refactor: add MissingDataError

docs/contributing/index.rst

@@ -210,7 +210,7 @@ Also test the functions available in the ``Dataset`` class.
 
 .. code:: python
 
-   > data.get_node_columns(['sources'])
+   > data.get_node_columns(['sources'], scope='SPRAS')
      sources NODEID
    0    True      A
 

spras/allpairs.py

@@ -29,9 +29,7 @@ def generate_inputs(data: Dataset, filename_map):
 
         # Get sources and targets for node input file
         # Borrowed code from pathlinker.py
-        sources_targets = data.get_node_columns(["sources", "targets"])
-        if sources_targets is None:
-            raise ValueError("All Pairs Shortest Paths requires sources and targets")
+        sources_targets = data.get_node_columns(["sources", "targets"], "All Pairs Shortest Paths")
 
         both_series = sources_targets.sources & sources_targets.targets
         for _index, row in sources_targets[both_series].iterrows():

spras/btb.py

@@ -40,19 +40,11 @@ def generate_inputs(data, filename_map):
 
         # Get sources and write to file, repeat for targets
         # Does not check whether a node is a source and a target
-        for node_type in ['sources', 'targets']:
-            nodes = data.get_node_columns([node_type])
-            if nodes is None:
-                raise ValueError(f'No {node_type} found in the node files')
-
+        for node_type, nodes in data.get_node_columns_separate(['sources', 'targets'], "BowTieBuilder").items():
             # TODO test whether this selection is needed, what values could the column contain that we would want to
             # include or exclude?
             nodes = nodes.loc[nodes[node_type]]
-            if node_type == "sources":
-                nodes.to_csv(filename_map["sources"], sep= '\t', index=False, columns=['NODEID'], header=False)
-            elif node_type == "targets":
-                nodes.to_csv(filename_map["targets"], sep= '\t', index=False, columns=['NODEID'], header=False)
-
+            nodes.to_csv(filename_map[node_type], sep='\t', index=False, columns=['NODEID'], header=False)
 
         # Create network file
         edges = data.get_interactome()

spras/dataset.py

@@ -10,6 +10,50 @@
 
 Methods and intermediate state for loading data and putting it into pandas tables for use by pathway reconstruction algorithms.
 """
+class MissingDataError(RuntimeError):
+    """
+    Raises when there is missing data from the input dataframe, for `generate_input`.
+    This is thrown by PRMs.
+    """
+
+    scope: str
+    """
+    This is usually the name of the PRM throwing this error.
+    We generically call this 'scope'
+    """
+
+    missing_message: list[str] | str
+    """
+    Either a list of some specific data is missing, or we provide a custom
+    error message.
+
+    This is in the format:
+
+    (If a string) {Scope} is missing data: {message}
+    (If a list) {Scope} requires columns {message joined by ", "}
+    """
+
+    def process_message(scope: str, missing_message: list[str] | str) -> str:
+        if isinstance(missing_message, str):
+            return f"{scope} is missing data: {missing_message}"
+        else:
+            return "{} requires columns: {}".format(scope, ", ".join(missing_message))
+
+    def __init__(self, scope: str, missing_message: list[str] | str):
+        """
+        Constructs a new MissingDataError.
+
+        @param message: The message or missing columns to let the user know about.
+        See the `MissingDataError#missing_message` docstring for more info
+        """
+
+        self.scope = scope
+        self.missing_message = missing_message
+
+        super(MissingDataError, self).__init__(MissingDataError.process_message(scope, missing_message))
+
+    def __str__(self):
+        return MissingDataError.process_message(self.algorithm, self.missing_message)
 
 
 class Dataset:
@@ -132,14 +176,23 @@ def load_files_from_dict(self, dataset_dict):
         self.node_table.insert(0, "NODEID", self.node_table.pop("NODEID"))
         self.other_files = dataset_dict["other_files"]
 
-    def get_node_columns(self, col_names: list[str]) -> pd.DataFrame:
+    def get_node_columns(self, col_names: list[str], scope: str) -> pd.DataFrame:
         """
-        returns: A table containing the requested column names and node IDs
+        @param scope: The name of the algorithm (or a more general 'scope' like SPRAS)
+            to fail on if get_node_columns fails.
+        @returns: A table containing the requested column names and node IDs
         for all nodes with at least 1 of the requested values being non-empty
         """
+        # Don't mutate the input col_names
+        col_names = col_names.copy()
+
         if self.node_table is None:
             raise ValueError("node_table is None: can't request node columns of an empty dataset.")
 
+        needed_columns = set(col_names).difference(self.node_table.columns)
+        if len(needed_columns) != 0:
+            raise MissingDataError(scope, needed_columns)
+
         col_names.append(self.NODE_ID)
         filtered_table = self.node_table[col_names]
         filtered_table = filtered_table.dropna(
@@ -156,6 +209,23 @@ def get_node_columns(self, col_names: list[str]) -> pd.DataFrame:
             )
         return filtered_table
 
+    def get_node_columns_separate(self, col_names: list[str], scope: str) -> dict[str, pd.DataFrame]:
+        """
+        Get each `col_name` in `col_names` as a separate call to `get_node_columns`,
+        allowing better column filtering for NODEIDs
+
+        This is useful for making separate node lists of specific column names.
+        """
+        needed_columns = set(col_names).difference(self.node_table.columns)
+        if len(needed_columns) != 0:
+            raise MissingDataError(scope, needed_columns)
+
+        result_dict: dict[str, pd.DataFrame] = dict()
+        for name in col_names:
+            result_dict[name] = self.get_node_columns([name], scope)
+
+        return result_dict
+
     def contains_node_columns(self, col_names: list[str] | str):
         if self.node_table is None:
             raise ValueError("node_table is None: can't request node columns of an empty dataset.")

spras/domino.py

@@ -41,11 +41,7 @@ def generate_inputs(data, filename_map):
         DOMINO.validate_required_inputs(filename_map)
 
         # Get active genes for node input file
-        if data.contains_node_columns('active'):
-            # NODEID is always included in the node table
-            node_df = data.get_node_columns(['active'])
-        else:
-            raise ValueError('DOMINO requires active genes')
+        node_df = data.get_node_columns(['active'], 'DOMINO')
         node_df = node_df[node_df['active'] == True]
 
         # Transform each node id with a prefix

spras/meo.py

@@ -98,11 +98,7 @@ def generate_inputs(data, filename_map):
 
         # Get sources and write to file, repeat for targets
         # Does not check whether a node is a source and a target
-        for node_type in ['sources', 'targets']:
-            nodes = data.get_node_columns([node_type])
-            if nodes is None:
-                raise ValueError(f'No {node_type} found in the node files')
-
+        for node_type, nodes in data.get_node_columns_separate(['sources', 'targets'], "MEO").items():
             # TODO test whether this selection is needed, what values could the column contain that we would want to
             # include or exclude?
             nodes = nodes.loc[nodes[node_type]]

spras/mincostflow.py

@@ -40,10 +40,7 @@ def generate_inputs(data, filename_map):
         MinCostFlow.validate_required_inputs(filename_map)
 
         # will take the sources and write them to files, and repeats with targets
-        for node_type in ['sources', 'targets']:
-            nodes = data.get_node_columns([node_type])
-            if nodes is None:
-                raise ValueError(f'No {node_type} found in the node files')
+        for node_type, nodes in data.get_node_columns_separate(['sources', 'targets'], "MinCostFlow").items():
             # take nodes one column data frame, call sources/ target series
             nodes = nodes.loc[nodes[node_type]]
             # creates with the node type without headers

spras/omicsintegrator1.py

@@ -1,6 +1,7 @@
 from pathlib import Path
 
 from spras.containers import prepare_volume, run_container_and_log
+from spras.dataset import MissingDataError
 from spras.interactome import reinsert_direction_col_mixed
 from spras.prm import PRM
 from spras.util import add_rank_column, duplicate_edges, raw_pathway_df
@@ -64,14 +65,14 @@ def generate_inputs(data, filename_map):
 
         if data.contains_node_columns('prize'):
             # NODEID is always included in the node table
-            node_df = data.get_node_columns(['prize'])
+            node_df = data.get_node_columns(['prize'], 'Omics Integrator 1')
         elif data.contains_node_columns(['sources', 'targets']):
             # If there aren't prizes but are sources and targets, make prizes based on them
-            node_df = data.get_node_columns(['sources','targets'])
+            node_df = data.get_node_columns(['sources', 'targets'], 'Omics Integrator 1')
             node_df.loc[node_df['sources']==True, 'prize'] = 1.0
             node_df.loc[node_df['targets']==True, 'prize'] = 1.0
         else:
-            raise ValueError("Omics Integrator 1 requires node prizes or sources and targets")
+            raise MissingDataError("Omics Integrator 1", "(node prizes) or (sources and targets)")
 
         # Omics Integrator already gives warnings for strange prize values, so we won't here
         node_df.to_csv(filename_map['prizes'],sep='\t',index=False,columns=['NODEID','prize'],header=['name','prize'])

spras/omicsintegrator2.py

@@ -3,7 +3,7 @@
 import pandas as pd
 
 from spras.containers import prepare_volume, run_container_and_log
-from spras.dataset import Dataset
+from spras.dataset import Dataset, MissingDataError
 from spras.interactome import reinsert_direction_col_undirected
 from spras.prm import PRM
 from spras.util import add_rank_column, duplicate_edges
@@ -36,14 +36,14 @@ def generate_inputs(data: Dataset, filename_map):
 
         if data.contains_node_columns('prize'):
             # NODEID is always included in the node table
-            node_df = data.get_node_columns(['prize'])
+            node_df = data.get_node_columns(['prize'], 'Omics Integrator 2')
         elif data.contains_node_columns(['sources', 'targets']):
             # If there aren't prizes but are sources and targets, make prizes based on them
-            node_df = data.get_node_columns(['sources', 'targets'])
+            node_df = data.get_node_columns(['sources', 'targets'], 'Omics Integrator 2')
             node_df.loc[node_df['sources']==True, 'prize'] = 1.0
             node_df.loc[node_df['targets']==True, 'prize'] = 1.0
         else:
-            raise ValueError("Omics Integrator 2 requires node prizes or sources and targets")
+            raise MissingDataError("Omics Integrator 2", "(node prizes) or (sources and targets)")
 
         # Omics Integrator already gives warnings for strange prize values, so we won't here
         node_df.to_csv(filename_map['prizes'], sep='\t', index=False, columns=['NODEID', 'prize'], header=['name','prize'])

spras/pathlinker.py

@@ -37,9 +37,7 @@ def generate_inputs(data, filename_map):
         PathLinker.validate_required_inputs(filename_map)
 
         # Get sources and targets for node input file
-        sources_targets = data.get_node_columns(["sources", "targets"])
-        if sources_targets is None:
-            return False
+        sources_targets = data.get_node_columns(["sources", "targets"], 'PathLinker')
         both_series = sources_targets.sources & sources_targets.targets
         for _index, row in sources_targets[both_series].iterrows():
             warn_msg = row.NODEID + " has been labeled as both a source and a target."

spras/responsenet.py

@@ -34,10 +34,7 @@ def generate_inputs(data, filename_map):
         ResponseNet.validate_required_inputs(filename_map)
 
         # will take the sources and write them to files, and repeats with targets
-        for node_type in ['sources', 'targets']:
-            nodes = data.get_node_columns([node_type])
-            if nodes is None:
-                raise ValueError(f'No {node_type} found in the node files')
+        for node_type, nodes in data.get_node_columns_separate(['sources', 'targets'], "MinCostFlow").items():
             # take nodes one column data frame, call sources/ target series
             nodes = nodes.loc[nodes[node_type]]
             # creates with the node type without headers

spras/rwr.py

@@ -19,13 +19,9 @@ def generate_inputs(data, filename_map):
         RWR.validate_required_inputs(filename_map)
 
         # Get sources and targets for node input file
-        if data.contains_node_columns(["sources","targets"]):
-            sources = data.get_node_columns(["sources"])
-            targets = data.get_node_columns(["targets"])
-            nodes = pd.DataFrame({'NODEID':sources['NODEID'].tolist() + targets['NODEID'].tolist()})
-            nodes.to_csv(filename_map['nodes'],sep='\t',index=False,columns=['NODEID'],header=False)
-        else:
-            raise ValueError("Invalid node data")
+        sources_targets = data.get_node_columns_separate(["sources", "targets"], "RWR")
+        nodes = pd.DataFrame({'NODEID': sources_targets["sources"]['NODEID'].tolist() + sources_targets["targets"]['NODEID'].tolist()})
+        nodes.to_csv(filename_map['nodes'],sep='\t',index=False,columns=['NODEID'],header=False)
 
         # Get edge data for network file
         edges = data.get_interactome()

spras/strwr.py

@@ -18,14 +18,8 @@ def generate_inputs(data, filename_map):
         ST_RWR.validate_required_inputs(filename_map)
 
         # Get separate source and target nodes for source and target files
-        if data.contains_node_columns(["sources","targets"]):
-            sources = data.get_node_columns(["sources"])
-            sources.to_csv(filename_map['sources'],sep='\t',index=False,columns=['NODEID'],header=False)
-
-            targets = data.get_node_columns(["targets"])
-            targets.to_csv(filename_map['targets'],sep='\t',index=False,columns=['NODEID'],header=False)
-        else:
-            raise ValueError("Invalid node data")
+        for node_type, nodes in data.get_node_columns_separate(["sources", "targets"], "Source-Target RWR").items():
+            nodes.to_csv(filename_map[node_type],sep='\t',index=False,columns=['NODEID'],header=False)
 
         # Get edge data for network file
         edges = data.get_interactome()