black am

drevalpy/datasets/dataset.py

@@ -105,7 +105,11 @@ def __str__(self):
                 f"Response {self.response[:3]}..."
             )
         else:
-            string = f"DrugResponseDataset: CLs {self.cell_line_ids}; " f"Drugs {self.drug_ids}; " f"Response {self.response}"
+            string = (
+                f"DrugResponseDataset: CLs {self.cell_line_ids}; "
+                f"Drugs {self.drug_ids}; "
+                f"Response {self.response}"
+            )
         if self.predictions is not None:
             if len(self.predictions) > 3:
                 string += f"; Predictions {self.predictions[:3]}..."

drevalpy/datasets/loader.py

@@ -5,8 +5,9 @@
 from .utils import download_dataset
 
 
-def load_gdsc1(path_data: str = "data", file_name: str = "response_GDSC1.csv", dataset_name: str = "GDSC1") -> (
-        DrugResponseDataset):
+def load_gdsc1(
+    path_data: str = "data", file_name: str = "response_GDSC1.csv", dataset_name: str = "GDSC1"
+) -> DrugResponseDataset:
     """
     Loads the GDSC1 dataset.
 

drevalpy/experiment.py

@@ -163,7 +163,9 @@ def drug_response_experiment(
 
             model = model_class()
 
-            if not os.path.isfile(prediction_file):  # if this split has not been run yet (or for a single drug model, this drug_id)
+            if not os.path.isfile(
+                prediction_file
+            ):  # if this split has not been run yet (or for a single drug model, this drug_id)
 
                 tuning_inputs = {
                     "model": model,
@@ -234,7 +236,9 @@ def drug_response_experiment(
                     print(f"Randomization tests for {model_class.model_name}")
                     # if this line changes, it also needs to be changed in pipeline:
                     # randomization_split.py
-                    randomization_test_views = get_randomization_test_views(model=model, randomization_mode=randomization_mode)
+                    randomization_test_views = get_randomization_test_views(
+                        model=model, randomization_mode=randomization_mode
+                    )
                     randomization_test(
                         randomization_test_views=randomization_test_views,
                         model=model,
@@ -308,7 +312,11 @@ def consolidate_single_drug_model_predictions(
                     "randomization": {},
                 }
                 # list all dirs in model_path/drugs
-                drugs = [d for d in os.listdir(os.path.join(model_path, "drugs")) if os.path.isdir(os.path.join(model_path, "drugs", d))]
+                drugs = [
+                    d
+                    for d in os.listdir(os.path.join(model_path, "drugs"))
+                    if os.path.isdir(os.path.join(model_path, "drugs", d))
+                ]
                 for drug in drugs:
                     single_drug_prediction_path = os.path.join(model_path, "drugs", drug)
 
@@ -343,7 +351,9 @@ def consolidate_single_drug_model_predictions(
                         f = f"robustness_{trial+1}_split_{split}.csv"
                         if trial not in predictions["robustness"]:
                             predictions["robustness"][trial] = []
-                        predictions["robustness"][trial].append(pd.read_csv(os.path.join(robustness_path, f), index_col=0))
+                        predictions["robustness"][trial].append(
+                            pd.read_csv(os.path.join(robustness_path, f), index_col=0)
+                        )
 
                     # Randomization predictions
                     if randomization_mode is not None:

drevalpy/models/SRMF/srmf.py

@@ -76,7 +76,9 @@ def train(
         drug_response_matrix = drug_response_matrix.groupby(["cell_line_id", "drug_id"]).mean().reset_index()
         drug_response_matrix = drug_response_matrix.pivot(index="cell_line_id", columns="drug_id", values="response")
 
