Add ticks to report_score

report_score.py

@@ -10,6 +10,7 @@ from sklearn.model_selection import KFold, cross_validate
 from experimentation.Sets import Datasets
 from experimentation.Database import MySQL
 from wodt import TreeClassifier
+from experimentation.Utils import TextColor
 
 
 def parse_arguments():
@@ -178,7 +179,36 @@ def store_string(
     return result
 
 
+def compute_status(dbh, name, model, accuracy):
+    better_default = "\N{heavy check mark}"
+    better_stree = TextColor.GREEN + "\N{heavy check mark}" + TextColor.ENDC
+    best = TextColor.RED + "\N{black star}" + TextColor.ENDC
+    best_default, _ = get_best_score(dbh, name, model)
+    best_stree, _ = get_best_score(dbh, name, "stree")
+    best_all, _ = get_best_score(dbh, name, models_tree)
+    status = better_default if accuracy >= best_default else " "
+    status = better_stree if accuracy >= best_stree else status
+    status = best if accuracy >= best_all else status
+    return status
+
+
+def get_best_score(dbh, name, model):
+    record = dbh.find_best(name, model, "crossval")
+    accuracy = record[5] if record is not None else 0.0
+    acc_std = record[11] if record is not None else 0.0
+    return accuracy, acc_std
+
+
 random_seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
+models_tree = [
+    "stree",
+    "stree_default",
+    "wodt",
+    "j48svm",
+    "oc1",
+    "cart",
+    "baseRaF",
+]
 standardize = False
 (set_of_files, model, dataset, sql, normalize, parameters) = parse_arguments()
 dbh = MySQL()
@@ -206,17 +236,22 @@ if dataset == "all":
         "Parameters",
     ]
     header_lengths = [30, 5, 3, 3, 7, 7, 7, 15, 15, 10]
+    parameters = json.dumps(json.loads(parameters))
+    if parameters != "{}" and len(parameters) > 10:
+        header_lengths.pop()
+        header_lengths.append(len(parameters))
     line_col = ""
     for field, underscore in zip(header_cols, header_lengths):
         print(f"{field:{underscore}s} ", end="")
         line_col += "=" * underscore + " "
     print(f"\n{line_col}")
     for dataset in dt:
-        X, y = dt.load(dataset[0])  # type: ignore
+        name = dataset[0]
+        X, y = dt.load(name)  # type: ignore
         samples, features = X.shape
         classes = len(np.unique(y))
         print(
-            f"{dataset[0]:30s} {samples:5d} {features:3d} {classes:3d} ",
+            f"{name:30s} {samples:5d} {features:3d} {classes:3d} ",
             end="",
         )
         scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
@@ -232,28 +267,35 @@ if dataset == "all":
             f"{nodes_item:7.2f} {leaves_item:7.2f} {depth_item:7.2f} ",
             end="",
         )
-        print(f"{np.mean(scores):8.6f}±{np.std(scores):6.4f} ", end="")
+        accuracy = np.mean(scores)
+        status = (
+            compute_status(dbh, name, model, accuracy)
+            if model == "stree_default"
+            else " "
+        )
+        print(f"{accuracy:8.6f}±{np.std(scores):6.4f}{status}", end="")
         print(f"{np.mean(times):8.6f}±{np.std(times):6.4f} {hyperparameters}")
         if sql:
             command = store_string(
-                dataset[0], model, scores, times, hyperparameters, complexity
+                name, model, scores, times, hyperparameters, complexity
             )
             print(command, file=sql_output)
 else:
     scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
         dataset, verbose=True, model=model, params=parameters
     )
-    record = dbh.find_best(dataset, model, "crossval")
+    best_accuracy, acc_best_std = get_best_score(dbh, dataset, model)
     accuracy = np.mean(scores)
-    accuracy_best = record[5] if record is not None else 0.0
-    acc_best_std = record[11] if record is not None else 0.0
     print(f"* Normalize/Standard.: {normalize} / {standardize}")
     print(
         f"* Accuracy Computed .: {accuracy:6.4f}±{np.std(scores):6.4f} "
         f"{np.mean(times):5.3f}s"
     )
-    print(f"* Accuracy Best .....: {accuracy_best:6.4f}±{acc_best_std:6.4f}")
-    print(f"* Difference ........: {accuracy_best - accuracy:6.4f}")
+    print(f"* Best Accuracy model: {best_accuracy:6.4f}±{acc_best_std:6.4f}")
+    print(f"* Difference ........: {best_accuracy - accuracy:6.4f}")
+    best_accuracy, acc_best_std = get_best_score(dbh, dataset, models_tree)
+    print(f"* Best Accuracy .....: {best_accuracy:6.4f}±{acc_best_std:6.4f}")
+    print(f"* Difference ........: {best_accuracy - accuracy:6.4f}")
     print(
         f"* Nodes/Leaves/Depth : {np.mean(nodes):.2f} {np.mean(leaves):.2f} "
         f"{np.mean(depth):.2f} "