Fix BinDisc quantile mistakes

tests/tests_do.py

@@ -0,0 +1,39 @@
+from sklearn.preprocessing import KBinsDiscretizer
+
+with open("datasets/tests.txt") as f:
+    data = f.readlines()
+
+data = [x.strip() for x in data if x[0] != "#"]
+
+for i in range(0, len(data), 3):
+    print("Experiment:", data[i])
+    from_, to_, step_, n_bins_, strategy_ = data[i].split(",")
+    strategy = "quantile" if strategy_.strip() == "Q" else "uniform"
+    disc = KBinsDiscretizer(
+        n_bins=int(n_bins_),
+        encode="ordinal",
+        strategy=strategy,
+    )
+    X = [[float(x)] for x in range(int(from_), int(to_), int(step_))]
+    # result = disc.fit_transform(X)
+    disc.fit(X)
+    result = disc.transform(X)
+    result = [int(x) for x in result.flatten()]
+    expected = [int(x) for x in data[i + 1].split(",")]
+    assert len(result) == len(expected)
+    for j in range(len(result)):
+        if result[j] != expected[j]:
+            print("Error at", j, "Expected=", expected[j], "Result=", result[j])
+    expected_cuts = disc.bin_edges_[0]
+    computed_cuts = [float(x) for x in data[i + 2].split(",")]
+    assert len(expected_cuts) == len(computed_cuts)
+    for j in range(len(expected_cuts)):
+        if round(expected_cuts[j], 5) != computed_cuts[j]:
+            print(
+                "Error at",
+                j,
+                "Expected=",
+                expected_cuts[j],
+                "Result=",
+                computed_cuts[j],
+            )