[script.compare] Implement system performance tabulation

bob/ip/binseg/utils/table.py

+#!/usr/bin/env python
+# coding=utf-8
+
+
+import tabulate
+
+
+def performance_table(data, fmt):
+    """Tables result comparison in a given format
+
+
+    Parameters
+    ----------
+
+    data : dict
+        A dictionary in which keys are strings defining plot labels and values
+        are dictionaries with two entries:
+
+        * ``df``: :py:class:`pandas.DataFrame`
+
+          A dataframe that is produced by our evaluator engine, indexed by
+          integer "thresholds", containing the following columns: ``threshold``
+          (sorted ascending), ``precision``, ``recall``, ``pr_upper`` (upper
+          precision bounds), ``pr_lower`` (lower precision bounds),
+          ``re_upper`` (upper recall bounds), ``re_lower`` (lower recall
+          bounds).
+
+        * ``threshold``: :py:class:`list`
+
+          A threshold to graph with a dot for each set.    Specific
+          threshold values do not affect "second-annotator" dataframes.
+
+
+    fmt : str
+        One of the formats supported by tabulate_.
+
+
+    Returns
+    -------
+
+    table : str
+        A table in a specific format
+
+    """
+
+    headers = [
+        "Dataset",
+        "T",
+        "F1",
+        "F1\nstd",
+        "P",
+        "R",
+        "F1\nmax",
+        "P\nmax",
+        "R\nmax",
+        ]
+
+    table = []
+    for k, v in data.items():
+        entry = [k, v["threshold"], ]
+
+        # statistics based on the "assigned" threshold (a priori, less biased)
+        bins = len(v["df"])
+        index = int(round(bins*v["threshold"]))
+        index = min(index, len(v["df"])-1)  #avoids out of range indexing
+        entry.append(v["df"].f1_score[index])
+        entry.append(v["df"].std_f1[index])
+        entry.append(v["df"].precision[index])
+        entry.append(v["df"].recall[index])
+
+        # statistics based on the best threshold (a posteriori, biased)
+        entry.append(v["df"].f1_score.max())
+        f1max_idx = v["df"].f1_score.idxmax()
+        entry.append(v["df"].precision[f1max_idx])
+        entry.append(v["df"].recall[f1max_idx])
+
+        table.append(entry)
+
+    return tabulate.tabulate(table, headers, tablefmt=fmt, floatfmt=".3f")