diff --git a/CMakeLists.txt b/CMakeLists.txt
index 37e7f9018b53b07fefd728de44f2172ad37759c9..59c8caca24e5a48a27f7f625124af64415c197fa 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -21,9 +21,7 @@ message(STATUS "Found xtensor: ${xtensor_INCLUDE_DIRS}/xtensor")
# define evalhyd library
add_library(
evalhyd
- src/probabilist/evaluator_brier.cpp
- src/probabilist/evaluator_elements.cpp
- src/probabilist/evaluator_quantiles.cpp
+ INTERFACE
)
add_library(EvalHyd::evalhyd ALIAS evalhyd)
@@ -36,30 +34,20 @@ set_target_properties(
target_include_directories(
evalhyd
- PUBLIC
+ INTERFACE
$<INSTALL_INTERFACE:include>
$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
- PRIVATE
- ${CMAKE_CURRENT_SOURCE_DIR}/src
)
target_link_libraries(
evalhyd
- PUBLIC
+ INTERFACE
xtensor
)
-if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
- target_compile_options(
- evalhyd
- PRIVATE
- "/bigobj"
- )
-endif()
-
target_compile_features(
evalhyd
- PUBLIC
+ INTERFACE
cxx_std_14
)
diff --git a/src/determinist/evaluator.hpp b/include/evalhyd/detail/determinist/evaluator.hpp
similarity index 100%
rename from src/determinist/evaluator.hpp
rename to include/evalhyd/detail/determinist/evaluator.hpp
diff --git a/src/masks.hpp b/include/evalhyd/detail/masks.hpp
similarity index 100%
rename from src/masks.hpp
rename to include/evalhyd/detail/masks.hpp
diff --git a/src/maths.hpp b/include/evalhyd/detail/maths.hpp
similarity index 100%
rename from src/maths.hpp
rename to include/evalhyd/detail/maths.hpp
diff --git a/src/probabilist/evaluator_brier.cpp b/include/evalhyd/detail/probabilist/evaluator.hpp
similarity index 54%
rename from src/probabilist/evaluator_brier.cpp
rename to include/evalhyd/detail/probabilist/evaluator.hpp
index 2528aef8fb811add2845b19bc1bb5709081c9c10..07bd764574e4dbfc4937fc7c46c6fd85559883f9 100644
--- a/src/probabilist/evaluator_brier.cpp
+++ b/include/evalhyd/detail/probabilist/evaluator.hpp
@@ -1,23 +1,251 @@
-#include <xtensor/xmath.hpp>
+#ifndef EVALHYD_PROBABILIST_EVALUATOR_HPP
+#define EVALHYD_PROBABILIST_EVALUATOR_HPP
+
+#include <stdexcept>
+#include <vector>
+
+#include <xtensor/xtensor.hpp>
#include <xtensor/xview.hpp>
#include <xtensor/xmasked_view.hpp>
+#include <xtensor/xslice.hpp>
#include <xtensor/xoperation.hpp>
+#include <xtensor/xmath.hpp>
+#include <xtensor/xsort.hpp>
-#include "probabilist/evaluator.hpp"
-
-namespace eh = evalhyd;
-
-// NOTE ------------------------------------------------------------------------
-// All equations in metrics below are following notations from
-// "Wilks, D. S. (2011). Statistical methods in the atmospheric sciences.
-// Amsterdam; Boston: Elsevier Academic Press. ISBN: 9780123850225".
-// In particular, pp. 302-303, 332-333.
