diff --git a/coverage.txt b/coverage.txt index 4c08b10..75575a4 100644 --- a/coverage.txt +++ b/coverage.txt @@ -42,4 +42,4 @@ pkg/vtreat/vtreat_impl.py 710 78 89% TOTAL 1410 154 89% -============================= 33 passed in 22.33s ============================== +============================= 33 passed in 21.03s ============================== diff --git a/docs/vtreat/vtreat_impl.html b/docs/vtreat/vtreat_impl.html index c0df4aa..bb1c933 100644 --- a/docs/vtreat/vtreat_impl.html +++ b/docs/vtreat/vtreat_impl.html @@ -369,7 +369,6 @@

self.extra_args_ = None self.params_ = None - def transform(self, data_frame: pandas.DataFrame) -> pandas.DataFrame: """ return a transformed data frame @@ -402,13 +401,13 @@

xforms: Tuple[VarTransform, ...] def __init__( - self, - *, - outcome_name: Optional[str] = None, - cols_to_copy: Optional[Iterable[str]] = None, - num_list: Optional[Iterable[str]] = None, - cat_list: Optional[Iterable[str]] = None, - xforms: Iterable[Optional[VarTransform]]): + self, + *, + outcome_name: Optional[str] = None, + cols_to_copy: Optional[Iterable[str]] = None, + num_list: Optional[Iterable[str]] = None, + cat_list: Optional[Iterable[str]] = None, + xforms: Iterable[Optional[VarTransform]]): self.outcome_name = outcome_name if cols_to_copy is None: self.cols_to_copy = tuple() @@ -1061,11 +1060,9 @@

imputation_map=imputation_map, ) if xform is not None: - # noinspection PyTypeChecker xforms.append(xform) for vi in cat_list: if "impact_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_regression_impact_code( incoming_column_name=vi, @@ -1076,7 +1073,6 @@

) ) if "deviation_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_regression_deviation_code( incoming_column_name=vi, @@ -1087,12 +1083,10 @@

) ) if "prevalence_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi])) ) if "indicator_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_indicator_code( incoming_column_name=vi, @@ -1156,12 +1150,10 @@

imputation_map=imputation_map, ) if xform is not None: - # noinspection PyTypeChecker xforms.append(xform) extra_args = {"outcome_target": outcome_target, "var_suffix": ""} for vi in cat_list: if "logit_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_binomial_impact_code( incoming_column_name=vi, @@ -1172,12 +1164,10 @@

) ) if "prevalence_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi])) ) if "indicator_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_indicator_code( incoming_column_name=vi, @@ -1243,7 +1233,6 @@

imputation_map=imputation_map, ) if xform is not None: - # noinspection PyTypeChecker xforms.append(xform) for vi in cat_list: for outcome in outcomes: @@ -1252,7 +1241,6 @@

"outcome_target": outcome, "var_suffix": ("_" + str(outcome)), } - # noinspection PyTypeChecker xforms.append( fit_binomial_impact_code( incoming_column_name=vi, @@ -1263,12 +1251,10 @@

) ) if "prevalence_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi])) ) if "indicator_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_indicator_code( incoming_column_name=vi, @@ -1330,16 +1316,13 @@

imputation_map=imputation_map, ) if xform is not None: - # noinspection PyTypeChecker xforms.append(xform) for vi in cat_list: if "prevalence_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi])) ) if "indicator_code" in params["coders"]: - # noinspection PyTypeChecker xforms.append( fit_indicator_code( incoming_column_name=vi, @@ -1666,7 +1649,7 @@

def pseudo_score_plan_variables( - *, cross_frame, plan:TreatmentPlan, params: Dict[str, Any] + *, cross_frame, plan: TreatmentPlan, params: Dict[str, Any] ) -> pandas.DataFrame: """ Build a score frame look-alike for unsupervised case. @@ -2298,7 +2281,6 @@

self.extra_args_ = None self.params_ = None - def transform(self, data_frame: pandas.DataFrame) -> pandas.DataFrame: """ return a transformed data frame @@ -2463,13 +2445,13 @@

xforms: Tuple[VarTransform, ...] def __init__( - self, - *, - outcome_name: Optional[str] = None, - cols_to_copy: Optional[Iterable[str]] = None, - num_list: Optional[Iterable[str]] = None, - cat_list: Optional[Iterable[str]] = None, - xforms: Iterable[Optional[VarTransform]]): + self, + *, + outcome_name: Optional[str] = None, + cols_to_copy: Optional[Iterable[str]] = None, + num_list: Optional[Iterable[str]] = None, + cat_list: Optional[Iterable[str]] = None, + xforms: Iterable[Optional[VarTransform]]): self.outcome_name = outcome_name if cols_to_copy is None: self.cols_to_copy = tuple() @@ -2523,13 +2505,13 @@

