[BUG] Fix GreedyEncoder max_len type hint and add tests (#376)

pyaptamer/trafos/encode/_greedy.py

@@ -60,7 +60,7 @@ class GreedyEncoder(BaseTransform):
     def __init__(
         self,
         words: dict[str, int],
-        max_len: int,
+        max_len: int | None = None,
         word_max_len: int = None,
     ):
         self.words = words

pyaptamer/trafos/encode/tests/test_greedy_encoder.py

@@ -0,0 +1,60 @@
+"""Tests for GreedyEncoder."""
+
+import pandas as pd
+
+from pyaptamer.trafos.encode import GreedyEncoder
+
+WORDS = {"A": 1, "C": 2, "G": 3, "U": 4, "AC": 5, "GU": 6}
+
+
+def test_get_test_params_instantiation():
+    """Both param sets from get_test_params() must produce a valid instance.
+
+    Regression test for issue #376: param0 was missing max_len,
+    causing a TypeError on instantiation.
+    """
+    params = GreedyEncoder(words=WORDS).get_test_params()
+    for i, param in enumerate(params):
+        enc = GreedyEncoder(**param)
+        assert isinstance(enc, GreedyEncoder), f"param{i} failed to instantiate"
+
+
+def test_max_len_none_by_default():
+    """max_len should default to None and pad to longest sequence length."""
+    enc = GreedyEncoder(words=WORDS)
+    assert enc.max_len is None
+
+    # "ACGU" -> greedy encodes as ["AC", "GU"] = 2 tokens
+    # "A"    -> 1 token, padded to 2
+    X = pd.DataFrame([["ACGU"], ["A"]])
+    result = enc.fit_transform(X)
+
+    # output length = longest encoded sequence = 2
+    assert result.shape[1] == 2
+    # shorter sequence should be zero-padded
+    assert result.iloc[1, 1] == 0
+
+
+def test_max_len_truncates():
+    """Sequences longer than max_len should be truncated."""
+    enc = GreedyEncoder(words=WORDS, max_len=2)
+    X = pd.DataFrame([["ACGU"]])
+    result = enc.fit_transform(X)
+    assert result.shape[1] == 2
+
+
+def test_max_len_pads():
+    """Sequences shorter than max_len should be zero-padded."""
+    enc = GreedyEncoder(words=WORDS, max_len=6)
+    X = pd.DataFrame([["AC"]])
+    result = enc.fit_transform(X)
+    assert result.shape[1] == 6
+    assert result.iloc[0, 2:].eq(0).all()
+
+
+def test_unknown_token_maps_to_zero():
+    """Characters not in words dict should map to unknown token (0)."""
+    enc = GreedyEncoder(words={"A": 1}, max_len=3)
+    X = pd.DataFrame([["AXA"]])
+    result = enc.fit_transform(X)
+    assert result.iloc[0, 1] == 0  # X is unknown