Move widget display to Out[...] (#308)

* fix repr to display string of df

* replace repr_html to move widget into Out

* replace repr_html in other files

* fix issue for LuxSeries

lux/core/frame.py

@@ -317,10 +317,6 @@ def current_vis(self):
     def current_vis(self, current_vis: Dict):
         self._current_vis = current_vis
 
-    def __repr__(self):
-        # TODO: _repr_ gets called from _repr_html, need to get rid of this call
-        return ""
-
     #######################################################
     ########## SQL Metadata, type, model schema ###########
     #######################################################
@@ -619,7 +615,7 @@ def set_intent_on_click(self, change):
         self._widget.observe(self.remove_deleted_recs, names="deletedIndices")
         self._widget.observe(self.set_intent_on_click, names="selectedIntentIndex")
 
-    def _repr_html_(self):
+    def _ipython_display_(self):
         from IPython.display import display
         from IPython.display import clear_output
         import ipywidgets as widgets

lux/core/series.py

@@ -105,7 +105,7 @@ def to_pandas(self) -> pd.Series:
 
         return lux.core.originalSeries(self, copy=False)
 
-    def __repr__(self):
+    def _ipython_display_(self):
         from IPython.display import display
         from IPython.display import clear_output
         import ipywidgets as widgets
@@ -188,7 +188,6 @@ def on_button_clicked(b):
             )
             warnings.warn(traceback.format_exc())
             display(self.to_pandas())
-        return ""
 
     @property
     def recommendation(self):

lux/vis/Vis.py

@@ -111,7 +111,7 @@ def set_intent(self, intent: List[Clause]) -> None:
         self._intent = intent
         self.refresh_source(self._source)
 
-    def _repr_html_(self):
+    def _ipython_display_(self):
         from IPython.display import display
 
         check_import_lux_widget()

lux/vis/VisList.py

@@ -257,7 +257,7 @@ def normalize_score(self, invert_order=False):
             if invert_order:
                 dobj.score = 1 - dobj.score
 
-    def _repr_html_(self):
+    def _ipython_display_(self):
         self._widget = None
         from IPython.display import display
         from lux.core.frame import LuxDataFrame

tests/test_action.py

@@ -23,7 +23,7 @@ def test_vary_filter_val(global_var):
     df = pytest.olympic
     vis = Vis(["Height", "SportType=Ball"], df)
     df.set_intent_as_vis(vis)
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["Filter"]) == len(df["SportType"].unique()) - 1
     linechart = list(filter(lambda x: x.mark == "line", df.recommendation["Enhance"]))[0]
     assert (
@@ -42,7 +42,7 @@ def test_filter_inequality(global_var):
             lux.Clause(attribute="Acceleration", filter_op=">", value=10),
         ]
     )
-    df._repr_html_()
+    df._ipython_display_()
 
     from lux.utils.utils import get_filter_specs
 
@@ -59,7 +59,7 @@ def test_generalize_action(global_var):
         df["Year"], format="%Y"
     )  # change pandas dtype for the column "Year" to datetype
     df.set_intent(["Acceleration", "MilesPerGal", "Cylinders", "Origin=USA"])
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["Generalize"]) == 4
     v1 = df.recommendation["Generalize"][0]
     v2 = df.recommendation["Generalize"][1]
@@ -86,14 +86,14 @@ def test_row_column_group(global_var):
     tseries[tseries.columns.min()] = tseries[tseries.columns.min()].fillna(0)
     tseries[tseries.columns.max()] = tseries[tseries.columns.max()].fillna(tseries.max(axis=1))
     tseries = tseries.interpolate("zero", axis=1)
-    tseries._repr_html_()
+    tseries._ipython_display_()
     assert list(tseries.recommendation.keys()) == ["Temporal"]
 
 
 def test_groupby(global_var):
     df = pytest.college_df
     groupbyResult = df.groupby("Region").sum()
-    groupbyResult._repr_html_()
+    groupbyResult._ipython_display_()
     assert list(groupbyResult.recommendation.keys()) == ["Column Groups"]
 
 
@@ -160,7 +160,7 @@ def test_crosstab():
 
     df = pd.DataFrame(d, columns=["Name", "Exam", "Subject", "Result"])
     result = pd.crosstab([df.Exam], df.Result)
-    result._repr_html_()
+    result._ipython_display_()
     assert list(result.recommendation.keys()) == ["Row Groups", "Column Groups"]
 
 
@@ -169,7 +169,7 @@ def test_custom_aggregation(global_var):
 
     df = pytest.college_df
     df.set_intent(["HighestDegree", lux.Clause("AverageCost", aggregation=np.ptp)])
-    df._repr_html_()
+    df._ipython_display_()
     assert list(df.recommendation.keys()) == ["Enhance", "Filter", "Generalize"]
     df.clear_intent()
 
