add start of new tests

tests/test_backward_compat.py

@@ -0,0 +1,65 @@
+import os
+
+import torch
+import pytest
+
+from netam.common import BIG, force_spawn
+from netam.framework import (
+    crepe_exists,
+    load_crepe,
+    load_pcp_df,
+)
+from netam.sequences import MAX_AA_TOKEN_IDX, translate_sequence
+from netam.models import TransformerBinarySelectionModelWiggleAct
+from netam.dasm import (
+    DASMBurrito,
+    DASMDataset,
+    zap_predictions_along_diagonal,
+)
+
+
+
+# @pytest.fixture(scope="module")
+# def dasm_old_burrito(pcp_df):
+#     force_spawn()
+#     """Fixture that returns the DNSM Burrito object."""
+#     pcp_df["in_train"] = True
+#     pcp_df.loc[pcp_df.index[-15:], "in_train"] = False
+#     train_dataset, val_dataset = DASMDataset.train_val_datasets_of_pcp_df(pcp_df)
+
+#     model = load_crepe("old_models/dasm_13kv1jaffe+v1tang-joint")
+
+#     burrito = DASMBurrito(
+#         train_dataset,
+#         val_dataset,
+#         model,
+#         batch_size=32,
+#         learning_rate=0.001,
+#         min_learning_rate=0.0001,
+#     )
+#     burrito.joint_train(
+#         epochs=1, cycle_count=2, training_method="full", optimize_bl_first_cycle=False
+#     )
+#     return burrito
+
+def test_old_model_outputs(pcp_df):
+    example_seq = "QVQLVESGGGVVQPGRSLRLSCAASGFTFSSSGMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVKGRFTISRDNSKNTVYLQMNSLRAEDTAVYYCAREGHSNYPYYYYYMDVWGKGTTVTVSS"
+    dasm_crepe = load_crepe("tests/old_models/dasm_13k-v1jaffe+v1tang-joint")
+    dnsm_crepe = load_crepe("tests/old_models/dnsm_13k-v1jaffe+v1tang-joint")
+
+    unmodified_df = load_pcp_df(
+        "data/wyatt-10x-1p5m_pcp_2023-11-30_NI.first100.csv.gz",
+    )
+    # evaluate on first sequence in pcp_df:
+    sequence = unmodified_df["parent"].iloc[0]
+    aa_sequence = translate_sequence(sequence)
+    print(aa_sequence)
+    print(dasm_crepe([example_seq]))
+    assert False
+
+
+    # aa_parents_idxs = pcp_df["aa_parents_idxs"].iloc[0]
+
+    # Test applying models to raw sequences (keep in mind that pcp_df may have
+    # tokens added, such as separator tokens, to sequences), and to sequences that have been
+    # processed by the Dataset initializer.