BPZ submission attempt

.github/workflows/submit_bpz_31temps.yaml

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+---
+# This workflow will install Python dependencies and run tests
+
+name: Unit test and code coverage
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.13']
+        submission: ['bpz_31temps']
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          sudo apt-get update
+          sudo apt install libbz2-dev
+          python -m pip install --upgrade pip
+          pip install wheel
+          pip install .
+          pip install .[dev]
+          if [ -f requirements_${{ matrix.submission }}.txt ]; then pip install -r requirements_${{ matrix.submission }}.txt; fi
+      - name: Run unit tests with pytest
+        run: |
+          python -m pytest tests/test_${{ matrix.submission }}.py

requirements_bpz_31temps.txt

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+pz-rail-base>=2.0.0
+pz-rail-bpz>=2.0.1
+desc-bpz>=0.3.3
+qp-prob>=1.0.0
+tables_io>=1.0.0

tests/test_bpz_31temps.py

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+import os
+from pathlib import Path
+import pytest
+
+# Put needed import here
+from rail.estimation.algos.bpz_lite import BPZliteInformer, BPZliteEstimator
+from rail.core.data import TableHandle
+from rail.utils import catalog_utils
+
+
+# These are used by test scripts
+from pz_data_challenge.taskset_1 import run_taskset_1
+from pz_data_challenge.taskset_2 import run_taskset_2
+from pz_data_challenge import submit_utils
+
+xbands = ['u','g','r','i','z','y']
+errbands = []
+for band in xbands:
+    errbands.append(f"mag_{band}_lsst_err")
+
+
+# Change these to match the name of the submission
+# and a URL to download the sumission data files
+# and needed model files
+SUBMISSION_NAME: str = "bpz_31temps"
+SUBMISSION_URL: str = "https://portal.nersc.gov/cfs/lsst/PZ/submit_bpz_31temps.tgz"
+
+# don't change these
+SUBMIT_DIR: str = f"submissions/{SUBMISSION_NAME}"
+PUBLIC_AREA: str = "tests/public"
+
+
+@pytest.fixture(name="setup_submit_area", scope="module")
+def setup_submit_area(request: pytest.FixtureRequest) -> int:
+    """
+    A pytest fixture to download the submission data
+
+    If all the submission data are in a tar file with the
+    proper structure you should not need to change this function.
+    """
+
+    if not os.path.exists(SUBMIT_DIR):
+        if not SUBMISSION_URL:
+            raise ValueError(f"SUBMISSION_URL in tests/test_{SUBMISSION_NAME}.py has not been set")
+        submit_utils.download_and_extract_tar(SUBMISSION_URL, SUBMIT_DIR)
+
+    def teardown_submit_area() -> None:
+        if not os.environ.get("NO_TEARDOWN"):
+            os.system(f"\\rm -rf {SUBMIT_DIR}")
+
+    try:
+        os.makedirs(os.path.join(SUBMIT_DIR, "outputs_2"))
+    except Exception:
+        pass
+
+    try:
+        os.makedirs(os.path.join(SUBMIT_DIR, "outputs_3"))
+    except Exception:
+        pass
+
+    request.addfinalizer(teardown_submit_area)
+
+    return 0
+
+
+def run_taskset_1_estimation_only(
+    model_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run estimation for task set 1
+
+    This function should use a model stored in model_file, which
+    is downloaded as part of the submission tar file.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    model_file:
+        Path to the model.  This should be part of the submission
+        tar file.
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    test_data = TableHandle("test", path=test_file)
+    estimator = BPZliteEstimator.make_stage(
+        name="estimate",
+        hdf5_groupname="",
+        model=model_file,
+        spectra_file="COSMOS_seds.list",
+        err_bands=errbands,
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_1_training_and_estimation(
+    train_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run training and estimation for task set 1
+
+    This function should train a model and use it.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    train_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be trained
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    train_data = TableHandle("train", path=train_file)
+    test_data = TableHandle("test", path=test_file)
+
+    informer = BPZliteInformer.make_stage(
+        name="inform",
+        output_HDFN=True,
+        nt_array=[10,9,12],
+    )
+    model = informer.inform(train_data)
+
+    estimator = BPZliteEstimator.make_stage(
+        name="estimate",
+        model=model,
+        hdf5_groupname="",
+        spectra_file="COSMOS_seds.list",
+        err_bands=errbands,
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_2_estimation_only(
+    model_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run estimation for task set 1
+
+    This function should use a model stored in model_file, which
+    is downloaded as part of the submission tar file.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    model_file:
+        Path to the model.  This should be part of the submission
+        tar file.
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    test_data = TableHandle("test", path=test_file)
+    estimator = BPZliteEstimator.make_stage(
+        name="estimate",
+        model=model_file,
+        hdf5_groupname="",
+        spectra_file="COSMOS_seds.list",
+        err_bands=errbands,
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_2_training_and_estimation(
+    train_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run training and estimation for task set 1
+
+    This function should train a model and use it.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    train_data = TableHandle("train", path=train_file)
+    test_data = TableHandle("test", path=test_file)
+
+    informer = BPZliteInformer.make_stage(
+        name="inform",
+        output_hdfn=True,
+        nt_array=[10,9,12],
+    )
+    model = informer.inform(train_data)
+
+    estimator = BPZliteEstimator.make_stage(
+        name="estimate",
+        model=model,
+        hdf5_groupname="",
+        spectra_file="COSMOS_seds.list",
+        err_bands=errbands,
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+def test_example_taskset_1(
+    setup_public_area: int,
+    setup_submit_area: int,
+) -> None:
+    """
+    Test fuction to validate a submisson for Taskset 1
+
+    You should not need to change this function
+    """
+
+    assert setup_public_area == 0
+    assert setup_submit_area == 0
+
+    run_taskset_1(
+        PUBLIC_AREA,
+        SUBMISSION_NAME,
+        run_taskset_1_estimation_only,
+        run_taskset_1_training_and_estimation,
+    )
+
+
+def test_example_taskset_2(
+    setup_public_area: int,
+    setup_submit_area: int,
+) -> None:
+    """
+    Test fuction to validate a submisson for Taskset 2
+
+    You should not need to change this function
+    """
+
+    assert setup_public_area == 0
+    assert setup_submit_area == 0
+
+    run_taskset_2(
+        PUBLIC_AREA,
+        SUBMISSION_NAME,
+        run_taskset_2_estimation_only,
+        run_taskset_2_training_and_estimation,
+    )