migrate tests from nbs to pytest

.github/workflows/pytest.yml

@@ -0,0 +1,37 @@
+name: pytest
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  all-tests:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+    steps:
+      - name: Clone repo
+        uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          git submodule status
+          git submodule update --init --recursive
+          pip install uv && uv pip install --system ".[all]"
+
+      - name: Run pytest
+        run: pytest tests --ignore tests/test_distributed_fugue.py

scripts/cli.py

@@ -0,0 +1,95 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: cli.ipynb.
+
+# %% auto 0
+__all__ = ['tst_flags', 'to_skip', 'mapper', 'print_execs', 'print_hide', 'other_tests', 'get_markdown', 'get_code',
+           'extract_dir', 'no_dir_and_dir', 'get_all_tests', 'print_dir_in_nb']
+
+# %% cli.ipynb 1
+from functools import partial
+
+from fastcore.script import call_parse
+from nbdev.processors import NBProcessor
+
+# %% cli.ipynb 5
+tst_flags = 'datasets distributed matplotlib polars pyarrow scipy'.split()
+to_skip = [
+    'showdoc',
+    'load_ext',
+    'from nbdev'
+]
+
+
+def print_execs(cell):
+    if 'exec' in cell.source: print(cell.source)
+
+def print_hide(cell):
+    if 'hide' in cell.directives_: print(cell.source)
+
+def other_tests(cell):
+    if len(cell.directives_) == 0:
+        print(cell.source)
+
+def get_markdown(cell):
+    if cell.cell_type == "markdown":
+        print(cell.source)
+
+def get_code(cell):
+    if cell.cell_type == "code":
+        print(cell.source)
+
+def extract_dir(cell, dirs):
+    for directive in dirs.split(','):
+        if directive in cell.directives_:
+            print(cell.source)
+
+def no_dir_and_dir(cell, dir):
+    if len(cell.directives_) == 0:
+        print(cell.source)
+
+    if dir in cell.directives_:
+        print(cell.source)
+
+def get_all_tests(cell):
+    if cell.cell_type == "code":
+
+        if len(cell.directives_) == 0:
+            print(cell.source)
+
+
+        elif any(x in tst_flags + ['hide'] for x in cell.directives_):
+            if not (x in cell.source for x in to_skip):
+                print(cell.source)
+
+
+# %% cli.ipynb 9
+mapper = {
+    'print_execs': print_execs,
+    'print_hide': print_hide,
+    'other_tests': other_tests,
+    'get_markdown': get_markdown,
+    'extract_dir': extract_dir,
+    'no_dir_and_dir': no_dir_and_dir,
+    'get_all_tests':get_all_tests
+}
+
+# %% cli.ipynb 10
+@call_parse
+def print_dir_in_nb(nb_path:str,
+                    dir:str=None,
+                    dir_name:str=None,
+                    ):
+    if dir_name not in mapper.keys():
+        raise ValueError(f'Choose processor from the the following: {mapper.keys()}')
+
+    if dir_name == 'extract_dir':
+        processor = NBProcessor(nb_path, partial(extract_dir, dir=dir))
+        processor.process()
+        return
+    elif dir_name == 'no_dir_and_dir':
+        processor = NBProcessor(nb_path, partial(no_dir_and_dir, dir=dir))
+        processor.process()
+        return
+
+    processor = NBProcessor(nb_path, mapper[dir_name])
+    processor.process()
+

scripts/cvt.py

@@ -0,0 +1,2 @@
+from nbdev.export import nb_export
+nb_export('cli.ipynb', lib_path='.', name='cli')

scripts/extract_test.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+echo Python: $(which python)
+source /Users/deven367/miniforge3/bin/activate
+conda activate nixtla
+
+echo Activated env: $(which python)
+
+
+mkdir -p ../tests
+
+tst_flags='datasets distributed matplotlib polars pyarrow scipy'
+
+nbs=$(find ../nbs/src -type f -iname '*.ipynb')
+# echo "Available notebooks: $nbs"
+
+
+# this approach3 that was discussed on Slack
+# mkdir -p ../tests3
+# for flag in $tst_flags; do
+#     # echo "Extracting $flag"
+
+#     for nb in $nbs; do
+#         # get name of notebook without extension
+#         nb_name=$(basename "$nb" .ipynb)
+
+#         # echo "Processing notebook: $nb"
+#         # print_dir_in_nb "$nb" --dir_name no_dir_and_dir --dir "$flag" >> "../tests/test_$flag_$nb_name.py"
+#         print_dir_in_nb "$nb" --dir_name no_dir_and_dir --dir "$flag" >> "../tests3/test_${flag}_$nb_name.py"
+#     done
+# done
+
+for nb in $nbs; do
+    # get name of notebook without extension
+    nb_name=$(basename "$nb" .ipynb)
+
+    # echo "Processing notebook: $nb"
+    # print_dir_in_nb "$nb" --dir_name no_dir_and_dir --dir "$flag" >> "../tests/test_$flag_$nb_name.py"
+    python cli.py "$nb" --dir_name get_all_tests2 >> "../tests/test_$nb_name.py"
+done

