Aggregate prediction explanations for derived features #1901

freddyaboulton · 2021-02-25T22:17:10Z

Pull Request Description

We will only aggregate the values for the features that we know the provenance of. Otherwise, no aggregation will happen. Which is basically the current behavior.

Sample output on titanic dataset

	Best 1 of 5

		Predicted Probabilities: [0: 0.996, 1: 0.004]
		Predicted Value: 0
		Target Value: 0
		Cross Entropy: 0.004
		Index ID: 322

		    Feature Name           Feature Value         Contribution to      SHAP Value
		                                                    Prediction                  
		================================================================================
		         Age                   20.00                    +                0.28   
		        Fare                   69.55                    +                0.04   
		  Parents/Children              2.00                    -               -0.16   
		       Aboard                                                                   
		        Name             Mr. George John Jr             -               -0.31   
		                                Sage                                            
		       Pclass                   3.00                    -               -1.10   
		         Sex                    male                    --              -1.42   
		  Siblings/Spouses              8.00                   ---              -2.50   
		       Aboard

Example of drill_down dict on titanic dataset

pred['explanations'][0]['explanations'][0]['drill_down']

{'Name': {'feature_names': ['POLARITY_SCORE(Name)',
   'LSA(Name)[1]',
   'DIVERSITY_SCORE(Name)',
   'LSA(Name)[0]',
   'MEAN_CHARACTERS_PER_WORD(Name)'],
  'feature_values': [0.0, -0.010237950687552993, 1.0, 0.3208657470990783, 3.6],
  'qualitative_explanation': ['+', '+', '+', '-', '-'],
  'quantitative_explanation': [0.06198793040637707,
   0.01567506508862504,
   0.0,
   -0.13351634379165223,
   -0.25702767905947055]},
 'Sex': {'feature_names': ['Sex_male', 'Sex_female'],
  'feature_values': [1.0, 0.0],
  'qualitative_explanation': ['-', '-'],
  'quantitative_explanation': [-0.36807136387713535, -1.0517515360270118]}}

Sample output of explain_predictions_best_worst on fraud dataset

	Best 1 of 5

		Predicted Probabilities: [False: 0.988, True: 0.012]
		Predicted Value: False
		Target Value: False
		Cross Entropy: 0.012
		Index ID: 754

		Feature Name      Feature Value      Contribution to Prediction   SHAP Value
		============================================================================
		  currency             SDG                       +                   0.00   
		  datetime     2019-04-05 11:16:41               -                  -0.00   
		  provider          Discover                     -                  -0.00   
		   amount             73.00                    -----                -2.36

After creating the pull request: in order to pass the release_notes_updated check you will need to update the "Future Release" section of docs/source/release_notes.rst to include this pull request by adding :pr:123.

codecov · 2021-02-25T22:25:44Z

Codecov Report

Merging #1901 (f8ffba7) into main (c499006) will increase coverage by 0.1%.
The diff coverage is 100.0%.

@@            Coverage Diff            @@
##             main    #1901     +/-   ##
=========================================
+ Coverage   100.0%   100.0%   +0.1%     
=========================================
  Files         267      267             
  Lines       21536    21715    +179     
=========================================
+ Hits        21530    21709    +179     
  Misses          6        6

Impacted Files	Coverage Δ
...derstanding/prediction_explanations/_algorithms.py	`97.8% <100.0%> (+0.7%)`	⬆️
...tanding/prediction_explanations/_user_interface.py	`100.0% <100.0%> (ø)`
...nderstanding/prediction_explanations/explainers.py	`100.0% <100.0%> (ø)`
evalml/tests/conftest.py	`100.0% <100.0%> (ø)`
...s/prediction_explanations_tests/test_algorithms.py	`100.0% <100.0%> (ø)`
...s/prediction_explanations_tests/test_explainers.py	`100.0% <100.0%> (ø)`
...ediction_explanations_tests/test_user_interface.py	`100.0% <100.0%> (ø)`

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update c499006...f8ffba7. Read the comment docs.

chukarsten

Wow, that's a great addition. Nice job.

chukarsten · 2021-03-01T18:59:16Z

evalml/model_understanding/prediction_explanations/_algorithms.py

+        provenance (dict): A mapping from a feature in the original data to the names of the features that were created
+            from that feature
+    Returns:
+        dict


Love this doc string. Very clear. I think the return just needs a description.

chukarsten · 2021-03-01T18:59:23Z

evalml/model_understanding/prediction_explanations/_algorithms.py

+    Arguments:
+        values (dict):  A mapping of feature names to a list of SHAP values for each data point.
+        provenance (dict): A mapping from a feature in the original data to the names of the features that were created
+            from that feature


supernit: period?

chukarsten · 2021-03-01T19:13:20Z

evalml/model_understanding/prediction_explanations/_user_interface.py

        json_rows = _rows_to_dict(rows)
+        drill_down = self.make_drill_down_dict(self.provenance, shap_values[1], normalized_values[1],
+                                               pipeline_features, original_features, self.include_shap_values)
+        json_rows["drill_down"] = drill_down


Is "json_rows" a carryover copy pasta? It's kinda weird in make_dict()

Changed the name to dict_rows!

bchen1116

LGTM! I left a few comments on docstrings and some nitpicks, but nothing blocking.

bchen1116 · 2021-03-02T16:02:13Z

evalml/tests/conftest.py

+
+
+@pytest.fixture
+def fraud_100():


bchen1116 · 2021-03-02T16:39:34Z

evalml/tests/model_understanding_tests/prediction_explanations_tests/test_user_interface.py


    table_maker = table_maker.make_text if output_format == "text" else table_maker.make_dict

