Make aggregation function selection consistent across Run and Chart pages (#42)

Currently, the Run page (e.g. /db_default/v4/nts/<runid>) provides a
way to select the aggregation function used when there are multiple
samples in the run (that option is located under "View Options").
The supported options are min and median.

Similarly, the Graph page (e.g. /db_default/v4/nts/graph?<params>)
provides a way to use mean instead of min to aggregate multiple
data points for the same order on the graph. That is provided as a
checkbox under the "Settings 🔧" menu.

This patch makes both selection mechanisms consistent by using a
dropdown menu that provides min, max, mean and median in both cases.

As a drive-by, it reimplements how we select the sample to use as a
reference for related data: we now select the point closest to the
aggregated value instead of selecting the first point unconditionally.

Author: ldionne

lnt/server/ui/templates/v4_graph.html

@@ -137,9 +137,18 @@ <h4>Controls</h4>
                     {{ 'checked="checked"' if options.xaxis_date else ""}}/></td>
               </tr>
               <tr>
-                <td>Mean() as Aggregation</td>
-                <td><input type="checkbox" name="switch_min_mean" value="yes"
-                    {{ 'checked="checked"' if options.switch_min_mean else ""}}/></td>
+                <td>Aggregation function:</td>
+              </tr>
+              {# Split this into a new row to avoid making the dialog wider. #}
+              <tr>
+                <td>
+                  <select name="aggregation_function" id="aggregation_function">
+                    <option value="min" {{ 'selected="selected"' if options.aggregation_function == 'min' else '' }}>Minimum</option>
+                    <option value="max" {{ 'selected="selected"' if options.aggregation_function == 'max' else '' }}>Maximum</option>
+                    <option value="mean" {{ 'selected="selected"' if options.aggregation_function == 'mean' else '' }}>Mean</option>
+                    <option value="median" {{ 'selected="selected"' if options.aggregation_function == 'median' else '' }}>Median</option>
+                  </select>
+                </td>
               </tr>
               <tr>
                 <td>Hide Line Plot:</td>

lnt/server/ui/templates/v4_run.html

@@ -252,13 +252,13 @@ <h4>Parameters</h4>
       <td><input id="test_min_value_filter" type="text" name="test_min_value_filter" value="{{ options.test_min_value_filter }}"/></td>
     </tr>
     <tr>
-      <td><label for="agg_func">Aggregation Function</label></td>
+      <td><label for="aggregation_function">Aggregation Function</label></td>
       <td>
-        <select id="agg_func" name="aggregation_fn">
-        <option value="min" {{ ('selected="selected"' if "min" == options.aggregation_fn else '')|safe}}>
-          Minimum</option>
-        <option value="median" {{ ('selected="selected"' if "median" == options.aggregation_fn else '')|safe}}>
-          Median</option>
+        <select id="aggregation_function" name="aggregation_function">
+          <option value="min" {{ 'selected="selected"' if options.aggregation_function == 'min' else '' }}>Minimum</option>
+          <option value="max" {{ 'selected="selected"' if options.aggregation_function == 'max' else '' }}>Maximum</option>
+          <option value="mean" {{ 'selected="selected"' if options.aggregation_function == 'mean' else '' }}>Mean</option>
+          <option value="median" {{ 'selected="selected"' if options.aggregation_function == 'median' else '' }}>Median</option>
         </select>
       </td>
     </tr>

lnt/server/ui/views.py

@@ -186,6 +186,25 @@ def ts_data(ts):
     }
 
 
+def determine_aggregation_function(function_name):
+    """
+    Return the aggregation function associated to the provided function name, or None if
+    the function name is unsupported.
+
+    This is used by dropdown menus that allow selecting from multiple aggregation functions.
+    """
+    if function_name == 'min':
+        return lnt.util.stats.safe_min
+    elif function_name == 'max':
+        return lnt.util.stats.safe_max
+    elif function_name == 'mean':
+        return lnt.util.stats.mean
+    elif function_name == 'median':
+        return lnt.util.stats.median
+    else:
+        return None
+
+
 @db_route('/submitRun', methods=('GET', 'POST'))
 def submit_run():
     """Compatibility url that hardcodes testsuite to 'nts'"""
@@ -353,10 +372,10 @@ def __init__(self, run_id):
             abort(404, "Invalid run id {}".format(run_id))
 
         # Get the aggregation function to use.
-        aggregation_fn_name = request.args.get('aggregation_fn')
-        self.aggregation_fn = {'min': lnt.util.stats.safe_min,
-                               'median': lnt.util.stats.median}.get(
-            aggregation_fn_name, lnt.util.stats.safe_min)
+        fn_name = request.args.get('aggregation_function', 'min')
+        aggregation_fn = determine_aggregation_function(fn_name)
+        if aggregation_fn is None:
+            abort(404, "Invalid aggregation function name {}".format(fn_name))
 
