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6 files changed

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lines changed

6 files changed

+40
-108
lines changed

‎coverage.txt‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -42,4 +42,4 @@ pkg/vtreat/vtreat_impl.py 710 78 89%
4242
TOTAL 1410 154 89%
4343

4444

45-
============================= 33 passed in 22.33s ==============================
45+
============================= 33 passed in 21.03s ==============================

‎docs/vtreat/vtreat_impl.html‎

Lines changed: 23 additions & 57 deletions
Large diffs are not rendered by default.

‎pkg/build/lib/vtreat/vtreat_impl.py‎

Lines changed: 8 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -109,7 +109,6 @@ def __init__(
109109
self.extra_args_ = None
110110
self.params_ = None
111111

112-
113112
def transform(self, data_frame: pandas.DataFrame) -> pandas.DataFrame:
114113
"""
115114
return a transformed data frame
@@ -142,13 +141,13 @@ class TreatmentPlan:
142141
xforms: Tuple[VarTransform, ...]
143142

144143
def __init__(
145-
self,
146-
*,
147-
outcome_name: Optional[str] = None,
148-
cols_to_copy: Optional[Iterable[str]] = None,
149-
num_list: Optional[Iterable[str]] = None,
150-
cat_list: Optional[Iterable[str]] = None,
151-
xforms: Iterable[Optional[VarTransform]]):
144+
self,
145+
*,
146+
outcome_name: Optional[str] = None,
147+
cols_to_copy: Optional[Iterable[str]] = None,
148+
num_list: Optional[Iterable[str]] = None,
149+
cat_list: Optional[Iterable[str]] = None,
150+
xforms: Iterable[Optional[VarTransform]]):
152151
self.outcome_name = outcome_name
153152
if cols_to_copy is None:
154153
self.cols_to_copy = tuple()
@@ -801,11 +800,9 @@ def fit_numeric_outcome_treatment(
801800
imputation_map=imputation_map,
802801
)
803802
if xform is not None:
804-
# noinspection PyTypeChecker
805803
xforms.append(xform)
806804
for vi in cat_list:
807805
if "impact_code" in params["coders"]:
808-
# noinspection PyTypeChecker
809806
xforms.append(
810807
fit_regression_impact_code(
811808
incoming_column_name=vi,
@@ -816,7 +813,6 @@ def fit_numeric_outcome_treatment(
816813
)
817814
)
818815
if "deviation_code" in params["coders"]:
819-
# noinspection PyTypeChecker
820816
xforms.append(
821817
fit_regression_deviation_code(
822818
incoming_column_name=vi,
@@ -827,12 +823,10 @@ def fit_numeric_outcome_treatment(
827823
)
828824
)
829825
if "prevalence_code" in params["coders"]:
830-
# noinspection PyTypeChecker
831826
xforms.append(
832827
fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
833828
)
834829
if "indicator_code" in params["coders"]:
835-
# noinspection PyTypeChecker
836830
xforms.append(
837831
fit_indicator_code(
838832
incoming_column_name=vi,
@@ -896,12 +890,10 @@ def fit_binomial_outcome_treatment(
896890
imputation_map=imputation_map,
897891
)
898892
if xform is not None:
899-
# noinspection PyTypeChecker
900893
xforms.append(xform)
901894
extra_args = {"outcome_target": outcome_target, "var_suffix": ""}
902895
for vi in cat_list:
903896
if "logit_code" in params["coders"]:
904-
# noinspection PyTypeChecker
905897
xforms.append(
906898
fit_binomial_impact_code(
907899
incoming_column_name=vi,
@@ -912,12 +904,10 @@ def fit_binomial_outcome_treatment(
912904
)
913905
)
914906
if "prevalence_code" in params["coders"]:
915-
# noinspection PyTypeChecker
916907
xforms.append(
917908
fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
918909
)
919910
if "indicator_code" in params["coders"]:
920-
# noinspection PyTypeChecker
921911
xforms.append(
922912
fit_indicator_code(
923913
incoming_column_name=vi,
@@ -983,7 +973,6 @@ def fit_multinomial_outcome_treatment(
983973
imputation_map=imputation_map,
984974
)
985975
if xform is not None:
986-
# noinspection PyTypeChecker
987976
xforms.append(xform)
988977
for vi in cat_list:
989978
for outcome in outcomes:
@@ -992,7 +981,6 @@ def fit_multinomial_outcome_treatment(
992981
"outcome_target": outcome,
993982
"var_suffix": ("_" + str(outcome)),
994983
}
995-
# noinspection PyTypeChecker
996984
xforms.append(
997985
fit_binomial_impact_code(
998986
incoming_column_name=vi,
@@ -1003,12 +991,10 @@ def fit_multinomial_outcome_treatment(
1003991
)
1004992
)
1005993
if "prevalence_code" in params["coders"]:
1006-
# noinspection PyTypeChecker
1007994
xforms.append(
1008995
fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
1009996
)
1010997
if "indicator_code" in params["coders"]:
1011-
# noinspection PyTypeChecker
1012998
xforms.append(
1013999
fit_indicator_code(
10141000
incoming_column_name=vi,
@@ -1070,16 +1056,13 @@ def fit_unsupervised_treatment(
10701056
imputation_map=imputation_map,
10711057
)
10721058
if xform is not None:
1073-
# noinspection PyTypeChecker
10741059
xforms.append(xform)
10751060
for vi in cat_list:
10761061
if "prevalence_code" in params["coders"]:
1077-
# noinspection PyTypeChecker
10781062
xforms.append(
10791063
fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
10801064
)
10811065
if "indicator_code" in params["coders"]:
1082-
# noinspection PyTypeChecker
10831066
xforms.append(
10841067
fit_indicator_code(
10851068
incoming_column_name=vi,
@@ -1406,7 +1389,7 @@ def describe_ut(ut):
14061389

