Remove few dev deps (#493)

* Remove mlflow, evidently and prefect package from dev deps * Update ruff to 0.1.0, unused remove type ignores * fix type annotation * Remove category-encoders dependency --------- Co-authored-by: Franklin <[email protected]>
VectorInstitute · Oct 20, 2023 · 3065072 · 3065072
1 parent 2300338
commit 3065072
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -21,7 +21,7 @@ repos:
     - id: black
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.0.288'
+    rev: 'v0.1.0'
     hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix]

diff --git a/cyclops/evaluate/evaluator.py b/cyclops/evaluate/evaluator.py
@@ -293,8 +293,8 @@ def _prepare_models(
 ) -> Dict[str, WrappedModel]:
     """Prepare models for evaluation."""
     if isinstance(model, get_args(WrappedModel)):
-        model_name: str = model.model_.__class__.__name__  # type: ignore
-        return {model_name: model}  # type: ignore[dict-item]
+        model_name: str = model.model_.__class__.__name__
+        return {model_name: model}
     if isinstance(model, (list, tuple)):
         assert all(isinstance(m, get_args(WrappedModel)) for m in model)
         return {m.getattr("model_").__class__.__name__: m for m in model}

diff --git a/cyclops/evaluate/fairness/evaluator.py b/cyclops/evaluate/fairness/evaluator.py
@@ -234,7 +234,7 @@ def evaluate_fairness(
             # since we have base values, remove overall slice
             slice_spec._registry.pop("overall", None)
 
-        results: Dict[str, Dict[str, Any]] = {}
+        results: Dict[str, Dict[str, Dict[str, Any]]] = {}
 
         for slice_name, slice_fn in slice_spec.slices():
             sliced_dataset = dataset.remove_columns(remove_columns or []).filter(
@@ -933,32 +933,33 @@ def _construct_base_slice_name(base_values: Dict[str, Any]) -> str:
 
 
 def _compute_parity_metrics(
-    results: Dict[str, Dict[str, Dict[str, Dict[str, float]]]],
+    results: Dict[str, Dict[str, Dict[str, Any]]],
     base_slice_name: str,
-) -> Dict[str, Dict[str, Dict[str, Dict[str, float]]]]:
+) -> Dict[str, Dict[str, Dict[str, Any]]]:
     """Compute the parity metrics for each group and threshold if specified.
 
     Parameters
     ----------
-    results : Dict[str, Dict[str, Dict[str, Dict[str, float]]]]
+    results : Dict[str, Dict[str, Dict[str, Any]]]
         A dictionary mapping the prediction column to the metrics dictionary.
     base_slice_name : str
         The name of the base slice.
 
     Returns
     -------
-    Dict[str, Dict[str, Dict[str, Dict[str, float]]]]
+    Dict[str, Dict[str, Dict[str, Any]]]
         A dictionary mapping the prediction column to the metrics dictionary.
 
     """
-    parity_results: Dict[str, Dict[str, Any]] = {}
+    parity_results: Dict[str, Dict[str, Dict[str, Any]]] = {}
 
     for key, prediction_result in results.items():
         parity_results[key] = {}
         for slice_name, slice_result in prediction_result.items():
             for metric_name, metric_value in slice_result.items():
                 if metric_name == "Group Size":
                     continue
+
                 # add 'Parity' to the metric name before @threshold, if specified
                 metric_name_parts = metric_name.split("@")
                 parity_metric_name = f"{metric_name_parts[0]} Parity"
@@ -967,14 +968,13 @@ def _compute_parity_metrics(
 
                 numerator = metric_value
                 denominator = prediction_result[base_slice_name][metric_name]
-                parity_metric_value = np.divide(  # type: ignore[call-overload]
+                parity_metric_value = np.divide(
                     numerator,
                     denominator,
                     out=np.zeros_like(numerator, dtype=np.float_),
                     where=denominator != 0,
                 )
 
-                # add the parity metric to the results
                 parity_results[key].setdefault(slice_name, {}).update(
                     {
                         parity_metric_name: _get_value_if_singleton_array(