-// -----------------------------------------------------------------------------
namespace evalhyd
{
namespace probabilist
{
+ template <class D2, class D4, class B4>
+ class Evaluator
+ {
+ private:
+ using view1d_xtensor2d_double_type = decltype(
+ xt::view(
+ std::declval<const D2&>(),
+ std::declval<int>(),
+ xt::all()
+ )
+ );
+
+ using view2d_xtensor4d_double_type = decltype(
+ xt::view(
+ std::declval<const D4&>(),
+ std::declval<int>(),
+ std::declval<int>(),
+ xt::all(),
+ xt::all()
+ )
+ );
+
+ using view2d_xtensor4d_bool_type = decltype(
+ xt::view(
+ std::declval<const B4&>(),
+ std::declval<int>(),
+ std::declval<int>(),
+ xt::all(),
+ xt::all()
+ )
+ );
+
+ // members for input data
+ const view1d_xtensor2d_double_type& q_obs;
+ const view2d_xtensor4d_double_type& q_prd;
+ const view1d_xtensor2d_double_type& q_thr;
+ xt::xtensor<bool, 2> t_msk;
+ const std::vector<xt::xkeep_slice<int>>& b_exp;
+
+ // members for dimensions
+ std::size_t n;
+ std::size_t n_msk;
+ std::size_t n_mbr;
+ std::size_t n_thr;
+ std::size_t n_exp;
+
+ // members for computational elements
+ xt::xtensor<double, 2> o_k;
+ xt::xtensor<double, 3> bar_o;
+ xt::xtensor<double, 2> y_k;
+ xt::xtensor<double, 2> q_qnt;
+
+ public:
+ // constructor method
+ Evaluator(const view1d_xtensor2d_double_type& obs,
+ const view2d_xtensor4d_double_type& prd,
+ const view1d_xtensor2d_double_type& thr,
+ const view2d_xtensor4d_bool_type& msk,
+ const std::vector<xt::xkeep_slice<int>>& exp) :
+ q_obs{obs}, q_prd{prd}, q_thr{thr}, t_msk(msk), b_exp(exp)
+ {
+ // initialise a mask if none provided
+ // (corresponding to no temporal subset)
+ if (msk.size() < 1)
+ t_msk = xt::ones<bool>({std::size_t {1}, q_obs.size()});
+
+ // determine size for recurring dimensions
+ n = q_obs.size();
+ n_msk = t_msk.shape(0);
+ n_mbr = q_prd.shape(0);
+ n_thr = q_thr.size();
+ n_exp = b_exp.size();
+
+ // drop time steps where observations and/or predictions are NaN
+ auto obs_nan = xt::isnan(q_obs);
+ auto prd_nan = xt::isnan(q_prd);
+ auto sum_nan = xt::eval(xt::sum(prd_nan, -1));
+
+ if (xt::amin(sum_nan) != xt::amax(sum_nan))
+ throw std::runtime_error(
+ "predictions across members feature non-equal lengths"
+ );
+
+ auto msk_nan = xt::where(obs_nan | xt::row(prd_nan, 0))[0];
+
+ xt::view(t_msk, xt::all(), xt::keep(msk_nan)) = false;
+ };
+
+ // members for intermediate evaluation metrics
+ // (i.e. before the reduction along the temporal axis)
+ xt::xtensor<double, 2> bs;
+ xt::xtensor<double, 2> qs;
+ xt::xtensor<double, 2> crps;
+
+ // members for evaluation metrics
+ xt::xtensor<double, 3> BS;
+ xt::xtensor<double, 4> BS_CRD;
+ xt::xtensor<double, 4> BS_LBD;
+ xt::xtensor<double, 3> BSS;
+ xt::xtensor<double, 3> QS;
+ xt::xtensor<double, 2> CRPS;
+
+ // methods to compute derived data
+ void calc_q_qnt();
+
+ // methods to compute elements
+ void calc_o_k();
+ void calc_bar_o();
+ void calc_y_k();
+
+ // methods to compute intermediate metrics
+ void calc_bs();
+ void calc_qs();
+ void calc_crps();
+
+ // methods to compute metrics
+ void calc_BS();
+ void calc_BS_CRD();
+ void calc_BS_LBD();
+ void calc_BSS();
+ void calc_QS();
+ void calc_CRPS();
+ };
+
+ // --------------------------------------------------------------------
+ // ELEMENTS
+ // --------------------------------------------------------------------
+
+ // Determine observed realisation of threshold(s) exceedance.
+ //
+ // \require q_obs:
+ // Streamflow observations.
+ // shape: (time,)
+ // \require q_thr:
+ // Streamflow exceedance threshold(s).
+ // shape: (thresholds,)
+ // \assign o_k:
+ // Event observed outcome.
+ // shape: (thresholds, time)
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_o_k()
+ {
+ // determine observed realisation of threshold(s) exceedance
+ o_k = q_obs >= xt::view(q_thr, xt::all(), xt::newaxis());
+ }
+
+ // Determine mean observed realisation of threshold(s) exceedance.
+ //
+ // \require o_k:
+ // Event observed outcome.
+ // shape: (thresholds, time)
+ // \require t_msk:
+ // Temporal subsets of the whole record.
+ // shape: (subsets, time)
+ // \assign bar_o:
+ // Mean event observed outcome.
+ // shape: (subsets, samples, thresholds)
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_bar_o()
+ {
+ // apply the mask
+ // (using NaN workaround until reducers work on masked_view)
+ auto o_k_masked = xt::where(
+ xt::view(t_msk, xt::all(), xt::newaxis(), xt::all()),
+ o_k, NAN
+ );
+
+ // compute variable one sample at a time
+ bar_o = xt::zeros<double>({n_msk, n_exp, n_thr});
+
+ for (int e = 0; e < n_exp; e++)
+ {
+ // apply the bootstrap sampling
+ auto o_k_masked_sampled =
+ xt::view(o_k_masked, xt::all(), xt::all(), b_exp[e]);
+
+ // compute mean "climatology" relative frequency of the event
+ // $\bar{o} = \frac{1}{n} \sum_{k=1}^{n} o_k$
+ xt::view(bar_o, xt::all(), e, xt::all()) =
+ xt::nanmean(o_k_masked_sampled, -1);
+ }
+ }
+
+ // Determine forecast probability of threshold(s) exceedance to occur.
+ //
+ // \require q_prd:
+ // Streamflow predictions.
+ // shape: (members, time)
+ // \require q_thr:
+ // Streamflow exceedance threshold(s).
+ // shape: (thresholds,)
+ // \assign y_k:
+ // Event probability forecast.
+ // shape: (thresholds, time)
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_y_k()
+ {
+ // determine if members have exceeded threshold(s)
+ auto e_frc =
+ q_prd
+ >= xt::view(q_thr, xt::all(), xt::newaxis(), xt::newaxis());
+
+ // calculate how many members have exceeded threshold(s)
+ auto n_frc = xt::sum(e_frc, 1);
+
+ // determine probability of threshold(s) exceedance
+ // /!\ probability calculation dividing by n (the number of
+ // members), not n+1 (the number of ranks) like in other metrics
+ y_k = xt::cast<double>(n_frc) / n_mbr;
+ }
+
+ // Compute the forecast quantiles from the ensemble members.
+ //
+ // \require q_prd:
+ // Streamflow predictions.
+ // shape: (members, time)
+ // \assign q_qnt:
+ // Streamflow forecast quantiles.
+ // shape: (quantiles, time)
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_q_qnt()
+ {
+ q_qnt = xt::sort(q_prd, 0);
+ }
+
+ // --------------------------------------------------------------------
+ // BRIER
+ // --------------------------------------------------------------------
+
// Compute the Brier score for each time step.
//
// \require o_k:
@@ -29,7 +257,8 @@ namespace evalhyd
// \assign bs:
// Brier score for each threshold for each time step.
// shape: (thresholds, time)
- void Evaluator::calc_bs()
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_bs()
{
// return computed Brier score(s)
// $bs = (o_k - y_k)^2$
@@ -47,7 +276,8 @@ namespace evalhyd
// \assign BS:
// Brier score for each subset and for each threshold.
// shape: (subsets, samples, thresholds)
- void Evaluator::calc_BS()
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_BS()
{
// initialise output variable
// shape: (subsets, thresholds)
@@ -105,7 +335,8 @@ namespace evalhyd
// Brier score components (reliability, resolution, uncertainty)
// for each subset and for each threshold.
// shape: (subsets, samples, thresholds, components)
- void Evaluator::calc_BS_CRD()
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_BS_CRD()
{
// declare internal variables
// shape: (bins, thresholds, time)
@@ -227,7 +458,8 @@ namespace evalhyd
// Brier score components (type 2 bias, discrimination, sharpness)
// for each subset and for each threshold.
// shape: (subsets, samples, thresholds, components)
- void Evaluator::calc_BS_LBD()
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_BS_LBD()
{
// declare internal variables
// shape: (bins, thresholds, time)
@@ -361,7 +593,8 @@ namespace evalhyd
// \assign BSS:
// Brier skill score for each subset and for each threshold.
// shape: (subsets, samples, thresholds)
- void Evaluator::calc_BSS()
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_BSS()
{
// declare and initialise output variable
// shape: (subsets, thresholds)
@@ -420,5 +653,147 @@ namespace evalhyd
xt::all()))
) = NAN;
}
+
+ // --------------------------------------------------------------------
+ // QUANTILES
+ // --------------------------------------------------------------------
+
+ // Compute the quantile scores for each time step.