         # Get the MW confidence level.
         try:
@@ -428,7 +447,7 @@ def __init__(self, run_id):
             session, self.run, baseurl=db_url_for('.index', _external=False),
             result=None, compare_to=compare_to, baseline=baseline,
             num_comparison_runs=self.num_comparison_runs,
-            aggregation_fn=self.aggregation_fn, confidence_lv=confidence_lv,
+            aggregation_fn=aggregation_fn, confidence_lv=confidence_lv,
             styles=styles, classes=classes)
         self.sri = self.data['sri']
         note = self.data['visible_note']
@@ -516,7 +535,7 @@ def v4_run(id):
     else:
         test_min_value_filter = 0.0
 
-    options['aggregation_fn'] = request.args.get('aggregation_fn', 'min')
+    options['aggregation_function'] = request.args.get('aggregation_function', 'min')
 
     # Get the test names.
     test_info = session.query(ts.Test.name, ts.Test.id).\
@@ -961,30 +980,13 @@ def v4_tableau():
 
 @v4_route("/graph")
 def v4_graph():
-
     session = request.session
     ts = request.get_testsuite()
-    switch_min_mean_local = False
 
-    if 'switch_min_mean_session' not in flask.session:
-        flask.session['switch_min_mean_session'] = False
     # Parse the view options.
-    options = {'min_mean_checkbox': 'min()'}
-    if 'submit' in request.args:  # user pressed a button
-        if 'switch_min_mean' in request.args:  # user checked mean() checkbox
-            flask.session['switch_min_mean_session'] = \
-                options['switch_min_mean'] = \
-                bool(request.args.get('switch_min_mean'))
-            switch_min_mean_local = flask.session['switch_min_mean_session']
-        else:  # mean() check box is not checked
-            flask.session['switch_min_mean_session'] = \
-                options['switch_min_mean'] = \
-                bool(request.args.get('switch_min_mean'))
-            switch_min_mean_local = flask.session['switch_min_mean_session']
-    else:  # new page was loaded by clicking link, not submit button
-        options['switch_min_mean'] = switch_min_mean_local = \
-            flask.session['switch_min_mean_session']
-
+    options = {}
+    options['aggregation_function'] = \
+        request.args.get('aggregation_function')  # default determined later based on the field being graphed
     options['hide_lineplot'] = bool(request.args.get('hide_lineplot'))
     show_lineplot = not options['hide_lineplot']
     options['show_mad'] = show_mad = bool(request.args.get('show_mad'))
@@ -1198,15 +1200,18 @@ def trace_name(name, test_name, field_name):
 
             is_multisample = (len(values) > 1)
 
-            aggregation_fn = min
-            if switch_min_mean_local:
-                aggregation_fn = lnt.util.stats.agg_mean
-            if field.bigger_is_better:
-                aggregation_fn = max
+            fn_name = options.get('aggregation_function') or ('max' if field.bigger_is_better else 'min')
+            aggregation_fn = determine_aggregation_function(fn_name)
+            if aggregation_fn is None:
+                abort(404, "Invalid aggregation function name {}".format(fn_name))
+            agg_value = aggregation_fn(values)
+
+            # When aggregating multiple samples, it becomes unclear which sample to use for
+            # associated data like the run date, the order, etc. Use the index of the closest
+            # value in all the samples.
+            closest_value = sorted(values, key=lambda val: abs(val - agg_value))[0]
+            agg_index = values.index(closest_value)
 
-            agg_value, agg_index = \
-                aggregation_fn((value, index)
-                               for (index, value) in enumerate(values))
             pts_y.append(agg_value)
 
             # Plotly does not sort X axis in case of type: 'category'.

lnt/util/stats.py

@@ -33,24 +33,6 @@ def geometric_mean(values):
     return reduce(lambda a, b: a * b, [v ** iPow for v in values])
 
 
-def agg_mean(pairs):
-    """Aggregation function in views.py receives input via enumerate and
-       produces a tuple.
-       Input: (value, index)
-       Output: (mean, 0), or (None, None) on invalid input.
-    """
-    if not pairs:
-        return None, None
-    my_sum = 0.0
-    counter = 0
-    for item in pairs:
-        my_sum += item[0]
-        counter += 1
-    if counter > 0:
-        return my_sum / counter, 0
-    return None, None
-
-
 def median(values):
     if not values:
         return None