View Source 
    def __init__(
-        self,
-        *,
-        outcome_name: Optional[str] = None,
-        cols_to_copy: Optional[Iterable[str]] = None,
-        num_list: Optional[Iterable[str]] = None,
-        cat_list: Optional[Iterable[str]] = None,
-        xforms: Iterable[Optional[VarTransform]]):
+            self,
+            *,
+            outcome_name: Optional[str] = None,
+            cols_to_copy: Optional[Iterable[str]] = None,
+            num_list: Optional[Iterable[str]] = None,
+            cat_list: Optional[Iterable[str]] = None,
+            xforms: Iterable[Optional[VarTransform]]):
         self.outcome_name = outcome_name
         if cols_to_copy is None:
             self.cols_to_copy = tuple()
@@ -3977,11 +3959,9 @@ 
Inherited Members

                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         if "impact_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_regression_impact_code(
                     incoming_column_name=vi,
@@ -3992,7 +3972,6 @@ 
Inherited Members

                 )
             )
         if "deviation_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_regression_deviation_code(
                     incoming_column_name=vi,
@@ -4003,12 +3982,10 @@ 
Inherited Members

                 )
             )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -4107,12 +4084,10 @@ 
Inherited Members

                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     extra_args = {"outcome_target": outcome_target, "var_suffix": ""}
     for vi in cat_list:
         if "logit_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_binomial_impact_code(
                     incoming_column_name=vi,
@@ -4123,12 +4098,10 @@ 
Inherited Members

                 )
             )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -4227,7 +4200,6 @@ 
Inherited Members

                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         for outcome in outcomes:
@@ -4236,7 +4208,6 @@ 
Inherited Members

                     "outcome_target": outcome,
                     "var_suffix": ("_" + str(outcome)),
                 }
-                # noinspection PyTypeChecker
                 xforms.append(
                     fit_binomial_impact_code(
                         incoming_column_name=vi,
@@ -4247,12 +4218,10 @@ 
Inherited Members

                     )
                 )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -4347,16 +4316,13 @@ 
Inherited Members

                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -4814,7 +4780,7 @@ 
Inherited Members

                 

             View Source
             
def pseudo_score_plan_variables(
-    *, cross_frame, plan:TreatmentPlan, params: Dict[str, Any]
+    *, cross_frame, plan: TreatmentPlan, params: Dict[str, Any]
 ) -> pandas.DataFrame:
     """
     Build a score frame look-alike for unsupervised case.
diff --git a/pkg/build/lib/vtreat/vtreat_impl.py b/pkg/build/lib/vtreat/vtreat_impl.py
index ba111af..aab366b 100644
--- a/pkg/build/lib/vtreat/vtreat_impl.py
+++ b/pkg/build/lib/vtreat/vtreat_impl.py
@@ -109,7 +109,6 @@ def __init__(
         self.extra_args_ = None
         self.params_ = None
 
-
     def transform(self, data_frame: pandas.DataFrame) -> pandas.DataFrame:
         """
         return a transformed data frame
@@ -142,13 +141,13 @@ class TreatmentPlan:
     xforms: Tuple[VarTransform, ...]
 
     def __init__(
-        self,
-        *,
-        outcome_name: Optional[str] = None,
-        cols_to_copy: Optional[Iterable[str]] = None,
-        num_list: Optional[Iterable[str]] = None,
-        cat_list: Optional[Iterable[str]] = None,
-        xforms: Iterable[Optional[VarTransform]]):
+            self,
+            *,
+            outcome_name: Optional[str] = None,
+            cols_to_copy: Optional[Iterable[str]] = None,
+            num_list: Optional[Iterable[str]] = None,
+            cat_list: Optional[Iterable[str]] = None,
+            xforms: Iterable[Optional[VarTransform]]):
         self.outcome_name = outcome_name
         if cols_to_copy is None:
             self.cols_to_copy = tuple()
@@ -801,11 +800,9 @@ def fit_numeric_outcome_treatment(
                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         if "impact_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_regression_impact_code(
                     incoming_column_name=vi,
@@ -816,7 +813,6 @@ def fit_numeric_outcome_treatment(
                 )
             )
         if "deviation_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_regression_deviation_code(
                     incoming_column_name=vi,
@@ -827,12 +823,10 @@ def fit_numeric_outcome_treatment(
                 )
             )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -896,12 +890,10 @@ def fit_binomial_outcome_treatment(
                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     extra_args = {"outcome_target": outcome_target, "var_suffix": ""}
     for vi in cat_list:
         if "logit_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_binomial_impact_code(
                     incoming_column_name=vi,
@@ -912,12 +904,10 @@ def fit_binomial_outcome_treatment(
                 )
             )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -983,7 +973,6 @@ def fit_multinomial_outcome_treatment(
                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         for outcome in outcomes:
@@ -992,7 +981,6 @@ def fit_multinomial_outcome_treatment(
                     "outcome_target": outcome,
                     "var_suffix": ("_" + str(outcome)),
                 }
-                # noinspection PyTypeChecker
                 xforms.append(
                     fit_binomial_impact_code(
                         incoming_column_name=vi,
@@ -1003,12 +991,10 @@ def fit_multinomial_outcome_treatment(
                     )
                 )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -1070,16 +1056,13 @@ def fit_unsupervised_treatment(
                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -1406,7 +1389,7 @@ def describe_ut(ut):
 