+ //
+ // \require q_obs:
+ // Streamflow observations.
+ // shape: (time,)
+ // \require q_qnt:
+ // Streamflow quantiles.
+ // shape: (quantiles, time)
+ // \assign qs:
+ // Quantile scores for each time step.
+ // shape: (quantiles, time)
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_qs()
+ {
+ // compute the quantile order $alpha$
+ xt::xtensor<double, 1> alpha =
+ xt::arange<double>(1., double(n_mbr + 1))
+ / double(n_mbr + 1);
+
+ // calculate the difference
+ xt::xtensor<double, 2> diff = q_qnt - q_obs;
+
+ // calculate the quantile scores
+ qs = xt::where(
+ diff > 0,
+ 2 * (1 - xt::view(alpha, xt::all(), xt::newaxis())) * diff,
+ - 2 * xt::view(alpha, xt::all(), xt::newaxis()) * diff
+ );
+ }
+
+ // Compute the quantile score (QS).
+ //
+ // \require t_msk:
+ // Temporal subsets of the whole record.
+ // shape: (subsets, time)
+ // \require qs:
+ // Quantile scores for each time step.
+ // shape: (quantiles, time)
+ // \assign QS:
+ // Quantile scores.
+ // shape: (subsets, quantiles)
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_QS()
+ {
+ // initialise output variable
+ // shape: (subsets, quantiles)
+ QS = xt::zeros<double>({n_msk, n_exp, n_mbr});
+
+ // compute variable one mask at a time to minimise memory imprint
+ for (int m = 0; m < n_msk; m++)
+ {
+ // apply the mask
+ // (using NaN workaround until reducers work on masked_view)
+ auto qs_masked = xt::where(xt::row(t_msk, m), qs, NAN);
+
+ // compute variable one sample at a time
+ for (int e = 0; e < n_exp; e++)
+ {
+ // apply the bootstrap sampling
+ auto qs_masked_sampled =
+ xt::view(qs_masked, xt::all(), b_exp[e]);
+
+ // calculate the mean over the time steps
+ // $QS = \frac{1}{n} \sum_{k=1}^{n} qs$
+ xt::view(QS, m, e, xt::all()) =
+ xt::nanmean(qs_masked_sampled, -1);
+ }
+ }
+ }
+
+ // Compute the continuous rank probability score(s) based
+ // on quantile scores for each time step, and integrating using the
+ // trapezoidal rule.
+ //
+ // /!\ The number of quantiles must be sufficiently large so that the
+ // cumulative distribution is smooth enough for the numerical
+ // integration to be accurate.
+ //
+ // \require qs:
+ // Quantile scores for each time step.
+ // shape: (quantiles, time)
+ // \assign crps:
+ // CRPS for each time step.
+ // shape: (1, time)
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_crps()
+ {
+ // integrate with trapezoidal rule
+ crps = xt::view(
+ // xt::trapz(y, dx=1/(n+1), axis=0)
+ xt::trapz(qs, 1./(double(n_mbr) + 1.), 0),
+ xt::newaxis(), xt::all()
+ );
+ }
+
+ // Compute the continuous rank probability score (CRPS) based
+ // on quantile scores.
+ //
+ // \require t_msk:
+ // Temporal subsets of the whole record.
+ // shape: (subsets, time)
+ // \require crps:
+ // CRPS for each time step.
+ // shape: (1, time)
+ // \assign CRPS:
+ // CRPS.
