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  • Thibault Hallouin's avatar
    allow masking conditions to be specified on predictions · f31664dd
    Thibault Hallouin authored 
    An earlier implementation of the masking conditions assumed that the
conditions on streamflow would only be on the observations, but this is
not always the case. For example, reliability scores cannot be done on
the observed streamflow and need to be performed on the predicted
streamflow. So this is now possible as the condition syntax is changed
and now *q_obs*/*q_prd_median*/*q_prd_mean* in place of *q*.
    f31664dd
test_determinist.cpp 10.51 KiB
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#include <fstream>
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#include <vector>
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#include <array>
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#include <gtest/gtest.h>
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#include <xtensor/xtensor.hpp>
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#include <xtensor/xview.hpp>
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#include <xtensor/xmanipulation.hpp>
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#include <xtensor/xcsv.hpp>
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#include "evalhyd/evald.hpp"
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using namespace xt::placeholders;  // required for `_` to work
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TEST(DeterministTests, TestMetrics2D)
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{
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    // read in data
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    std::ifstream ifs;
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    ifs.open("./data/q_obs.csv");
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    xt::xtensor<double, 2> observed =xt::load_csv<int>(ifs);
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    ifs.close();
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    ifs.open("./data/q_prd.csv");
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    xt::xtensor<double, 2> predicted = xt::view(
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            xt::load_csv<double>(ifs), xt::range(0, 5), xt::all()
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    );
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    ifs.close();
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    // compute scores (with 2D tensors)
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    std::vector<xt::xarray<double>> metrics =
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            evalhyd::evald<2>(
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                    observed, predicted, {"RMSE", "NSE", "KGE", "KGEPRIME"}
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            );
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    // check results on all metrics
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    xt::xtensor<double, 2> rmse =
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            {{777.034272},
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             {776.878479},
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             {777.800217},
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             {778.151082},
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             {778.61487}};
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    EXPECT_TRUE(xt::allclose(metrics[0], rmse));
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    xt::xtensor<double, 2> nse =
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            {{0.718912},
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             {0.719025},
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             {0.718358},
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             {0.718104},
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             {0.717767}};
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    EXPECT_TRUE(xt::allclose(metrics[1], nse));
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    xt::xtensor<double, 2> kge =
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            {{0.748088},
