Add score_all_cfs

score_all_cfs.py

@@ -0,0 +1,64 @@
+import sys
+import time
+import warnings
+
+from experimentation.Sets import Datasets
+from stree import Stree
+from mdlp import MDLP
+from mfs import MFS
+
+
+def header(filter_name):
+    print(f"Score files")
+    initial = f"{'Dataset':30s} T. Disc T.Selec "
+    sec_line = "=" * 30 + " ======= ======= "
+    for item in ["Normal", "Discret.", filter_name.upper()]:
+        initial += f"{item:10s} "
+        sec_line += "=" * 10 + " "
+    initial += "Reduction"
+    sec_line += "========="
+    print(initial)
+    print(sec_line)
+
+
+warnings.filterwarnings("ignore")
+if len(sys.argv) > 1:
+    filter_name = sys.argv[1]
+else:
+    filter_name = "cfs"
+if filter_name not in ["cfs", "fcbs"]:
+    print("First parameter has to be one of: {cfs, fcbs}")
+datasets = Datasets(False, False, "tanveer")
+header(filter_name)
+better = worse = equal = 0
+for dataset in datasets:
+    mdlp = MDLP(random_state=1)
+    X, y = datasets.load(dataset[0])
+    mfs = MFS()
+    now_disc = time.time()
+    X_disc = mdlp.fit_transform(X, y)
+    time_disc = time.time() - now_disc
+    now_selec = time.time()
+    if filter_name == "cfs":
+        features_selected = mfs.cfs(X_disc, y).get_results()
+    else:
+        features_selected = mfs.fcbs(X_disc, y, 5e-2).get_results()
+    time_selec = time.time() - now_selec
+    output = ""
+    odte_score = stree_score = 0.0
+    now = time.time()
+    clf = Stree(random_state=1, multiclass_strategy="ovo")
+    score_norm = clf.fit(X, y).score(X, y)
+    clf = Stree(random_state=1, multiclass_strategy="ovo")
+    score_disc = clf.fit(X_disc, y).score(X_disc, y)
+    if len(features_selected) > 0:
+        X_feat = X_disc[:, features_selected]
+        clf = Stree(random_state=1, multiclass_strategy="ovo")
+        score_fs = clf.fit(X_feat, y).score(X_feat, y)
+    else:
+        score_fs = 0.0
+    output = f"{dataset[0]:30s} {time_disc:7.3f} {time_selec:7.3f} "
+    output += f"{score_norm:.8f} "
+    output += f"{score_disc:.8f} {score_fs:.8f} "
+    output += f"{X.shape[1]:3} - {len(features_selected):3}"
+    print(output)