+ // shape: (subsets,)
+ template <class D2, class D4, class B4>
+ void Evaluator<D2, D4, B4>::calc_CRPS()
+ {
+ // initialise output variable
+ // shape: (subsets,)
+ CRPS = xt::zeros<double>({n_msk, n_exp});
+
+ // compute variable one mask at a time to minimise memory imprint
+ for (int m = 0; m < n_msk; m++)
+ {
+ // apply the mask
+ // (using NaN workaround until reducers work on masked_view)
+ auto crps_masked = xt::where(xt::row(t_msk, m), crps, NAN);
+
+ // compute variable one sample at a time
+ for (int e = 0; e < n_exp; e++)
+ {
+ // apply the bootstrap sampling
+ auto crps_masked_sampled =
+ xt::view(crps_masked, xt::all(), b_exp[e]);
+
+ // calculate the mean over the time steps
+ // $CRPS = \frac{1}{n} \sum_{k=1}^{n} crps$
+ xt::view(CRPS, m, e) =
+ xt::squeeze(xt::nanmean(crps_masked_sampled, -1));
+ }
+ }
+ }
}
}
+
+#endif //EVALHYD_PROBABILIST_EVALUATOR_HPP
diff --git a/src/uncertainty.hpp b/include/evalhyd/detail/uncertainty.hpp
similarity index 100%
rename from src/uncertainty.hpp
rename to include/evalhyd/detail/uncertainty.hpp
diff --git a/src/utils.hpp b/include/evalhyd/detail/utils.hpp
similarity index 100%
rename from src/utils.hpp
rename to include/evalhyd/detail/utils.hpp
diff --git a/include/evalhyd/evald.hpp b/include/evalhyd/evald.hpp
index 8ef12b0e561a65ec77231945a1f7eab41e27e22d..5dc85412c0d0bdb264e44e8aa787b7a8774428d9 100644
--- a/include/evalhyd/evald.hpp
+++ b/include/evalhyd/evald.hpp
@@ -9,11 +9,11 @@
#include <xtensor/xtensor.hpp>
#include <xtensor/xarray.hpp>
-#include "utils.hpp"
-#include "masks.hpp"
-#include "maths.hpp"
-#include "uncertainty.hpp"
-#include "determinist/evaluator.hpp"
+#include "detail/utils.hpp"
+#include "detail/masks.hpp"
+#include "detail/maths.hpp"
+#include "detail/uncertainty.hpp"
+#include "detail/determinist/evaluator.hpp"
namespace evalhyd
diff --git a/include/evalhyd/evalp.hpp b/include/evalhyd/evalp.hpp
index fdf58215c9f0de240ef1d6242b89e5425dd38ce7..2a9725c5eb5da5664fe51bed93583bc2f155b6db 100644
--- a/include/evalhyd/evalp.hpp
+++ b/include/evalhyd/evalp.hpp
@@ -8,11 +8,11 @@
#include <xtensor/xtensor.hpp>
#include <xtensor/xarray.hpp>
-#include "utils.hpp"
-#include "masks.hpp"
-#include "maths.hpp"
-#include "uncertainty.hpp"
-#include "probabilist/evaluator.hpp"
+#include "detail/utils.hpp"
+#include "detail/masks.hpp"
+#include "detail/maths.hpp"
+#include "detail/uncertainty.hpp"
+#include "detail/probabilist/evaluator.hpp"
namespace evalhyd
@@ -307,7 +307,7 @@ namespace evalhyd
xt::view(c_msk, s, l, xt::all(), xt::all()) :
xt::view(t_msk_, s, l, xt::all(), xt::all()));
- eh::probabilist::Evaluator evaluator(
+ eh::probabilist::Evaluator<D2, D4, B4> evaluator(
q_obs_v, q_prd_v, q_thr_v, t_msk_v, b_exp
);
diff --git a/src/probabilist/evaluator.hpp b/src/probabilist/evaluator.hpp
deleted file mode 100644
index 1df1e0c566609838f4cf51f336084b3bc547894b..0000000000000000000000000000000000000000
--- a/src/probabilist/evaluator.hpp
+++ /dev/null
@@ -1,140 +0,0 @@
-#ifndef EVALHYD_PROBABILIST_EVALUATOR_HPP
-#define EVALHYD_PROBABILIST_EVALUATOR_HPP
-
-#include <stdexcept>
-#include <vector>
-
-#include <xtensor/xtensor.hpp>
-#include <xtensor/xview.hpp>
-#include <xtensor/xslice.hpp>
-
-using view1d_xtensor2d_double_type = decltype(
- xt::view(
- std::declval<const xt::xtensor<double, 2>&>(),
- std::declval<int>(),
- xt::all()
- )
-);
-
-using view2d_xtensor4d_double_type = decltype(
- xt::view(
- std::declval<const xt::xtensor<double, 4>&>(),
- std::declval<int>(),
- std::declval<int>(),
- xt::all(),
- xt::all()
- )
-);