-        drug_response_matrix = drug_response_matrix.reindex(index=cell_lines, columns=drugs)  # missing rows and columns are filled with NaN
+        drug_response_matrix = drug_response_matrix.reindex(
+            index=cell_lines, columns=drugs
+        )  # missing rows and columns are filled with NaN
 
         self.w = ~np.isnan(drug_response_matrix)
         drug_response_matrix = drug_response_matrix.copy()

drevalpy/models/utils.py

@@ -112,22 +112,10 @@ def load_drug_fingerprint_features(data_path: str, dataset_name: str) -> Feature
     :return:
     """
     if dataset_name == "Toy_Data":
-        fingerprints = pd.read_csv(
-            os.path.join(
-                data_path,
-                dataset_name,
-                "fingerprints.csv"
-            ),
-            index_col=0
-        )
+        fingerprints = pd.read_csv(os.path.join(data_path, dataset_name, "fingerprints.csv"), index_col=0)
     else:
         fingerprints = pd.read_csv(
-            os.path.join(
-                data_path,
-                dataset_name,
-                "drug_fingerprints",
-                "drug_name_to_demorgan_128_map.csv"
-            ),
+            os.path.join(data_path, dataset_name, "drug_fingerprints", "drug_name_to_demorgan_128_map.csv"),
             index_col=0,
         ).T
     return FeatureDataset(

drevalpy/utils.py

@@ -295,4 +295,7 @@ def get_response_transformation(response_transformation: str):
         return MinMaxScaler()
     if response_transformation == "robust":
         return RobustScaler()
-    raise ValueError(f"Unknown response transformation {response_transformation}. Choose from 'None', " f"'standard', 'minmax', 'robust'")
+    raise ValueError(
+        f"Unknown response transformation {response_transformation}. Choose from 'None', "
+        f"'standard', 'minmax', 'robust'"
+    )

drevalpy/visualization/corr_comp_scatter.py

@@ -154,7 +154,10 @@ def write_to_html(lpo_lco_ldo: str, f: TextIO, *args, **kwargs) -> TextIO:
                 ]
                 listed_files.sort()
                 for group_comparison in listed_files:
-                    f.write(f'<li><a href="corr_comp_scatter/{group_comparison}" target="_blank">' f"{group_comparison}</a></li>\n")
+                    f.write(
+                        f'<li><a href="corr_comp_scatter/{group_comparison}" target="_blank">'
+                        f"{group_comparison}</a></li>\n"
+                    )
                 f.write("</ul>\n")
         return f
 

drevalpy/visualization/utils.py

@@ -47,7 +47,9 @@ def parse_results(path_to_results: str):
     result_dir = pathlib.Path(path_to_results)
     result_files = list(result_dir.rglob("*.csv"))
     # filter for all files that follow this pattern: result_dir/*/{predictions|cross_study|randomization|robustness}/*.csv
-    pattern = re.compile(fr"{result_dir}/(LPO|LCO|LDO)/[^/]+/(predictions|cross_study|randomization|robustness)/.*\.csv$")
+    pattern = re.compile(
+        rf"{result_dir}/(LPO|LCO|LDO)/[^/]+/(predictions|cross_study|randomization|robustness)/.*\.csv$"
+    )
     result_files = [file for file in result_files if pattern.match(str(file))]
 
     # inititalize dictionaries to store the evaluation results
@@ -371,7 +373,8 @@ def create_index_html(custom_id: str, test_modes: list[str], prefix_results: str
             )
             shutil.copyfile(img_path, os.path.join(prefix_results, f"{lpo_lco_ldo}.png"))
             f.write(
-                f'<a href="{lpo_lco_ldo}.html" target="_blank"><img src="{lpo_lco_ldo}.png" ' f'style="width:300px;height:300px;"></a>\n'
+                f'<a href="{lpo_lco_ldo}.html" target="_blank"><img src="{lpo_lco_ldo}.png" '
+                f'style="width:300px;height:300px;"></a>\n'
             )
         f.write("</div>\n")
         f.write("</div>\n")

