Merge pull request #29 from sam-writer/master

More verbose logging output for compare m2

errant/commands/compare_m2.py

@@ -22,9 +22,11 @@ def main():
         # Process the edits for detection/correction based on args
         hyp_dict = process_edits(hyp_edits, args)
         ref_dict = process_edits(ref_edits, args)
+        # original sentence for logging
+        original_sentence = sent[0][2:].split("\nA")[0]
         # Evaluate edits and get best TP, FP, FN hyp+ref combo.
         count_dict, cat_dict = evaluate_edits(
-            hyp_dict, ref_dict, best_dict, sent_id, args)
+            hyp_dict, ref_dict, best_dict, sent_id, original_sentence, args)
         # Merge these dicts with best_dict and best_cats
         best_dict += Counter(count_dict)
         best_cats = merge_dict(best_cats, cat_dict)
@@ -198,7 +200,11 @@ def process_edits(edits, args):
 # Input 5: Command line args
 # Output 1: A dict of the best corpus level TP, FP and FN for the input sentence.
 # Output 2: The corresponding error type dict for the above dict.
-def evaluate_edits(hyp_dict, ref_dict, best, sent_id, args):
+def evaluate_edits(hyp_dict, ref_dict, best, sent_id, original_sentence, args):
+    # Verbose output: display the original sentence
+    if args.verbose:
+        print('{:-^40}'.format(""))
+        print("Original sentence " + str(sent_id) + ": " + original_sentence)
     # Store the best sentence level scores and hyp+ref combination IDs
     # best_f is initialised as -1 cause 0 is a valid result.
     best_tp, best_fp, best_fn, best_f, best_hyp, best_ref = 0, 0, 0, -1, 0, 0
@@ -230,6 +236,11 @@ def evaluate_edits(hyp_dict, ref_dict, best, sent_id, args):
                 # Prepare verbose output edits.
                 hyp_verb = list(sorted(hyp_dict[hyp_id].keys()))
                 ref_verb = list(sorted(ref_dict[ref_id].keys()))
+                # add categories
+                # hyp_dict[hyp_id] looks like (0, 1, "str")
+                # hyp_dict[hyp_id][h] is a list, always length one, of the corresponding category
+                hyp_verb = [h + (hyp_dict[hyp_id][h][0],) for h in hyp_verb]
+                ref_verb = [r + (ref_dict[ref_id][r][0],) for r in ref_verb]
                 # Ignore noop edits
                 if not hyp_verb or hyp_verb[0][0] == -1: hyp_verb = []
                 if not ref_verb or ref_verb[0][0] == -1: ref_verb = []
@@ -246,6 +257,10 @@ def evaluate_edits(hyp_dict, ref_dict, best, sent_id, args):
     if args.verbose:
         print('{:-^40}'.format(""))
         print("^^ HYP "+str(best_hyp)+", REF "+str(best_ref)+" chosen for sentence "+str(sent_id))
+        print("Local results:")
+        header = ["Category", "TP", "FP", "FN"]
+        body = [[k, *v] for k, v in best_cat.items()]
+        print_table([header] + body)
     # Save the best TP, FP and FNs as a dict, and return this and the best_cat dict
     best_dict = {"tp":best_tp, "fp":best_fp, "fn":best_fn}
     return best_dict, best_cat
@@ -254,7 +269,7 @@ def evaluate_edits(hyp_dict, ref_dict, best, sent_id, args):
 # Input 2: A dictionary of reference edits for a single annotator.
 # Output 1-3: The TP, FP and FN for the hyp vs the given ref annotator.
 # Output 4: A dictionary of the error type counts.
-def compareEdits(hyp_edits, ref_edits):    
+def compareEdits(hyp_edits, ref_edits):
     tp = 0    # True Positives
     fp = 0    # False Positives
     fn = 0    # False Negatives
@@ -360,22 +375,31 @@ def print_results(best, best_cats, args):
         best_cats = processCategories(best_cats, args.cat)
         print("")
         print('{:=^66}'.format(title))
-        print("Category".ljust(14), "TP".ljust(8), "FP".ljust(8), "FN".ljust(8), 
+        print("Category".ljust(14), "TP".ljust(8), "FP".ljust(8), "FN".ljust(8),
             "P".ljust(8), "R".ljust(8), "F"+str(args.beta))
         for cat, cnts in sorted(best_cats.items()):
             cat_p, cat_r, cat_f = computeFScore(cnts[0], cnts[1], cnts[2], args.beta)
-            print(cat.ljust(14), str(cnts[0]).ljust(8), str(cnts[1]).ljust(8), 
+            print(cat.ljust(14), str(cnts[0]).ljust(8), str(cnts[1]).ljust(8),
                 str(cnts[2]).ljust(8), str(cat_p).ljust(8), str(cat_r).ljust(8), cat_f)
 
     # Print the overall results.
     print("")
     print('{:=^46}'.format(title))
     print("\t".join(["TP", "FP", "FN", "Prec", "Rec", "F"+str(args.beta)]))
-    print("\t".join(map(str, [best["tp"], best["fp"], 
+    print("\t".join(map(str, [best["tp"], best["fp"],
         best["fn"]]+list(computeFScore(best["tp"], best["fp"], best["fn"], args.beta)))))
     print('{:=^46}'.format(""))
     print("")
 
+def print_table(table):
+    longest_cols = [
+        (max([len(str(row[i])) for row in table]) + 3)
+        for i in range(len(table[0]))
+    ]
+    row_format = "".join(["{:>" + str(longest_col) + "}" for longest_col in longest_cols])
+    for row in table:
+        print(row_format.format(*row))
+
 if __name__ == "__main__":
     # Run the program
-    main()
+    main()