Merge pull request #28 from HEP-PBSP/fix-python-version-test

Fix tests

Author: LucaMantani

.github/workflows/tests.yml

@@ -14,8 +14,6 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-      matrix:
-        python-version: ["3.11"]
 
     steps:
     - uses: actions/checkout@v3

ci_environment.yml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 
 dependencies:
-  - python >= 3.11
+  - python >= 3.11,<3.14
   - mpich
   - lhapdf
   - pandoc

environment.yml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 
 dependencies:
-  - python >= 3.11
+  - python >= 3.11,<3.14
   - mpich
   - lhapdf
   - pandoc

wmin/runcards/shift_lhadf_members.py

@@ -7,6 +7,7 @@
   ...
 If a file with index 0000 exists, it will be deleted before renaming to avoid collisions.
 """
+
 import os
 import re
 import argparse

wmin/tests/regression_runcards/test_fit_wmin_mc_dis_L0.yaml

@@ -27,8 +27,14 @@ positivity:                            # Positivity datasets, used in the positi
   posdatasets:
   - {dataset: POSF2U, maxlambda: 1e6}
 
-alpha: 1e-7                           
-lambda_positivity: 0                 
+positivity_penalty_settings:
+  positivity_penalty: False
+  alpha: 1e-7                           
+  lambda_positivity: 0  
+
+# Integrability Settings
+integrability_settings:
+  integrability: False                  
 
 use_fit_t0: True                       # Whether the t0 covariance is used in the chi2 loss.
 t0pdfset: NNPDF40_nnlo_as_01180         # The t0 PDF used to build the t0 covariance matrix.

wmin/tests/regression_runcards/test_fit_wmin_mc_had_L0.yaml

@@ -27,8 +27,14 @@ positivity:                            # Positivity datasets, used in the positi
   posdatasets:
   - {dataset: POSF2U, maxlambda: 1e6}
 
-alpha: 1e-7                           
-lambda_positivity: 0                 
+positivity_penalty_settings:
+  positivity_penalty: False
+  alpha: 1e-7                           
+  lambda_positivity: 0  
+
+# Integrability Settings
+integrability_settings:
+  integrability: False                 
 
 use_fit_t0: True                       # Whether the t0 covariance is used in the chi2 loss.
 t0pdfset: NNPDF40_nnlo_as_01180         # The t0 PDF used to build the t0 covariance matrix.

wmin/tests/test_wmin_utils.py

@@ -15,9 +15,10 @@
 mock_penalty_posdata = (
     lambda pdf, alpha, lambda_positivity, fast_kernel_arrays: jnp.array([0])
 )
-central_inv_covmat_index = Mock()
-central_inv_covmat_index.central_values = jnp.ones(N_MOCK_DATA)
-central_inv_covmat_index.inv_covmat = jnp.eye(N_MOCK_DATA)
+central_covmat_index = Mock()
+central_covmat_index.central_values = jnp.ones(N_MOCK_DATA)
+central_covmat_index.covmat = jnp.eye(N_MOCK_DATA)
+central_covmat_index.central_values_idx = jnp.arange(N_MOCK_DATA)
 
 fast_kernel_arrays = ((jnp.ones(N_MOCK_DATA)),)
 positivity_fast_kernel_arrays = ((jnp.ones(N_MOCK_DATA)),)
@@ -58,7 +59,7 @@ def mock_bayesian_prior(rng):
 
 SETUP = {
     "_penalty_posdata": mock_penalty_posdata,
-    "central_inv_covmat_index": central_inv_covmat_index,
+    "central_covmat_index": central_covmat_index,
     "fast_kernel_arrays": fast_kernel_arrays,
     "positivity_fast_kernel_arrays": positivity_fast_kernel_arrays,
     "_pred_data": mock_pred_data,

wmin/utils.py

@@ -10,14 +10,11 @@
 import jax
 import numpy as np
 import pandas as pd
-from colibri.likelihood import LogLikelihood
-from colibri.loss_functions import chi2
 
 from colibri.likelihood import LogLikelihood
 from colibri.constants import FLAVOUR_TO_ID_MAPPING, LHAPDF_XGRID
 from reportengine.table import table
 
-
 log = logging.getLogger(__name__)
 
 
@@ -85,7 +82,7 @@
 @table
 def likelihood_time(
     _penalty_posdata,
-    central_inv_covmat_index,
+    central_covmat_index,
     fast_kernel_arrays,
     positivity_fast_kernel_arrays,
     _pred_data,
@@ -142,17 +139,16 @@ def likelihood_time(
     res = RSS_MB()
     log.info(f"RSS: {res - init:.2f}MB")
 
-    central_values = central_inv_covmat_index.central_values
+    central_values = central_covmat_index.central_values
     ndata = len(central_values)
 
     log_likelihood = LogLikelihood(
-        central_inv_covmat_index,
+        central_covmat_index,
         pdf_model,
         FIT_XGRID,
         _pred_data,
         fast_kernel_arrays,
         positivity_fast_kernel_arrays,
-        chi2,
         _penalty_posdata,
         positivity_penalty_settings=positivity_penalty_settings,
         integrability_penalty=integrability_penalty,