drevalpy/visualization/vioheat.py

@@ -65,7 +65,9 @@ def write_to_html(lpo_lco_ldo: str, f: TextIO, *args, **kwargs) -> TextIO:
         ]
         f.write(f'<h2 id="{nav_id}">{plot} Plots of Performance Measures over CV runs</h2>\n')
         f.write(f"<h3>{plot} plots comparing all models</h3>\n")
-        f.write(f'<iframe src="{dir_name}/{prefix}_algorithms_{lpo_lco_ldo}.html" width="100%" height="100%" frameBorder="0"></iframe>\n')
+        f.write(
+            f'<iframe src="{dir_name}/{prefix}_algorithms_{lpo_lco_ldo}.html" width="100%" height="100%" frameBorder="0"></iframe>\n'
+        )
         f.write(f"<h3>{plot} plots comparing all models with normalized metrics</h3>\n")
         f.write(
             f"Before calculating the evaluation metrics, all values were normalized by the mean of the drug or cell line. "

drevalpy/visualization/violin.py

@@ -97,7 +97,14 @@ def __draw__(self) -> None:
                         {
                             "visible": [False] * (self.count_r2 + self.count_pearson + self.count_spearman)
                             + [True] * self.count_kendall
-                            + [False] * (count_sum - self.count_r2 - self.count_pearson - self.count_spearman - self.count_kendall)
+                            + [False]
+                            * (
+                                count_sum
+                                - self.count_r2
+                                - self.count_pearson
+                                - self.count_spearman
+                                - self.count_kendall
+                            )
                         },
                         {"title": "Kendall"},
                     ],
@@ -108,7 +115,13 @@ def __draw__(self) -> None:
                     args=[
                         {
                             "visible": [False]
-                            * (count_sum - self.count_partial_correlation - self.count_mse - self.count_rmse - self.count_mae)
+                            * (
+                                count_sum
+                                - self.count_partial_correlation
+                                - self.count_mse
+                                - self.count_rmse
+                                - self.count_mae
+                            )
                             + [True] * self.count_partial_correlation
                             + [False] * (self.count_mse + self.count_rmse + self.count_mae)
                         },

tests/individual_models/conftest.py

@@ -2,17 +2,19 @@
 
 from drevalpy.datasets.dataset import DrugResponseDataset, FeatureDataset
 from drevalpy.datasets.loader import load_toy
-from drevalpy.models.utils import (get_multiomics_feature_dataset, load_drug_fingerprint_features,
-                                   load_drug_ids_from_csv, load_cl_ids_from_csv)
+from drevalpy.models.utils import (
+    get_multiomics_feature_dataset,
+    load_drug_fingerprint_features,
+    load_drug_ids_from_csv,
+    load_cl_ids_from_csv,
+)
 
 
 @pytest.fixture(scope="session")
 def sample_dataset() -> tuple[DrugResponseDataset, FeatureDataset, FeatureDataset]:
     path_data = "../data"
     drug_response = load_toy(path_data)
-    cell_line_input = get_multiomics_feature_dataset(
-        data_path=path_data, dataset_name="Toy_Data", gene_list=None
-    )
+    cell_line_input = get_multiomics_feature_dataset(data_path=path_data, dataset_name="Toy_Data", gene_list=None)
     cell_line_ids = load_cl_ids_from_csv(path=path_data, dataset_name="Toy_Data")
     cell_line_input._add_features(cell_line_ids)
     # Load the drug features

tests/test_run_suite.py

@@ -44,7 +44,7 @@ def test_run_suite(args):
     args = Namespace(**args)
     main(args)
     assert os.listdir(temp_dir.name) == ["test_run"]
-    '''
+    """
     (
         evaluation_results,
         evaluation_results_per_drug,
@@ -86,4 +86,4 @@ def test_run_suite(args):
     assert all(test_mode in evaluation_results.LPO_LCO_LDO.unique() for test_mode in args.test_mode)
     assert evaluation_results.CV_split.astype(int).max() == (args.n_cv_splits - 1)
     assert evaluation_results.Pearson.astype(float).max() > 0.5
-    '''
+    """