refactor: merge WeightedDataGenerator into DataGenerator (#458)

* chore: upgrade Jupyter notebook kernels
* docs: add link to TR-018
* feat: embed weights as key to `DataSample`
* feat: implement phase space weights in `UnbinnedNLL`

docs/amplitude-analysis.ipynb

@@ -299,6 +299,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "::::{margin}\n",
+    ":::{tip}\n",
+    "{doc}`TR-018<compwa-org:report/018>` explains some of the mechanisms behind the phase space generator as well as how to do {ref}`importance sampling<compwa-org:report/018:Intensity distribution>`.\n",
+    ":::\n",
+    "::::\n",
+    "\n",
     "In this section, we use the {class}`~ampform.helicity.HelicityModel` that we created with {mod}`ampform` in {ref}`the previous step <compwa-step-1>` to generate a data sample via hit & miss Monte Carlo. We do this with the {mod}`.data` module.\n",
     "\n",
     "First, we {func}`~pickle.load` the {class}`~ampform.helicity.HelicityModel` that was created in the previous step. This does not have to be done if the model has been generated in the same script or notebook, but can be useful if the model was generated elsewhere."
@@ -353,7 +359,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The {class}`~qrules.transition.ReactionInfo` class defines the constraints of the phase space. As such, we have enough information to generate a **phase-space sample** for this particle reaction. We do this with a {class}`.TFPhaseSpaceGenerator` class, which is an implementation of the {class}`.DataGenerator` for a {obj}`.DataSample` of **four-momenta** arrays (using {obj}`tensorflow <tf.Tensor>` and the [`phasespace`](https://phasespace.readthedocs.io) package as a back-end). We also need to construct a {class}`.RealNumberGenerator` that can generate random numbers. {class}`.TFUniformRealNumberGenerator` is the natural choice here.\n",
+    "The {class}`~qrules.transition.ReactionInfo` class defines the constraints of the phase space. As such, we have enough information to generate a **phase-space sample** for this particle reaction. We do this with a {class}`.TFPhaseSpaceGenerator` class, which is a {class}`.DataGenerator` for a {obj}`.DataSample` of **four-momenta** arrays (using {obj}`tensorflow <tf.Tensor>` and the [`phasespace`](https://phasespace.readthedocs.io) package as a back-end). We also need to construct a {class}`.RealNumberGenerator` that can generate random numbers. {class}`.TFUniformRealNumberGenerator` is the natural choice here.\n",
     "\n",
     "As opposed to the main {ref}`amplitude-analysis:Step 2: Generate data` of the main usage example page, we will generate a **deterministic** data sample. This can be done by feeding a {class}`.RealNumberGenerator` with a specific {attr}`~.RealNumberGenerator.seed` and giving that generator to the {meth}`.TFPhaseSpaceGenerator.generate` method:"
    ]
@@ -1935,8 +1941,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.8.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/amplitude-analysis/analytic-continuation.ipynb

@@ -290,7 +290,15 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
    "version": "3.8.13"
   }
  },

docs/usage.ipynb

@@ -857,8 +857,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.8.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/usage/basics.ipynb

@@ -1291,8 +1291,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.8.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/usage/binned-fit.ipynb

@@ -328,8 +328,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.8.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/usage/caching.ipynb

@@ -540,8 +540,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.8.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/usage/chi-squared.ipynb

@@ -274,8 +274,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.8.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/usage/faster-lambdify.ipynb

@@ -413,8 +413,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.8.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/usage/unbinned-fit.ipynb

@@ -351,8 +351,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.8.12"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

src/tensorwaves/data/__init__.py

@@ -13,7 +13,6 @@
     DataTransformer,
     Function,
     RealNumberGenerator,
-    WeightedDataGenerator,
 )
 
 from ._data_sample import (
@@ -71,7 +70,7 @@ class IntensityDistributionGenerator(DataGenerator):
 
     def __init__(
         self,
-        domain_generator: DataGenerator | WeightedDataGenerator,
+        domain_generator: DataGenerator,
         function: Function,
         domain_transformer: DataTransformer | None = None,
         bunch_size: int = 50_000,
@@ -115,18 +114,14 @@ def generate(self, size: int, rng: RealNumberGenerator) -> DataSample:
         return select_events(returned_data, selector=slice(None, size))
 
     def _generate_bunch(self, rng: RealNumberGenerator) -> tuple[DataSample, float]:
-        domain_generator = self.__domain_generator
-        if isinstance(domain_generator, WeightedDataGenerator):
-            domain, weights = domain_generator.generate(self.__bunch_size, rng)
-        else:
-            domain = _generate_without_progress_bar(
-                domain_generator, self.__bunch_size, rng
-            )
-            weights = 1  # type: ignore[assignment]
+        domain = _generate_without_progress_bar(
+            self.__domain_generator, self.__bunch_size, rng
+        )
         transformed_domain = self.__domain_transformer(domain)
         computed_intensities = self.__function(transformed_domain)
         max_intensity: float = np.max(computed_intensities)
         random_intensities = rng(size=self.__bunch_size, max_value=max_intensity)
+        weights = domain.get("weights", 1)
         hit_and_miss_sample = select_events(
             domain,
             selector=weights * computed_intensities > random_intensities,
@@ -139,9 +134,9 @@ def _generate_without_progress_bar(
 ) -> DataSample:
     # https://github.com/ComPWA/tensorwaves/issues/395
     show_progress = getattr(domain_generator, "show_progress", None)
-    if show_progress:
+    if show_progress is not None:
         domain_generator.show_progress = False  # type: ignore[attr-defined]
     domain = domain_generator.generate(bunch_size, rng)
-    if show_progress:
+    if show_progress is not None:
         domain_generator.show_progress = show_progress  # type: ignore[attr-defined]
     return domain

src/tensorwaves/data/phasespace.py

@@ -1,20 +1,14 @@
 # pylint: disable=import-outside-toplevel
-"""Implementations of `.DataGenerator` and `.WeightedDataGenerator`."""
+"""Implementations of a `.DataGenerator` for four-momentum samples."""
 from __future__ import annotations
 
 import logging
 from typing import Mapping
 
-import numpy as np
 from tqdm.auto import tqdm
 
 from tensorwaves.function._backend import raise_missing_module_error
-from tensorwaves.interface import (
-    DataGenerator,
-    DataSample,
-    RealNumberGenerator,
-    WeightedDataGenerator,
-)
+from tensorwaves.interface import DataGenerator, DataSample, RealNumberGenerator
 
 from ._data_sample import (
     finalize_progress_bar,
@@ -64,20 +58,30 @@ def generate(self, size: int, rng: RealNumberGenerator) -> DataSample:
         )
         momentum_pool: DataSample = {}
         while get_number_of_events(momentum_pool) < size:
-            phsp_momenta, weights = self.__phsp_generator.generate(
-                self.__bunch_size, rng
-            )
+            phsp_momenta = self.__phsp_generator.generate(self.__bunch_size, rng)
+            weights = phsp_momenta.get("weights")
+            if weights is None:
+                raise ValueError(
+                    "DataSample returned by"
+                    f" {type(self.__phsp_generator).__name__} doesn't contain"
+                    ' "weights"'
+                )
             hit_and_miss_randoms = rng(self.__bunch_size)
             bunch = select_events(phsp_momenta, selector=weights > hit_and_miss_randoms)
             momentum_pool = merge_events(momentum_pool, bunch)
             progress_bar.update(n=get_number_of_events(bunch))
         finalize_progress_bar(progress_bar)
-        return select_events(momentum_pool, selector=slice(None, size))
+        phsp = select_events(momentum_pool, selector=slice(None, size))
+        del phsp["weights"]
+        return phsp
 
 
-class TFWeightedPhaseSpaceGenerator(WeightedDataGenerator):
+class TFWeightedPhaseSpaceGenerator(DataGenerator):
     """Implements a phase space generator **with weights** using tensorflow.
 
