Adding support to improve temporal action to display different timesc…

…ales (#262) * Add support to improve temporal action to display different timescales * Resolve PR comments * Add support to improve temporal action to display different timescales * Resolve PR comments * Reformat files using black * "All-column" vis when only few columns in dataframe #199 (#336) Co-authored-by: Caitlyn Chen <[email protected]> Co-authored-by: Doris Lee <[email protected]> * documentation and cleaning * added notebook gallery * update README * removed scatterplot message in SQLExecutor * fixed typo in SQL documentation * update README and bump version * bump version * clear propagated vis data intent after PandasExecutor completes execute (#297) * fix black to stable version * Scalability: incorporate early pruning optimizations (#368) * changes from perf branch to config * added flag for turning on/off lazy maintain optimization * merged in approx early pruning code * increase overall sampling start and cap * Adjust width and length criteria for early pruning vislist based on experiment results; Add warning message and test for early pruning * black version update * version lock on black * * fixed sql tests (added approx to execute constructor) * fixed sampling config test * improved Executor documentation * timescale feature * adding weekday * adding docs * bugfix for y axis line chart export * fixing temporal axis by adding timescale variable in Clause Co-authored-by: Doris Lee <[email protected]> Co-authored-by: Caitlyn Chen <[email protected]> Co-authored-by: Caitlyn Chen <[email protected]>
lux-org · Apr 30, 2021 · ada6173 · ada6173
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diff --git a/doc/source/advanced/date.rst b/doc/source/advanced/date.rst
@@ -89,6 +89,22 @@ After changing the Pandas data type to datetime, we see that date field is recog
   :align: center
   :alt: add screenshot
 
+Visualizing Trends across Different Timescales
+----------------------------------------------
+
+Lux automatically detects the temporal attribute and plots the visualizations across different timescales to showcase any cyclical patterns. Here, we see that the `Temporal` tab displays the yearly, monthly, and weekly trends for the number of stock records.
+
+.. code-block:: python
+
+  from vega_datasets import data
+  df = data.stocks()
+
+  df.recommendation["Temporal"]
+
+.. image:: https://github.com/lux-org/lux-resources/blob/master/doc_img/date-12.png?raw=true
+  :width: 600
+  :align: center
+
 Advanced Date Manipulation
 --------------------------
 

diff --git a/doc/source/reference/gen/lux.core.frame.LuxDataFrame.rst b/doc/source/reference/gen/lux.core.frame.LuxDataFrame.rst
@@ -41,6 +41,7 @@
       ~LuxDataFrame.combine
       ~LuxDataFrame.combine_first
       ~LuxDataFrame.compare
+      ~LuxDataFrame.compute_metadata
       ~LuxDataFrame.convert_dtypes
       ~LuxDataFrame.copy
       ~LuxDataFrame.copy_intent

diff --git a/doc/source/reference/gen/lux.executor.Executor.Executor.rst b/doc/source/reference/gen/lux.executor.Executor.Executor.rst
@@ -1,4 +1,4 @@
-lux.executor.Executor.Executor
+lux.executor.Executor.Executor
 ==============================
 
 .. currentmodule:: lux.executor.Executor
@@ -19,6 +19,7 @@ lux.executor.Executor.Executor
       ~Executor.compute_data_type
       ~Executor.compute_stats
       ~Executor.execute
+      ~Executor.execute_2D_binning
       ~Executor.execute_aggregate
       ~Executor.execute_binning
       ~Executor.execute_filter

diff --git a/doc/source/reference/gen/lux.executor.PandasExecutor.PandasExecutor.rst b/doc/source/reference/gen/lux.executor.PandasExecutor.PandasExecutor.rst
@@ -1,4 +1,4 @@
-lux.executor.PandasExecutor.PandasExecutor
+lux.executor.PandasExecutor.PandasExecutor
 ==========================================
 
 .. currentmodule:: lux.executor.PandasExecutor
@@ -23,6 +23,7 @@ lux.executor.PandasExecutor.PandasExecutor
       ~PandasExecutor.execute
       ~PandasExecutor.execute_2D_binning
       ~PandasExecutor.execute_aggregate
+      ~PandasExecutor.execute_approx_sample
       ~PandasExecutor.execute_binning
       ~PandasExecutor.execute_filter
       ~PandasExecutor.execute_sampling

diff --git a/lux/action/default.py b/lux/action/default.py
@@ -6,6 +6,7 @@ def register_default_actions():
     from lux.action.enhance import enhance
     from lux.action.filter import add_filter
     from lux.action.generalize import generalize
+    from lux.action.temporal import temporal
 