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             {0.746106},
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             {0.744111},
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             {0.743011},
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             {0.741768}};
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    EXPECT_TRUE(xt::allclose(metrics[2], kge));
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    xt::xtensor<double, 2> kgeprime =
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            {{0.813141},
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             {0.812775},
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             {0.812032},
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             {0.811787},
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             {0.811387}};
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    EXPECT_TRUE(xt::allclose(metrics[3], kgeprime));
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}
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TEST(DeterministTests, TestMetrics1D)
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{
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    // read in data
7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140
std::ifstream ifs; ifs.open("./data/q_obs.csv"); xt::xtensor<double, 1> observed = xt::squeeze(xt::load_csv<int>(ifs)); ifs.close(); ifs.open("./data/q_prd.csv"); xt::xtensor<double, 1> predicted = xt::row( xt::view(xt::load_csv<double>(ifs), xt::range(0, 5), xt::all()), 0 ); ifs.close(); // compute scores (with 1D tensors) std::vector<xt::xarray<double>> metrics = evalhyd::evald<1>( observed, predicted, {"RMSE", "NSE", "KGE", "KGEPRIME"} ); // check results on all metrics xt::xtensor<double, 1> rmse = {777.034272}; EXPECT_TRUE(xt::allclose(metrics[0], rmse)); xt::xtensor<double, 1> nse = {0.718912}; EXPECT_TRUE(xt::allclose(metrics[1], nse)); xt::xtensor<double, 1> kge = {0.748088}; EXPECT_TRUE(xt::allclose(metrics[2], kge)); xt::xtensor<double, 1> kgeprime = {0.813141}; EXPECT_TRUE(xt::allclose(metrics[3], kgeprime)); } TEST(DeterministTests, TestTransform) { // read in data std::ifstream ifs; ifs.open("./data/q_obs.csv"); xt::xtensor<double, 2> observed =xt::load_csv<int>(ifs); ifs.close(); ifs.open("./data/q_prd.csv"); xt::xtensor<double, 2> predicted = xt::view( xt::load_csv<double>(ifs), xt::range(0, 5), xt::all() ); ifs.close(); // compute and check results on square-rooted streamflow series std::vector<xt::xarray<double>> metrics = evalhyd::evald<2>(observed, predicted, {"NSE"}, "sqrt"); xt::xtensor<double, 2> nse_sqrt = {{0.882817}, {0.883023}, {0.883019}, {0.883029}, {0.882972}}; EXPECT_TRUE(xt::allclose(metrics[0], nse_sqrt)); // compute and check results on inverted streamflow series metrics = evalhyd::evald<2>(observed, predicted, {"NSE"}, "inv"); xt::xtensor<double, 2> nse_inv = {{0.737323}, {0.737404}, {0.737429}, {0.737546}, {0.737595}}; EXPECT_TRUE(xt::allclose(metrics[0], nse_inv)); // compute and check results on square-rooted streamflow series metrics = evalhyd::evald<2>(observed, predicted, {"NSE"}, "log");
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xt::xtensor<double, 2> nse_log = {{0.893344}, {0.893523}, {0.893585}, {0.893758}, {0.893793}}; EXPECT_TRUE(xt::allclose(metrics[0], nse_log)); // compute and check results on power-transformed streamflow series metrics = evalhyd::evald<2>(observed, predicted, {"NSE"}, "pow", 0.2); xt::xtensor<double, 2> nse_pow = {{0.899207}, {0.899395}, {0.899451}, {0.899578}, {0.899588}}; EXPECT_TRUE(xt::allclose(metrics[0], nse_pow)); } TEST(DeterministTests, TestMasks) { // read in data std::ifstream ifs; ifs.open("./data/q_obs.csv"); xt::xtensor<double, 2> observed = xt::load_csv<int>(ifs); ifs.close(); ifs.open("./data/q_prd.csv"); xt::xtensor<double, 2> predicted = xt::load_csv<double>(ifs); ifs.close(); // generate temporal subset by dropping 20 first time steps xt::xtensor<double, 2> masks = xt::ones<bool>({std::size_t {1}, std::size_t {observed.size()}}); xt::view(masks, 0, xt::range(0, 20)) = 0; // compute scores using masks to subset whole record std::vector<std::string> metrics = {"RMSE", "NSE", "KGE", "KGEPRIME"}; std::vector<xt::xarray<double>> metrics_masked = evalhyd::evald<2>(observed, predicted, metrics, "none", 1, -9, masks); // compute scores on pre-computed subset of whole record xt::xtensor<double, 2> obs = xt::view(observed, xt::all(), xt::range(20, _)); xt::xtensor<double, 2> prd = xt::view(predicted, xt::all(), xt::range(20, _)); std::vector<xt::xarray<double>> metrics_subset = evalhyd::evald<2>(obs, prd, metrics); // check results are identical for (int m = 0; m < metrics.size(); m++) { EXPECT_TRUE(xt::allclose(metrics_masked[m], metrics_subset[m])) << "Failure for (" << metrics[m] << ")"; } } TEST(DeterministTests, TestMaskingConditions) { std::vector<std::string> metrics = {"RMSE", "NSE", "KGE", "KGEPRIME"}; // read in data std::ifstream ifs; ifs.open("./data/q_obs.csv"); xt::xtensor<double, 2> observed = xt::load_csv<int>(ifs);
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ifs.close(); ifs.open("./data/q_prd.csv"); xt::xtensor<double, 2> predicted = xt::load_csv<double>(ifs); ifs.close(); // generate dummy empty masks required to access next optional argument xt::xtensor<bool, 2> masks; // conditions on streamflow ________________________________________________ // compute scores using masking conditions on streamflow to subset whole record xt::xtensor<std::array<char, 32>, 2> q_conditions ={ {{"q_obs{<2000,>3000}"}} }; std::vector<xt::xarray<double>> metrics_q_conditioned = evalhyd::evald<2>( observed, predicted, metrics, "none", 1, -9, masks, q_conditions ); // compute scores using "NaN-ed" time indices where conditions on streamflow met std::vector<xt::xarray<double>> metrics_q_preconditioned = evalhyd::evald<2>( xt::where((observed < 2000) | (observed > 3000), observed, NAN), predicted, metrics ); // check results are identical for (int m = 0; m < metrics.size(); m++) { EXPECT_TRUE( xt::allclose( metrics_q_conditioned[m], metrics_q_preconditioned[m] ) ) << "Failure for (" << metrics[m] << ")"; } // conditions on temporal indices __________________________________________ // compute scores using masking conditions on time indices to subset whole record xt::xtensor<std::array<char, 32>, 2> t_conditions = { {{"t{0,1,2,3,4,5:97,97,98,99}"}} }; std::vector<xt::xarray<double>> metrics_t_conditioned = evalhyd::evald<2>( observed, predicted, metrics, "none", 1, -9, masks, t_conditions ); // compute scores on already subset time series std::vector<xt::xarray<double>> metrics_t_subset = evalhyd::evald<2>( xt::view(observed, xt::all(), xt::range(0, 100)), xt::view(predicted, xt::all(), xt::range(0, 100)), metrics ); // check results are identical for (int m = 0; m < metrics.size(); m++) { EXPECT_TRUE( xt::allclose( metrics_t_conditioned[m], metrics_t_subset[m] ) ) << "Failure for (" << metrics[m] << ")"; }
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} TEST(DeterministTests, TestMissingData) { std::vector<std::string> metrics = {"RMSE", "NSE", "KGE", "KGEPRIME"}; // read in data std::ifstream ifs; ifs.open("./data/q_obs.csv"); xt::xtensor<double, 2> observed = xt::load_csv<int>(ifs); ifs.close(); ifs.open("./data/q_prd.csv"); xt::xtensor<double, 2> predicted = xt::view( xt::load_csv<double>(ifs), xt::range(0, 5), xt::all() ); ifs.close(); // add some missing observations artificially by assigning NaN values xt::view(observed, xt::all(), xt::range(0, 20)) = NAN; // add some missing predictions artificially by assigning NaN values xt::view(observed, 0, xt::range(20, 23)) = NAN; xt::view(observed, 1, xt::range(20, 26)) = NAN; xt::view(observed, 2, xt::range(20, 29)) = NAN; xt::view(observed, 3, xt::range(20, 32)) = NAN; xt::view(observed, 4, xt::range(20, 35)) = NAN; // compute metrics with observations containing NaN values std::vector<xt::xarray<double>> metrics_nan = evalhyd::evald<2>(observed, predicted, metrics); for (int m = 0; m < metrics.size(); m++) { for (int p = 0; p < predicted.shape(0); p++) { // compute metrics on subset of observations and predictions (i.e. // eliminating region with NaN in observations or predictions manually) xt::xtensor<double, 1> obs = xt::view(observed, 0, xt::range(20+(3*(p+1)), _)); xt::xtensor<double, 1> prd = xt::view(predicted, p, xt::range(20+(3*(p+1)), _)); std::vector<xt::xarray<double>> metrics_sbs = evalhyd::evald<1>(obs, prd, {metrics[m]}); // compare to check results are the same EXPECT_TRUE( xt::allclose( xt::view(metrics_nan[m], p), metrics_sbs[0] ) ) << "Failure for (" << metrics[m] << ")"; } } }