Global shared variable in test (#144) (#149)

* using global shared variable in test (#144)

* modified fixture scope as session, resolved dependency test cases

* run black

Co-authored-by: Doris Lee <dorisjunglinlee@gmail.com>

.gitignore

@@ -10,5 +10,6 @@ static/
 *.egg-info*
 build/
 .DS_Store
+.idea/
 tests/.coverage
 tests/coverage.xml

lux/core/frame.py

@@ -246,6 +246,7 @@ def intent(self, intent_input: Union[List[Union[str, Clause]], Vis]):
 
     def clear_intent(self):
         self.intent = []
+        self.expire_recs()
 
     def set_intent(self, intent: List[Union[str, Clause]]):
         """

tests/conftest.py

@@ -0,0 +1,10 @@
+import pytest
+import pandas as pd
+
+
+@pytest.fixture(scope="session")
+def global_var():
+    url = "https://github.com/lux-org/lux-datasets/blob/master/data/olympic.csv?raw=true"
+    pytest.olympic = pd.read_csv(url)
+    pytest.car_df = pd.read_csv("lux/data/car.csv")
+    pytest.college_df = pd.read_csv("lux/data/college.csv")

tests/test_action.py

@@ -19,16 +19,16 @@
 from lux.vis.Vis import Vis
 
 
-def test_vary_filter_val():
-    df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/olympic.csv?raw=true")
+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_()
     assert len(df.recommendation["Filter"]) == len(df["SportType"].unique()) - 1
 
 
-def test_filter_inequality():
-    df = pd.read_csv("lux/data/car.csv")
+def test_filter_inequality(global_var):
+    df = pytest.car_df
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
 
     df.set_intent(
@@ -48,9 +48,9 @@ def test_filter_inequality():
     assert fltr_clause.value == 10
 
 
-def test_generalize_action():
+def test_generalize_action(global_var):
     # test that generalize action creates all unique visualizations
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     df["Year"] = pd.to_datetime(
         df["Year"], format="%Y"
     )  # change pandas dtype for the column "Year" to datetype
@@ -72,9 +72,10 @@ def test_generalize_action():
     assert check1 and check2 and check3
 
 
-def test_row_column_group():
-    url = "https://github.com/lux-org/lux-datasets/blob/master/data/state_timeseries.csv?raw=true"
-    df = pd.read_csv(url)
+def test_row_column_group(global_var):
+    df = pd.read_csv(
+        "https://github.com/lux-org/lux-datasets/blob/master/data/state_timeseries.csv?raw=true"
+    )
     df["Date"] = pd.to_datetime(df["Date"])
     tseries = df.pivot(index="State", columns="Date", values="Value")
     # Interpolating missing values
@@ -85,8 +86,8 @@ def test_row_column_group():
     assert list(tseries.recommendation.keys()) == ["Row Groups", "Column Groups"]
 
 
-def test_groupby():
-    df = pd.read_csv("lux/data/college.csv")
+def test_groupby(global_var):
+    df = pytest.college_df
     groupbyResult = df.groupby("Region").sum()
     groupbyResult._repr_html_()
     assert list(groupbyResult.recommendation.keys()) == ["Column Groups"]
@@ -159,17 +160,17 @@ def test_crosstab():
     assert list(result.recommendation.keys()) == ["Row Groups", "Column Groups"]
 
 
-def test_custom_aggregation():
+def test_custom_aggregation(global_var):
     import numpy as np
 
-    df = pd.read_csv("lux/data/college.csv")
+    df = pytest.college_df
     df.set_intent(["HighestDegree", lux.Clause("AverageCost", aggregation=np.ptp)])
     df._repr_html_()
     assert list(df.recommendation.keys()) == ["Enhance", "Filter", "Generalize"]
 
 
-def test_year_filter_value():
-    df = pd.read_csv("lux/data/car.csv")
+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_()
@@ -191,3 +192,4 @@ def test_year_filter_value():
     assert (
         "T00:00:00.000000000" not in vis.to_Altair()
     ), "Year filter title contains extraneous string, not displayed as summarized string"
+    df.clear_intent()

tests/test_compiler.py

@@ -19,21 +19,21 @@
 from lux.vis.VisList import VisList
 
 
-def test_underspecified_no_vis(test_recs):
+def test_underspecified_no_vis(global_var, test_recs):
     no_vis_actions = ["Correlation", "Distribution", "Occurrence", "Temporal"]
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     test_recs(df, no_vis_actions)
-    assert len(df.current_vis) == 0
+    assert df.current_vis is None or len(df.current_vis) == 0
 
     # test only one filter context case.
     df.set_intent([lux.Clause(attribute="Origin", filter_op="=", value="USA")])
     test_recs(df, no_vis_actions)
     assert len(df.current_vis) == 0
 
 
-def test_underspecified_single_vis(test_recs):
+def test_underspecified_single_vis(global_var, test_recs):
     one_vis_actions = ["Enhance", "Filter", "Generalize"]
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     df.set_intent([lux.Clause(attribute="MilesPerGal"), lux.Clause(attribute="Weight")])
     test_recs(df, one_vis_actions)
     assert len(df.current_vis) == 1
@@ -42,6 +42,7 @@ def test_underspecified_single_vis(test_recs):
         assert attr.data_model == "measure"
     for attr in df.current_vis[0]._inferred_intent:
         assert attr.data_type == "quantitative"
+    df.clear_intent()
 
 
 # def test_underspecified_vis_collection(test_recs):
@@ -75,8 +76,8 @@ def test_underspecified_single_vis(test_recs):
 # 	df.set_intent([lux.Clause(attribute ="?", data_model="measure"), lux.Clause(attribute ="?", data_model="measure")])
 # 	assert len(df.current_vis) == len([vis.get_attr_by_data_model("measure") for vis in df.current_vis]) #should be 25
 # 	test_recs(df, multiple_vis_actions)
-def test_set_intent_as_vis(test_recs):
-    df = pd.read_csv("lux/data/car.csv")
+def test_set_intent_as_vis(global_var, test_recs):
+    df = pytest.car_df
     df._repr_html_()
     vis = df.recommendation["Correlation"][0]
     df.intent = vis
@@ -95,19 +96,19 @@ def test_recs_function(df, actions):
     return test_recs_function
 
 
-def test_parse():
-    df = pd.read_csv("lux/data/car.csv")
+def test_parse(global_var):
+    df = pytest.car_df
     vlst = VisList([lux.Clause("Origin=?"), lux.Clause(attribute="MilesPerGal")], df)
     assert len(vlst) == 3
 
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     vlst = VisList([lux.Clause("Origin=?"), lux.Clause("MilesPerGal")], df)
     assert len(vlst) == 3
 
 
-def test_underspecified_vis_collection_zval():
+def test_underspecified_vis_collection_zval(global_var):
     # check if the number of charts is correct
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     vlst = VisList(
         [
             lux.Clause(attribute="Origin", filter_op="=", value="?"),
@@ -123,11 +124,11 @@ def test_underspecified_vis_collection_zval():
     # assert len(vlst) == 8
 
 
-def test_sort_bar():
+def test_sort_bar(global_var):
     from lux.processor.Compiler import Compiler
     from lux.vis.Vis import Vis
 
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     vis = Vis(
         [
             lux.Clause(attribute="Acceleration", data_model="measure", data_type="quantitative"),
@@ -138,7 +139,7 @@ def test_sort_bar():
     assert vis.mark == "bar"
     assert vis._inferred_intent[1].sort == ""
 
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     vis = Vis(
         [
             lux.Clause(attribute="Acceleration", data_model="measure", data_type="quantitative"),
@@ -150,8 +151,8 @@ def test_sort_bar():
     assert vis._inferred_intent[1].sort == "ascending"
 
 
-def test_specified_vis_collection():
-    df = pd.read_csv("lux/data/car.csv")
+def test_specified_vis_collection(global_var):
+    df = pytest.car_df
     # change pandas dtype for the column "Year" to datetype
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
 
@@ -181,8 +182,8 @@ def test_specified_vis_collection():
     assert "Origin = Europe" not in chart_titles
 
 
-def test_specified_channel_enforced_vis_collection():
-    df = pd.read_csv("lux/data/car.csv")
+def test_specified_channel_enforced_vis_collection(global_var):
+    df = pytest.car_df
     # change pandas dtype for the column "Year" to datetype
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
     visList = VisList(
@@ -193,9 +194,9 @@ def test_specified_channel_enforced_vis_collection():
         check_attribute_on_channel(vis, "MilesPerGal", "x")
 
 
-def test_autoencoding_scatter():
+def test_autoencoding_scatter(global_var):
     # No channel specified
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     # change pandas dtype for the column "Year" to datetype
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
     vis = Vis([lux.Clause(attribute="MilesPerGal"), lux.Clause(attribute="Weight")], df)
@@ -234,9 +235,9 @@ def test_autoencoding_scatter():
         )
 