+    The weights are provided in the returned `.DataSample` under the key
+    :code:`"weights"`.
+
     Args:
         initial_state_mass: Mass of the decaying state.
         final_state_masses: A mapping of final state IDs to the corresponding masses.
@@ -102,9 +106,7 @@ def __init__(
             names=list(map(str, sorted_ids)),
         )
 
-    def generate(
-        self, size: int, rng: RealNumberGenerator
-    ) -> tuple[DataSample, np.ndarray]:
+    def generate(self, size: int, rng: RealNumberGenerator) -> DataSample:
         r"""Generate a `.DataSample` of phase space four-momenta with weights.
 
         Returns:
@@ -122,4 +124,7 @@ def generate(
             f"p{label}": momenta.numpy()[:, [3, 0, 1, 2]]
             for label, momenta in particles.items()
         }
-        return phsp_momenta, weights.numpy()
+        return {
+            "weights": weights.numpy(),
+            **phsp_momenta,
+        }

src/tensorwaves/estimator.py

@@ -193,7 +193,8 @@ def __init__(  # pylint: disable=too-many-arguments
         backend: str = "numpy",
     ) -> None:
         self.__data = {k: np.array(v) for k, v in data.items()}
-        self.__phsp = {k: np.array(v) for k, v in phsp.items()}
+        self.__phsp = {k: np.array(v) for k, v in phsp.items() if k != "weights"}
+        self.__phsp_weights = phsp.get("weights")
         self.__function = function
         self.__gradient = gradient_creator(self.__call__, backend)
 
@@ -207,6 +208,8 @@ def __call__(self, parameters: Mapping[str, ParameterValue]) -> float:
         self.__function.update_parameters(parameters)
         bare_intensities = self.__function(self.__data)
         phsp_intensities = self.__function(self.__phsp)
+        if self.__phsp_weights is not None:
+            phsp_intensities *= self.__phsp_weights
         normalization_factor = 1.0 / (
             self.__phsp_volume * self.__mean_function(phsp_intensities)
         )

src/tensorwaves/interface.py

@@ -231,17 +231,7 @@ class DataGenerator(ABC):
 
     @abstractmethod
     def generate(self, size: int, rng: RealNumberGenerator) -> DataSample:
-        ...
-
-
-class WeightedDataGenerator(ABC):
-    """Abstract class for generating a `.DataSample` with weights."""
-
-    @abstractmethod
-    def generate(
-        self, size: int, rng: RealNumberGenerator
-    ) -> tuple[DataSample, np.ndarray]:
-        r"""Generate `.DataSample` with weights.
+        r"""Generate a `.DataSample` with :code:`size` events.
 
         Returns:
             A `tuple` of a `.DataSample` with an array of weights.

tests/data/test_data.py

@@ -1,4 +1,6 @@
 # pylint: disable=import-outside-toplevel
+from __future__ import annotations
+
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -96,7 +98,7 @@ def test_generate_four_momenta_on_flat_distribution(self):
             assert pytest.approx(phsp[i]) == data[i]
 
 
-def test_generate_without_progress_bar(capsys: "CaptureFixture"):
+def test_generate_without_progress_bar(capsys: CaptureFixture):
     class SilentGenerator(DataGenerator):
         def generate(self, size: int, rng: RealNumberGenerator) -> DataSample:
             return {"x": 1}  # type: ignore[dict-item]

tests/data/test_phasespace.py

@@ -137,7 +137,10 @@ def test_generate_deterministic(self, pdg: "ParticleCollection"):
                 i: pdg[name].mass for i, name in enumerate(final_state_names)
             },
         )
-        phsp_momenta, weights = phsp_generator.generate(sample_size, rng)
+        phsp_momenta = phsp_generator.generate(sample_size, rng)
+        assert list(phsp_momenta) == ["weights", "p0", "p1", "p2"]
+        weights = phsp_momenta.get("weights", [])
+        del phsp_momenta["weights"]
         print("Expected values, get by running pytest with the -s flag")
         pprint(
             {