WIP - Parallelization of DistToPoint metrics

src/qp/metrics/base_metric_classes.py

@@ -139,6 +139,14 @@ class DistToPointMetric(BaseMetric):
     def evaluate(self, estimate, reference):
         raise NotImplementedError()
 
+    def initialize(self):  #pragma: no cover
+        pass
+
+    def accumulate(self, estimate, reference):  #pragma: no cover
+        raise NotImplementedError()
+
+    def finalize(self):  #pragma: no cover
+        raise NotImplementedError()
 
 class PointToPointMetric(BaseMetric):
     """A base class for metrics that require a point estimate as input for both

src/qp/metrics/brier.py

@@ -36,6 +36,11 @@ def evaluate(self):
         self._validate_data()
         return self._calculate_metric()
 
+    def accumulate(self):
+        self._manipulate_data()
+        self._validate_data()
+        return self._calculate_metric_for_accumulation()
+
     def _manipulate_data(self):
         """
         Placeholder for data manipulation as required. i.e. converting from
@@ -95,3 +100,8 @@ def _calculate_metric(self):
         return np.mean(
             np.sum((self._prediction - self._truth) ** 2, axis=self._axis_for_summation)
         )
+
+    def _calculate_metric_for_accumulation(self):
+        return np.sum(
+            np.sum((self._prediction - self._truth) ** 2, axis=self._axis_for_summation)
+        )

src/qp/metrics/concrete_metric_classes.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 
-from qp.ensemble import Ensemble
 from qp.metrics.base_metric_classes import (
     MetricOutputType,
     DistToDistMetric,
@@ -11,6 +10,7 @@
 )
 from qp.metrics.metrics import (
     calculate_brier,
+    calculate_brier_for_accumulation,
     calculate_goodness_of_fit,
     calculate_kld,
     calculate_moment,
@@ -20,11 +20,56 @@
 )
 from qp.metrics.pit import PIT
 
+from pytdigest import TDigest
+from functools import reduce
+from operator import add
+
+
+class DistToPointMetricDigester(DistToPointMetric):
+
+    def __init__(self, tdigest_compression: int = 1000, **kwargs) -> None:
+        super().__init__(**kwargs)
+        self._tdigest_compression = tdigest_compression
+
+    def initialize(self):
+        pass
+
+    def accumulate(self, estimate, reference):  #pragma: no cover
+        raise NotImplementedError()
+
+    def finalize(self, centroids: np.ndarray = []):
+        """This function combines all the centroids that were calculated for the
+        input estimate and reference subsets and returns the resulting TDigest
+        object.
+
+        Parameters
+        ----------
+        centroids : Numpy 2d array, optional
+            The output collected from prior calls to `accumulate`, by default []
+
+        Returns
+        -------
+        float
+            The result of the specific metric calculation defined in the subclasses
+            `compute_from_digest` method.
+        """
+        digests = (
+            TDigest.of_centroids(np.array(centroid), compression=self._tdigest_compression)
+            for centroid in centroids
+        )
+        digest = reduce(add, digests)
+
+        return self.compute_from_digest(digest)
+
+    def compute_from_digest(self, digest):  #pragma: no cover
+        raise NotImplementedError()
+
 
 class MomentMetric(SingleEnsembleMetric):
     """Class wrapper around the `calculate_moment` function."""
 
     metric_name = "moment"
+    metric_output_type = MetricOutputType.one_value_per_distribution
 
     def __init__(
         self,
@@ -80,7 +125,7 @@ def evaluate(self, estimate) -> list:
 
 
 #! Should this be implemented as `DistToPointMetric` or `DistToDistMetric` ???
-class BrierMetric(DistToPointMetric):
+class BrierMetric(DistToPointMetricDigester):
     """Class wrapper around the calculate_brier function. (Which itself is a
     wrapper around the `Brier` metric evaluator class).
     """
@@ -89,11 +134,23 @@ class BrierMetric(DistToPointMetric):
     metric_output_type = MetricOutputType.one_value_per_distribution
 
     def __init__(self, limits: tuple = (0.0, 3.0), dx: float = 0.01, **kwargs) -> None:
-        super().__init__(limits, dx)
+        kwargs.update({"limits": limits, "dx": dx})
+        super().__init__(**kwargs)
 
     def evaluate(self, estimate, reference) -> list:
         return calculate_brier(estimate, reference, self._limits, self._dx)
 
+    def accumulate(self, estimate, reference):
+        brier_sum_npdf_tuple = calculate_brier_for_accumulation(estimate, reference, self._limits, self._dx)
+        return brier_sum_npdf_tuple
+
+    def finalize(self, tuples):
+        # tuples is a list of tuples. The first value in the tuple is the Brier sum
+        # The second value is the number of PDFs
+        summed_terms = np.sum(np.atleast_2d(tuples), axis=0)
+
+        # calculate the mean from the summed terms
+        return summed_terms[0] / summed_terms[1]
 
 class OutlierMetric(SingleEnsembleMetric):
     """Class wrapper around the outlier calculation metric."""
@@ -204,24 +261,47 @@ def evaluate(self, estimate, reference) -> list:
         )
 