@@ -178,7 +178,7 @@ def test_year_filter_value(global_var):
     df = pytest.car_df
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
     df.set_intent(["Acceleration", "Horsepower"])
-    df._repr_html_()
+    df._ipython_display_()
     list_of_vis_with_year_filter = list(
         filter(
             lambda vis: len(
@@ -210,7 +210,7 @@ def test_similarity(global_var):
             lux.Clause("Origin=USA"),
         ]
     )
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["Similarity"]) == 2
     ranked_list = df.recommendation["Similarity"]
 
@@ -264,13 +264,13 @@ def test_similarity2():
 def test_intent_retained():
     df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/employee.csv")
     df.intent = ["Attrition"]
-    df._repr_html_()
+    df._ipython_display_()
 
     df["%WorkingYearsAtCompany"] = df["YearsAtCompany"] / df["TotalWorkingYears"]
     assert df.current_vis != None
     assert df.intent != None
     assert df._recs_fresh == False
     assert df._metadata_fresh == False
 
-    df._repr_html_()
+    df._ipython_display_()
     assert list(df.recommendation.keys()) == ["Enhance", "Filter"]

tests/test_compiler.py

@@ -81,17 +81,17 @@ def test_underspecified_single_vis(global_var, test_recs):
 
 def test_set_intent_as_vis(global_var, test_recs):
     df = pytest.car_df
-    df._repr_html_()
+    df._ipython_display_()
     vis = df.recommendation["Correlation"][0]
     df.intent = vis
-    df._repr_html_()
+    df._ipython_display_()
     test_recs(df, ["Enhance", "Filter", "Generalize"])
 
 
 @pytest.fixture
 def test_recs():
     def test_recs_function(df, actions):
-        df._repr_html_()
+        df._ipython_display_()
         assert len(df.recommendation) > 0
         recKeys = list(df.recommendation.keys())
         list_equal(recKeys, actions)
@@ -350,7 +350,7 @@ def test_populate_options(global_var):
             lux.Clause(attribute="MilesPerGal"),
         ]
     )
-    df._repr_html_()
+    df._ipython_display_()
     col_set = set()
     for specOptions in Compiler.populate_wildcard_options(df._intent, df)["attributes"]:
         for clause in specOptions:
@@ -372,7 +372,7 @@ def test_remove_all_invalid(global_var):
             lux.Clause(attribute="Origin"),
         ]
     )
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.current_vis) == 0
     df.clear_intent()
 

tests/test_config.py

@@ -51,7 +51,7 @@ def contain_horsepower(df):
 
 def test_default_actions_registered(global_var):
     df = pytest.car_df
-    df._repr_html_()
+    df._ipython_display_()
     assert "Distribution" in df.recommendation
     assert len(df.recommendation["Distribution"]) > 0
 
@@ -67,7 +67,7 @@ def test_default_actions_registered(global_var):
 
 def test_fail_validator():
     df = register_new_action()
-    df._repr_html_()
+    df._ipython_display_()
     assert (
         "bars" not in df.recommendation,
         "Bars should not be rendered when there is no intent 'horsepower' specified.",
@@ -77,7 +77,7 @@ def test_fail_validator():
 def test_pass_validator():
     df = register_new_action()
     df.set_intent(["Acceleration", "Horsepower"])
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["bars"]) > 0
     assert (
         "bars" in df.recommendation,
@@ -87,7 +87,7 @@ def test_pass_validator():
 
 def test_no_validator():
     df = register_new_action(False)
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["bars"]) > 0
     assert "bars" in df.recommendation
 
@@ -121,7 +121,7 @@ def random_categorical(ldf):
 def test_remove_action():
     df = register_new_action()
     df.set_intent(["Acceleration", "Horsepower"])
-    df._repr_html_()
+    df._ipython_display_()
     assert (
         "bars" in df.recommendation,
         "Bars should be rendered after it has been registered with correct intent.",
@@ -131,7 +131,7 @@ def test_remove_action():
         "Bars should be rendered after it has been registered with correct intent.",
     )
     lux.config.remove_action("bars")
-    df._repr_html_()
+    df._ipython_display_()
     assert (
         "bars" not in df.recommendation,
         "Bars should not be rendered after it has been removed.",
@@ -148,22 +148,22 @@ def test_remove_invalid_action(global_var):
 # TODO: This test does not pass in pytest but is working in Jupyter notebook.
 def test_remove_default_actions(global_var):
     df = pytest.car_df
-    df._repr_html_()
+    df._ipython_display_()
 
     lux.config.remove_action("distribution")
-    df._repr_html_()
+    df._ipython_display_()
     assert "Distribution" not in df.recommendation
 
     lux.config.remove_action("occurrence")
-    df._repr_html_()
+    df._ipython_display_()
     assert "Occurrence" not in df.recommendation
 
     lux.config.remove_action("temporal")
-    df._repr_html_()
+    df._ipython_display_()
     assert "Temporal" not in df.recommendation
 
