Add recommendation property to LuxSeries objects

lux/core/series.py

@@ -202,3 +202,16 @@ def on_button_clicked(b):
             warnings.warn(traceback.format_exc())
             display(self.to_pandas())
         return ""
+
+    @property
+    def recommendation(self):
+        from lux.core.frame import LuxDataFrame
+
+        if self.name is None:
+            self.name = " "
+        ldf = LuxDataFrame(self)
+
+        if self._recommendation is not None and self._recommendation == {}:
+            ldf.maintain_metadata()
+            ldf.maintain_recs()
+        return ldf._recommendation

tests/test_interestingness.py

@@ -102,7 +102,6 @@ def test_interestingness_0_1_0(global_var):
         ):
             rank3 = f
     assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
-
 
     # check that top recommended filter graph score is not none and that ordering makes intuitive sense
     assert interestingness(df.recommendation["Filter"][0], df) != None

tests/test_series.py

@@ -65,3 +65,17 @@ def test_print_iterrow(global_var):
             print(row)
             break
         assert len(w) == 0, "Warning displayed when printing iterrow"
+
+
+def test_series_recommendation():
+    lux.config.plotting_backend = "matplotlib"
+    df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/employee.csv")
+    df.plot_config = None
+    df = df["YearsAtCompany"] / df["TotalWorkingYears"]
+    assert len(df.recommendation["Distribution"]) > 0
+
+    lux.config.plotting_backend = "vegalite"
+    df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/employee.csv")
+    df.plot_config = None
+    df = df["YearsAtCompany"] / df["TotalWorkingYears"]
+    assert len(df.recommendation["Distribution"]) > 0