Feature/refactor #37

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft

jwdink wants to merge 48 commits into develop from feature/refactor

.github/workflows/main.yml

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -11,7 +11,7 @@ jobs:
  
        runs-on: ubuntu-latest

        strategy:

          matrix:

            py-version: [ '3.8', '3.9', '3.10' ]

            py-version: [ '3.9', '3.10', '3.11' ]

        steps:

          - uses: actions/checkout@v4

    @@ -22,10 +22,10 @@ jobs:
  
              python-version: ${{ matrix.py-version }}

          - name: Install torchcast

            run: pip install .[tests]

            run: pip install .[tests] --extra-index-url https://download.pytorch.org/whl/cpu

          - name: Run tests

            run: python3 -m unittest

            run: pytest

      release:

        needs: test

    @@ -74,7 +74,7 @@ jobs:
  
          - name: Install dependencies

            run: |

              pip install awscli

              pip install ".[docs]"

              pip install ".[docs]" --extra-index-url https://download.pytorch.org/whl/cpu

          - name: Install pandoc

            uses: pandoc/actions/setup@main

docs/examples/air_quality.ipynb

Large diffs are not rendered by default.

docs/examples/electricity.ipynb

Large diffs are not rendered by default.

docs/quick_start.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -129,7 +129,7 @@ @@
     # %%
     pred.plot(
-        pred.to_dataframe(dataset_all, type='components').query("group=='Changping'"), split_dt=SPLIT_DT,
+        pred.to_dataframe(dataset_all, type='observed_states').query("group=='Changping'"), split_dt=SPLIT_DT,
         time_colname='time', group_colname='group'
     )
@@ Expand Down @@

pyproject.toml

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -2,14 +2,14 @@
  
    name = "torchcast"

    description = "Forecasting in PyTorch"

    readme = "README.md"

    requires-python = ">= 3.8"

    requires-python = ">= 3.9"

    classifiers = [

        "Development Status :: 5 - Production/Stable",

        "Intended Audience :: Developers",

        "Programming Language :: Python :: 3",

        "Programming Language :: Python :: 3.8",

        "Programming Language :: Python :: 3.9",

        "Programming Language :: Python :: 3.10",

        "Programming Language :: Python :: 3.11",

    ]

    license = { file = "LICENSE" }

    authors = [

    @@ -20,7 +20,8 @@ dependencies = [
  
        "numpy>=1.4",

        "scipy>=1.10",

        "tqdm>=4.59",

        "platformdirs>=4.3"

        "platformdirs>=4.3",

        "pandas<3"

    ]

    dynamic = ["version"]

    @@ -35,9 +36,8 @@ dev = [
  
        "black",

    ]

    tests = [

        "parameterized>=0.7",

        "pytest>=7.0",

        "filterpy>=1.4",

        "pandas>=1.0"

    ]

    docs = [

        "jupytext>=1.11",

tests/test_covariance.py

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -1,27 +1,26 @@
  
    import torch

    from torchcast.covariance import Covariance

    import unittest

    class TestCovariance(unittest.TestCase):

        torch.no_grad()

    @torch.no_grad()

    def test_from_log_cholesky():

        module = Covariance(id='test', rank=3)

        def test_from_log_cholesky(self):

            module = Covariance(id='test', rank=3)

        module.state_dict()['cholesky_log_diag'][:] = torch.arange(1., 3.1)

        module.state_dict()['cholesky_off_diag'][:] = torch.arange(1., 3.1)

            module.state_dict()['cholesky_log_diag'][:] = torch.arange(1., 3.1)

            module.state_dict()['cholesky_off_diag'][:] = torch.arange(1., 3.1)

        expected = torch.tensor([[7.3891, 2.7183, 5.4366],

                                 [2.7183, 55.5982, 24.1672],

                                 [5.4366, 24.1672, 416.4288]])

        diff = (expected - module({}, num_groups=1, num_times=1)).abs()

        assert (diff < .0001).all()

            expected = torch.tensor([[7.3891, 2.7183, 5.4366],

                                     [2.7183, 55.5982, 24.1672],

                                     [5.4366, 24.1672, 416.4288]])

            diff = (expected - module({}, num_groups=1, num_times=1)).abs()

            self.assertTrue((diff < .0001).all())

        def test_empty_idx(self):

            module = torch.jit.script(Covariance(id='test', rank=3, empty_idx=[0]))

            cov = module({}, num_groups=1, num_times=1)

            cov = cov.squeeze()

            self.assertTrue((cov[0, :] == 0).all())

            self.assertTrue((cov[:, 0] == 0).all())

            self.assertTrue((cov == cov.t()).all())