 
-#! Confirm metric output type - perhaps a new type is appropriate ???
-class PITMetric(DistToPointMetric):
+class PITMetric(DistToPointMetricDigester):
     """Class wrapper for the PIT Metric class."""
 
     metric_name = "pit"
     metric_output_type = MetricOutputType.single_distribution
     default_eval_grid = np.linspace(0, 1, 100)
 
     def __init__(self, eval_grid: list = default_eval_grid, **kwargs) -> None:
-        super().__init__()
+        super().__init__(**kwargs)
         self._eval_grid = eval_grid
 
-    def evaluate(self, estimate, reference) -> Ensemble:
+    def evaluate(self, estimate, reference):
         pit_object = PIT(estimate, reference, self._eval_grid)
         return pit_object.pit
 
-
-class CDELossMetric(DistToPointMetric):
+    def accumulate(self, estimate, reference):
+        pit_samples = PIT(estimate, reference, self._eval_grid)._gather_pit_samples(estimate, reference)
+        digest = TDigest.compute(pit_samples, compression=self._tdigest_compression)
+        centroids = digest.get_centroids()
+        return centroids
+
+    def compute_from_digest(self, digest):
+        # Since we use equal weights for all the values in the digest
+        # digest.weight is the total number of values, it is stored as a float,
+        # so we cast to int.
+        eval_grid = self._eval_grid
+        total_samples = int(digest.weight)
+        n_pit = np.min([total_samples, len(eval_grid)])
+        if n_pit < len(eval_grid):  # pragma: no cover
+            #! TODO: Determine what the appropriate style of logging is going to be for metrics.
+            print(
+                "Number of pit samples is smaller than the evaluation grid size. "
+                "Will create a new evaluation grid with size = number of pit samples"
+            )
+            eval_grid = np.linspace(0, 1, n_pit)
+
+        data_quants = digest.inverse_cdf(eval_grid)
+        return PIT._produce_output_ensemble(data_quants, eval_grid)
+
+
+class CDELossMetric(DistToPointMetricDigester):
     """Conditional density loss"""
 
     metric_name = "cdeloss"
@@ -248,3 +328,19 @@ def evaluate(self, estimate, reference):
         term2 = np.mean(pdfs[range(npdf), nns])
         cdeloss = term1 - 2 * term2
         return cdeloss
+
+    def accumulate(self, estimate, reference):
+        pdfs = estimate.pdf(self._xvals)
+        npdf = estimate.npdf
+        term1_sum = np.sum(np.trapz(pdfs**2, x=self._xvals))
+
+        nns = [np.argmin(np.abs(self._xvals - z)) for z in reference]
+        term2_sum = np.sum(pdfs[range(npdf), nns])
+
+        return (term1_sum, term2_sum, npdf)
+
+    def finalize(self, tuples):
+        summed_terms = np.sum(np.atleast_2d(tuples), axis=0)
+        term1 = summed_terms[0] / summed_terms[2]
+        term2 = summed_terms[1] / summed_terms[2]
+        return term1 - 2 * term2

src/qp/metrics/metrics.py

@@ -217,32 +217,7 @@ def evaluate_pdf_at_z(z, dist):
     return np.array(rbpes)
 
 
-def calculate_brier(p, truth, limits, dx=0.01):
-    """This function will do the following:
-
-    1) Generate a Mx1 sized grid based on `limits` and `dx`.
-    2) Produce an NxM array by evaluating the pdf for each of the N distribution objects
-       in the Ensemble p on the grid.
-    3) Produce an NxM truth_array using the input truth and the generated grid. All values will be 0 or 1.
-    4) Create a Brier metric evaluation object
-    5) Return the result of the Brier metric calculation.
-
-    Parameters
-    ----------
-    p: qp.Ensemble object
-        of N distributions probability distribution functions that will be gridded and compared against truth.
-    truth: Nx1 sequence
-        the list of true values, 1 per distribution in p.
-    limits: 2-tuple of floats
-        endpoints grid to evaluate the PDFs for the distributions in p
-    dx: float
-        resolution of the grid Defaults to 0.01.
-
-    Returns
-    -------
-    Brier_metric: float
-    """
-
+def _prepare_for_brier(p, truth, limits, dx=0.01):
     # Ensure that the number of distributions objects in the Ensemble
     # is equal to the length of the truth array
     if p.npdf != len(truth):
@@ -270,11 +245,45 @@ def calculate_brier(p, truth, limits, dx=0.01):
     truth_array = np.squeeze([np.histogram(t, grid.hist_bin_edges)[0] for t in truth])
 
