Add who dataset, examples and tests

.github/workflows/lint.yml

@@ -26,7 +26,7 @@ jobs:
       - name: Lint with flake8
         run: |
           # ignore formatting, it will be checked by black
-          export FORMATTING_RULES="E101,E111,E114,E115,E116,E117,E12,E13,E2,E3,E401,E5,E70,W1,W2,W3,W5"
+                export FORMATTING_RULES="E101,E111,E114,E115,E116,E117,E12,E13,E2,E3,E401,E5,E70,W1,W2,W3,W5"
           flake8 --ignore=$FORMATTING_RULES .
       - name: Lint with black
         run: |

docs/requirements.txt

@@ -9,3 +9,4 @@ sphinx_rtd_theme
 tqdm
 traitlets>=5.0
 jinja2 < 3.1
+pandas

examples/requirements.txt

@@ -1,3 +1,4 @@
+pandas
 jupyter
 jupyter_contrib_nbextensions
 matplotlib

setup.cfg

@@ -23,4 +23,4 @@ zip_safe = True
 packages = find:
 install_requires =
 	numpy
-    scikit-learn>=1.1.0
+        scikit-learn>=1.1.0

skcosmo/datasets/__init__.py

@@ -2,6 +2,12 @@
     load_csd_1000r,
     load_degenerate_CH4_manifold,
     load_nice_dataset,
+    load_who_dataset,
 )
 
-__all__ = ["load_degenerate_CH4_manifold", "load_csd_1000r", "load_nice_dataset"]
+__all__ = [
+    "load_degenerate_CH4_manifold",
+    "load_csd_1000r",
+    "load_nice_dataset",
+    "load_who_dataset",
+]

skcosmo/datasets/_base.py

@@ -4,7 +4,10 @@
 )
 
 import numpy as np
-from sklearn.utils import Bunch
+from sklearn.utils import (
+    Bunch,
+    check_pandas_support,
+)
 
 
 def load_nice_dataset():
@@ -91,3 +94,23 @@ def load_csd_1000r(return_X_y=False):
         return Bunch(data=data, DESCR=fdescr)
     else:
         return raw_data["X"], raw_data["Y"]
+
+
+def load_who_dataset():
+    """Load and returns WHO dataset.
+    Returns
+    -------
+    who_dataset : sklearn.utils.Bunch
+      Dictionary-like object, with the following attributes:
+          data : `pandas.core.frame.DataFrame` -- the WHO dataset
+                  as a Pandas dataframe.
+          DESCR: `str` -- The full description of the dataset.
+    """
+
+    module_path = dirname(__file__)
+    target_filename = join(module_path, "data", "who_dataset.csv")
+    pd = check_pandas_support("load_who_dataset")
+    raw_data = pd.read_csv(target_filename)
+    with open(join(module_path, "descr", "who_dataset.rst")) as rst_file:
+        fdescr = rst_file.read()
+    return Bunch(data=raw_data, DESCR=fdescr)

tests/test_datasets.py

@@ -1,9 +1,11 @@
 import unittest
+import numpy as np
 
 from skcosmo.datasets import (
     load_degenerate_CH4_manifold,
     load_csd_1000r,
     load_nice_dataset,
+    load_who_dataset,
 )
 
 
@@ -58,5 +60,49 @@ def test_load_csd_1000r_access_descr(self):
         self.csd.DESCR
 
 
+class WHOTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.size = 24240
+        cls.shape = (2020, 12)
+        cls.value = 5.00977993011475
+        try:
+            import pandas as pd  # noqa F401
+
+            cls.has_pandas = True
+            cls.who = load_who_dataset()
+        except ImportError:
+            cls.has_pandas = False
+
+    def test_load_dataset_without_pandas(self):
+        """
+        Check if the correct exception occurs when pandas isn't present.
+        """
+        if self.has_pandas is False:
+            with self.assertRaises(ImportError) as cm:
+                _ = load_who_dataset()
+            self.assertEqual(str(cm.exception), "load_who_dataset requires pandas.")
+
+    def test_dataset_size_and_shape(self):
+        """
+        Check if the correct number of datapoints are present in the dataset.
+        Also check if the size of the dataset is correct.
+        """
+        if self.has_pandas is True:
+            self.assertEqual(self.who["data"].size, self.size)
+            self.assertEqual(self.who["data"].shape, self.shape)
+
+    def test_datapoint_value(self):
+        """
+        Check if the value of a datapoint at a certain location is correct.
+        """
+        if self.has_pandas is True:
+            self.assertTrue(
+                np.allclose(
+                    self.who["data"]["SE.XPD.TOTL.GD.ZS"][1924], self.value, rtol=1e-6
+                )
+            )
+
+
 if __name__ == "__main__":
     unittest.main()