Closes #86: Discard ApplicationContext post-apply

.gitignore

@@ -104,3 +104,6 @@ tags
 # don't like pipfiles
 Pipfile
 Pipfile.lock
+
+# Temporary testing file
+notebooks/debug*.py

pdpipe/core.py

@@ -810,7 +810,8 @@ def _transform(self, df, verbose):
         raise NotImplementedError
 
     def _post_transform_lock(self):
-        self.application_context.lock()
+        # Application context is discarded after pipeline application
+        self.application_context = None
         self.fit_context.lock()
 
     def apply(
@@ -819,7 +820,8 @@ def apply(
         exraise: Optional[bool] = None,
         verbose: Optional[bool] = False,
         time: Optional[bool] = False,
-        context: Optional[dict] = {},
+        fit_context: Optional[dict] = {},
+        application_context: Optional[dict] = {},
     ):
         """Applies this pipeline stage to the given dataframe.
 
@@ -860,15 +862,16 @@ def apply(
                 exraise=exraise,
                 verbose=verbose,
                 time=time,
-                context=context,
+                application_context=application_context,
             )
             return res
         res = self.fit_transform(
             X=df,
             exraise=exraise,
             verbose=verbose,
             time=time,
-            context=context,
+            fit_context=fit_context,
+            application_context=application_context,
         )
         return res
 
@@ -878,12 +881,13 @@ def __timed_fit_transform(
         y: Optional[Iterable] = None,
         exraise: Optional[bool] = None,
         verbose: Optional[bool] = False,
-        context: Optional[dict] = {},
+        fit_context: Optional[dict] = {},
+        application_context: Optional[dict] = {},
     ):
         self.fit_context = PdpApplicationContext()
-        self.fit_context.update(context)
+        self.fit_context.update(fit_context)
         self.application_context = PdpApplicationContext()
-        self.application_context.update(context)
+        self.application_context.update(application_context)
         inter_x = X
         times = []
         prev = time.time()
@@ -918,7 +922,8 @@ def fit_transform(
         exraise: Optional[bool] = None,
         verbose: Optional[bool] = False,
         time: Optional[bool] = False,
-        context: Optional[dict] = {},
+        fit_context: Optional[dict] = {},
+        application_context: Optional[dict] = {},
     ):
         """Fits this pipeline and transforms the input dataframe.
 
@@ -942,7 +947,10 @@ def fit_transform(
         time : bool, default False
             If True, per-stage application time is measured and reported when
             pipeline application is done.
-        context : dict, optional
+        fit_context : dict, option
+            Context for the entire pipeline, is retained after the pipeline
+            application is completed.
+        application_context : dict, optional
             Context to add to the application context of this call. Can map
             str keys to arbitrary object values to be used by pipeline stages
             during this pipeline application.
@@ -954,12 +962,15 @@ def fit_transform(
         """
         if time:
             return self.__timed_fit_transform(
-                X=X, y=y, exraise=exraise, verbose=verbose, context=context)
+                X=X, y=y, exraise=exraise,
+                verbose=verbose,
+                fit_context=fit_context,
+                application_context=application_context)
         inter_x = X
         self.application_context = PdpApplicationContext()
-        self.application_context.update(context)
+        self.application_context.update(application_context)
         self.fit_context = PdpApplicationContext()
-        self.fit_context.update(context)
+        self.fit_context.update(fit_context)
         for i, stage in enumerate(self._stages):
             try:
                 stage.fit_context = self.fit_context
@@ -985,7 +996,8 @@ def fit(
         exraise: Optional[bool] = None,
         verbose: Optional[bool] = False,
         time: Optional[bool] = False,
-        context: Optional[dict] = {},
+        fit_context: Optional[dict] = {},
+        application_context: Optional[dict] = {},
     ):
         """Fits this pipeline without transforming the input dataframe.
 
@@ -1009,7 +1021,10 @@ def fit(
         time : bool, default False
             If True, per-stage application time is measured and reported when
             pipeline application is done.
-        context : dict, optional
+        fit_context : dict, option
+            Context for the entire pipeline, is retained after the pipeline
+            application is completed.
+        application_context : dict, optional
             Context to add to the application context of this call. Can map
             str keys to arbitrary object values to be used by pipeline stages
             during this pipeline application.
@@ -1025,7 +1040,8 @@ def fit(
             exraise=exraise,
             verbose=verbose,
             time=time,
-            context=context,
+            fit_context=fit_context,
+            application_context=application_context,
         )
         return X
 
@@ -1035,13 +1051,13 @@ def __timed_transform(
         y: Optional[Iterable[float]] = None,
         exraise: Optional[bool] = None,
         verbose: Optional[bool] = None,
-        context: Optional[dict] = {},
+        application_context: Optional[dict] = {},
     ) -> pandas.DataFrame:
         inter_x = X
         times = []
         prev = time.time()
         self.application_context = PdpApplicationContext()
-        self.application_context.update(context)
+        self.application_context.update(application_context)
         for i, stage in enumerate(self._stages):
             try:
                 stage.fit_context = self.fit_context
@@ -1073,7 +1089,7 @@ def transform(
         exraise: Optional[bool] = None,
         verbose: Optional[bool] = None,
         time: Optional[bool] = False,
-        context: Optional[dict] = {},
+        application_context: Optional[dict] = {},
     ) -> pandas.DataFrame:
         """Transforms the given dataframe without fitting this pipeline.
 
@@ -1100,7 +1116,7 @@ def transform(
         time : bool, default False
             If True, per-stage application time is measured and reported when
             pipeline application is done.
-        context : dict, optional
+        application_context : dict, optional
             Context to add to the application context of this call. Can map
             str keys to arbitrary object values to be used by pipeline stages
             during this pipeline application.
@@ -1117,10 +1133,11 @@ def transform(
                     " unfitted!").format(stage))
         if time:
             return self.__timed_transform(
-                X=X, y=y, exraise=exraise, verbose=verbose, context=context)
+                X=X, y=y, exraise=exraise, verbose=verbose,
+                application_context=application_context)
         inter_df = X
         self.application_context = PdpApplicationContext()
-        self.application_context.update(context)
+        self.application_context.update(application_context)
         for i, stage in enumerate(self._stages):
             try:
                 stage.application_context = self.application_context

tests/core/test_app_context.py

@@ -132,7 +132,7 @@ def test_application_context_injection():
     assert len(pipeline) == 2
     df = _test_df()
     val = randint(840, 921)
-    res_df = pipeline.apply(df, verbose=True, context={'a': val})
+    res_df = pipeline.apply(df, verbose=True, fit_context={'a': val})
     assert 'num1' in res_df.columns
     assert 'num1+val' in res_df.columns
     assert 'num2' in res_df.columns