     # instantiate the Brier metric object
-    brier_metric_evaluation = Brier(pdf_values, truth_array)
+    return Brier(pdf_values, truth_array)
+
+def calculate_brier(p, truth, limits, dx=0.01):
+    """This function will do the following:
+
+    1) Generate a Mx1 sized grid based on `limits` and `dx`.
+    2) Produce an NxM array by evaluating the pdf for each of the N distribution objects
+       in the Ensemble p on the grid.
+    3) Produce an NxM truth_array using the input truth and the generated grid. All values will be 0 or 1.
+    4) Create a Brier metric evaluation object
+    5) Return the result of the Brier metric calculation.
+
+    Parameters
+    ----------
+    p: qp.Ensemble object
+        of N distributions probability distribution functions that will be gridded and compared against truth.
+    truth: Nx1 sequence
+        the list of true values, 1 per distribution in p.
+    limits: 2-tuple of floats
+        endpoints grid to evaluate the PDFs for the distributions in p
+    dx: float
+        resolution of the grid Defaults to 0.01.
+
+    Returns
+    -------
+    Brier_metric: float
+    """
+
+    brier_metric_evaluation = _prepare_for_brier(p, truth, limits, dx)
 
     # return the results of evaluating the Brier metric
     return brier_metric_evaluation.evaluate()
 
+def calculate_brier_for_accumulation(p, truth, limits, dx=0.01):
+    brier_metric_evaluation = _prepare_for_brier(p, truth, limits, dx)
+
+    brier_sum = brier_metric_evaluation.accumulate()
+
+    return (brier_sum, p.npdf)
 
 @deprecated(
     reason="""

src/qp/metrics/pit.py

@@ -42,25 +42,8 @@ def __init__(self, qp_ens, true_vals, eval_grid=DEFAULT_QUANTS):
         eval_grid : [float], optional
             A strictly increasing array-like sequence in the range [0,1], by default DEFAULT_QUANTS
         """
-        self._true_vals = true_vals
-
-        # For each distribution in the Ensemble, calculate the CDF where x = known_true_value
-        self._pit_samps = np.array(
-            [
-                qp_ens[i].cdf(self._true_vals[i])[0][0]
-                for i in range(len(self._true_vals))
-            ]
-        )
 
-        # These two lines set all `NaN` values to 0. This may or may not make sense
-        # Alternatively if it's better to simply remove the `NaN`, this can be done
-        # efficiently on line 61 with `data_quants = np.nanquantile(...)`.`
-        samp_mask = np.isfinite(self._pit_samps)
-        self._pit_samps[~samp_mask] = 0
-        if not np.all(samp_mask):  #pragma: no cover
-            logging.warning(
-                "Some PIT samples were `NaN`. They have been replacd with 0."
-            )
+        self._pit_samps = self._gather_pit_samples(qp_ens, true_vals)
 
         n_pit = np.min([len(self._pit_samps), len(eval_grid)])
         if n_pit < len(eval_grid):
@@ -72,19 +55,7 @@ def __init__(self, qp_ens, true_vals, eval_grid=DEFAULT_QUANTS):
 
         data_quants = np.quantile(self._pit_samps, eval_grid)
 
-        # Remove duplicates values as well as values outside the range (0,1)
-        _, unique_indices = np.unique(data_quants, return_index=True)
-        unique_data_quants = data_quants[unique_indices]
-        unique_eval_grid = eval_grid[unique_indices]
-        quant_mask = self._create_quant_mask(unique_data_quants)
-
-        self._pit = qp.Ensemble(
-            qp.quant,
-            data=dict(
-                quants=unique_eval_grid[quant_mask],
-                locs=np.atleast_2d(unique_data_quants[quant_mask]),
-            ),
-        )
+        self._pit = self._produce_output_ensemble(data_quants, eval_grid)
 
     @property
     def pit_samps(self):
@@ -201,6 +172,38 @@ def evaluate_PIT_outlier_rate(self, pit_min=0.0001, pit_max=0.9999):
         """
         return calculate_outlier_rate(self._pit, pit_min, pit_max)[0]
 
+    @classmethod
+    def _gather_pit_samples(cls, qp_ens, true_vals):
+        pit_samples = np.squeeze(qp_ens.cdf(np.vstack(true_vals)))
+
+        # These two lines set all `NaN` values to 0. This may or may not make sense
+        # Alternatively if it's better to simply remove the `NaN`, this can be done
+        # efficiently on line 61 with `data_quants = np.nanquantile(...)`.`
+        sample_mask = np.isfinite(pit_samples)
+        pit_samples[~sample_mask] = 0
+        if not np.all(sample_mask):  #pragma: no cover
+            logging.warning(
+                "Some PIT samples were `NaN`. They have been replacd with 0."
+            )
+
+        return pit_samples
+
+    @classmethod
+    def _produce_output_ensemble(cls, data_quants, eval_grid):
+        # Remove duplicates values as well as values outside the range (0,1)
+        _, unique_indices = np.unique(data_quants, return_index=True)
+        unique_data_quants = data_quants[unique_indices]
+        unique_eval_grid = eval_grid[unique_indices]
+        quant_mask = cls._create_quant_mask(unique_data_quants)
+
+        return qp.Ensemble(
+            qp.quant,
+            data=dict(
+                quants=unique_eval_grid[quant_mask],
+                locs=np.atleast_2d(unique_data_quants[quant_mask]),
+            ),
+        )
+
     @classmethod
     def _create_quant_mask(cls, data_quants):
         """Create a numpy mask such that, when applied only values greater than