Check feature input for NaNs

src/copairs/map/filter.py

@@ -3,13 +3,16 @@
 from typing import List, Tuple
 
 import pandas as pd
+import numpy as np
 
 
 def validate_pipeline_input(meta, feats, columns):
     if meta[columns].isna().any(axis=None):
         raise ValueError("metadata columns should not have null values.")
     if len(meta) != len(feats):
         raise ValueError("meta and feats have different number of rows")
+    if np.isnan(feats).any():
+        raise ValueError("features should not have null values.")
 
 
 def flatten_str_list(*args):

tests/test_map.py

@@ -1,7 +1,7 @@
-import numpy as np
 import pandas as pd
 import pytest
 from sklearn.metrics import average_precision_score
+import numpy as np
 
 from copairs import compute
 from copairs.map import average_precision
@@ -140,3 +140,28 @@ def test_raise_no_pairs():
         average_precision(meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
     with pytest.raises(UnpairedException, match="Unable to find negative pairs."):
         average_precision(meta, feats, pos_diffby, [], pos_sameby, [])
+
+
+def test_raise_nan_error():
+    length = 10
+    vocab_size = {"p": 5, "w": 3, "l": 4}
+    n_feats = 8
+    pos_sameby = ["l"]
+    pos_diffby = ["p"]
+    neg_sameby = []
+    neg_diffby = ["l"]
+    rng = np.random.default_rng(SEED)
+    meta = simulate_random_dframe(length, vocab_size, pos_sameby, pos_diffby, rng)
+    length = len(meta)
+    feats = rng.uniform(size=(length, n_feats))
+
+    # add null values
+    feats_nan = feats.copy()
+    feats_nan[2,2] = None
+    meta_nan = meta.copy()
+    meta_nan.loc[1,"p"] = None
+
+    with pytest.raises(ValueError, match="features should not have null values."):
+        average_precision(meta, feats_nan, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    with pytest.raises(ValueError, match="metadata columns should not have null values."):
+        average_precision(meta_nan, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby)