diff --git a/include/evalhyd/probabilist.hpp b/include/evalhyd/probabilist.hpp
index f66ade2b033a351acf986c0e5e7db7fa914d69c2..95cfdd914dc1a81203450fd25259290e099f1073 100644
--- a/include/evalhyd/probabilist.hpp
+++ b/include/evalhyd/probabilist.hpp
@@ -42,7 +42,7 @@ namespace evalhyd
// check that the metrics to be computed are valid
utils::check_metrics(
metrics,
- {"bs", "bss", "bs_crd", "bs_lbd", "QS", "CRPS"}
+ {"BS", "BSS", "BS_CRD", "BS_LBD", "QS", "CRPS"}
);
// instantiate probabilist evaluator
@@ -53,14 +53,14 @@ namespace evalhyd
std::unordered_map<std::string, std::vector<std::string>> dep;
// register potentially recurring computation elements across metrics
- elt["bs"] = {"o_k", "y_k"};
- elt["bss"] = {"o_k", "bar_o"};
- elt["bs_crd"] = {"o_k", "bar_o", "y_k"};
- elt["bs_lbd"] = {"o_k", "y_k"};
- elt["qs"] = {"q_qnt"};
+ elt["BS"] = {"o_k", "y_k"};
+ elt["BSS"] = {"o_k", "bar_o"};
+ elt["BS_CRD"] = {"o_k", "bar_o", "y_k"};
+ elt["BS_LBD"] = {"o_k", "y_k"};
+ elt["QS"] = {"q_qnt"};
// register nested metrics (i.e. metric dependent on another metric)
- dep["bss"] = {"bs"};
+ dep["BSS"] = {"BS"};
dep["QS"] = {"qs"};
dep["CRPS"] = {"qs", "crps"};
@@ -86,8 +86,8 @@ namespace evalhyd
// pre-compute required dep
for (const auto& dependency : req_dep)
{
- if ( dependency == "bs" )
- evaluator.calc_bs();
+ if ( dependency == "BS" )
+ evaluator.calc_BS();
else if ( dependency == "qs" )
evaluator.calc_qs();
else if ( dependency == "crps" )
@@ -99,33 +99,33 @@ namespace evalhyd
for (const auto& metric : metrics)
{
- if ( metric == "bs" )
+ if ( metric == "BS" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
- evaluator.calc_bs();
- r.emplace_back(evaluator.bs);
+ evaluator.calc_BS();
+ r.emplace_back(evaluator.BS);
}
- else if ( metric == "bss" )
+ else if ( metric == "BSS" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
- evaluator.calc_bss();
- r.emplace_back(evaluator.bss);
+ evaluator.calc_BSS();
+ r.emplace_back(evaluator.BSS);
}
- else if ( metric == "bs_crd" )
+ else if ( metric == "BS_CRD" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
- evaluator.calc_bs_crd();
- r.emplace_back(evaluator.bs_crd);
+ evaluator.calc_BS_CRD();
+ r.emplace_back(evaluator.BS_CRD);
}
- else if ( metric == "bs_lbd" )
+ else if ( metric == "BS_LBD" )
{
if (std::find(req_dep.begin(), req_dep.end(), metric)
== req_dep.end())
- evaluator.calc_bs_lbd();
- r.emplace_back(evaluator.bs_lbd);
+ evaluator.calc_BS_LBD();
+ r.emplace_back(evaluator.BS_LBD);
}
else if ( metric == "QS" )
{
diff --git a/include/evalhyd/probabilist/evaluator.h b/include/evalhyd/probabilist/evaluator.h
index d35730de2420d049b8f92fab6c1614d60d2190da..c5d5b990664706c8162408cfd3aeab177837cd86 100644
--- a/include/evalhyd/probabilist/evaluator.h
+++ b/include/evalhyd/probabilist/evaluator.h
@@ -34,10 +34,10 @@ namespace evalhyd
xt::xtensor<double, 2> crps;
// members for evaluation metrics
- xt::xtensor<double, 2> bs;
- xt::xtensor<double, 2> bs_crd;
- xt::xtensor<double, 2> bs_lbd;
- xt::xtensor<double, 2> bss;
+ xt::xtensor<double, 2> BS;
+ xt::xtensor<double, 2> BS_CRD;
+ xt::xtensor<double, 2> BS_LBD;
+ xt::xtensor<double, 2> BSS;
xt::xtensor<double, 2> QS;
xt::xtensor<double, 2> CRPS;
@@ -54,10 +54,10 @@ namespace evalhyd
void calc_crps();
// methods to compute metrics
- void calc_bs();
- void calc_bs_crd();
- void calc_bs_lbd();
- void calc_bss();
+ void calc_BS();
+ void calc_BS_CRD();
+ void calc_BS_LBD();
+ void calc_BSS();
void calc_QS();
void calc_CRPS();
};
diff --git a/include/evalhyd/probabilist/evaluator_brier.cpp b/include/evalhyd/probabilist/evaluator_brier.cpp
index cbfe7e36bb23a56332f80b6b20f54cd63718bbf1..af2372b89fc791a90267b53299a5957fbfda5214 100644
--- a/include/evalhyd/probabilist/evaluator_brier.cpp
+++ b/include/evalhyd/probabilist/evaluator_brier.cpp
@@ -25,14 +25,14 @@ namespace evalhyd
// \require y_k:
// Event probability forecast.
// shape: (thresholds, time)
- // \assign bs:
+ // \assign BS:
// 2D array of Brier score for each threshold.
