regression tests for all methods

SALib/tests/test_regression.py

@@ -7,6 +7,14 @@
 
 from ..sample.morris import sample
 from ..analyze import morris
+from SALib.sample import saltelli
+from SALib.analyze import sobol
+from SALib.sample import fast_sampler
+from SALib.analyze import fast
+from SALib.sample import finite_diff
+from SALib.analyze import dgsm
+from SALib.sample import latin
+from SALib.analyze import delta
 from ..test_functions import Ishigami
 from ..util import read_param_file
 
@@ -76,3 +84,59 @@ def test_regression_morris_optimal():
                         num_levels=4, grid_jump=2)
 
     assert_allclose(Si['mu_star'], [8.1, 2.2, 5.4], rtol=10)
+
+
+def test_regression_sobol():
+    param_file = 'SALib/test_functions/params/Ishigami.txt'
+    param_values = saltelli.sample(10000, param_file, calc_second_order=True)
+
+    Y = Ishigami.evaluate(param_values)
+    np.savetxt('model_output.txt', Y, delimiter=' ')
+
+    Si = sobol.analyze(param_file, 'model_output.txt', column=0,
+                       calc_second_order=True, conf_level=0.95, print_to_console=False)
+
+    assert_allclose(Si['S1'], [0.31, 0.44, 0.00], atol=5e-2, rtol=1e-1)
+    assert_allclose(Si['ST'], [0.55, 0.44, 0.24], atol=5e-2, rtol=1e-1)
+    assert_allclose([Si['S2'][0][1], Si['S2'][0][2], Si['S2'][1][2]], [0.00, 0.25, 0.00], atol=5e-2, rtol=1e-1)
+
+
+def test_regression_fast():
+    param_file = 'SALib/test_functions/params/Ishigami.txt'
+    param_values = fast_sampler.sample(1000, param_file)
+
+    Y = Ishigami.evaluate(param_values)
+    np.savetxt("model_output.txt", Y, delimiter=' ')
+
+    Si = fast.analyze(param_file, 'model_output.txt', column=0, print_to_console=True)
+    assert_allclose(Si['S1'], [0.31, 0.44, 0.00], atol=5e-2, rtol=1e-1)
+    assert_allclose(Si['ST'], [0.55, 0.44, 0.24], atol=5e-2, rtol=1e-1)
+
+
+def test_regression_dgsm():
+    param_file = 'SALib/test_functions/params/Ishigami.txt'
+    param_values = finite_diff.sample(10000, param_file, delta=0.001)
+
+    np.savetxt('model_input.txt', param_values, delimiter=' ')
+    Y = Ishigami.evaluate(param_values)
+    np.savetxt('model_output.txt', Y, delimiter=' ')
+
+    Si = dgsm.analyze(param_file, 'model_input.txt', 'model_output.txt',
+                      column=0, conf_level=0.95, print_to_console=False)
+
+    assert_allclose(Si['dgsm'], [2.229, 7.066, 3.180], atol=5e-2, rtol=1e-1)
+
+
+def test_regression_delta():
+    param_file = 'SALib/test_functions/params/Ishigami.txt'
+    param_values = latin.sample(10000, param_file)
+
+    np.savetxt('model_input.txt', param_values, delimiter=' ')
+    Y = Ishigami.evaluate(param_values)
+    np.savetxt('model_output.txt', Y, delimiter=' ')
+
+    Si = delta.analyze(param_file, 'model_input.txt', 'model_output.txt',
+                   column=0, num_resamples=10, conf_level=0.95, print_to_console=True)
+
+    assert_allclose(Si['delta'], [0.210, 0.358, 0.155], atol=5e-2, rtol=1e-1)
+    assert_allclose(Si['S1'], [0.31, 0.44, 0.00], atol=5e-2, rtol=1e-1)

examples/delta/delta.py

@@ -11,6 +11,6 @@
 # Perform the sensitivity analysis using the model output
 # Specify which column of the output file to analyze (zero-indexed)
 Si = delta.analyze(param_file, 'model_input.txt', 'model_output.txt',
-                   column=0, num_resamples=10, conf_level=0.95, print_to_console=False)
+                   column=0, num_resamples=10, conf_level=0.95, print_to_console=True)
 # Returns a dictionary with keys 'delta', 'delta_conf', 'S1', 'S1_conf'
 print(str(Si['delta']))

examples/dgsm/dgsm.py

@@ -23,7 +23,7 @@
 # Perform the sensitivity analysis using the model output
 # Specify which column of the output file to analyze (zero-indexed)
 Si = dgsm.analyze(param_file, 'model_input.txt', 'model_output.txt',
-                  column=0, conf_level=0.95, print_to_console=False)
+                  column=0, conf_level=0.95, print_to_console=True)
 # Returns a dictionary with keys 'vi', 'vi_std', 'dgsm', and 'dgsm_conf'
 # e.g. Si['vi'] contains the sensitivity measure for each parameter, in
 # the same order as the parameter file

examples/fast/fast.py

@@ -20,7 +20,7 @@
 # Perform the sensitivity analysis using the model output
 # Specify which column of the output file to analyze (zero-indexed)
 Si = fast.analyze(
-    param_file, 'model_output.txt', column=0, print_to_console=False)
+    param_file, 'model_output.txt', column=0, print_to_console=True)
 # Returns a dictionary with keys 'S1' and 'ST'
 # e.g. Si['S1'] contains the first-order index for each parameter, in the
 # same order as the parameter file