Merge pull request #90 from Quantmetry/chp_test_docstrings_examples

add test on docstrings examples

Author: JulienRoussel77

.github/workflows/test.yml

@@ -35,8 +35,9 @@ jobs:
       - name: Test with pytest
         run: |
           conda install pytest
-          pytest
-          echo you should uncomment pytest and delete this line
+          make coverage
+      - name: Test docstrings examples
+        run: make doctest
       - name: typing with mypy
         run: |
           mypy qolmat

Makefile

@@ -0,0 +1,9 @@
+coverage:
+	pytest --cov-branch --cov=qolmat --cov-report=xml
+
+doctest:
+	pytest --doctest-modules --pyargs qolmat
+
+clean:
+	rm -rf .mypy_cache .pytest_cache .coverage*
+	rm -rf **__pycache__

qolmat/imputations/em_sampler.py

@@ -668,10 +668,10 @@ class VARpEM(EM):
     >>> from qolmat.imputations.em_sampler import VARpEM
     >>> imputer = VARpEM(method="sample")
     >>> X = pd.DataFrame(data=[[1, 1, 1, 1],
-    >>>                        [np.nan, np.nan, 3, 2],
-    >>>                        [1, 2, 2, 1], [2, 2, 2, 2]],
-    >>>                        columns=["var1", "var2", "var3", "var4"])
-    >>> imputer.fit_transform(X)
+    ...                        [np.nan, np.nan, 3, 2],
+    ...                        [1, 2, 2, 1], [2, 2, 2, 2]],
+    ...                        columns=["var1", "var2", "var3", "var4"])
+    >>> imputer.fit_transform(X) # doctest: +SKIP
     """
 
     def __init__(

qolmat/imputations/imputers.py

@@ -1117,7 +1117,7 @@ class ImputerResiduals(_Imputer):
     TODO review/remake this exemple
     >>> import numpy as np
     >>> import pandas as pd
-    >>> from qolmat.imputations.models import ImputeOnResiduals
+    >>> from qolmat.imputations.imputers import ImputerResiduals
     >>> df = pd.DataFrame(index=pd.date_range('2015-01-01','2020-01-01'))
     >>> mean = 5
     >>> offset = 10
@@ -1130,8 +1130,8 @@ class ImputerResiduals(_Imputer):
     >>> np.random.seed(100)
     >>> mask = np.random.choice([True, False], size=df.shape)
     >>> df = df.mask(mask)
-    >>> imputor = ImputeOnResiduals(period=365, model="additive")
-    >>> imputor.fit_transform(df)
+    >>> imputor.fit_transform(df)  # doctest: +SKIP
+    >>> imputor = ImputerResiduals(period=365, model_tsa="additive")
     """
 
     def __init__(
@@ -1353,10 +1353,10 @@ class ImputerMICE(_Imputer):
     ...                        columns=["var1", "var2", "var3", "var4"])
     >>> imputer.fit_transform(df)
        var1  var2  var3  var4
-    0   1.0   1.0   1.0   1.0
-    1   1.0   2.0   2.0   5.0
-    2   1.0   2.0   2.0   5.0
-    3   2.0   2.0   2.0   2.0
+    0  1.00  1.00  1.00  1.00
+    1  1.51  1.99  1.99  3.55
+    2  1.00  2.00  2.00  5.00
+    3  2.00  2.00  2.00  2.00
     """
 
     def __init__(
@@ -1470,18 +1470,18 @@ class ImputerRegressor(_Imputer):
     >>> import pandas as pd
     >>> from qolmat.imputations import imputers
     >>> from sklearn.ensemble import ExtraTreesRegressor
-    >>> imputer = imputers.ImputerRegressor(model=ExtraTreesRegressor())
+    >>> imputer = imputers.ImputerRegressor(estimator=ExtraTreesRegressor())
     >>> df = pd.DataFrame(data=[[1, 1, 1, 1],
     ...                        [np.nan, np.nan, np.nan, np.nan],
     ...                        [1, 2, 2, 5],
     ...                        [2, 2, 2, 2]],
     ...                        columns=["var1", "var2", "var3", "var4"])
     >>> imputer.fit_transform(df)
-           var1      var2      var3      var4
-    0  1.000000  1.000000  1.000000  1.000000
-    1  1.333333  1.666667  1.666667  2.666667
-    2  1.000000  2.000000  2.000000  5.000000
-    3  2.000000  2.000000  2.000000  2.000000
+       var1  var2  var3  var4
+    0   1.0   1.0   1.0   1.0
+    1   1.0   2.0   2.0   2.0
+    2   1.0   2.0   2.0   5.0
+    3   2.0   2.0   2.0   2.0
     """
 
     def __init__(

qolmat/imputations/imputers_pytorch.py

@@ -471,12 +471,10 @@ def build_autoencoder(
 
     Examples
     --------
-    >>> encoder, decoder = build_autoencoder(
-                                                        input_dim=10,
-                                                        latent_dim=4,
-                                                        list_num_neurons=[32, 64, 128],
-                                                        output_dim=252
-                                                    )
+    >>> encoder, decoder = build_autoencoder(input_dim=10,
+    ...                                      latent_dim=4,
+    ...                                      list_num_neurons=[32, 64, 128],
+    ...                                      output_dim=252)
     >>> print(encoder)
     Sequential(
       (0): Linear(in_features=10, out_features=128, bias=True)

qolmat/imputations/softimpute.py

@@ -48,6 +48,10 @@ class SoftImpute(BaseEstimator, TransformerMixin):
     >>> X = np.array([[1, 2, np.nan, 4], [1, 5, 3, np.nan], [4, 2, 3, 2], [1, 1, 5, 4]])
     >>> X_imputed = SoftImpute().fit_transform(X)
     >>> print(X_imputed)
+    [[ 1.          2.          4.7369014   4.        ]
+     [ 1.          5.          3.         -0.52477073]
+     [ 4.          2.          3.          2.        ]
+     [ 1.          1.          5.          4.        ]]
     """
 
     def __init__(