Commit d43a467c authored by hazrmard's avatar hazrmard
Browse files

tensor type/device conversion for score(), optional keyword-arguments for fit()/predict()

parent e70275cd

.gitignore

+1 −0
Changes for .gitignore: 1 added line, 0 removed lines.
Original line number Diff line number Diff line 
*.ini

pytorchbridge.egg-info

__pycache__/
 
 No newline at end of file

pytorchbridge/pytorchbridge.py

+9 −4
Changes for pytorchbridge/pytorchbridge.py: 9 added lines, 4 removed lines.
Original line number Diff line number Diff line
@@ -90,7 +90,7 @@ class TorchEstimator(BaseEstimator): 
            self.loss = nn.MSELoss()





    def fit(self, X: torch.Tensor, y: torch.Tensor) -> 'TorchEstimator':

    def fit(self, X: torch.Tensor, y: torch.Tensor, **kwargs) -> 'TorchEstimator':

        """

        Fit target to features.
@@ -99,6 +99,7 @@ class TorchEstimator(BaseEstimator): 
            for recurrent modules or (N, Features) for other modules.

        y {torch.Tensor} -- `Tensor` of shape ([SeqLen,] N, OutputFeatures) for recurrent

            modules of (N, OutputFeatures).

        **kwargs -- Keyword arguments passed to `self.module(X, **kwargs)`



        Returns:

        self
@@ -117,7 +118,7 @@ class TorchEstimator(BaseEstimator): 
            for instance, target in zip(self._to_batches(X), self._to_batches(y)):

                instance, target = instance.to(self._device), target.to(self._device)

                self.module.zero_grad()

                output = self.module(instance)

                output = self.module(instance, **kwargs)

                loss = self.loss(output, target)

                loss.backward()

                self.optimizer.step()
@@ -128,13 +129,14 @@ class TorchEstimator(BaseEstimator): 
        return self





    def predict(self, X: torch.Tensor) -> torch.Tensor:

    def predict(self, X: torch.Tensor, **kwargs) -> torch.Tensor:

        """

        Predict output from inputs.



        Arguments:

        X {torch.Tensor} -- `Tensor` of shape (SeqLen, N, Features) or (N, SeqLen, Features)

            for recurrent modules or (N, Features) for other modules.

        **kwargs -- Keyword arguments passed to `self.module(X, **kwargs)`



        Returns:

        torch.Tensor -- of shape ([SeqLen,] N, OutputFeatures) for recurrent
@@ -144,7 +146,7 @@ class TorchEstimator(BaseEstimator): 
        is_numpy = isinstance(X, np.ndarray)

        X = torch.as_tensor(X, dtype=self._dtype, device=self._device)

        with torch.no_grad():

            result = self.module(X)

            result = self.module(X, **kwargs)

        if is_numpy:

            return result.numpy()

        return result
@@ -164,6 +166,9 @@ class TorchEstimator(BaseEstimator): 
        Returns:

        float -- Coefficient of determination.

        """

        # pylint: disable=no-member

        X = torch.as_tensor(X, dtype=self._dtype, device=self._device)

        y = torch.as_tensor(y, dtype=self._dtype, device=self._device)

        y_pred = self.predict(X)

        residual_squares_sum = ((y - y_pred) ** 2).sum()

        total_squares_sum = ((y - y.mean()) ** 2).sum()

setup.py

+1 −1
Changes for setup.py: 1 added line, 1 removed line.
Original line number Diff line number Diff line
@@ -3,7 +3,7 @@ from distutils.core import setup 




setup(name='pytorchbridge',

      version='0.1.0',

      version='0.1.1',

      packages=['pytorchbridge'],

      install_requires=['tqdm', 'scikit-learn>=0.20'],

      author='Ibrahim Ahmed',