#324 create table from state space results - work in progress

* tests TBA

scripts/vandv/ssm_reporting.py

@@ -0,0 +1,57 @@
+import pandas as pd
+from scipy.stats import trim_mean
+
+
+def __html_table_from_dataframe(df_results, terms, term_style='{:.0}'):
+    df_summary_table = df_results.style.hide_index()
+    df_summary_table = df_summary_table.set_table_styles([
+        dict(selector='table', props=[('border-collapse', 'collapse')]),
+        dict(selector='td', props=[('border', '2px solid black'),
+                                   ('text-align', 'right'),
+                                   ('padding-left', '15px'),
+                                   ('padding-right', '15px')])
+    ])
+    for term in terms:
+        df_summary_table = df_summary_table.format({term: term_style})
+    df_summary_table = df_summary_table.highlight_max(axis=1)
+
+    heading = '<style type="text/css">table {border-collapse: collapse;} </style>\n'
+    return heading + df_summary_table.render()
+
+
+def html_table(results, prediction_lengths):
+    df_results = pd.DataFrame({'terms': list(results.keys())})
+    for prediction_length in prediction_lengths:
+        prediction_length_results = []
+        for term_name in results:
+            prediction_length_results.append(results[term_name][prediction_length])
+
+        df_term_column = pd.DataFrame({f'{prediction_length}': prediction_length_results})
+        df_results = df_results.join(df_term_column)
+
+    results_table = __html_table_from_dataframe(df_results, 'SSM')
+
+    trimmed_mean_proportion_to_cut = 0.1
+    trimmed_means = {
+        'terms': f'<b>Trimmed ({trimmed_mean_proportion_to_cut * 100.0:.0f}% cut) <br/> mean</b>'}
+    for prediction_length in prediction_lengths:
+        trimmed_means[f'{prediction_length}'] = trim_mean(df_results[f'{prediction_length}'],
+                                                          trimmed_mean_proportion_to_cut)
+    # print(f'{term_name} trimmed mean={trimmed_means:.1f}')
+    df_results = df_results.append(trimmed_means, ignore_index=True)
+
+    # standard_deviations = {
+    #     'terms': f'<b>Standard deviation <br/>of {metric_name}</b>'}
+    # for predictor_name in predictor_names:
+    #     predictor_display_name = predictor_name.replace('-', '<br/>')
+    #     results_without_nan = [x for x in df_results[predictor_display_name] if not math.isnan(x)]
+    #     standard_deviations[predictor_display_name] = stdev(results_without_nan)
+    #     print(f'{predictor_name} {metric_name} standard deviation={standard_deviations[predictor_display_name]:.1f}')
+    # df_results = df_results.append(standard_deviations, ignore_index=True)
+
+    summary_df = pd.DataFrame(trimmed_means, index=[0])
+    summary_table = __html_table_from_dataframe(summary_df, 'SSM summary')
+
+    # summary_df = summary_df.append(standard_deviations, ignore_index=True)
+
+    return f'<h2>State Space Model Results</h2>\n{results_table}<p/>{summary_table}\n'

tests/vandv/test_ssm_reporting.py

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+import unittest
+
+from scripts.vandv.ssm_reporting import html_table
+
+
+class SSMReporting(unittest.TestCase):
+    def test_html_table(self):
+        results = {
+            'sample term': {2: 78, 3: 60}
+        }
+
+        output_html = html_table(results, [2, 3])
+
+        self.assertEqual(
+            '''
+            '''
+            , output_html
+        )