Add wodt clf

Add execution results of RaF, RoF and RRoF
Fix fit time in database records

testwodt.py

@@ -0,0 +1,125 @@
+import argparse
+from wodt import TreeClassifier
+from sklearn.model_selection import cross_val_score
+import numpy as np
+import random
+from experimentation.Sets import Datasets
+
+
+def parse_arguments():
+    ap = argparse.ArgumentParser()
+    ap.add_argument(
+        "-S",
+        "--set-of-files",
+        type=str,
+        choices=["aaai", "tanveer"],
+        required=False,
+        default="aaai",
+    )
+    ap.add_argument(
+        "-d",
+        "--dataset",
+        type=str,
+        required=False,
+        help="Dataset name",
+    )
+    ap.add_argument(
+        "-n",
+        "--normalize",
+        default=False,
+        type=bool,
+        required=False,
+        help="Normalize dataset (True/False)",
+    )
+    ap.add_argument(
+        "-s",
+        "--standardize",
+        default=False,
+        type=bool,
+        required=False,
+        help="Standardize dataset (True/False)",
+    )
+    ap.add_argument(
+        "-p",
+        "--paper-norm",
+        default=False,
+        type=bool,
+        required=False,
+        help="[-1, 1] normalization like on paper (True/False)",
+    )
+    ap.add_argument(
+        "-r",
+        "--random-set",
+        default=0,
+        type=int,
+        required=False,
+        help="Set of random seeds: {0, 1}",
+    )
+    args = ap.parse_args()
+    return (
+        args.set_of_files,
+        args.dataset,
+        args.normalize,
+        args.standardize,
+        args.paper_norm,
+        args.random_set,
+    )
+
+
+def normalize_paper(data):
+    min_data = data.min()
+    return 2 * (data - min_data) / (data.max() - min_data) - 1
+
+
+def process_dataset(dataset, verbose):
+    X, y = dt.load(dataset)
+    if paper_norm:
+        X = normalize_paper(X)
+    scores = []
+    if verbose:
+        print(f"* Processing dataset [{dataset}] from Set: {set_of_files}")
+        print(f"X.shape: {X.shape}")
+        print(f"{X[:4]}")
+        print(f"Random seeds: {random_seeds}")
+        print(f"[-1, 1]: {paper_norm} norm: {normalize} std: {standardize}")
+    for random_state in random_seeds:
+        random.seed(random_state)
+        np.random.seed(random_state)
+        clf = TreeClassifier(random_state=random_state)
+        res = cross_val_score(clf, X, y, cv=5)
+        scores.append(res)
+        if verbose:
+            print(
+                f"Random seed: {random_state:5d} Accuracy: {res.mean():6.4f}"
+                f"±{res.std():6.4f}"
+            )
+    return scores
+
+
+(
+    set_of_files,
+    dataset,
+    normalize,
+    standardize,
+    paper_norm,
+    random_set,
+) = parse_arguments()
+random_seeds = (
+    [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
+    if random_set == 0
+    else [32, 24, 56, 18, 2, 94, 1256, 84, 156, 42]
+)
+dt = Datasets(normalize, standardize, set_of_files)
+if dataset == "all":
+    print(
+        f"* Process all datasets set: {set_of_files} [-1, 1]: {paper_norm} "
+        f"norm: {normalize} std: {standardize}"
+    )
+    print(f"5 Fold Cross Validation with 10 random seeds {random_seeds}")
+    for dataset in dt:
+        print(f"- {dataset[0]:20s} ", end="")
+        scores = process_dataset(dataset[0], verbose=False)
+        print(f"{np.mean(scores):6.4f}±{np.std(scores):6.4f}")
+else:
+    scores = process_dataset(dataset, verbose=True)
+    print(f"* Accuracy: {np.mean(scores):6.4f}±{np.std(scores):6.4f}")