Sourcery Starbot ⭐ refactored rodrigosnader/deepdow by SourceryAI · Pull Request #1 · rodrigosnader/deepdow

SourceryAI · 2021-09-25T03:58:35Z

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SourceryAI · 2021-09-25T03:58:36Z

deepdow/benchmarks.py

-        weights = ivols / ivols.sum(dim=1, keepdim=True)
-
-        return weights
+        return ivols / ivols.sum(dim=1, keepdim=True)


Function InverseVolatility.__call__ refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

SourceryAI · 2021-09-25T03:58:36Z

deepdow/callbacks.py

-        if not (len(stats['lookback'].unique()) == 1 and len(stats['model'].unique()) == 1):
+        if (
+            len(stats['lookback'].unique()) != 1
+            or len(stats['model'].unique()) != 1
+        ):


Function EarlyStoppingCallback.on_epoch_end refactored with the following changes:

Simplify logical expression using De Morgan identities (de-morgan)

SourceryAI · 2021-09-25T03:58:36Z

deepdow/callbacks.py

-        if not (len(stats['lookback'].unique()) == 1 and len(stats['model'].unique()) == 1):
+        if (
+            len(stats['lookback'].unique()) != 1
+            or len(stats['model'].unique()) != 1
+        ):


Function ModelCheckpointCallback.on_epoch_end refactored with the following changes:

Simplify logical expression using De Morgan identities (de-morgan)

SourceryAI · 2021-09-25T03:58:36Z

deepdow/experiments.py

-
-        else:
-            df = self.metrics_per_epoch(epoch)
+        df = self.metrics if epoch is None else self.metrics_per_epoch(epoch)


Function History.pretty_print refactored with the following changes:

Replace if statement with if expression (assign-if-exp)

SourceryAI · 2021-09-25T03:58:37Z

deepdow/experiments.py

-            if not all([isinstance(x, Loss) for x in metrics.values()]):
+            if not all(isinstance(x, Loss) for x in metrics.values()):


Function Run.__init__ refactored with the following changes:

Replace unneeded comprehension with generator (comprehension-to-generator)

SourceryAI · 2021-09-25T03:58:38Z

deepdow/layers/allocate.py

-        res = torch.stack(w_l, dim=0)
-
-        return res
+        return torch.stack(w_l, dim=0)


Function NCO.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

SourceryAI · 2021-09-25T03:58:38Z

deepdow/layers/allocate.py

-        cons = [cp.sum(w) == 1,
-                0. <= w,
-                w <= max_weight]
+        cons = [cp.sum(w) == 1, w >= 0., w <= max_weight]


Function SparsemaxAllocator.__init__ refactored with the following changes:

Ensure constant in comparison is on the right (flip-comparison)

SourceryAI · 2021-09-25T03:58:38Z

deepdow/layers/misc.py

-        corr = covmat / torch.matmul(stds_, stds_.permute(0, 2, 1))
-
-        return corr
+        return covmat / torch.matmul(stds_, stds_.permute(0, 2, 1))


Function Cov2Corr.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

SourceryAI · 2021-09-25T03:58:39Z

deepdow/layers/misc.py

-        if shrinkage_strategy is not None:
-            if shrinkage_strategy not in {'diagonal', 'identity', 'scaled_identity'}:
-                raise ValueError('Unrecognized shrinkage strategy {}'.format(shrinkage_strategy))
+        if shrinkage_strategy is not None and shrinkage_strategy not in {
+            'diagonal',
+            'identity',
+            'scaled_identity',
+        }:
+            raise ValueError('Unrecognized shrinkage strategy {}'.format(shrinkage_strategy))


Function CovarianceMatrix.__init__ refactored with the following changes:

Merge nested if conditions (merge-nested-ifs)

SourceryAI · 2021-09-25T03:58:39Z

deepdow/layers/transform.py

-        x_warped = nn.functional.grid_sample(x,
+        return nn.functional.grid_sample(x,
                                             grid,
                                             mode=self.mode,
                                             padding_mode=self.padding_mode,
                                             align_corners=True,
                                             )

-        return x_warped


Function Warp.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

SourceryAI · 2021-09-25T03:58:41Z

deepdow/layers/transform.py

-        x_zoomed = nn.functional.grid_sample(x,
+        return nn.functional.grid_sample(x,
                                             grid,
                                             mode=self.mode,
                                             padding_mode=self.padding_mode,
                                             align_corners=True,
                                             )
-
-        return x_zoomed


Function Zoom.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

SourceryAI · 2021-09-25T03:58:41Z

examples/end_to_end/getting_started.py

-        weights = self.allocate_layer(x, temperatures)
-
-        return weights
+        return self.allocate_layer(x, temperatures)


Function GreatNet.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

SourceryAI · 2021-09-25T03:58:42Z

examples/end_to_end/iid.py

-        weights_filled = torch.repeat_interleave(weights, n, dim=0)
-
-        return weights_filled
+        return torch.repeat_interleave(weights, n, dim=0)


Function Net.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

SourceryAI · 2021-09-25T03:58:42Z

tests/test_layers.py

-            assert n_parameters == n_dir * (
-                    (n_channels * hidden_size_a) + (hidden_size_a * hidden_size_a) + 2 * hidden_size_a)
+            assert n_parameters == (
+                n_dir
+                * (
+                    n_channels * hidden_size_a
+                    + hidden_size_a ** 2
+                    + 2 * hidden_size_a
+                )
+            )
+

        else:
-            assert n_parameters == n_dir * 4 * (
-                    (n_channels * hidden_size_a) + (hidden_size_a * hidden_size_a) + 2 * hidden_size_a)
+            assert n_parameters == (
+                n_dir
+                * 4
+                * (
+                    n_channels * hidden_size_a
+                    + hidden_size_a ** 2
+                    + 2 * hidden_size_a
+                )
+            )


Function TestRNN.test_n_parameters refactored with the following changes:

Replace x * x with x ** 2 (square-identity)

'Refactored by Sourcery'

532fe1b

SourceryAI commented Sep 25, 2021

View reviewed changes

		if not all([isinstance(x, Loss) for x in metrics.values()]):
		if not all(isinstance(x, Loss) for x in metrics.values()):

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