Merge pull request #41 from GauravPandeyLab/minor-fixes

Minor fixes

docs/source/datasets.rst

@@ -1,4 +1,6 @@
 Datasets
 --------
 
+If using the below datasets in a scientific study, please cite the relevant publication in the doc string.
+
 .. autofunction:: eipy.datasets.load_diabetes

eipy/datasets.py

@@ -6,45 +6,6 @@
 import zipfile
 
 
-def _load_csv(file_path, fn, suffix):
- return pd.read_csv(join(file_path, f"{fn}_{suffix}.csv"), index_col=0)
-
-
-def get_data_home(data_home=None):
- """Return the path of the eipy data directory.
-
- This function is referring from scikit-learn.
-
- This folder is used by some large dataset loaders to avoid downloading the
- data several times.
-
- By default the data directory is set to a folder named 'eipy_data' in the
- user home folder.
-
- Alternatively, it can be set by the 'EIPY_DATA' environment
- variable or programmatically by giving an explicit folder path. The '~'
- symbol is expanded to the user home folder.
-
- If the folder does not already exist, it is automatically created.
-
- Parameters
- ----------
- data_home : str or path-like, default=None
- The path to scikit-learn data directory. If `None`, the default path
- is `~/eipy_data`.
-
- Returns
- -------
- data_home: str
- The path to eipy data directory.
- """
- if data_home is None:
- data_home = environ.get("EIPY_DATA", join("~", "eipy_data"))
- data_home = expanduser(data_home)
- makedirs(data_home, exist_ok=True)
- return data_home
-
-
 def load_diabetes():
  """
  Loads a multi-modal youth diabetes dataset.
@@ -56,6 +17,11 @@ def load_diabetes():
  dataset available through a public web portal. medRxiv 2023.08.02.23293517.
  https://doi.org/10.1101/2023.08.02.23293517
 
+ Returns
+ -------
+ data : dict
+ Dictionary with keys 'X_train', 'y_train', 'X_test', 'y_test', 'data_dict'.
+
  """
  zenodo_link = "https://zenodo.org/records/10035422/files/diabetes.zip?download=1"
  # Get data path
@@ -93,4 +59,43 @@ def load_diabetes():
  "X_test": X_test,
  "y_test": y_test,
  "data_dict": dictionary,
- }
+ }
+
+
+def _load_csv(file_path, fn, suffix):
+ return pd.read_csv(join(file_path, f"{fn}_{suffix}.csv"), index_col=0)
+
+
+def get_data_home(data_home=None):
+ """Return the path of the eipy data directory.
+
+ This function is referring from scikit-learn.
+
+ This folder is used by some large dataset loaders to avoid downloading the
+ data several times.
+
+ By default the data directory is set to a folder named 'eipy_data' in the
+ user home folder.
+
+ Alternatively, it can be set by the 'EIPY_DATA' environment
+ variable or programmatically by giving an explicit folder path. The '~'
+ symbol is expanded to the user home folder.
+
+ If the folder does not already exist, it is automatically created.
+
+ Parameters
+ ----------
+ data_home : str or path-like, default=None
+ The path to scikit-learn data directory. If `None`, the default path
+ is `~/eipy_data`.
+
+ Returns
+ -------
+ data_home: str
+ The path to eipy data directory.
+ """
+ if data_home is None:
+ data_home = environ.get("EIPY_DATA", join("~", "eipy_data"))
+ data_home = expanduser(data_home)
+ makedirs(data_home, exist_ok=True)
+ return data_home

eipy/metrics.py

@@ -5,14 +5,11 @@
 from sklearn.metrics import roc_auc_score, precision_recall_curve
 
 
-def fmax_score(y_test, y_score, beta=1.0, pos_label=1, return_threshold=True):
+def fmax_score(y_test, y_score, beta=1.0, pos_label=1):
  fmax_score, _, _, threshold_fmax = fmax_precision_recall_threshold(
  y_test, y_score, beta=beta, pos_label=pos_label
  )
- if return_threshold:
- return fmax_score, threshold_fmax
- else:
- return fmax_score
+ return fmax_score, threshold_fmax
 
 
 def fmax_precision_recall_threshold(labels, y_score, beta=1.0, pos_label=1):