Merge pull request #405 from UCL-CCS/feature/decrease_needed_memory

Feature/decrease needed memory (Issue 404)

.readthedocs.yaml

@@ -0,0 +1,35 @@
+# Read the Docs configuration file for Sphinx projects
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+    # You can also specify other tool versions:
+    # nodejs: "20"
+    # rust: "1.70"
+    # golang: "1.20"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/source/conf.py
+  # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs
+  # builder: "dirhtml"
+  # Fail on all warnings to avoid broken references
+  # fail_on_warning: true
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+#    - pdf
+#    - epub
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+# python:
+#    install:
+#    - requirements: docs/requirements.txt

easyvvuq/analysis/pce_analysis.py

@@ -182,7 +182,7 @@ def get_distribution(self, qoi):
 
 class PCEAnalysis(BaseAnalysisElement):
 
-    def __init__(self, sampler=None, qoi_cols=None, sampling=False):
+    def __init__(self, sampler=None, qoi_cols=None, sampling=False, CorrelationMatrices=True, OutputDistributions=True):
         """Analysis element for polynomial chaos expansion (PCE).
 
         Parameters
@@ -193,7 +193,13 @@ def __init__(self, sampler=None, qoi_cols=None, sampling=False):
             Column names for quantities of interest (for which analysis is
             performed).
         sampling : True if chaospy sampling method to be used for calculating
-            statitical quantities; otherwise [default] the pce coefficients are used
+            statistical quantities; otherwise [default] the pce coefficients are used
+        CorrelationMatrices : boolean
+            if False then disable the calculation of the Correlation Matrices, otherwise 
+            [default] calculate them
+        OutputDistributions : boolean
+            if False then disable the calculation of the Output Distributions, otherwise
+            [default] calculate them
         """
 
         if sampler is None:
@@ -211,6 +217,8 @@ def __init__(self, sampler=None, qoi_cols=None, sampling=False):
         self.sampling = sampling
         self.output_type = OutputType.SUMMARY
         self.sampler = sampler
+        self.CorrelationMatrices = CorrelationMatrices
+        self.OutputDistributions = OutputDistributions
 
     def element_name(self):
         """Name for this element for logging purposes.
@@ -506,23 +514,31 @@ def build_surrogate_der(Y_hat, verbose=False):
 
             # Correlation matrix
             try:
-                warnings.warn(f"Skipping computation of cp.Corr", RuntimeWarning)
                 if self.sampler._is_dependent:
+                    warnings.warn(f"Skipping computation of cp.Corr", RuntimeWarning)
                     results['correlation_matrices'][k] = None
                 else:
-                    results['correlation_matrices'][k] = cp.Corr(fit, self.sampler.distribution)
+                    if self.CorrelationMatrices:
+                        results['correlation_matrices'][k] = cp.Corr(fit, self.sampler.distribution)
+                    else:
+                        warnings.warn(f"Skipping computation of cp.Corr", RuntimeWarning)
+                        results['correlation_matrices'][k] = None
             except Exception as e:
                 print ('Error %s for %s when computing cp.Corr()'% (e.__class__.__name__, k))
                 results['correlation_matrices'][k] = None
 
 
             # Output distributions
             try:
-                warnings.warn(f"Skipping computation of cp.QoI_Dist", RuntimeWarning)
                 if self.sampler._is_dependent:
+                    warnings.warn(f"Skipping computation of cp.QoI_Dist", RuntimeWarning)
                     results['output_distributions'][k] = None
                 else:
-                    results['output_distributions'][k] = cp.QoI_Dist( fit, self.sampler.distribution)
+                    if self.OutputDistributions:
+                        results['output_distributions'][k] = cp.QoI_Dist( fit, self.sampler.distribution)
+                    else:
+                        warnings.warn(f"Skipping computation of cp.QoI_Dist", RuntimeWarning)
+                        results['output_distributions'][k] = None                        
             except Exception as e:
                 print ('Error %s for %s when computing cp.QoI_Dist()'% (e.__class__.__name__, k))
 #                from traceback import print_exc