Adding CCPP data and data provider.

mlp/data_providers.py

@@ -163,7 +163,7 @@ class MetOfficeDataProvider(DataProvider):
 
     def __init__(self, window_size, batch_size=10, max_num_batches=-1,
                  shuffle_order=True, rng=None):
-        """Create a new Met Offfice data provider object.
+        """Create a new Met Office data provider object.
 
         Args:
             window_size (int): Size of windows to split weather time series
@@ -204,3 +204,50 @@ class MetOfficeDataProvider(DataProvider):
         targets = windowed[:, -1]
         super(MetOfficeDataProvider, self).__init__(
             inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
+
+
+class CCPPDataProvider(DataProvider):
+
+    def __init__(self, which_set='train', input_dims=None, batch_size=10,
+                 max_num_batches=-1, shuffle_order=True, rng=None):
+        """Create a new Combined Cycle Power Plant data provider object.
+
+        Args:
+            which_set: One of 'train' or 'valid'. Determines which portion of
+                data this object should provide.
+            input_dims: Which of the four input dimension to use. If `None` all
+                are used. If an iterable of integers are provided (consisting
+                of a subset of {0, 1, 2, 3}) then only the corresponding
+                input dimensions are included.
+            batch_size (int): Number of data points to include in each batch.
+            max_num_batches (int): Maximum number of batches to iterate over
+                in an epoch. If `max_num_batches * batch_size > num_data` then
+                only as many batches as the data can be split into will be
+                used. If set to -1 all of the data will be used.
+            shuffle_order (bool): Whether to randomly permute the order of
+                the data before each epoch.
+            rng (RandomState): A seeded random number generator.
+        """
+        data_path = os.path.join(
+            os.environ['MLP_DATA_DIR'], 'ccpp_data.npz')
+        assert os.path.isfile(data_path), (
+            'Data file does not exist at expected path: ' + data_path
+        )
+        # check a valid which_set was provided
+        assert which_set in ['train', 'valid'], (
+            'Expected which_set to be either train or valid '
+            'Got {0}'.format(which_set)
+        )
+        # check input_dims are valid
+        if not input_dims is not None:
+            input_dims = set(input_dims)
+            assert input_dims.issubset({0, 1, 2, 3}), (
+                'input_dims should be a subset of {0, 1, 2, 3}'
+            )
+        loaded = np.load(data_path)
+        inputs = loaded[which_set + '_inputs']
+        if input_dims is not None:
+            inputs = inputs[:, input_dims]
+        targets = loaded[which_set + '_targets']
+        super(CCPPDataProvider, self).__init__(
+            inputs, targets, batch_size, max_num_batches, shuffle_order, rng)