-
-using view2d_xtensor4d_bool_type = decltype(
- xt::view(
- std::declval<const xt::xtensor<bool, 4>&>(),
- std::declval<int>(),
- std::declval<int>(),
- xt::all(),
- xt::all()
- )
-);
-
-namespace evalhyd
-{
- namespace probabilist
- {
- class Evaluator
- {
- private:
- // members for input data
- const view1d_xtensor2d_double_type& q_obs;
- const view2d_xtensor4d_double_type& q_prd;
- const view1d_xtensor2d_double_type& q_thr;
- xt::xtensor<bool, 2> t_msk;
- const std::vector<xt::xkeep_slice<int>>& b_exp;
-
- // members for dimensions
- std::size_t n;
- std::size_t n_msk;
- std::size_t n_mbr;
- std::size_t n_thr;
- std::size_t n_exp;
-
- // members for computational elements
- xt::xtensor<double, 2> o_k;
- xt::xtensor<double, 3> bar_o;
- xt::xtensor<double, 2> y_k;
- xt::xtensor<double, 2> q_qnt;
-
- public:
- // constructor method
- Evaluator(const view1d_xtensor2d_double_type& obs,
- const view2d_xtensor4d_double_type& prd,
- const view1d_xtensor2d_double_type& thr,
- const view2d_xtensor4d_bool_type& msk,
- const std::vector<xt::xkeep_slice<int>>& exp) :
- q_obs{obs}, q_prd{prd}, q_thr{thr}, t_msk(msk), b_exp(exp)
- {
- // initialise a mask if none provided
- // (corresponding to no temporal subset)
- if (msk.size() < 1)
- t_msk = xt::ones<bool>({std::size_t {1}, q_obs.size()});
-
- // determine size for recurring dimensions
- n = q_obs.size();
- n_msk = t_msk.shape(0);
- n_mbr = q_prd.shape(0);
- n_thr = q_thr.size();
- n_exp = b_exp.size();
-
- // drop time steps where observations and/or predictions are NaN
- auto obs_nan = xt::isnan(q_obs);
- auto prd_nan = xt::isnan(q_prd);
- auto sum_nan = xt::eval(xt::sum(prd_nan, -1));
-
- if (xt::amin(sum_nan) != xt::amax(sum_nan))
- throw std::runtime_error(
- "predictions across members feature non-equal lengths"
- );
-
- auto msk_nan = xt::where(obs_nan | xt::row(prd_nan, 0))[0];
-
- xt::view(t_msk, xt::all(), xt::keep(msk_nan)) = false;
- };
-
- // members for intermediate evaluation metrics
- // (i.e. before the reduction along the temporal axis)
- xt::xtensor<double, 2> bs;
- xt::xtensor<double, 2> qs;
- xt::xtensor<double, 2> crps;
-
- // members for evaluation metrics
- xt::xtensor<double, 3> BS;
- xt::xtensor<double, 4> BS_CRD;
- xt::xtensor<double, 4> BS_LBD;
- xt::xtensor<double, 3> BSS;
- xt::xtensor<double, 3> QS;
- xt::xtensor<double, 2> CRPS;
-
- // methods to compute derived data
- void calc_q_qnt();
-
- // methods to compute elements
- void calc_o_k();
- void calc_bar_o();
- void calc_y_k();
-
- // methods to compute intermediate metrics
- void calc_bs();
- void calc_qs();
- void calc_crps();
-
- // methods to compute metrics
- void calc_BS();
- void calc_BS_CRD();
- void calc_BS_LBD();
- void calc_BSS();
- void calc_QS();
- void calc_CRPS();
- };
- }
-}
-
-#endif //EVALHYD_PROBABILIST_EVALUATOR_HPP
diff --git a/src/probabilist/evaluator_elements.cpp b/src/probabilist/evaluator_elements.cpp
deleted file mode 100644
index bdce6c1b729a600343ac2bea710caea604a7658c..0000000000000000000000000000000000000000
--- a/src/probabilist/evaluator_elements.cpp
+++ /dev/null
@@ -1,104 +0,0 @@
-#include <xtensor/xmath.hpp>
-#include <xtensor/xview.hpp>
-#include <xtensor/xsort.hpp>
-
-#include "probabilist/evaluator.hpp"
-
-namespace evalhyd
-{
- namespace probabilist
- {
- // Determine observed realisation of threshold(s) exceedance.
- //
- // \require q_obs:
- // Streamflow observations.
- // shape: (time,)
- // \require q_thr:
- // Streamflow exceedance threshold(s).
- // shape: (thresholds,)
- // \assign o_k:
- // Event observed outcome.
