diff --git a/drevalpy/datasets/dataset.py b/drevalpy/datasets/dataset.py index d653afa..9049b57 100644 --- a/drevalpy/datasets/dataset.py +++ b/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]}..." diff --git a/drevalpy/datasets/loader.py b/drevalpy/datasets/loader.py index e829e5a..d2a5d5d 100644 --- a/drevalpy/datasets/loader.py +++ b/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. diff --git a/drevalpy/experiment.py b/drevalpy/experiment.py index c0d43bf..e2a86bc 100644 --- a/drevalpy/experiment.py +++ b/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: diff --git a/drevalpy/models/SRMF/srmf.py b/drevalpy/models/SRMF/srmf.py index 1881e2c..0f293a5 100644 --- a/drevalpy/models/SRMF/srmf.py +++ b/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() diff --git a/drevalpy/models/utils.py b/drevalpy/models/utils.py index 9ccf8a9..538a223 100644 --- a/drevalpy/models/utils.py +++ b/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( diff --git a/drevalpy/utils.py b/drevalpy/utils.py index a407135..d0a43ba 100644 --- a/drevalpy/utils.py +++ b/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'" + ) diff --git a/drevalpy/visualization/corr_comp_scatter.py b/drevalpy/visualization/corr_comp_scatter.py index 3daef17..fe03e38 100644 --- a/drevalpy/visualization/corr_comp_scatter.py +++ b/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'
  • ' f"{group_comparison}
    • \n") + f.write( + f'
  • ' + f"{group_comparison}
    • \n" + ) f.write("\n") return f diff --git a/drevalpy/visualization/utils.py b/drevalpy/visualization/utils.py index df82e5e..da35c91 100644 --- a/drevalpy/visualization/utils.py +++ b/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'\n' + f'\n' ) f.write("\n") f.write("\n") diff --git a/drevalpy/visualization/vioheat.py b/drevalpy/visualization/vioheat.py index d4ca7ec..29f4ec4 100644 --- a/drevalpy/visualization/vioheat.py +++ b/drevalpy/visualization/vioheat.py @@ -65,7 +65,9 @@ def write_to_html(lpo_lco_ldo: str, f: TextIO, *args, **kwargs) -> TextIO: ] f.write(f'

    {plot} Plots of Performance Measures over CV runs

    \n') f.write(f"

    {plot} plots comparing all models

    \n") - f.write(f'\n') + f.write( + f'\n' + ) f.write(f"

    {plot} plots comparing all models with normalized metrics

    \n") f.write( f"Before calculating the evaluation metrics, all values were normalized by the mean of the drug or cell line. " diff --git a/drevalpy/visualization/violin.py b/drevalpy/visualization/violin.py index 7e19cc7..21267c6 100644 --- a/drevalpy/visualization/violin.py +++ b/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) }, diff --git a/tests/individual_models/conftest.py b/tests/individual_models/conftest.py index 0c188c2..75b2895 100644 --- a/tests/individual_models/conftest.py +++ b/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 diff --git a/tests/test_run_suite.py b/tests/test_run_suite.py index 5813511..f3859aa 100644 --- a/tests/test_run_suite.py +++ b/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 - ''' + """