Refactor samples and fix Metrics tests

samples/sample.py

@@ -0,0 +1,44 @@
+import time
+import argparse
+import os
+from scipy.io import arff
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from fimdlp.mdlp import FImdlp
+
+datasets = {
+    "mfeat-factors": True,
+    "iris": True,
+    "letter": True,
+    "kdd_JapaneseVowels": False,
+}
+
+ap = argparse.ArgumentParser()
+ap.add_argument("--proposal", action="store_true")
+ap.add_argument("--original", dest="proposal", action="store_false")
+ap.add_argument("dataset", type=str, choices=datasets.keys())
+args = ap.parse_args()
+relative = "" if os.path.isdir("fimdlp") else ".."
+file_name = os.path.join(
+    relative, "fimdlp", "testcpp", "datasets", args.dataset
+)
+data = arff.loadarff(file_name + ".arff")
+df = pd.DataFrame(data[0])
+class_column = -1 if datasets[args.dataset] else 0
+class_name = df.columns.to_list()[class_column]
+X = df.drop(class_name, axis=1)
+y, _ = pd.factorize(df[class_name])
+X = X.to_numpy()
+test = FImdlp(proposal=args.proposal)
+now = time.time()
+test.fit(X, y)
+fit_time = time.time()
+print("Fitting: ", fit_time - now)
+now = time.time()
+Xt = test.transform(X)
+print("Transforming: ", time.time() - now)
+print(test.get_cut_points())
+clf = RandomForestClassifier(random_state=0)
+print(
+    "Random Forest score with discretized data: ", clf.fit(Xt, y).score(Xt, y)
+)