Bug in tests fixed

.github/workflows/test.yaml

@@ -9,14 +9,12 @@ on:
     branches:
       - main
       - dev
-      - dev_1.0
-      - dev_1.0_weighting
+      - dev_1.1.0
   pull_request:
     branches:
       - main
       - dev
-      - dev_1.0
-      - dev_1.0_weighting
+      - dev_1.1.0
   workflow_dispatch:  # make is manually start-able
 
 # A workflow run is made up of one or more jobs that can run sequentially or in parallel

datasail/reader/read_genomes.py

@@ -43,5 +43,6 @@ def read_dir(ds: DataSet, path: Path) -> None:
     read_data_input(data, dataset, read_dir)
 
     dataset = read_data(weights, strats, sim, dist, inter, index, num_clusters, tool_args, dataset)
-    dataset = remove_duplicate_values(dataset, dataset.data)
+    if dataset.data is not None:
+        dataset = remove_duplicate_values(dataset, dataset.data)
     return dataset

datasail/reader/read_molecules.py

@@ -40,7 +40,6 @@ def read_molecule_data(
         index: Optional[int] = None,
         num_clusters: Optional[int] = None,
         tool_args: str = "",
-        detect_duplicates: bool = True,
 ) -> DataSet:
     """
     Read in molecular data, compute the weights, and distances or similarities of every entity.
@@ -80,7 +79,7 @@ def read_dir(ds: DataSet, path: Path) -> None:
     read_data_input(data, dataset, read_dir)
 
     dataset = read_data(weights, strats, sim, dist, inter, index, num_clusters, tool_args, dataset)
-    if detect_duplicates:
+    if dataset.data is not None:
         dataset = remove_molecule_duplicates(dataset)
 
     return dataset

datasail/reader/read_other.py

@@ -44,6 +44,7 @@ def read_dir(ds: DataSet, path: Path) -> None:
     read_data_input(data, dataset, read_dir)
 
     dataset = read_data(weights, strats, sim, dist, inter, index, num_clusters, tool_args, dataset)
-    dataset = remove_duplicate_values(dataset, dataset.data)
+    if dataset.data is not None:
+        dataset = remove_duplicate_values(dataset, dataset.data)
 
     return dataset

datasail/reader/read_proteins.py

@@ -46,7 +46,8 @@ def read_dir(ds: DataSet, path: Path) -> None:
     dataset.format = FORM_PDB if str(next(iter(dataset.data.values()))).endswith(".pdb") else FORM_FASTA
 
     dataset = read_data(weights, strats, sim, dist, inter, index, num_clusters, tool_args, dataset)
-    dataset = remove_duplicate_values(dataset, dataset.data)
+    if dataset.data is not None:
+        dataset = remove_duplicate_values(dataset, dataset.data)
 
     return dataset
 

datasail/reader/utils.py

@@ -14,7 +14,7 @@
 
 DATA_INPUT = Optional[Union[str, Path, Dict[str, Union[str, np.ndarray]],
     Callable[..., Dict[str, Union[str, np.ndarray]]], Generator[Tuple[str, Union[str, np.ndarray]], None, None]]]
-MATRIX_INPUT = Optional[Union[str, Path, Tuple[List[str], np.ndarray], Callable[..., Tuple[List[str], np.ndarray]]]]
+MATRIX_INPUT = Optional[Union[str, Path, np.ndarray, Tuple[np.ndarray, List[str]], Callable[..., Tuple[np.ndarray, Optional[List[str]]]]]]
 DictMap = Dict[str, List[Dict[str, str]]]
 
 
@@ -63,7 +63,10 @@ def __hash__(self) -> int:
             elif isinstance(obj, Namespace):
                 hv = hash(tuple(obj.__dict__.items()))
             else:
-                hv = hash(obj)
+                try:
+                    hv = hash(obj)
+                except TypeError:
+                    hv = 0
             hash_val ^= hv
         return hash_val
 
@@ -178,7 +181,7 @@ def count_inter(inter: List[Tuple[str, str]], mode: int) -> Generator[Tuple[str,
         yield key, tmp[mode].count(key)
 
 
-def read_clustering_file(filepath: Path, sep: str = "\t") -> Tuple[List[str], np.ndarray]:
+def read_clustering_file(filepath: Path, sep: str = "\t") -> Tuple[np.ndarray, Optional[List[str]]]:
     """
     Read a similarity or distance matrix from a file.
 
