[refactor] improve test coverage for dataset.

extreme_fit/estimator/abstract_estimator.py

@@ -43,7 +43,7 @@ class AbstractEstimator(object):
 
     @property
     def train_split(self):
-        return self.dataset.train_split
+        return self.dataset.slicer.train_split
 
 
 

spatio_temporal_dataset/coordinates/abstract_coordinates.py

@@ -158,9 +158,6 @@ class AbstractCoordinates(object):
     def coordinates_values(self, split: Split = Split.all, transformed=True) -> np.ndarray:
         return self.df_coordinates(split, transformed=transformed).values
 
-    def coordinates_index(self, split: Split = Split.all) -> pd.Index:
-        return self.df_coordinates(split).index
-
     @property
     def ind_train_spatial(self) -> pd.Series:
         return ind_train_from_s_split(s_split=self.s_split_spatial)

spatio_temporal_dataset/dataset/abstract_dataset.py

@@ -19,21 +19,6 @@ class AbstractDataset(object):
         assert pd.Index.equals(observations.index, coordinates.index), '\n{}\n{}'.format(observations.index, coordinates.index)
         self.observations = observations  # type: AbstractSpatioTemporalObservations
         self.coordinates = coordinates  # type: AbstractCoordinates
-        self.subset_id_to_column_idxs = None  # type: Dict[int, List[int]]
-
-    @classmethod
-    def from_csv(cls, csv_path: str):
-        assert op.exists(csv_path)
-        df = pd.read_csv(csv_path, index_col=0)
-        coordinates = AbstractCoordinates.from_df(df)
-        temporal_maxima = AbstractSpatioTemporalObservations.from_df(df)
-        return cls(temporal_maxima, coordinates)
-
-    def to_csv(self, csv_path: str):
-        dirname = op.dirname(csv_path)
-        if not op.exists(dirname):
-            os.makedirs(dirname)
-        self.df_dataset.to_csv(csv_path)
 
     @property
     def df_dataset(self) -> pd.DataFrame:
@@ -83,49 +68,13 @@ class AbstractDataset(object):
     def coordinates_values(self, split: Split = Split.all) -> np.ndarray:
         return self.coordinates.coordinates_values(split=split)
 
-    def coordinates_index(self, split: Split = Split.all) -> pd.Index:
-        return self.coordinates.coordinates_index(split=split)
-
     # Slicer wrapper
 
     @property
     def slicer(self) -> AbstractSlicer:
         return self.coordinates.slicer
 
-    @property
-    def train_split(self) -> Split:
-        return self.slicer.train_split
-
-    @property
-    def test_split(self) -> Split:
-        return self.slicer.test_split
-
-    @property
-    def splits(self) -> List[Split]:
-        return self.slicer.splits
-
-    # Dataset subsets
-
-    def create_subsets(self, nb_subsets):
-        self.subset_id_to_column_idxs = {}
-        for subset_id in range(nb_subsets):
-            column_idxs = [idx for idx in range(self.observations.nb_obs) if idx % nb_subsets == subset_id]
-            self.subset_id_to_column_idxs[subset_id] = column_idxs
-
     # Special methods
 
     def __str__(self) -> str:
         return 'coordinates:\n{}\nobservations:\n{}'.format(self.coordinates.__str__(), self.observations.__str__())
-
-
-def get_subset_dataset(dataset: AbstractDataset, subset_id) -> AbstractDataset:
-    columns_idxs = dataset.subset_id_to_column_idxs[subset_id]
-    assert dataset.subset_id_to_column_idxs is not None, 'You need to create subsets'
-    assert subset_id in dataset.subset_id_to_column_idxs.keys()
-    subset_dataset = copy.deepcopy(dataset)
-    observations = subset_dataset.observations
-    if observations.df_maxima_gev is not None:
-        observations.df_maxima_gev = observations.df_maxima_gev.iloc[:, columns_idxs]
-    if observations.df_maxima_frech is not None:
-        observations.df_maxima_frech = observations.df_maxima_frech.iloc[:, columns_idxs]
-    return subset_dataset

test/test_spatio_temporal_dataset/test_dataset.py

@@ -63,6 +63,7 @@ class TestSpatioTemporalDataset(unittest.TestCase):
         self.dataset = MarginDataset.from_sampling(nb_obs=nb_obs,
                                                    margin_model=smooth_margin_model,
                                                    coordinates=self.coordinates)
+        print(self.dataset.__str__())
 
     def test_spatio_temporal_array_wrt_time(self):
         # The test could have been on a given station. But we decided to do it for a given time step.