Fix formatting

mlpp_lib/__init__.py

@@ -1,4 +1,5 @@
 __version__ = "0.1.0"
 
 import os
+
 os.environ["TF_USE_LEGACY_KERAS"] = "1"

mlpp_lib/datasets.py

@@ -451,7 +451,9 @@ def drop_nans(self, group_size: int = 1):
         event_axes = [self.dims.index(dim) for dim in self.dims if dim != "s"]
         mask = da.any(~da.isfinite(da.from_array(x, name="x")), axis=event_axes)
         if y is not None:
-            mask = mask | da.any(~da.isfinite(da.from_array(y, name="y")), axis=event_axes)
+            mask = mask | da.any(
+                ~da.isfinite(da.from_array(y, name="y")), axis=event_axes
+            )
         mask = (~mask).compute()
 
         # with grouped samples, nans have to be removed in blocks:

mlpp_lib/losses.py

@@ -432,9 +432,7 @@ class MultivariateLoss(tf.keras.losses.Loss):
     """
 
     def mse_metric(y_true, y_pred):
-        return tf.reduce_mean(
-            tf.square(y_true - y_pred), axis=0
-        )
+        return tf.reduce_mean(tf.square(y_true - y_pred), axis=0)
 
     def mae_metric(y_true, y_pred):
         return tf.reduce_mean(tf.abs(y_true - y_pred), axis=0)

mlpp_lib/normalizers.py

@@ -211,7 +211,9 @@ def f(ds: xr.Dataset) -> xr.Dataset:
                 ds = ds.fillna(self.fillvalue)
             else:
                 if ds.isnull().any():
-                    raise ValueError("Missing values found in the data. Please provide a fill value.")
+                    raise ValueError(
+                        "Missing values found in the data. Please provide a fill value."
+                    )
             return ds.astype("float32")
 
         return tuple(f(ds) for ds in datasets)

mlpp_lib/probabilistic_layers.py

@@ -88,9 +88,7 @@ def new_from_t(t):
             return IndependentBeta.new(t, event_shape, validate_args)
 
         super(IndependentBeta, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -200,9 +198,7 @@ def new_from_t(t):
             return Independent4ParamsBeta.new(t, event_shape, validate_args)
 
         super(Independent4ParamsBeta, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -313,14 +309,10 @@ def __init__(
         kwargs.pop("make_distribution_fn", None)
 
         def new_from_t(t):
-            return  IndependentDoublyCensoredNormal.new(
-                        t, event_shape, validate_args
-                    )
+            return IndependentDoublyCensoredNormal.new(t, event_shape, validate_args)
 
         super(IndependentDoublyCensoredNormal, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -390,9 +382,11 @@ def _mean(self):
                     cdf = lambda x: tfd.Normal(0, 1).cdf(x)
                     pdf = lambda x: tfd.Normal(0, 1).prob(x)
 
-                    return 1 * (1 - cdf(high_bound_standard)) + mu * (
-                        cdf(high_bound_standard) - cdf(low_bound_standard)) + sigma * (
-                            pdf(low_bound_standard) - pdf(high_bound_standard))
+                    return (
+                        1 * (1 - cdf(high_bound_standard))
+                        + mu * (cdf(high_bound_standard) - cdf(low_bound_standard))
+                        + sigma * (pdf(low_bound_standard) - pdf(high_bound_standard))
+                    )
 
                 def _log_prob(self, value):
 
@@ -402,9 +396,15 @@ def _log_prob(self, value):
 
                     logprob_left = lambda x: tf.math.log(cdf(-mu / sigma) + 1e-3)
                     logprob_middle = lambda x: self.normal.log_prob(x)
-                    logprob_right = lambda x: tf.math.log(1 - cdf((1 - mu) / sigma) + 1e-3)
+                    logprob_right = lambda x: tf.math.log(
+                        1 - cdf((1 - mu) / sigma) + 1e-3
+                    )
 