14071390

14081391
def pseudo_score_plan_variables(
1409-
*, cross_frame, plan:TreatmentPlan, params: Dict[str, Any]
1392+
*, cross_frame, plan: TreatmentPlan, params: Dict[str, Any]
14101393
) -> pandas.DataFrame:
14111394
"""
14121395
Build a score frame look-alike for unsupervised case.

‎pkg/dist/vtreat-1.2.0-py3-none-any.whl‎

-31 Bytes
Binary file not shown.

‎pkg/dist/vtreat-1.2.0.tar.gz‎

-93 Bytes
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‎pkg/vtreat/vtreat_impl.py‎

Lines changed: 8 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -109,7 +109,6 @@ def __init__(
109109
self.extra_args_ = None
110110
self.params_ = None
111111

112-
113112
def transform(self, data_frame: pandas.DataFrame) -> pandas.DataFrame:
114113
"""
115114
return a transformed data frame
@@ -142,13 +141,13 @@ class TreatmentPlan:
142141
xforms: Tuple[VarTransform, ...]
143142

144143
def __init__(
145-
self,
146-
*,
147-
outcome_name: Optional[str] = None,
148-
cols_to_copy: Optional[Iterable[str]] = None,
149-
num_list: Optional[Iterable[str]] = None,
150-
cat_list: Optional[Iterable[str]] = None,
151-
xforms: Iterable[Optional[VarTransform]]):
144+
self,
145+
*,
146+
outcome_name: Optional[str] = None,
147+
cols_to_copy: Optional[Iterable[str]] = None,
148+
num_list: Optional[Iterable[str]] = None,
149+
cat_list: Optional[Iterable[str]] = None,
150+
xforms: Iterable[Optional[VarTransform]]):
152151
self.outcome_name = outcome_name
153152
if cols_to_copy is None:
154153
self.cols_to_copy = tuple()
@@ -801,11 +800,9 @@ def fit_numeric_outcome_treatment(
801800
imputation_map=imputation_map,
802801
)
803802
if xform is not None:
804-
# noinspection PyTypeChecker
805803
xforms.append(xform)
806804
for vi in cat_list:
807805
if "impact_code" in params["coders"]:
808-
# noinspection PyTypeChecker
809806
xforms.append(
810807
fit_regression_impact_code(
811808
incoming_column_name=vi,
@@ -816,7 +813,6 @@ def fit_numeric_outcome_treatment(
816813
)
817814
)
818815
if "deviation_code" in params["coders"]:
819-
# noinspection PyTypeChecker
820816
xforms.append(
821817
fit_regression_deviation_code(
822818
incoming_column_name=vi,
@@ -827,12 +823,10 @@ def fit_numeric_outcome_treatment(
827823
)
828824
)
829825
if "prevalence_code" in params["coders"]:
830-
# noinspection PyTypeChecker
831826
xforms.append(
832827
fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
833828
)
834829
if "indicator_code" in params["coders"]:
835-
# noinspection PyTypeChecker
836830
xforms.append(
837831
fit_indicator_code(
838832
incoming_column_name=vi,
@@ -896,12 +890,10 @@ def fit_binomial_outcome_treatment(
896890
imputation_map=imputation_map,
897891
)
898892
if xform is not None:
899-
# noinspection PyTypeChecker
900893
xforms.append(xform)
901894
extra_args = {"outcome_target": outcome_target, "var_suffix": ""}
902895
for vi in cat_list:
903896
if "logit_code" in params["coders"]:
904-
# noinspection PyTypeChecker
905897
xforms.append(
906898
fit_binomial_impact_code(
907899
incoming_column_name=vi,
@@ -912,12 +904,10 @@ def fit_binomial_outcome_treatment(
912904
)
913905
)
914906
if "prevalence_code" in params["coders"]:
915-
# noinspection PyTypeChecker
916907
xforms.append(
917908
fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
918909
)
919910
if "indicator_code" in params["coders"]:
920-
# noinspection PyTypeChecker
921911
xforms.append(
922912
fit_indicator_code(
923913
incoming_column_name=vi,
@@ -983,7 +973,6 @@ def fit_multinomial_outcome_treatment(
983973
imputation_map=imputation_map,
984974
)
985975
if xform is not None:
986-
# noinspection PyTypeChecker
987976
xforms.append(xform)
988977
for vi in cat_list:
989978
for outcome in outcomes:
@@ -992,7 +981,6 @@ def fit_multinomial_outcome_treatment(
992981
"outcome_target": outcome,
993982
"var_suffix": ("_" + str(outcome)),
994983
}
995-
# noinspection PyTypeChecker
996984
xforms.append(
997985
fit_binomial_impact_code(
998986
incoming_column_name=vi,
@@ -1003,12 +991,10 @@ def fit_multinomial_outcome_treatment(
1003991
)
1004992
)
1005993
if "prevalence_code" in params["coders"]:
1006-
# noinspection PyTypeChecker
1007994
xforms.append(
1008995
fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
1009996
)
1010997
if "indicator_code" in params["coders"]:
1011-
# noinspection PyTypeChecker
1012998
xforms.append(
1013999
fit_indicator_code(
10141000
incoming_column_name=vi,
@@ -1070,16 +1056,13 @@ def fit_unsupervised_treatment(
10701056
imputation_map=imputation_map,
10711057
)
10721058
if xform is not None:
1073-
# noinspection PyTypeChecker
10741059
xforms.append(xform)
10751060
for vi in cat_list:
10761061
if "prevalence_code" in params["coders"]:
1077-
# noinspection PyTypeChecker
10781062
xforms.append(
10791063
fit_prevalence_code(incoming_column_name=vi, x=numpy.asarray(X[vi]))
10801064
)
10811065
if "indicator_code" in params["coders"]:
1082-
# noinspection PyTypeChecker
10831066
xforms.append(
10841067
fit_indicator_code(
10851068
incoming_column_name=vi,
@@ -1406,7 +1389,7 @@ def describe_ut(ut):
14061389

14071390

14081391
def pseudo_score_plan_variables(
1409-
*, cross_frame, plan:TreatmentPlan, params: Dict[str, Any]
1392+
*, cross_frame, plan: TreatmentPlan, params: Dict[str, Any]
14101393
) -> pandas.DataFrame:
14111394
"""
14121395
Build a score frame look-alike for unsupervised case.

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