     # display conditions for default actions
     no_vis = lambda ldf: (ldf.current_vis is None) or (
@@ -18,7 +19,7 @@ def register_default_actions():
     lux.config.register_action("correlation", correlation, no_vis)
     lux.config.register_action("distribution", univariate, no_vis, "quantitative")
     lux.config.register_action("occurrence", univariate, no_vis, "nominal")
-    lux.config.register_action("temporal", univariate, no_vis, "temporal")
+    lux.config.register_action("temporal", temporal, no_vis)
     lux.config.register_action("geographical", univariate, no_vis, "geographical")
 
     lux.config.register_action("Enhance", enhance, one_current_vis)

diff --git a/lux/action/temporal.py b/lux/action/temporal.py
@@ -0,0 +1,128 @@
+#  Copyright 2019-2020 The Lux Authors.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import lux
+from lux.vis.VisList import VisList
+from lux.vis.Vis import Vis
+import pandas as pd
+from lux.core.frame import LuxDataFrame
+from lux.interestingness.interestingness import interestingness
+from lux.utils import utils
+
+
+def temporal(ldf):
+    """
+    Generates line charts for temporal fields at different granularities.
+    Parameters
+    ----------
+    ldf : lux.core.frame
+            LuxDataFrame with underspecified intent.
+    Returns
+    -------
+    recommendations : Dict[str,obj]
+            Object with a collection of visualizations that result from the Temporal action.
+    """
+    vlist = []
+    recommendation = {
+        "action": "Temporal",
+        "description": "Show trends over <p class='highlight-descriptor'>time-related</p> attributes.",
+        "long_description": "Temporal displays line charts for all attributes related to datetimes in the dataframe.",
+    }
+    for c in ldf.columns:
+        if ldf.data_type[c] == "temporal":
+            try:
+                generated_vis = create_temporal_vis(ldf, c)
+                vlist.extend(generated_vis)
+            except:
+                pass
+
+    # If no temporal visualizations were generated via parsing datetime, fallback to default behavior.
+    if len(vlist) == 0:
+        intent = [lux.Clause("?", data_type="temporal")]
+        intent.extend(utils.get_filter_specs(ldf._intent))
+        vlist = VisList(intent, ldf)
+        for vis in vlist:
+            vis.score = interestingness(vis, ldf)
+    else:
+        vlist = VisList(vlist)
+        recommendation["long_description"] += (
+            " Lux displays the overall temporal trend first,"
+            + " followed by trends across other timescales (e.g., year, month, week, day)."
+        )
+
+    # Doesn't make sense to generate a line chart if there is less than 3 datapoints (pre-aggregated)
+    if len(ldf) < 3:
+        recommendation["collection"] = []
+        return recommendation
+    vlist.sort()
+    recommendation["collection"] = vlist
+    return recommendation
+
+
+def create_temporal_vis(ldf, col):
+    """
+    Creates and populates Vis objects for different timescales in the provided temporal column.
+    Parameters
+    ----------
+    ldf : lux.core.frame
+            LuxDataFrame with underspecified intent.
+
+    col : str
+            Name of temporal column.
+
+    Returns
+    -------
+    vlist : [Vis]
+            Collection of Vis objects.
+    """
+    formatted_date = pd.to_datetime(ldf[col], format="%Y-%m-%d")
+
+    overall_vis = Vis([lux.Clause(col, data_type="temporal")], source=ldf, score=5)
+
+    year_col = col + " (year)"
+    year_df = LuxDataFrame({year_col: pd.to_datetime(formatted_date.dt.year, format="%Y")})
+    year_vis = Vis([lux.Clause(year_col, data_type="temporal")], source=year_df, score=4)
+
+    month_col = col + " (month)"
+    month_df = LuxDataFrame({month_col: formatted_date.dt.month})
+    month_vis = Vis(
+        [lux.Clause(month_col, data_type="temporal", timescale="month")], source=month_df, score=3
+    )
+
+    day_col = col + " (day)"
+    day_df = LuxDataFrame({day_col: formatted_date.dt.day})
+    day_df.set_data_type(
+        {day_col: "nominal"}
+    )  # Since day is high cardinality 1-31, it can get recognized as quantitative
+    day_vis = Vis([lux.Clause(day_col, data_type="temporal", timescale="day")], source=day_df, score=2)
+
+    week_col = col + " (day of week)"
+    week_df = lux.LuxDataFrame({week_col: formatted_date.dt.dayofweek})
+    week_vis = Vis(
+        [lux.Clause(week_col, data_type="temporal", timescale="day of week")], source=week_df, score=1
+    )
+
+    unique_year_values = len(year_df[year_col].unique())
+    unique_month_values = len(month_df[month_col].unique())
+    unique_week_values = len(week_df[week_col].unique())
+    vlist = []
+
+    vlist.append(overall_vis)
+    if unique_year_values != 1:
+        vlist.append(year_vis)
+    if unique_month_values != 1:
+        vlist.append(month_vis)
+    if unique_week_values != 1:
+        vlist.append(week_vis)
+    return vlist
diff --git a/lux/action/univariate.py b/lux/action/univariate.py
@@ -89,17 +89,6 @@ def univariate(ldf, *args):
             "description": "Show choropleth maps of <p class='highlight-descriptor'>geographic</p> attributes",
             "long_description": f"Occurence displays choropleths of averages for some geographic attribute{examples}. Visualizations are ranked by diversity of the geographic attribute.",
         }
-    elif data_type_constraint == "temporal":
-        intent = [lux.Clause("?", data_type="temporal")]
-        intent.extend(filter_specs)
-        recommendation = {
-            "action": "Temporal",
-            "description": "Show trends over <p class='highlight-descriptor'>time-related</p> attributes.",
-            "long_description": "Temporal displays line charts for all attributes related to datetimes in the dataframe.",
-        }
-        # Doesn't make sense to generate a line chart if there is less than 3 datapoints (pre-aggregated)
-        if len(ldf) < 3:
-            ignore_rec_flag = True
     if ignore_rec_flag:
         recommendation["collection"] = []
         return recommendation

diff --git a/lux/utils/date_utils.py b/lux/utils/date_utils.py
@@ -83,7 +83,8 @@ def compute_date_granularity(date_column: pd.core.series.Series):
             A str specifying the granularity of dates for the inspected temporal column
     """
     # supporting a limited set of Vega-Lite TimeUnit (https://vega.github.io/vega-lite/docs/timeunit.html)
-    date_fields = ["day", "month", "year"]
+    # corresponding to Pandas timescales
+    date_fields = ["day", "month", "year", "dayofweek"]
     date_index = pd.DatetimeIndex(date_column)
     for field in date_fields:
         # can be changed to sum(getattr(date_index, field)) != 0

diff --git a/lux/vis/Clause.py b/lux/vis/Clause.py
@@ -33,6 +33,7 @@ def __init__(
         bin_size: int = 0,
         weight: float = 1,
         sort: str = "",
+        timescale: str = "",
         exclude: typing.Union[str, list] = "",
     ):
         """
@@ -67,6 +68,9 @@ def __init__(
                 Number of bins for histograms, by default 0
         weight : float, optional
                 A number between 0 and 1 indicating the importance of this Clause, by default 1
+        timescale : str, optional
+                If data type is temporal, indicate whether temporal associated with timescale (if empty, then plot overall).
+                If timescale is present, the line chart axis is based on ordinal data type (non-date axis).
         sort : str, optional
                 Specifying whether and how the bar chart should be sorted
                 Possible values: 'ascending', 'descending', by default ""
@@ -86,6 +90,7 @@ def __init__(
         self.bin_size = bin_size
         self.weight = weight
         self.sort = sort
+        self.timescale = timescale
         self.exclude = exclude
 
     def get_attr(self):

diff --git a/lux/vislib/altair/LineChart.py b/lux/vislib/altair/LineChart.py
@@ -43,6 +43,11 @@ def initialize_chart(self):
         x_attr_abv = str(x_attr.attribute)
         y_attr_abv = str(y_attr.attribute)
 