-                    return logprob_left(value) + logprob_middle(value) + logprob_right(value)
+                    return (
+                        logprob_left(value)
+                        + logprob_middle(value)
+                        + logprob_right(value)
+                    )
 
             return independent_lib.Independent(
                 CustomCensored(normal_dist),
@@ -486,9 +486,7 @@ def new_from_t(t):
             return IndependentConcaveBeta.new(t, event_shape, validate_args)
 
         super(IndependentConcaveBeta, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -602,9 +600,7 @@ def new_from_t(t):
             return IndependentGamma.new(t, event_shape, validate_args)
 
         super(IndependentGamma, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -715,9 +711,7 @@ def new_from_t(t):
             return IndependentLogNormal.new(t, event_shape, validate_args)
 
         super(IndependentLogNormal, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -826,9 +820,7 @@ def new_from_t(t):
             return IndependentLogitNormal.new(t, event_shape, validate_args)
 
         super(IndependentLogitNormal, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -940,9 +932,7 @@ def new_from_t(t):
             return IndependentMixtureNormal.new(t, event_shape, validate_args)
 
         super(IndependentMixtureNormal, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -1116,9 +1106,7 @@ def new_from_t(t):
             return IndependentTruncatedNormal.new(t, event_shape, validate_args)
 
         super(IndependentTruncatedNormal, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -1229,9 +1217,7 @@ def new_from_t(t):
             return IndependentWeibull.new(t, event_shape, validate_args)
 
         super(IndependentWeibull, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_shape = event_shape
@@ -1346,9 +1332,7 @@ def new_from_t(t):
             return MultivariateNormalDiag.new(t, event_size, validate_args)
 
         super(MultivariateNormalDiag, self).__init__(
-            new_from_t,
-            convert_to_tensor_fn,
-            **kwargs
+            new_from_t, convert_to_tensor_fn, **kwargs
         )
 
         self._event_size = event_size

mlpp_lib/train.py

@@ -39,8 +39,10 @@ def get_log_params(param_run: dict) -> dict:
 
 def get_lr(optimizer: tf.keras.optimizers.Optimizer) -> float:
     """Get the learning rate of the optimizer"""
+
     def lr(y_true, y_pred):
         return optimizer.lr
+
     return lr
 
 

tests/__init__.py

@@ -1,2 +1,3 @@
 import os
+
 os.environ["TF_USE_LEGACY_KERAS"] = "1"

tests/conftest.py

@@ -76,7 +76,9 @@ def datatransformations() -> list:
     import mlpp_lib.normalizers as no
 
     datatransformations = [
-        no.create_transformation_from_str(n.name, inputs={"fillvalue": -5} if n.name == "Identity" else {}) # temporary fix, do we want to let the user define different fillvalue for each transformation ?
+        no.create_transformation_from_str(
+            n.name, inputs={"fillvalue": -5} if n.name == "Identity" else {}
+        )  # temporary fix, do we want to let the user define different fillvalue for each transformation ?
         for n in no.DataTransformation.__subclasses__()
     ]
 
@@ -96,7 +98,11 @@ def data_transformer() -> xr.Dataset:
         for i, transformation in enumerate(transformations_list)
     }
     data_transformer = no.DataTransformer(method_var_dict)
-    data_transformer.transformers['Identity'][0].fillvalue = -5 # temporary fix, do we want to let the user define different fillvalue for each transformation ?
+    data_transformer.transformers["Identity"][
+        0
+    ].fillvalue = (
+        -5
+    )  # temporary fix, do we want to let the user define different fillvalue for each transformation ?
 
     return data_transformer
 

tests/test_normalizers.py

@@ -165,20 +165,22 @@ def test_retro_compatibility(self, standardizer, features_multi):
         data_transformer = DataTransformer.from_dict(dict_stand)
 
         assert all(
-            [
-                np.allclose(
-                    getattr(data_transformer.transformers["Standardizer"][0], attr)[
-                        var
-                    ].values,
-                    getattr(standardizer, attr)[var].values,
-                    equal_nan=True,
+            (
+                [
+                    np.allclose(
+                        getattr(data_transformer.transformers["Standardizer"][0], attr)[
+                            var
+                        ].values,
+                        getattr(standardizer, attr)[var].values,
+                        equal_nan=True,
+                    )
+                    for var in getattr(standardizer, attr).data_vars
+                ]
+                if isinstance(getattr(standardizer, attr), xr.Dataset)
+                else np.allclose(
+                    getattr(data_transformer.transformers["Standardizer"][0], attr),
+                    getattr(standardizer, attr),
                 )
-                for var in getattr(standardizer, attr).data_vars
-            ]
-            if isinstance(getattr(standardizer, attr), xr.Dataset)
-            else np.allclose(
-                getattr(data_transformer.transformers["Standardizer"][0], attr),
-                getattr(standardizer, attr),
             )
             for attr in get_class_attributes(standardizer)
         )