Fix FImdlp tests

tests/tests_do.py

@@ -1,3 +1,4 @@
+import json
 from sklearn.preprocessing import KBinsDiscretizer
 
 with open("datasets/tests.txt") as f:
@@ -5,27 +6,37 @@ with open("datasets/tests.txt") as f:
 
 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(",")
+for i in range(0, len(data), 4):
+    experiment_type = data[i]
+    print("Experiment:", data[i + 1])
+    if experiment_type == "RANGE":
+        range_data = data[i + 1]
+        from_, to_, step_, n_bins_, strategy_ = range_data.split(",")
+        X = [[float(x)] for x in range(int(from_), int(to_), int(step_))]
+    else:
+        strategy_ = data[i + 1][0]
+        n_bins_ = data[i + 1][1]
+        vector = data[i + 1][2:]
+        X = [[float(x)] for x in json.loads(vector)]
+
     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)
+    expected_data = data[i + 2]
+    cuts_data = data[i + 3]
     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(",")]
+    expected = [int(x) for x in expected_data.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(",")]
+    computed_cuts = [float(x) for x in cuts_data.split(",")]
     assert len(expected_cuts) == len(computed_cuts)
     for j in range(len(expected_cuts)):
         if round(expected_cuts[j], 5) != computed_cuts[j]: