Formatting changes

spras/analysis/ml.py

@@ -88,9 +88,11 @@ def summarize_networks(file_paths: Iterable[Union[str, PathLike]]) -> pd.DataFra
 
     return concated_df
 
+
 def validate_df(dataframe: pd.DataFrame):
     """
     Raises an error if the dataframe is empty or contains one pathway (one row)
+    @param dataframe: datafrom of pathways to validate
     """
     if dataframe.empty:
         raise ValueError("ML post-processing cannot proceed because the summarize network dataframe is empty.\nWe "
@@ -100,6 +102,7 @@ def validate_df(dataframe: pd.DataFrame):
                       f"The ml post-processing requires more than one pathway, but currently "
                       f"there are only {min(dataframe.shape)} pathways.")
 
+
 def create_palette(column_names):
     """
     Generates a dictionary mapping each column name (algorithm name)

spras/config.py

@@ -80,11 +80,13 @@ def __init__(self, raw_config):
         # Only includes algorithms that are set to be run with 'include: true'.
         self.algorithm_params = None
         # Deprecated. Previously a dict mapping algorithm names to a Boolean tracking whether they used directed graphs.
-        self.algorithm_directed  = None
+        self.algorithm_directed = None
         # A dict with the analysis settings
         self.analysis_params = None
         # A dict with the ML settings
         self.ml_params = None
+        # A Boolean specifying whether to run ML analysis for individual algorithms
+        self.analysis_include_ml_aggregate_algo = None
         # A dict with the PCA settings
         self.pca_params = None
         # A dict with the hierarchical clustering settings
@@ -254,7 +256,7 @@ def process_config(self, raw_config):
             raise ValueError("Evaluation analysis cannot run as gold standard data not provided. "
                              "Please set evaluation include to false or provide gold standard data.")
 
-        if 'aggregate_per_algorithm' in self.ml_params and self.analysis_include_ml == True:
+        if 'aggregate_per_algorithm' in self.ml_params and self.analysis_include_ml:
             self.analysis_include_ml_aggregate_algo = raw_config["analysis"]["ml"]["aggregate_per_algorithm"]
         else:
             self.analysis_include_ml_aggregate_algo = False

test/ml/test_ml.py

@@ -97,7 +97,6 @@ def test_pca_robustness(self):
 
             assert coord.equals(expected)
 
-
     def test_hac_horizontal(self):
         dataframe = ml.summarize_networks([INPUT_DIR + 'test-data-s1/s1.txt', INPUT_DIR + 'test-data-s2/s2.txt', INPUT_DIR + 'test-data-s3/s3.txt'])
         ml.hac_horizontal(dataframe, OUT_DIR + 'hac-horizontal.png', OUT_DIR + 'hac-clusters-horizontal.txt')
@@ -138,6 +137,5 @@ def test_ensemble_network_empty(self):
 
         en = pd.read_table(OUT_DIR + 'ensemble-network-empty.tsv')
         expected = pd.read_table(EXPECT_DIR + 'expected-ensemble-network-empty.tsv')
-        expected = expected.round(5)
 
         assert en.equals(expected)