// shape: (thresholds, 1)
- void Evaluator::calc_bs()
+ void Evaluator::calc_BS()
{
// return computed Brier score(s)
// $BS = \frac{1}{n} \sum_{k=1}^{n} (o_k - y_k)^2$
- bs = xt::mean(xt::square(o_k - y_k), -1, xt::keep_dims);
+ BS = xt::mean(xt::square(o_k - y_k), -1, xt::keep_dims);
}
// Compute the calibration-refinement decomposition of the Brier score
@@ -49,11 +49,11 @@ namespace evalhyd
// \require y_k:
// Event probability forecast.
// shape: (thresholds, time)
- // \assign bs_crd:
+ // \assign BS_CRD:
// 2D array of Brier score components (reliability, resolution,
// uncertainty) for each threshold.
// shape: (thresholds, components)
- void Evaluator::calc_bs_crd()
+ void Evaluator::calc_BS_CRD()
{
// declare internal variables
// shape: (bins, thresholds, time)
@@ -71,7 +71,7 @@ namespace evalhyd
// initialise output variable
// shape: (components, thresholds)
- bs_crd = xt::zeros<double>({n_thr, n_cmp});
+ BS_CRD = xt::zeros<double>({n_thr, n_cmp});
// compute range of forecast values $y_i$
y_i = xt::arange<double>(double(n_mbr + 1)) / n_mbr;
@@ -103,7 +103,7 @@ namespace evalhyd
// calculate reliability =
// $\frac{1}{n} \sum_{i=1}^{I} N_i (y_i - \bar{o_i})^2$
- xt::col(bs_crd, 0) =
+ xt::col(BS_CRD, 0) =
xt::sum(
xt::square(
xt::view(y_i, xt::all(), xt::newaxis())
@@ -114,7 +114,7 @@ namespace evalhyd
// calculate resolution =
// $\frac{1}{n} \sum_{i=1}^{I} N_i (\bar{o_i} - \bar{o})^2$
- xt::col(bs_crd, 1) =
+ xt::col(BS_CRD, 1) =
xt::sum(
xt::square(
bar_o_i - bar_o
@@ -123,7 +123,7 @@ namespace evalhyd
) / n;
// calculate uncertainty = $\bar{o} (1 - \bar{o})$
- xt::col(bs_crd, 2) = bar_o * (1 - bar_o);
+ xt::col(BS_CRD, 2) = bar_o * (1 - bar_o);
}
// Compute the likelihood-base rate decomposition of the Brier score
@@ -137,11 +137,11 @@ namespace evalhyd
// \require y_k:
// Event probability forecast.
// shape: (thresholds, time)
- // \return bs_lbd:
+ // \return BS_LBD:
// 2D array of Brier score components (type 2 bias, discrimination,
// sharpness) for each threshold.
// shape: (thresholds, components)
- void Evaluator::calc_bs_lbd()
+ void Evaluator::calc_BS_LBD()
{
// declare internal variables
// shape: (bins, thresholds, time)
@@ -160,7 +160,7 @@ namespace evalhyd
// declare and initialise output variable
// shape: (components, thresholds)
- bs_lbd = xt::zeros<double>({n_thr, n_cmp});
+ BS_LBD = xt::zeros<double>({n_thr, n_cmp});
// set the range of observed values $o_j$
o_j = {0., 1.};
@@ -196,7 +196,7 @@ namespace evalhyd
// calculate type 2 bias =
// $\frac{1}{n} \sum_{j=1}^{J} M_j (o_j - \bar{y_j})^2$
- xt::col(bs_lbd, 0) =
+ xt::col(BS_LBD, 0) =
xt::sum(
xt::square(
xt::view(o_j, xt::all(), xt::newaxis())
@@ -207,7 +207,7 @@ namespace evalhyd
// calculate discrimination =
// $\frac{1}{n} \sum_{j=1}^{J} M_j (\bar{y_j} - \bar{y})^2$
- xt::col(bs_lbd, 1) =
+ xt::col(BS_LBD, 1) =
xt::sum(
xt::square(
bar_y_j - bar_y
@@ -217,7 +217,7 @@ namespace evalhyd
// calculate sharpness =
// $\frac{1}{n} \sum_{k=1}^{n} (\bar{y_k} - \bar{y})^2$
- xt::col(bs_lbd, 2) =
+ xt::col(BS_LBD, 2) =
xt::sum(
xt::square(
y_k -
@@ -235,13 +235,13 @@ namespace evalhyd
// \require bar_o:
// Mean event observed outcome.
// shape: (thresholds,)
- // \require bs:
+ // \require BS:
// Brier score(s).
// shape: (thresholds,)
- // \assign bss:
+ // \assign BSS:
// 2D array of Brier skill score for each threshold.
// shape: (thresholds, 1)
- void Evaluator::calc_bss()
+ void Evaluator::calc_BSS()
{
// calculate reference Brier score(s)
// $BS_{ref} = \frac{1}{n} \sum_{k=1}^{n} (o_k - \bar{o})^2$
@@ -256,7 +256,7 @@ namespace evalhyd
// return computed Brier skill score(s)
// $BSS = 1 - \frac{BS}{BS_{ref}}
- bss = 1 - (bs / bs_ref);
+ BSS = 1 - (BS / bs_ref);
}
}
}
diff --git a/tests/test_probabilist.cpp b/tests/test_probabilist.cpp
index 67870f41acdbcac69264d3c47ef1c3a52574cfbf..8cb117467d8ee9e6cde87cb989bb7426180606c5 100644
--- a/tests/test_probabilist.cpp
+++ b/tests/test_probabilist.cpp
@@ -24,7 +24,7 @@ TEST(ProbabilistTests, TestBrier) {
std::vector<xt::xtensor<double, 2>> metrics =
evalhyd::probabilist::evaluate(
- {"bs", "bss", "bs_crd", "bs_lbd"},
+ {"BS", "BSS", "BS_CRD", "BS_LBD"},
xt::transpose(observed), xt::transpose(forecast),
thresholds
);