     lux.config.remove_action("correlation")
-    df._repr_html_()
+    df._ipython_display_()
     assert "Correlation" not in df.recommendation
 
     assert (
@@ -173,7 +173,7 @@ def test_remove_default_actions(global_var):
 
     df = register_new_action()
     df.set_intent(["Acceleration", "Horsepower"])
-    df._repr_html_()
+    df._ipython_display_()
     assert (
         "bars" in df.recommendation,
         "Bars should be rendered after it has been registered with correct intent.",
@@ -195,7 +195,7 @@ def add_title(fig, ax):
 
     df = pd.read_csv("lux/data/car.csv")
     lux.config.plotting_style = add_title
-    df._repr_html_()
+    df._ipython_display_()
     title_addition = 'ax.set_title("Test Title")'
     exported_code_str = df.recommendation["Correlation"][0].to_Altair()
     assert title_addition in exported_code_str
@@ -211,7 +211,7 @@ def change_color_make_transparent_add_title(chart):
 
     df = pd.read_csv("lux/data/car.csv")
     lux.config.plotting_style = change_color_make_transparent_add_title
-    df._repr_html_()
+    df._ipython_display_()
     config_mark_addition = 'chart = chart.configure_mark(color="green", opacity=0.2)'
     title_addition = 'chart.title = "Test Title"'
     exported_code_str = df.recommendation["Correlation"][0].to_Altair()
@@ -221,47 +221,47 @@ def change_color_make_transparent_add_title(chart):
 
 def test_sampling_flag_config():
     df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/airbnb_nyc.csv")
-    df._repr_html_()
+    df._ipython_display_()
     assert df.recommendation["Correlation"][0].data.shape[0] == 30000
     lux.config.sampling = False
     df = df.copy()
-    df._repr_html_()
+    df._ipython_display_()
     assert df.recommendation["Correlation"][0].data.shape[0] == 48895
     lux.config.sampling = True
 
 
 def test_sampling_parameters_config():
     df = pd.read_csv("lux/data/car.csv")
-    df._repr_html_()
+    df._ipython_display_()
     assert df.recommendation["Correlation"][0].data.shape[0] == 392
     lux.config.sampling_start = 50
     lux.config.sampling_cap = 100
     df = pd.read_csv("lux/data/car.csv")
-    df._repr_html_()
+    df._ipython_display_()
     assert df.recommendation["Correlation"][0].data.shape[0] == 100
     lux.config.sampling_cap = 30000
     lux.config.sampling_start = 10000
 
 
 def test_heatmap_flag_config():
     df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/airbnb_nyc.csv")
-    df._repr_html_()
+    df._ipython_display_()
     assert df.recommendation["Correlation"][0]._postbin
     lux.config.heatmap = False
     df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/airbnb_nyc.csv")
-    df._repr_html_()
+    df._ipython_display_()
     assert not df.recommendation["Correlation"][0]._postbin
     lux.config.heatmap = True
 
 
 def test_topk(global_var):
     df = pd.read_csv("lux/data/college.csv")
     lux.config.topk = False
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["Correlation"]) == 45, "Turn off top K"
     lux.config.topk = 20
     df = pd.read_csv("lux/data/college.csv")
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["Correlation"]) == 20, "Show top 20"
     for vis in df.recommendation["Correlation"]:
         assert vis.score > 0.2
@@ -270,20 +270,20 @@ def test_topk(global_var):
 def test_sort(global_var):
     df = pd.read_csv("lux/data/college.csv")
     lux.config.topk = 15
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["Correlation"]) == 15, "Show top 15"
     for vis in df.recommendation["Correlation"]:
         assert vis.score > 0.5
     df = pd.read_csv("lux/data/college.csv")
     lux.config.sort = "ascending"
-    df._repr_html_()
+    df._ipython_display_()
     assert len(df.recommendation["Correlation"]) == 15, "Show bottom 15"
     for vis in df.recommendation["Correlation"]:
         assert vis.score < 0.35
 
     lux.config.sort = "none"
     df = pd.read_csv("lux/data/college.csv")
-    df._repr_html_()
+    df._ipython_display_()
     scorelst = [x.score for x in df.recommendation["Distribution"]]
     assert sorted(scorelst) != scorelst, "unsorted setting"
     lux.config.sort = "descending"
@@ -300,7 +300,7 @@ def test_sort(global_var):
 
 # 	df.plot_config = change_color_add_title
 
-# 	df._repr_html_()
+# 	df._ipython_display_()
 
 # 	vis_code = df.recommendation["Correlation"][0].to_Altair()
 # 	print (vis_code)

tests/test_dates.py

@@ -88,7 +88,7 @@ def test_refresh_inplace():
         }
     )
     with pytest.warns(UserWarning, match="Lux detects that the attribute 'date' may be temporal."):
-        df._repr_html_()
+        df._ipython_display_()
     assert df.data_type["date"] == "temporal"
 
     from lux.vis.Vis import Vis