Add nodes, leaves and depth to report_score

report_score.py

@@ -9,8 +9,6 @@ from sklearn.model_selection import KFold, cross_validate
 from experimentation.Sets import Datasets
 from experimentation.Database import MySQL
 
-8
-
 
 def parse_arguments():
     ap = argparse.ArgumentParser()
@@ -57,6 +55,17 @@ def parse_arguments():
     return (args.set_of_files, args.model, args.dataset, args.sql, args.param)
 
 
+def nodes_leaves(clf):
+    nodes = 0
+    leaves = 0
+    for node in clf:
+        if node.is_leaf():
+            leaves += 1
+        else:
+            nodes += 1
+    return nodes, leaves
+
+
 def compute_auto_hyperparams(X, y):
     params = {"max_iter": 1e4, "C": 0.1}
     classes = len(np.unique(y))
@@ -87,7 +96,9 @@ def process_dataset(dataset, verbose, model, auto_params):
         kfold = KFold(shuffle=True, random_state=random_state, n_splits=5)
         clf = Stree(random_state=random_state)
         clf.set_params(**hyperparameters)
-        res = cross_validate(clf, X, y, cv=kfold)
+        res = cross_validate(clf, X, y, cv=kfold, return_estimator=True)
+        nodes, leaves = nodes_leaves(res["estimator"][0])
+        depth = res["estimator"][0].depth_
         scores.append(res["test_score"])
         times.append(res["fit_time"])
         if verbose:
@@ -97,7 +108,7 @@ def process_dataset(dataset, verbose, model, auto_params):
                 f"{res['test_score'].std():6.4f} "
                 f"{res['fit_time'].mean():5.3f}s"
             )
-    return scores, times, json.dumps(hyperparameters)
+    return scores, times, json.dumps(hyperparameters), nodes, leaves, depth
 
 
 def store_string(dataset, model, accuracy, time_spent, hyperparameters):
@@ -160,7 +171,8 @@ if dataset == "all":
     )
     print(f"5 Fold Cross Validation with 10 random seeds {random_seeds}\n")
     print(
-        "{0:30s} {5:4s} {6:3s} {7:2s} {1:13s} {2:13s} {3:8s} {4:90s}".format(
+        "{0:30s} {5:4s} {6:3s} {7:2s} {8:2s} {9:2s} {10:2s} {1:13s} {2:13s} "
+        "{3:8s} {4:90s}".format(
             "Dataset",
             "Acc. computed",
             "Best Accuracy",
@@ -169,12 +181,18 @@ if dataset == "all":
             "Samp",
             "Var",
             "Cls",
+            "N",
+            "L",
+            "D",
         )
     )
     print("=" * 30, end=" ")
     print("=" * 4, end=" ")
     print("=" * 3, end=" ")
     print("=" * 3, end=" ")
+    print("=" * 2, end=" ")
+    print("=" * 2, end=" ")
+    print("=" * 2, end=" ")
     print("=" * 13, end=" ")
     print("=" * 13, end=" ")
     print("=" * 8, end=" ")
@@ -187,9 +205,13 @@ if dataset == "all":
             f"{dataset[0]:30s} {samples:4d} {features:3d} " f"{classes:3d} ",
             end="",
         )
-        scores, times, hyperparameters = process_dataset(
+        scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
             dataset[0], verbose=False, model=model, auto_params=auto_params
         )
+        print(
+            f"{nodes:2d} {leaves:2d} " f"{depth:2d} ",
+            end="",
+        )
         record = dbh.find_best(dataset[0], model, "crossval")
         if record is not None:
             parameters = json.loads(record[8] if record[8] != "" else "{}")
@@ -213,7 +235,7 @@ if dataset == "all":
             )
             print(command, file=sql_output)
 else:
-    scores, times, hyperparameters = process_dataset(
+    scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
         dataset, verbose=True, model=model, auto_params=auto_params
     )
     record = dbh.find_best(dataset, model, "crossval")
@@ -224,10 +246,11 @@ else:
         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"* Accuracy Best .....: {accuracy_best:6.4f}±{acc_best_std:6.4f}")
+    print(f"* Difference ........: {accuracy_best - accuracy:6.4f}")
+    print(f"* Nodes/Leaves/Depth :{nodes:2d} {leaves:2d} " f"{depth:2d} ")
 stop = time.time()
-print(f"- Auto Hyperparams .: {hyperparameters}")
+print(f"- Auto Hyperparams ..: {hyperparameters}")
 hours, rem = divmod(stop - start, 3600)
 minutes, seconds = divmod(rem, 60)
 print(f"Time: {int(hours):2d}h {int(minutes):2d}m {int(seconds):2d}s")