settings.ini

@@ -21,7 +21,7 @@ ray_requirements = fugue[ray]>=0.8.1 protobuf>=3.15.3,<4.0.0 numpy<2 pandas<2.2
 dask_requirements = dask<=2024.12.1 fugue[dask]>=0.8.1
 spark_requirements = fugue[spark]>=0.8.1
 plotly_requirements = plotly plotly-resampler
-dev_requirements = black datasetsforecast fastcore<=1.7.29 fire nbdev==2.3.25 nbformat nbdev_plotly pandas[plot] polars[numpy] pre-commit prophet pyarrow pybind11 pytest scikit-learn setuptools<70 supersmoother yfinance
+dev_requirements = black datasetsforecast fastcore<=1.7.29 fire nbdev==2.3.25 nbformat nbdev_plotly pandas[plot] polars[numpy] pre-commit prophet pyarrow<21.0.0 pybind11 pytest scikit-learn setuptools<70 supersmoother yfinance
 nbs_path = nbs
 doc_path = _docs
 recursive = True

tests/conftest.py

@@ -0,0 +1,18 @@
+import pandas as pd
+import pytest
+from statsforecast.utils import AirPassengers as ap
+
+
+@pytest.fixture(scope="module")
+def sample_data_prophet():
+    """Load sample data for Prophet testing."""
+    df = pd.read_csv(
+        "https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv"
+    )
+    return df
+
+
+@pytest.fixture
+def air_passengers():
+    """AirPassengers dataset."""
+    return ap

tests/test_adapters_prophet.py

@@ -0,0 +1,132 @@
+import warnings
+
+import pandas as pd
+import pytest
+from statsforecast.adapters.prophet import Prophet
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+
+
+@pytest.fixture
+def prophet_model():
+    """Create a Prophet model instance."""
+    return Prophet(daily_seasonality=False)
+
+
+@pytest.fixture
+def holidays_data():
+    """Create sample holidays data."""
+    playoffs = pd.DataFrame(
+        {
+            "holiday": "playoff",
+            "ds": pd.to_datetime(
+                [
+                    "2008-01-13",
+                    "2009-01-03",
+                    "2010-01-16",
+                    "2010-01-24",
+                    "2010-02-07",
+                    "2011-01-08",
+                    "2013-01-12",
+                    "2014-01-12",
+                    "2014-01-19",
+                    "2014-02-02",
+                    "2015-01-11",
+                    "2016-01-17",
+                    "2016-01-24",
+                    "2016-02-07",
+                ]
+            ),
+            "lower_window": 0,
+            "upper_window": 1,
+        }
+    )
+    superbowls = pd.DataFrame(
+        {
+            "holiday": "superbowl",
+            "ds": pd.to_datetime(["2010-02-07", "2014-02-02", "2016-02-07"]),
+            "lower_window": 0,
+            "upper_window": 1,
+        }
+    )
+    return pd.concat((playoffs, superbowls))
+
+
+def test_prophet_initialization():
+    """Test Prophet model initialization."""
+    model = Prophet(daily_seasonality=False)
+    assert model is not None
+    assert hasattr(model, "fit")
+    assert hasattr(model, "predict")
+
+
+def test_prophet_fit(sample_data_prophet, prophet_model):
+    """Test Prophet model fitting."""
+    prophet_model.fit(sample_data_prophet)
+    # Check that the model has been fitted by verifying it has the necessary attributes
+    assert hasattr(prophet_model, "history")
+    assert prophet_model.history is not None
+
+
+def test_prophet_predict(sample_data_prophet, prophet_model):
+    """Test Prophet model prediction."""
+    prophet_model.fit(sample_data_prophet)
+    future = prophet_model.make_future_dataframe(365)
+    forecast = prophet_model.predict(future)
+
+    assert forecast is not None
+    assert isinstance(forecast, pd.DataFrame)
+    assert len(forecast) > len(sample_data_prophet)
+
+
+def test_prophet_make_future_dataframe(sample_data_prophet, prophet_model):
+    """Test making future dataframe."""
+    prophet_model.fit(sample_data_prophet)
+    future = prophet_model.make_future_dataframe(365)
+
+    assert isinstance(future, pd.DataFrame)
+    assert len(future) == len(sample_data_prophet) + 365
+    assert "ds" in future.columns
+
+
+def test_prophet_plot(sample_data_prophet, prophet_model):
+    """Test Prophet plotting functionality."""
+    prophet_model.fit(sample_data_prophet)
+    future = prophet_model.make_future_dataframe(365)
+    forecast = prophet_model.predict(future)
+
+    # Test that plot method exists and can be called
+    fig = prophet_model.plot(forecast)
+    assert fig is not None
+
+
+def test_prophet_with_holidays(sample_data_prophet, holidays_data):
+    """Test Prophet model with holidays."""
+    model = Prophet(holidays=holidays_data, daily_seasonality=False)
+    model.fit(sample_data_prophet)
+    future = model.make_future_dataframe(365)
+    forecast = model.predict(future)
+
+    assert forecast is not None
+    assert isinstance(forecast, pd.DataFrame)
+
+
+def test_prophet_forecast_components(sample_data_prophet, prophet_model):
+    """Test Prophet forecast components."""
+    prophet_model.fit(sample_data_prophet)
+    future = prophet_model.make_future_dataframe(10)
+    forecast = prophet_model.predict(future)
+
+    # Check that the forecast contains expected columns
+    expected_columns = ["yhat", "yhat_lower", "yhat_upper", "trend"]
+    for col in expected_columns:
+        assert col in forecast.columns, f"Expected column '{col}' not found in forecast"
+
+
+def test_prophet_empty_dataframe():
+    """Test Prophet behavior with empty dataframe."""
+    model = Prophet(daily_seasonality=False)
+    empty_df = pd.DataFrame(columns=["ds", "y"])
+
+    with pytest.raises(Exception):  # Prophet should raise an error with empty data
+        model.fit(empty_df)