-    table = table_maker(values, normalized_values, pipeline_features, top_k=3, include_shap_values=include_shap)
+    table = table_maker(values, normalized_values, values, normalized_values, pipeline_features, pipeline_features)


nit-pick: I was really confused when I saw these input params repeated. Any chance you can add the keys, like:

table = table_maker(aggregated_shap_values=values, aggregated_normalized_values=normalized_values, shap_values=values, normalized_values=normalized_values, pipeline_features=pipeline_features, original_features=pipeline_features)

just to make it a little clearer?

bchen1116 · 2021-03-02T16:40:54Z

evalml/model_understanding/prediction_explanations/_user_interface.py

    @abc.abstractmethod
-    def make_text(self, shap_values, normalized_values, pipeline_features, top_k, include_shap_values=False):
+    def make_text(self, aggregated_shap_values, aggregated_normalized_values,
+                  shap_values, normalized_values, pipeline_features, orignal_features):


typo: original_features

bchen1116 · 2021-03-02T18:06:22Z

evalml/model_understanding/prediction_explanations/_user_interface.py

            json_output_for_class["class_name"] = _make_json_serializable(class_name)
            json_output.append(json_output_for_class)
        return {"explanations": json_output}


-def _make_single_prediction_shap_table(pipeline, pipeline_features, index_to_explain, top_k=3,
+def _make_single_prediction_shap_table(pipeline, pipeline_features, input_features, index_to_explain, top_k=3,


Should we update this docstring to include input_features and index_to_explain?

bchen1116 · 2021-03-02T18:07:05Z

evalml/model_understanding/prediction_explanations/_user_interface.py

@@ -395,7 +473,7 @@ def __init__(self, top_k_features, include_shap_values):
        self.top_k_features = top_k_features
        self.include_shap_values = include_shap_values

-    def make_text(self, index, pipeline, pipeline_features):
+    def make_text(self, index, pipeline, pipeline_features, input_features):


update doc string

…data.

freddyaboulton marked this pull request as ready for review February 26, 2021 16:26

auto-assign bot assigned freddyaboulton Feb 26, 2021

chukarsten approved these changes Mar 1, 2021

View reviewed changes

freddyaboulton requested review from dsherry, angela97lin, bchen1116, ParthivNaresh and jeremyliweishih March 1, 2021 20:23

freddyaboulton force-pushed the 1347-aggregate-prediction-explanations-for-categorical-text-features branch from edf28c1 to 0bebd6a Compare March 1, 2021 21:21

bchen1116 approved these changes Mar 2, 2021

View reviewed changes

freddyaboulton force-pushed the 1347-aggregate-prediction-explanations-for-categorical-text-features branch 3 times, most recently from f681308 to e497258 Compare March 3, 2021 15:08

freddyaboulton added 15 commits March 3, 2021 13:45

Rough draft.

698c49f

Displaying pipeline features when the feature is not in the original …

00bdcc5

…data.

Displaying aggregated shap values.

49287d5

Fixing some tests.

26d8030

Unit tests passing.

777e59a

Aggregating all features we can aggregate. Adding tests.

91a6f57

Adding comment explaining feature selection in _make_rows.

c3e0e39

Adding comments to _aggregate_shap_values.

3fbd52e

Fixing docstring in _aggregate_shap_values.

9f82dfb

Adding PR 1901 to release notes.

ee21707

Adding extra test case for when some created features are dropped.

225bde0

Adding comments to _user_interface.

d7c484e

Updating comment in _aggregate_shap_values_dict.

fb0b025

Fixing docstrings.

830775a

Updating docstring in _aggregate_shap_values_dict.

f8ffba7

freddyaboulton force-pushed the 1347-aggregate-prediction-explanations-for-categorical-text-features branch from e497258 to f8ffba7 Compare March 3, 2021 18:45

freddyaboulton merged commit 3b01866 into main Mar 3, 2021

freddyaboulton deleted the 1347-aggregate-prediction-explanations-for-categorical-text-features branch March 3, 2021 19:21

dsherry mentioned this pull request Mar 11, 2021

v0.20.0 #1961

Merged

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Aggregate prediction explanations for derived features #1901

Aggregate prediction explanations for derived features #1901

freddyaboulton commented Feb 25, 2021 •

edited

codecov bot commented Feb 25, 2021 •

edited

chukarsten left a comment

chukarsten Mar 1, 2021

freddyaboulton Mar 2, 2021

chukarsten Mar 1, 2021

chukarsten Mar 1, 2021

freddyaboulton Mar 2, 2021

bchen1116 left a comment

bchen1116 Mar 2, 2021

bchen1116 Mar 2, 2021

bchen1116 Mar 2, 2021

bchen1116 Mar 2, 2021

freddyaboulton Mar 2, 2021

bchen1116 Mar 2, 2021

Aggregate prediction explanations for derived features #1901

Aggregate prediction explanations for derived features #1901

Conversation

freddyaboulton commented Feb 25, 2021 • edited

Pull Request Description

Sample output on titanic dataset

Example of drill_down dict on titanic dataset

Sample output of explain_predictions_best_worst on fraud dataset

codecov bot commented Feb 25, 2021 • edited

Codecov Report

chukarsten left a comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

bchen1116 left a comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

freddyaboulton commented Feb 25, 2021 •

edited

codecov bot commented Feb 25, 2021 •

edited