main' into main

c01a8b5

wrapping the final only bootstraps

907c02f

fixing the stratifying issue

ff564ec

alternative approach to bootstrapping

1100432

fixed bootstrapping for Dynamic DML?

6b4951e

remove output_T

8475d4d

kbattocchi requested changes Jan 24, 2023

View reviewed changes

Sathwik Karnik (Populus Group LLC) added 6 commits January 24, 2023 15:37

responding to most changes requested

c79f28f

fixed bootstrapping function name

2d0fdee

added parallel summarize proxy function

91c89e5

parallel process not threads

223e9d5

fixed remaining requested changes, revert back to non-parallel proxy

cb4a084

remove unused function

de62eb9

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Bootstrapping Final Model #716

Bootstrapping Final Model #716

S-Karnik commented Jan 13, 2023

microsoft-github-policy-service bot commented Jan 13, 2023

Contribution License Agreement

S-Karnik commented Jan 13, 2023 •

edited by kbattocchi

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S-Karnik commented Feb 3, 2023

	assert (all_bootstrapping_time > final_bootstrapping_time) or np.isclose(all_bootstrapping_time, final_bootstrapping_time)
	assert (all_bootstrapping_time > final_bootstrapping_time)

Bootstrapping Final Model #716

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S-Karnik commented Jan 13, 2023 •

edited by kbattocchi

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