- // shape: (thresholds, time)
- void Evaluator::calc_o_k()
- {
- // determine observed realisation of threshold(s) exceedance
- o_k = q_obs >= xt::view(q_thr, xt::all(), xt::newaxis());
- }
-
- // Determine mean observed realisation of threshold(s) exceedance.
- //
- // \require o_k:
- // Event observed outcome.
- // shape: (thresholds, time)
- // \require t_msk:
- // Temporal subsets of the whole record.
- // shape: (subsets, time)
- // \assign bar_o:
- // Mean event observed outcome.
- // shape: (subsets, samples, thresholds)
- void Evaluator::calc_bar_o()
- {
- // apply the mask
- // (using NaN workaround until reducers work on masked_view)
- auto o_k_masked = xt::where(
- xt::view(t_msk, xt::all(), xt::newaxis(), xt::all()),
- o_k, NAN
- );
-
- // compute variable one sample at a time
- bar_o = xt::zeros<double>({n_msk, n_exp, n_thr});
-
- for (int e = 0; e < n_exp; e++)
- {
- // apply the bootstrap sampling
- auto o_k_masked_sampled =
- xt::view(o_k_masked, xt::all(), xt::all(), b_exp[e]);
-
- // compute mean "climatology" relative frequency of the event
- // $\bar{o} = \frac{1}{n} \sum_{k=1}^{n} o_k$
- xt::view(bar_o, xt::all(), e, xt::all()) =
- xt::nanmean(o_k_masked_sampled, -1);
- }
- }
-
- // Determine forecast probability of threshold(s) exceedance to occur.
- //
- // \require q_prd:
- // Streamflow predictions.
- // shape: (members, time)
- // \require q_thr:
- // Streamflow exceedance threshold(s).
- // shape: (thresholds,)
- // \assign y_k:
- // Event probability forecast.
- // shape: (thresholds, time)
- void Evaluator::calc_y_k()
- {
- // determine if members have exceeded threshold(s)
- auto e_frc =
- q_prd
- >= xt::view(q_thr, xt::all(), xt::newaxis(), xt::newaxis());
-
- // calculate how many members have exceeded threshold(s)
- auto n_frc = xt::sum(e_frc, 1);
-
- // determine probability of threshold(s) exceedance
- // /!\ probability calculation dividing by n (the number of
- // members), not n+1 (the number of ranks) like in other metrics
- y_k = xt::cast<double>(n_frc) / n_mbr;
- }
-
- // Compute the forecast quantiles from the ensemble members.
- //
- // \require q_prd:
- // Streamflow predictions.
- // shape: (members, time)
- // \assign q_qnt:
- // Streamflow forecast quantiles.
- // shape: (quantiles, time)
- void Evaluator::calc_q_qnt()
- {
- q_qnt = xt::sort(q_prd, 0);
- }
- }
-}
diff --git a/src/probabilist/evaluator_quantiles.cpp b/src/probabilist/evaluator_quantiles.cpp
deleted file mode 100644
index c6f37e23a716251c046fe35d3c11e7c5a6b46f73..0000000000000000000000000000000000000000
--- a/src/probabilist/evaluator_quantiles.cpp
+++ /dev/null
@@ -1,146 +0,0 @@
-#include <unordered_map>
-#include <xtensor/xmath.hpp>
-#include <xtensor/xview.hpp>
-#include <xtensor/xoperation.hpp>
-
-#include "probabilist/evaluator.hpp"
-
-namespace eh = evalhyd;
-
-namespace evalhyd
-{
- namespace probabilist
- {
- // Compute the quantile scores for each time step.
- //
- // \require q_obs:
- // Streamflow observations.
- // shape: (time,)
- // \require q_qnt:
- // Streamflow quantiles.
- // shape: (quantiles, time)
- // \assign qs:
- // Quantile scores for each time step.
- // shape: (quantiles, time)
- void Evaluator::calc_qs()
- {
- // compute the quantile order $alpha$
- xt::xtensor<double, 1> alpha =
- xt::arange<double>(1., double(n_mbr + 1))
- / double(n_mbr + 1);
-
- // calculate the difference
- xt::xtensor<double, 2> diff = q_qnt - q_obs;
-
- // calculate the quantile scores
- qs = xt::where(
- diff > 0,
- 2 * (1 - xt::view(alpha, xt::all(), xt::newaxis())) * diff,
- - 2 * xt::view(alpha, xt::all(), xt::newaxis()) * diff
- );
- }
-
- // Compute the quantile score (QS).