 
-def test_autoencoding_histogram():
+def test_autoencoding_histogram(global_var):
     # No channel specified
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     # change pandas dtype for the column "Year" to datetype
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
     vis = Vis([lux.Clause(attribute="MilesPerGal", channel="y")], df)
@@ -247,8 +248,8 @@ def test_autoencoding_histogram():
     assert vis.get_attr_by_channel("y")[0].attribute == "Record"
 
 
-def test_autoencoding_line_chart():
-    df = pd.read_csv("lux/data/car.csv")
+def test_autoencoding_line_chart(global_var):
+    df = pytest.car_df
     # change pandas dtype for the column "Year" to datetype
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
     vis = Vis([lux.Clause(attribute="Year"), lux.Clause(attribute="Acceleration")], df)
@@ -287,8 +288,8 @@ def test_autoencoding_line_chart():
         )
 
 
-def test_autoencoding_color_line_chart():
-    df = pd.read_csv("lux/data/car.csv")
+def test_autoencoding_color_line_chart(global_var):
+    df = pytest.car_df
     # change pandas dtype for the column "Year" to datetype
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
     intent = [
@@ -302,8 +303,8 @@ def test_autoencoding_color_line_chart():
     check_attribute_on_channel(vis, "Origin", "color")
 
 
-def test_autoencoding_color_scatter_chart():
-    df = pd.read_csv("lux/data/car.csv")
+def test_autoencoding_color_scatter_chart(global_var):
+    df = pytest.car_df
     # change pandas dtype for the column "Year" to datetype
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
     vis = Vis(
@@ -327,10 +328,10 @@ def test_autoencoding_color_scatter_chart():
     check_attribute_on_channel(vis, "Acceleration", "color")
 
 
-def test_populate_options():
+def test_populate_options(global_var):
     from lux.processor.Compiler import Compiler
 
-    df = pd.read_csv("lux/data/car.csv")
+    df = pytest.car_df
     df.set_intent([lux.Clause(attribute="?"), lux.Clause(attribute="MilesPerGal")])
     col_set = set()
     for specOptions in Compiler.populate_wildcard_options(df._intent, df)["attributes"]:
@@ -355,8 +356,8 @@ def test_populate_options():
     )
 
 
-def test_remove_all_invalid():
-    df = pd.read_csv("lux/data/car.csv")
+def test_remove_all_invalid(global_var):
+    df = pytest.car_df
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")
     # with pytest.warns(UserWarning,match="duplicate attribute specified in the intent"):
     df.set_intent(

tests/test_config.py

@@ -48,8 +48,8 @@ def contain_horsepower(df):
     return df
 
 
-def test_default_actions_registered():
-    df = pd.read_csv("lux/data/car.csv")
+def test_default_actions_registered(global_var):
+    df = pytest.car_df
     df._repr_html_()
     assert "Distribution" in df.recommendation
     assert len(df.recommendation["Distribution"]) > 0
@@ -91,13 +91,13 @@ def test_no_validator():
     assert "bars" in df.recommendation
 
 
-def test_invalid_function():
+def test_invalid_function(global_var):
     df = pd.read_csv("lux/data/car.csv")
     with pytest.raises(ValueError, match="Value must be a callable"):
         lux.register_action("bars", "not a Callable")
 
 
-def test_invalid_validator():
+def test_invalid_validator(global_var):
     df = pd.read_csv("lux/data/car.csv")
 
     def random_categorical(ldf):
@@ -136,14 +136,14 @@ def test_remove_action():
     )
 
 
-def test_remove_invalid_action():
-    df = pd.read_csv("lux/data/car.csv")
+def test_remove_invalid_action(global_var):
+    df = pytest.car_df
     with pytest.raises(ValueError, match="Option 'bars' has not been registered"):
         lux.remove_action("bars")
 
 
-def test_remove_default_actions():
-    df = pd.read_csv("lux/data/car.csv")
+def test_remove_default_actions(global_var):
+    df = pytest.car_df
     df._repr_html_()
 
     lux.remove_action("Distribution")
@@ -178,8 +178,8 @@ def test_remove_default_actions():
 
 
 # TODO: This test does not pass in pytest but is working in Jupyter notebook.
-# def test_plot_setting():
-# 	df = pd.read_csv("lux/data/car.csv")
+# def test_plot_setting(global_var):
+# 	df = pytest.car_df
 # 	df["Year"] = pd.to_datetime(df["Year"], format='%Y')
 # 	def change_color_add_title(chart):
 # 		chart = chart.configure_mark(color="green") # change mark color to green