Add more doctests #3002
Add more doctests #3002
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# Conflicts: # evalml/data_checks/invalid_targets_data_check.py # evalml/data_checks/target_distribution_data_check.py
Codecov Report
@@ Coverage Diff @@
## main #3002 +/- ##
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+ Coverage 99.7% 99.7% +0.1%
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Files 312 312
Lines 29853 29856 +3
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+ Hits 29762 29765 +3
Misses 91 91
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|
ParthivNaresh
requested review from
angela97lin,
jeremyliweishih,
freddyaboulton,
bchen1116 and
christopherbunn
| @@ -25,12 +25,12 @@ def validate(self, X, y): | |||
| Returns: | |||
| dict (DataCheckError): List with DataCheckErrors if unequal intervals are found in the datetime column. | |||
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| Example: | |||
| Examples: | |||
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Could just be personal opinion, but I think if we add this many examples (which is great!), it gets a little harder to read. Maybe what we need here is an extra line above each example to better understand what each example is trying to convey? From a quick glance, it's a bit hard to gauge the purpose of each example 😬
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Sure thing, so maybe a comment above a doctest briefly explaining what it does?
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@ParthivNaresh Thank you for this! This looks good to me. I agree with @angela97lin that since some docstrings test different things, it might be helpful to add some in-line comments above each one to document what's happening.
Other than that, I think it would be helpful if some of the assert float1 == float2 checks used rounding so that these don't start to flake if we're off by negligible amount in a future release.
| metadata={"columns": too_sparse_cols}, | ||
| ).to_dict() | ||
| ) | ||
| if too_sparse_cols: |
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How come we need to add this?
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If none of the columns are returned from the sparsity_score, results still appends warning and actions. The warning message ends up being "Input columns ({}) for multiclass problem type are too sparse."
| Examples: | ||
| >>> from evalml.pipelines.components.estimators.regressors.decision_tree_regressor import DecisionTreeRegressor | ||
| >>> assert generate_component_code(DecisionTreeRegressor()) == "from evalml.pipelines.components.estimators.regressors.decision_tree_regressor import DecisionTreeRegressor\n\ndecisionTreeRegressor = DecisionTreeRegressor(**{'criterion': 'mse', 'max_features': 'auto', 'max_depth': 6, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0})" | ||
| ... |
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Docs don't build without the r prefix? Is it cause of these three dots here? The example renders fine in the doc so I'm just wondering what happened but no need to change anything.
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The lint check doesn't pass, I think due to the \n characters in the output
| @@ -84,7 +110,7 @@ def validate(self, X, y=None): | |||
| percent_null_rows >= self.pct_null_row_threshold | |||
| ] | |||
| if len(highly_null_rows) > 0: | |||
| warning_msg = f"{len(highly_null_rows)} out of {len(X)} rows are more than {self.pct_null_row_threshold*100}% null" | |||
| warning_msg = f"{len(highly_null_rows)} out of {len(X)} rows are {self.pct_null_row_threshold*100}% or more null" | |||
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Same comment as in the sparsity data check: Just wondering we were modifying this.
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Because the threshold check is percent_null_rows >= self.pct_null_row_threshold, so the phrasing or more seemed more appropriate than more than
Completes #2936