- //
- // \require t_msk:
- // Temporal subsets of the whole record.
- // shape: (subsets, time)
- // \require qs:
- // Quantile scores for each time step.
- // shape: (quantiles, time)
- // \assign QS:
- // Quantile scores.
- // shape: (subsets, quantiles)
- void Evaluator::calc_QS()
- {
- // initialise output variable
- // shape: (subsets, quantiles)
- QS = xt::zeros<double>({n_msk, n_exp, n_mbr});
-
- // compute variable one mask at a time to minimise memory imprint
- for (int m = 0; m < n_msk; m++)
- {
- // apply the mask
- // (using NaN workaround until reducers work on masked_view)
- auto qs_masked = xt::where(xt::row(t_msk, m), qs, NAN);
-
- // compute variable one sample at a time
- for (int e = 0; e < n_exp; e++)
- {
- // apply the bootstrap sampling
- auto qs_masked_sampled =
- xt::view(qs_masked, xt::all(), b_exp[e]);
-
- // calculate the mean over the time steps
- // $QS = \frac{1}{n} \sum_{k=1}^{n} qs$
- xt::view(QS, m, e, xt::all()) =
- xt::nanmean(qs_masked_sampled, -1);
- }
- }
- }
-
- // Compute the continuous rank probability score(s) based
- // on quantile scores for each time step, and integrating using the
- // trapezoidal rule.
- //
- // /!\ The number of quantiles must be sufficiently large so that the
- // cumulative distribution is smooth enough for the numerical
- // integration to be accurate.
- //
- // \require qs:
- // Quantile scores for each time step.
- // shape: (quantiles, time)
- // \assign crps:
- // CRPS for each time step.
- // shape: (1, time)
- void Evaluator::calc_crps()
- {
- // integrate with trapezoidal rule
- crps = xt::view(
- // xt::trapz(y, dx=1/(n+1), axis=0)
- xt::trapz(qs, 1./(double(n_mbr) + 1.), 0),
- xt::newaxis(), xt::all()
- );
- }
-
- // Compute the continuous rank probability score (CRPS) based
- // on quantile scores.
- //
- // \require t_msk:
- // Temporal subsets of the whole record.
- // shape: (subsets, time)
- // \require crps:
- // CRPS for each time step.
- // shape: (1, time)
- // \assign CRPS:
- // CRPS.
- // shape: (subsets,)
- void Evaluator::calc_CRPS()
- {
- // initialise output variable
- // shape: (subsets,)
- CRPS = xt::zeros<double>({n_msk, n_exp});
-
- // compute variable one mask at a time to minimise memory imprint
- for (int m = 0; m < n_msk; m++)
- {
- // apply the mask
- // (using NaN workaround until reducers work on masked_view)
- auto crps_masked = xt::where(xt::row(t_msk, m), crps, NAN);
-
- // compute variable one sample at a time
- for (int e = 0; e < n_exp; e++)
- {
- // apply the bootstrap sampling
- auto crps_masked_sampled =
- xt::view(crps_masked, xt::all(), b_exp[e]);
-
- // calculate the mean over the time steps
- // $CRPS = \frac{1}{n} \sum_{k=1}^{n} crps$
- xt::view(CRPS, m, e) =
- xt::squeeze(xt::nanmean(crps_masked_sampled, -1));
- }
- }
- }
- }
-}
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 436ab16c76a7128a95ca96e8b878b8dd14fb89d8..1ef9682e651c7db211bd5a8f7447341a42437f5a 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -24,7 +24,7 @@ set_target_properties(
target_include_directories(
evalhyd_tests
PRIVATE
- ${CMAKE_SOURCE_DIR}/src
+ ${CMAKE_SOURCE_DIR}/include/evalhyd
)
target_compile_definitions(
diff --git a/tests/test_uncertainty.cpp b/tests/test_uncertainty.cpp
index 1bfb312240310b6ec5fe3849b65127644394c9e6..d87a97288213c98b3e38876def1596bb63f5583f 100644
--- a/tests/test_uncertainty.cpp
+++ b/tests/test_uncertainty.cpp
@@ -5,7 +5,7 @@
#include <xtensor/xrandom.hpp>
#include <xtensor/xio.hpp>
-#include "uncertainty.hpp"
+#include "detail/uncertainty.hpp"
TEST(UncertaintyTests, TestBootstrapGenerator)
{