    @torch.no_grad()

    def test_empty_idx():

        module = Covariance(id='test', rank=3, empty_idx=[0])

        cov = module({}, num_groups=1, num_times=1)

        cov = cov.squeeze()

        assert (cov[0, :] == 0).all()

        assert (cov[:, 0] == 0).all()

        assert (cov == cov.t()).all()

tests/test_data_utils.py

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -1,4 +1,3 @@
  
    import unittest

    from warnings import warn

    import numpy as np

    @@ -7,53 +6,53 @@
  
    from torchcast.utils.data import TimeSeriesDataset

    class TestDataUtils(unittest.TestCase):

        def test_time_series_dataset(self):

            values = torch.randn((3, 39, 2))

            batch = TimeSeriesDataset(

                values,

                group_names=['one', 'two', 'three'],

                start_times=[0, 0, 0],

                measures=[['y1', 'y2']],

                dt_unit=None

            )

            try:

                import pandas as pd

            except ImportError:

                warn("Not testing TimeSeriesDataset.to_dataframe, pandas not installed.")

                return

            df1 = batch.to_dataframe()

            df2 = pd.concat([

                pd.DataFrame(values[i].numpy(), columns=batch.all_measures).assign(group=group, time=batch.times()[0])

                for i, group in enumerate(batch.group_names)

            ])

            self.assertTrue((df1 == df2).all().all())

        def test_pad_x(self, num_times: int = 10):

            from pandas import DataFrame

            df = DataFrame({'x1': np.random.randn(num_times), 'x2': np.random.randn(num_times)})

            df['y'] = 1.5 * df['x1'] + -.5 * df['x2'] + .1 * np.random.randn(num_times)

            df['time'] = df.index.values

            df['group'] = '1'

            dataset1 = TimeSeriesDataset.from_dataframe(

                dataframe=df,

                group_colname='group',

                time_colname='time',

                dt_unit=None,

                X_colnames=['x1', 'x2'],

                y_colnames=['y']

            )

            dataset2 = TimeSeriesDataset.from_dataframe(

                dataframe=df,

                group_colname='group',

                time_colname='time',

                dt_unit=None,

                X_colnames=['x1', 'x2'],

                y_colnames=['y'],

                pad_X=None

            )

            self.assertFalse(torch.isnan(dataset1.tensors[1]).any())

            self.assertFalse(torch.isnan(dataset2.tensors[1]).any())

            self.assertTrue((dataset1.tensors[1] == dataset2.tensors[1]).all())

    def test_time_series_dataset():

        values = torch.randn((3, 39, 2))

        batch = TimeSeriesDataset(

            values,

            group_names=['one', 'two', 'three'],

            start_times=[0, 0, 0],

            measures=[['y1', 'y2']],

            dt_unit=None

        )

        try:

            import pandas as pd

        except ImportError:

            warn("Not testing TimeSeriesDataset.to_dataframe, pandas not installed.")

            return

        df1 = batch.to_dataframe()

        df2 = pd.concat([

            pd.DataFrame(values[i].numpy(), columns=batch.all_measures).assign(group=group, time=batch.times()[0])

            for i, group in enumerate(batch.group_names)

        ])

        assert (df1 == df2).all().all()

    def test_pad_x(num_times: int = 10):

        from pandas import DataFrame

        df = DataFrame({'x1': np.random.randn(num_times), 'x2': np.random.randn(num_times)})

        df['y'] = 1.5 * df['x1'] + -.5 * df['x2'] + .1 * np.random.randn(num_times)

        df['time'] = df.index.values

        df['group'] = '1'

        dataset1 = TimeSeriesDataset.from_dataframe(

            dataframe=df,

            group_colname='group',

            time_colname='time',

            dt_unit=None,

            X_colnames=['x1', 'x2'],

            y_colnames=['y']

        )

        dataset2 = TimeSeriesDataset.from_dataframe(

            dataframe=df,

            group_colname='group',

            time_colname='time',

            dt_unit=None,

            X_colnames=['x1', 'x2'],

            y_colnames=['y'],

            pad_X=None

        )

        assert not torch.isnan(dataset1.tensors[1]).any()

        assert not torch.isnan(dataset2.tensors[1]).any()

        assert (dataset1.tensors[1] == dataset2.tensors[1]).all()

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Feature/refactor #37

Uh oh!

Diff view

Diff view

There are no files selected for viewing

Uh oh!

Uh oh!

Uh oh!

Feature/refactor #37

Are you sure you want to change the base?

Uh oh!

Feature/refactor #37

Uh oh!

Uh oh!

Diff view

Diff view

There are no files selected for viewing

Uh oh!

Uh oh!

Uh oh!