Use a fixed seed for convergent cross mapping + infotheory SPIs.

Author: joshuabmoore

pyspi/statistics/causal.py

@@ -125,6 +125,7 @@ def _from_cache(self, data):
                         target=targname,
                         libSizes=lib_sizes,
                         sample=100,
+                        seed=42,
                     )
                     ccmf[_i, _j] = ccm_df.iloc[:, 1].values[: (nlibs + 1)]
                     ccmf[_j, _i] = ccm_df.iloc[:, 2].values[: (nlibs + 1)]

pyspi/statistics/infotheory.py

@@ -38,6 +38,7 @@ class JIDTBase(Unsigned):
     _TAU_SEARCH_MAX_PROP_NAME = "AUTO_EMBED_TAU_SEARCH_MAX"
     _BIAS_CORRECTION = "BIAS_CORRECTION"
     _NORMALISE = "NORMALISE"
+    _SEED = "NOISE_SEED"
 
     _base_class = jp.JPackage("infodynamics.measures.continuous")
 
@@ -104,6 +105,7 @@ def _setup(self, calc):
             calc.setProperty(self._NNK_PROP_NAME, str(self._prop_k))
 
         calc.setProperty(self._BIAS_CORRECTION, "false")
+        calc.setProperty(self._SEED, "42")
 
         return calc
 

tests/conftest.py

@@ -30,7 +30,7 @@ def pytest_sessionfinish(session, exitstatus):
 
     # print problematic SPIs in table format
     if spi_warnings:
-        print(f"\nDetected {len(spi_warnings)} SPI(s) with outputs exceeding the specified 2 sigma threshold.\n")
+        print(f"\nDetected {len(spi_warnings)} SPI(s) with outputs exceeding the specified 1 sigma threshold.\n")
 
         # table header
         print(f"{'SPI':<25}{'Cat':<10}{'Max ZSc.':>10}{'# Exceed. Pairs':>20}{'Unq. Pairs':>15}")

tests/test_SPIs.py

@@ -55,7 +55,7 @@ def pytest_generate_tests(metafunc):
 
 def test_mpi(est, est_ob, mpi_benchmark, mpi_new, spi_warning_logger):
     """Run the benchmarking tests."""
-    zscore_threshold = 2 # 2 sigma
+    zscore_threshold = 1 # 2 sigma
 
     # separate the the mean and std. dev tables for the benchmark
     mean_table = mpi_benchmark['mean']