 
 def pseudo_score_plan_variables(
-    *, cross_frame, plan:TreatmentPlan, params: Dict[str, Any]
+    *, cross_frame, plan: TreatmentPlan, params: Dict[str, Any]
 ) -> pandas.DataFrame:
     """
     Build a score frame look-alike for unsupervised case.
diff --git a/pkg/dist/vtreat-1.2.0-py3-none-any.whl b/pkg/dist/vtreat-1.2.0-py3-none-any.whl
index fcab6d8..c75976d 100644
Binary files a/pkg/dist/vtreat-1.2.0-py3-none-any.whl and b/pkg/dist/vtreat-1.2.0-py3-none-any.whl differ
diff --git a/pkg/dist/vtreat-1.2.0.tar.gz b/pkg/dist/vtreat-1.2.0.tar.gz
index ed515d9..f067686 100644
Binary files a/pkg/dist/vtreat-1.2.0.tar.gz and b/pkg/dist/vtreat-1.2.0.tar.gz differ
diff --git a/pkg/vtreat/vtreat_impl.py b/pkg/vtreat/vtreat_impl.py
index ba111af..aab366b 100644
--- a/pkg/vtreat/vtreat_impl.py
+++ b/pkg/vtreat/vtreat_impl.py
@@ -109,7 +109,6 @@ def __init__(
         self.extra_args_ = None
         self.params_ = None
 
-
     def transform(self, data_frame: pandas.DataFrame) -> pandas.DataFrame:
         """
         return a transformed data frame
@@ -142,13 +141,13 @@ class TreatmentPlan:
     xforms: Tuple[VarTransform, ...]
 
     def __init__(
-        self,
-        *,
-        outcome_name: Optional[str] = None,
-        cols_to_copy: Optional[Iterable[str]] = None,
-        num_list: Optional[Iterable[str]] = None,
-        cat_list: Optional[Iterable[str]] = None,
-        xforms: Iterable[Optional[VarTransform]]):
+            self,
+            *,
+            outcome_name: Optional[str] = None,
+            cols_to_copy: Optional[Iterable[str]] = None,
+            num_list: Optional[Iterable[str]] = None,
+            cat_list: Optional[Iterable[str]] = None,
+            xforms: Iterable[Optional[VarTransform]]):
         self.outcome_name = outcome_name
         if cols_to_copy is None:
             self.cols_to_copy = tuple()
@@ -801,11 +800,9 @@ def fit_numeric_outcome_treatment(
                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         if "impact_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_regression_impact_code(
                     incoming_column_name=vi,
@@ -816,7 +813,6 @@ def fit_numeric_outcome_treatment(
                 )
             )
         if "deviation_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_regression_deviation_code(
                     incoming_column_name=vi,
@@ -827,12 +823,10 @@ def fit_numeric_outcome_treatment(
                 )
             )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -896,12 +890,10 @@ def fit_binomial_outcome_treatment(
                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     extra_args = {"outcome_target": outcome_target, "var_suffix": ""}
     for vi in cat_list:
         if "logit_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_binomial_impact_code(
                     incoming_column_name=vi,
@@ -912,12 +904,10 @@ def fit_binomial_outcome_treatment(
                 )
             )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -983,7 +973,6 @@ def fit_multinomial_outcome_treatment(
                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         for outcome in outcomes:
@@ -992,7 +981,6 @@ def fit_multinomial_outcome_treatment(
                     "outcome_target": outcome,
                     "var_suffix": ("_" + str(outcome)),
                 }
-                # noinspection PyTypeChecker
                 xforms.append(
                     fit_binomial_impact_code(
                         incoming_column_name=vi,
@@ -1003,12 +991,10 @@ def fit_multinomial_outcome_treatment(
                     )
                 )
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -1070,16 +1056,13 @@ def fit_unsupervised_treatment(
                 imputation_map=imputation_map,
             )
             if xform is not None:
-                # noinspection PyTypeChecker
                 xforms.append(xform)
     for vi in cat_list:
         if "prevalence_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
             )
         if "indicator_code" in params["coders"]:
-            # noinspection PyTypeChecker
             xforms.append(
                 fit_indicator_code(
                     incoming_column_name=vi,
@@ -1406,7 +1389,7 @@ def describe_ut(ut):
 
 
 def pseudo_score_plan_variables(
-    *, cross_frame, plan:TreatmentPlan, params: Dict[str, Any]
+    *, cross_frame, plan: TreatmentPlan, params: Dict[str, Any]
 ) -> pandas.DataFrame:
     """
     Build a score frame look-alike for unsupervised case.