IMP: replace volatility with lineplot for evaluate_* visualizers

ci/recipe/meta.yaml

@@ -33,7 +33,7 @@ test:
   requires:
     - pytest
     - q2-types >={{ q2_types }}
-    - q2-longitudinal >={{ q2_longitudinal }}
+    - q2-vizard >={{ q2_vizard }}
     - q2-feature-classifier >={{ q2_feature_classifier }}
     - qiime2 >={{ qiime2 }}
 

rescript/cross_validate.py

@@ -162,11 +162,9 @@ def _split_fasta(sequences, train_ids, test_ids):
     return train_seqs, test_seqs
 
 
-def evaluate_classifications(ctx,
-                             expected_taxonomies,
-                             observed_taxonomies,
-                             labels=None):
-    volatility = ctx.get_action('longitudinal', 'volatility')
+def _evaluate_classifications_stats(expected_taxonomies,
+                                    observed_taxonomies,
+                                    labels=None):
     # Validate inputs.
     if len(expected_taxonomies) != len(observed_taxonomies):
         raise ValueError('Expected and Observed Taxonomies do not match. '
@@ -201,10 +199,26 @@ def evaluate_classifications(ctx,
     # convert index to strings
     precision_recall.index = pd.Index(
         [str(i) for i in range(1, len(precision_recall.index) + 1)], name='id')
-    plots, = volatility(metadata=q2.Metadata(precision_recall),
-                        state_column='Level',
-                        default_group_column='Dataset',
-                        default_metric='F-Measure')
+
+    return q2.Metadata(precision_recall)
+
+
+def evaluate_classifications(ctx,
+                             expected_taxonomies,
+                             observed_taxonomies,
+                             labels=None):
+    lineplot = ctx.get_action('vizard', 'lineplot')
+
+    md = _evaluate_classifications_stats(
+        expected_taxonomies=expected_taxonomies,
+        observed_taxonomies=observed_taxonomies,
+        labels=labels)
+
+    plots, = lineplot(metadata=md,
+                      x_measure='Level',
+                      y_measure='F-Measure',
+                      group_by='Dataset',
+                      title='RESCRIPt Evaluate Classifications')
     return plots
 
 

rescript/evaluate.py

@@ -48,11 +48,13 @@ def evaluate_taxonomy(ctx,
     results.index = pd.Index(
         [str(i) for i in range(1, len(results.index) + 1)], name='id')
     results = q2.Metadata(results)
-    volatility = ctx.get_action('longitudinal', 'volatility')
-    plots, = volatility(metadata=results,
-                        state_column='Level',
-                        default_group_column='Dataset',
-                        default_metric='Taxonomic Entropy')
+
+    lineplot = ctx.get_action('vizard', 'lineplot')
+    plots, = lineplot(metadata=results,
+                      x_measure='Level',
+                      y_measure='Taxonomic Entropy',
+                      group_by='Dataset',
+                      title='RESCRIPt Evaluate Taxonomy')
     return plots
 
 

rescript/plugin_setup.py

@@ -82,7 +82,7 @@
     'CC BY-SA 4.0. To learn more, please visit https://unite.ut.ee/cite.php '
     'and https://creativecommons.org/licenses/by-sa/4.0/.')
 
-VOLATILITY_PLOT_XAXIS_INTERPRETATION = (
+LINEPLOT_XAXIS_INTERPRETATION = (
     'The x-axis in these plots represents the taxonomic '
     'levels present in the input taxonomies so are labeled numerically '
     'instead of by rank, but typically for 7-level taxonomies these will '
@@ -225,7 +225,7 @@
         'sets of true taxonomic labels to the predicted taxonomies for the '
         'same set(s) of features. Output an interactive line plot of '
         'classification accuracy for each pair of expected/observed '
-        'taxonomies. ' + VOLATILITY_PLOT_XAXIS_INTERPRETATION),
+        'taxonomies. ' + LINEPLOT_XAXIS_INTERPRETATION),
     citations=[citations['bokulich2018optimizing'],
                citations['bokulich2017q2']]
 )
@@ -395,7 +395,7 @@
         'unique labels, taxonomic entropy, and the number of features that '
         'are (un)classified at each taxonomic level. This action is useful '
         'for both reference taxonomies and classification results. ' +
-        VOLATILITY_PLOT_XAXIS_INTERPRETATION),
+        LINEPLOT_XAXIS_INTERPRETATION),
     citations=[citations['bokulich2017q2']]
 )
 

rescript/tests/test_cross_validate.py

@@ -6,15 +6,14 @@
 # The full license is in the file LICENSE, distributed with this software.
 # ----------------------------------------------------------------------------
 
-import os
-
 from qiime2.plugin.testing import TestPluginBase
 from qiime2.plugins import rescript
 import qiime2
 import pandas as pd
 import pandas.testing as pdt
 
 from rescript import cross_validate
+from ..cross_validate import _evaluate_classifications_stats
 
 
 import_data = qiime2.Artifact.import_data
@@ -92,22 +91,19 @@ def test_evaluate_fit_classifier(self):
         pdt.assert_series_equal(
             obs.view(pd.Series).sort_index(), exp_obs, check_names=False)
 
-    def test_evaluate_classifications(self):
+    def test_evaluate_classifications_stats(self):
         # simulate predicted classifications at genus level
         taxa = self.taxa_series.copy().apply(
             lambda x: ';'.join(x.split(';')[:6]))
         taxa = qiime2.Artifact.import_data('FeatureData[Taxonomy]', taxa)
         # first round we just make sure this runs
-        rescript.actions.evaluate_classifications([self.taxa], [taxa])
+        _evaluate_classifications_stats([self.taxa], [taxa])
         # now the same but input multiple times to test lists of inputs
-        vol, = rescript.actions.evaluate_classifications(
-            [self.taxa, taxa], [taxa, taxa])
+        obs = _evaluate_classifications_stats([self.taxa, taxa], [taxa, taxa])
         # now inspect and validate the contents
         # we inspect the second eval results to compare perfect match vs.
         # simulated genus-level classification (when species are expected)
-        vol.export_data(self.temp_dir.name)
-        html_path = os.path.join(self.temp_dir.name, 'data.tsv')
-        vol = qiime2.Metadata.load(html_path).to_dataframe()
+        obs_df = obs.to_dataframe()
         exp = pd.DataFrame({
             'Level': {
                 '1': 1.0, '2': 2.0, '3': 3.0, '4': 4.0, '5': 5.0, '6': 6.0,
@@ -128,7 +124,7 @@ def test_evaluate_classifications(self):
             'Dataset': {'1': '1', '2': '1', '3': '1', '4': '1', '5': '1',
                         '6': '1', '7': '1', '8': '2', '9': '2', '10': '2',
                         '11': '2', '12': '2', '13': '2'}}).sort_index()
-        pdt.assert_frame_equal(vol.sort_index(), exp, check_names=False)
+        pdt.assert_frame_equal(obs_df.sort_index(), exp, check_names=False)
 
     def test_evaluate_classifications_mismatch_input_count(self):
         with self.assertRaisesRegex(