@@ -189,14 +192,16 @@ def read_clustering_file(filepath: Path, sep: str = "\t") -> Tuple[List[str], np
     Returns:
         A list of names of the entities and their pairwise interactions in and numpy array
     """
-    names = []
-    measures = []
+    measures, names = [], []
     with open(filepath, "r") as data:
-        for line in data.readlines()[1:]:
+        names_given = not data.readlines()[0].split(sep)[1].replace('.','',1).isdigit()
+        data.seek(0)  # Reset filepointer to beginning
+        for line in data.readlines()[(1 if names_given else 0):]:
             parts = line.strip().split(sep)
-            names.append(parts[0])
-            measures.append([float(x) for x in parts[1:]])
-    return names, np.array(measures)
+            if names_given:
+                names.append(parts[0])
+            measures.append([float(x) for x in parts[(1 if names_given else 0):]])
+    return np.array(measures), names
 
 
 def read_csv(filepath: Path, sep: str = ",") -> Generator[Tuple[str, str], None, None]:
@@ -217,7 +222,7 @@ def read_csv(filepath: Path, sep: str = ",") -> Generator[Tuple[str, str], None,
 
 def read_matrix_input(
         in_data: MATRIX_INPUT,
-) -> Tuple[List[str], Union[np.ndarray, str]]:
+) -> Tuple[np.ndarray, Optional[List[str]]]:
     """
     Read the data from different types of similarity or distance.
 
@@ -228,17 +233,20 @@ def read_matrix_input(
         Tuple of names of the data samples and a matrix holding their similarities/distances or a string encoding a
         method to compute the fore-mentioned
     """
+    names = None
     if isinstance(in_data, str):
         in_data = Path(in_data)
     if isinstance(in_data, Path) and in_data.is_file():
-        names, similarity = read_clustering_file(in_data)
+        matrix, names = read_clustering_file(in_data)
+    elif isinstance(in_data, np.ndarray):
+        matrix = in_data
     elif isinstance(in_data, tuple):
-        names, similarity = in_data
+        matrix, names = in_data
     elif isinstance(in_data, Callable):
-        names, similarity = in_data()
+        matrix = in_data()
     else:
         raise ValueError()
-    return names, similarity
+    return matrix, names
 
 
 def read_data(
@@ -295,18 +303,26 @@ def read_data(
     # parse the protein similarity measure
     if sim is None and dist is None:
         dataset.similarity, dataset.distance = get_default(dataset.type, dataset.format)
-        dataset.names = list(dataset.data.keys())
     elif sim is not None and not (isinstance(sim, str) and sim.lower() in SIM_ALGOS):
-        dataset.names, dataset.similarity = read_matrix_input(sim)
+        dataset.similarity, names = read_matrix_input(sim)
+        if names is not None:
+            dataset.names = names
     elif dist is not None and not (isinstance(dist, str) and dist.lower() in DIST_ALGOS):
-        dataset.names, dataset.distance = read_matrix_input(dist)
+        dataset.distance, names = read_matrix_input(dist)
+        if names is not None:
+            dataset.names = names
     else:
         if sim is not None:
             dataset.similarity = sim
         else:
             dataset.distance = dist
         dataset.names = list(dataset.data.keys())
 
+    if dataset.data is not None and dataset.names is None:
+        dataset.names = list(dataset.data.keys())
+    elif dataset.similarity is None and dataset.distance is None:
+        raise ValueError("No data provided for splitting.")
+
     dataset.args = validate_user_args(dataset.type, dataset.format, sim, dist, tool_args)
 
     return dataset

datasail/routine.py

@@ -96,7 +96,6 @@ def datasail_main(**kwargs) -> Tuple[Dict, Dict, Dict]:
                             f_dataset.id_map.get(f, ""), NOT_ASSIGNED)
                     else:
                         raise ValueError()
-
     LOGGER.info("BQP splitting finished and results stored.")
     LOGGER.info(f"Total runtime: {time.time() - start:.5f}s")
 

tests/test_caching.py

@@ -22,8 +22,9 @@ def test_caching(size):
         num_clusters=50,
     )
 
-    original_dataset = cluster(dataset, num_clusters=50, output=base / "splits", threads=1, log_dir=Path("log.txt"),
-                               linkage="average")
+    original_dataset = cluster(
+        dataset, num_clusters=50, output=base / "splits", threads=1, log_dir=Path("log.txt"), linkage="average"
+    )
 
     # test caching
     store_to_cache(dataset, **{"cache": True, "cache_dir": Path("test_cache")})

tests/test_pipeline.py

@@ -381,10 +381,10 @@ def test_c2_impossible():
         names=["train", "test"],
         e_type="P",
         e_data={str(i): "A" * (i + 1) for i in range(10)},
-        e_sim=([str(i) for i in range(10)], sims),
+        e_sim=sims,
         e_clusters=2,
         f_type="P",
-        f_sim=([str(i) for i in range(10)], sims),
+        f_sim=sims,
         f_data={str(i): "A" * (i + 1) for i in range(10)},
         f_clusters=2,
     )