tests/server/ui/V4Pages.py

@@ -636,10 +636,11 @@ def main():
     assert 2 == lines_in_function
 
     # Make sure the new option does not break anything
-    check_html(client, '/db_default/v4/nts/graph?switch_min_mean=yes&plot.0=1.3.2&submit=Update')
-    check_json(client, '/db_default/v4/nts/graph?switch_min_mean=yes&plot.0=1.3.2&json=true&submit=Update')
-    check_html(client, '/db_default/v4/nts/graph?switch_min_mean=yes&plot.0=1.3.2')
-    check_json(client, '/db_default/v4/nts/graph?switch_min_mean=yes&plot.0=1.3.2&json=true')
+    check_html(client, '/db_default/v4/nts/graph?aggregation_function=mean&plot.0=1.3.2&submit=Update')
+    check_json(client, '/db_default/v4/nts/graph?aggregation_function=mean&plot.0=1.3.2&json=true&submit=Update')
+    check_html(client, '/db_default/v4/nts/graph?aggregation_function=mean&plot.0=1.3.2')
+    check_json(client, '/db_default/v4/nts/graph?aggregation_function=mean&plot.0=1.3.2&json=true')
+    check_html(client, '/db_default/v4/nts/graph?aggregation_function=nonexistent&plot.0=1.3.2', expected_code=404)
     app.testing = False
     error_page = check_html(client, '/explode', expected_code=500)
     assert re.search("division (or modulo )?by zero",

tests/server/ui/statsTester.py

@@ -4,44 +4,35 @@
 
 import lnt.util.stats as stats
 
-INDEX = 0
-
 
 class TestLNTStatsTester(unittest.TestCase):
-
-    @staticmethod
-    def _loc_test_agg_mean(values):
-        if values is None:
-            return stats.agg_mean(None)
-        agg_value, agg_index = stats.agg_mean(
-            (value, index) for (index, value) in enumerate(values))
-        return agg_value, agg_index
-
-    def test_agg_mean(self):
-        test_list1 = [1, 2, 3, 4, 6]
-        self.assertEqual(TestLNTStatsTester._loc_test_agg_mean(test_list1),
-                         (3.2, INDEX))
-        test_list2 = [1.0, 2.0, 3.0, 4.0]
-        self.assertEqual(TestLNTStatsTester._loc_test_agg_mean(test_list2),
-                         (2.5, INDEX))
-        test_list3 = [1.0]
-        self.assertEqual(TestLNTStatsTester._loc_test_agg_mean(test_list3),
-                         (1.0, INDEX))
-        self.assertEqual(TestLNTStatsTester._loc_test_agg_mean([]),
-                         (None, None))
-        self.assertEqual(TestLNTStatsTester._loc_test_agg_mean(None),
-                         (None, None))
-
-        # Test it exactly how it is called in views.py without indirection
-        agg_value, agg_index = stats.agg_mean(
-            (value, index) for (index, value) in enumerate(test_list1))
-        self.assertEqual((3.2, INDEX), (agg_value, agg_index))
-        agg_value, agg_index = stats.agg_mean(
-            (value, index) for (index, value) in enumerate(test_list2))
-        self.assertEqual((2.5, INDEX), (agg_value, agg_index))
-        agg_value, agg_index = stats.agg_mean(
-            (value, index) for (index, value) in enumerate(test_list3))
-        self.assertEqual((1.0, INDEX), (agg_value, agg_index))
+    def test_safe_min(self):
+        self.assertEqual(stats.safe_min([]), None)
+        self.assertEqual(stats.safe_min([1]), 1)
+        self.assertEqual(stats.safe_min([1, 2, 3]), 1)
+        self.assertEqual(stats.safe_min([3, 2, 1]), 1)
+        self.assertEqual(stats.safe_min([1, 1, 1]), 1)
+
+    def test_safe_max(self):
+        self.assertEqual(stats.safe_max([]), None)
+        self.assertEqual(stats.safe_max([1]), 1)
+        self.assertEqual(stats.safe_max([1, 2, 3]), 3)
+        self.assertEqual(stats.safe_max([3, 2, 1]), 3)
+        self.assertEqual(stats.safe_max([1, 1, 1]), 1)
+
+    def test_mean(self):
+        self.assertEqual(stats.mean([]), None)
+        self.assertEqual(stats.mean([1]), 1)
+        self.assertEqual(stats.mean([1, 2, 3]), 2)
+        self.assertEqual(stats.mean([3, 2, 1]), 2)
+        self.assertEqual(stats.mean([1, 1, 1]), 1)
+
+    def test_median(self):
+        self.assertEqual(stats.median([]), None)
+        self.assertEqual(stats.median([1]), 1)
+        self.assertEqual(stats.median([1, 2, 3]), 2)
+        self.assertEqual(stats.median([3, 2, 1]), 2)
+        self.assertEqual(stats.median([1, 1, 1]), 1)
 
 
 if __name__ == '__main__':