Provide details on how to fix error when ww schema is broken

[freddyaboulton]

Pull Request Description

Fixes #2353

This is the new stacktrace:

from evalml.pipelines import ComponentGraph
from evalml.demos import load_fraud
cg = ComponentGraph({"Imputer": ["Imputer"],
                     "DateTime": ["DateTime Featurization Component", "Imputer.x"],
                     "OneHot": ["One Hot Encoder", "DateTime.x"],
                     "TargetImputer": ["Target Imputer", "OneHot.x", "OneHot.y"],
                     "Logistic": ["Logistic Regression Classifier", "TargetImputer.x", "TargetImputer.y"]})
cg.instantiate({})
X, y = load_fraud(1000)
X["card_id"] = X["provider"].astype("category")
cg.fit(X, y)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-74772725e1f8> in <module>
      9 X, y = load_fraud(1000)
     10 X["card_id"] = X["provider"].astype("category")
---> 11 cg.fit(X, y)

~/sources/evalml/evalml/pipelines/component_graph.py in fit(self, X, y)
    194             y (pd.Series): The target training data of length [n_samples]
    195         """
--> 196         X = infer_feature_types(X)
    197         y = infer_feature_types(y)
    198         self._compute_features(self.compute_order, X, y, fit=True)

~/sources/evalml/evalml/utils/woodwork_utils.py in infer_feature_types(data, feature_types)
     75                     f"get rid of this message. This is a more detailed message about the mismatch: {ww_error}"
     76                 )
---> 77             raise ValueError(ww_error)
     78         data.ww.init(schema=data.ww.schema)
     79         return data

ValueError: Dataframe types are not consistent with logical types. This usually happens when a data transformation does not go through the ww accessor. Call df.ww.init() to get rid of this message. This is a more detailed message about the mismatch: dtype mismatch for column card_id between DataFrame dtype, category, and Integer dtype, int64

---

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]

Codecov Report

Merging #2466 (08a1084) into main (2ce178e) will increase coverage by 0.1%.
The diff coverage is 100.0%.

❗ Current head 08a1084 differs from pull request most recent head dd87381. Consider uploading reports for the commit dd87381 to get more accurate results

@@           Coverage Diff           @@
##            main   #2466     +/-   ##
=======================================
+ Coverage   99.7%   99.7%   +0.1%     
=======================================
  Files        283     283             
  Lines      25566   25577     +11     
=======================================
+ Hits       25464   25475     +11     
  Misses       102     102             

Impacted Files	Coverage Δ
evalml/tests/utils_tests/test_woodwork_utils.py	100.0% <100.0%> (ø)
evalml/utils/woodwork_utils.py	100.0% <100.0%> (ø)
evalml/objectives/fraud_cost.py	100.0% <0.0%> (ø)
...alml/objectives/binary_classification_objective.py	100.0% <0.0%> (ø)
...alml/tests/objective_tests/test_fraud_detection.py	100.0% <0.0%> (ø)

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[bchen1116]

Nice fix! The message is clear!

[angela97lin]

LGTM, thanks for doing this! 😁

[chukarsten]

Send it.