+        if x_attr.timescale != "":
+            x_attr.data_type = "ordinal"
+        if y_attr.timescale != "":
+            y_attr.data_type = "ordinal"
+
         if len(x_attr_abv) > 25:
             x_attr_abv = x_attr.attribute[:15] + "..." + x_attr.attribute[-10:]
         if len(y_attr_abv) > 25:
@@ -72,7 +77,7 @@ def initialize_chart(self):
                 axis=alt.Axis(title=y_attr_abv),
             )
             x_attr_field_code = f"alt.X('{x_attr.attribute}', type = '{x_attr.data_type}', axis=alt.Axis(title='{x_attr_abv}'))"
-            y_attr_fieldCode = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', title='{agg_title}', axis=alt.Axis(title='{y_attr_abv}')"
+            y_attr_field_code = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', title='{agg_title}', axis=alt.Axis(title='{y_attr_abv}'))"
         else:
             agg_title = get_agg_title(x_attr)
             x_attr_spec = alt.X(
@@ -84,14 +89,14 @@ def initialize_chart(self):
             y_attr_spec = alt.Y(
                 str(y_attr.attribute), type=y_attr.data_type, axis=alt.Axis(title=y_attr_abv)
             )
-            x_attr_field_code = f"alt.X('{x_attr.attribute}', type = '{x_attr.data_type}', title='{agg_title}', axis=alt.Axis(title='{x_attr_abv}')"
-            y_attr_fieldCode = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', axis=alt.Axis(title='{y_attr_abv}')"
+            x_attr_field_code = f"alt.X('{x_attr.attribute}', type = '{x_attr.data_type}', title='{agg_title}', axis=alt.Axis(title='{x_attr_abv}'))"
+            y_attr_field_code = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', axis=alt.Axis(title='{y_attr_abv}'))"
         self.data = AltairChart.sanitize_dataframe(self.data)
         chart = alt.Chart(self.data).mark_line().encode(x=x_attr_spec, y=y_attr_spec)
         chart = chart.interactive()  # Enable Zooming and Panning
         self.code += f"""
 		chart = alt.Chart(visData).mark_line().encode(
-		    y = {y_attr_fieldCode},
+		    y = {y_attr_field_code},
 		    x = {x_attr_field_code},
 		)
 		chart = chart.interactive() # Enable Zooming and Panning

diff --git a/tests/test_action.py b/tests/test_action.py
@@ -19,6 +19,56 @@
 from lux.vis.Vis import Vis
 
 
+def test_temporal_action(global_var):
+    airbnb_df = pd.read_csv(
+        "https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/airbnb_nyc.csv"
+    )
+    flights_df = pd.read_csv(
+        "https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/flights.csv"
+    )
+    date_df = pd.DataFrame(
+        {
+            "date": [
+                "2019-01",
+                "2014-02",
+                "2020-03",
+                "2013-04",
+                "2012-05",
+                "2019-01",
+                "2020-02",
+                "2013-03",
+                "2020-04",
+                "2000-05",
+                "2000-01",
+                "2000-02",
+                "2004-12",
+                "2004-06",
+                "2020-05",
+                "2020-01",
+                "2020-02",
+                "2020-03",
+                "2020-04",
+                "2020-05",
+            ]
+        }
+    )
+    test_data = [airbnb_df, flights_df, date_df, pytest.car_df, pytest.olympic]
+    test_data_vis_count = [4, 4, 2, 1, 1]
+    for entry in zip(test_data, test_data_vis_count):
+        df, num_vis = entry[0], entry[1]
+        df._repr_html_()
+        assert ("Temporal" in df.recommendation, "Temporal visualizations should be generated.")
+        recommended = df.recommendation["Temporal"]
+        assert (len(recommended) == num_vis, "Incorrect number of temporal visualizations generated.")
+        temporal_col = [c for c in df.columns if df.data_type[c] == "temporal"]
+        overall_vis = [
+            vis.get_attr_by_channel("x")[0].attribute
+            for vis in recommended
+            if vis.score == 4 or vis.score == 5
+        ]
+        assert temporal_col.sort() == overall_vis.sort()
+
+
 def test_vary_filter_val(global_var):
     lux.config.set_executor_type("Pandas")
     df = pytest.olympic