Release v0.3.0

Author: dilpath

doc/analysis.rst

@@ -0,0 +1,28 @@
+Analysis
+========
+
+After using PEtab Select to perform model selection, you may want to operate on all "good" calibrated models.
+The PEtab Select Python library provides some methods to help with this. Please request any missing methods.
+
+See the Python API docs for the :class:`petab_select.Models` class, which provides some methods. In particular, :attr:`petab_select.Models.df` can be used
+to get a quick overview over all models, as a pandas dataframe.
+
+Additionally, see the Python API docs for the :mod:`petab_select.analyze` module, which contains some methods to subset and group models,
+or compute "weights" (e.g. Akaike weights).
+
+Model hashes
+^^^^^^^^^^^^
+
+Model hashes are special objects in the library, that are generated from model-specific information that is unique within a single PEtab Select problem.
+
+This means you can reconstruct the model given some model hash. For example, with this model hash `M1-000`, you can reconstruct the :class:`petab_select.ModelHash` from a string, then reconstruct the :class:`petab_select.Model`.
+
+.. code-block:: language
+
+   ModelHash.from_hash("M1-000").get_model(petab_select_problem)
+
+You can use this to get the uncalibrated version of a calibrated model.
+
+.. code-block:: language
+
+   model.hash.get_model(petab_select_problem)

doc/api.rst

@@ -7,6 +7,7 @@ petab-select Python API
    :toctree: generated
 
    petab_select
+   petab_select.analyze
    petab_select.candidate_space
    petab_select.constants
    petab_select.criteria

doc/conf.py

@@ -58,7 +58,7 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
 html_theme = "sphinx_rtd_theme"
-# html_static_path = ['_static']
+html_static_path = ["standard"]
 html_logo = "logo/logo-wide.svg"
 
 

doc/examples/README.md

@@ -0,0 +1 @@
+These notebooks need to be run to see the output. Pre-computed output can be viewed in the documentation, at https://petab-select.readthedocs.io/en/stable/examples.html

doc/examples/calibrated_models/calibrated_models.yaml

@@ -2,114 +2,142 @@
     AICc: 37.97523003111246
     NLLH: 17.48761501555623
   estimated_parameters:
-    sigma_x2: 4.462298385653177
-  model_hash: M_0-000
-  model_id: M_0-000
-  model_subspace_id: M_0
+    sigma_x2: 4.462298422134608
+  iteration: 1
+  model_hash: M-000
+  model_id: M-000
+  model_subspace_id: M
   model_subspace_indices:
   - 0
   - 0
   - 0
   parameters:
-    k1: 0
-    k2: 0
+    k1: 0.2
+    k2: 0.1
     k3: 0
-  petab_yaml: petab_problem.yaml
-  predecessor_model_hash: virtual_initial_model
+  model_subspace_petab_yaml: petab_problem.yaml
+  predecessor_model_hash: virtual_initial_model-
 - criteria:
-    AICc: -0.1754060811089051
-    NLLH: -4.0877030405544525
+    AICc: -0.17540608110890332
+    NLLH: -4.087703040554452
   estimated_parameters:
     k3: 0.0
-    sigma_x2: 0.12242920113658744
-  model_hash: M_1-000
-  model_id: M_1-000
-  model_subspace_id: M_1
+    sigma_x2: 0.12242920113658338
+  iteration: 2
+  model_hash: M-001
+  model_id: M-001
+  model_subspace_id: M
   model_subspace_indices:
   - 0
   - 0
-  - 0
+  - 1
   parameters:
     k1: 0.2
     k2: 0.1
     k3: estimate
-  petab_yaml: petab_problem.yaml
-  predecessor_model_hash: M_0-000
+  model_subspace_petab_yaml: petab_problem.yaml
+  predecessor_model_hash: M-000
 - criteria:
-    AICc: -0.27451405630430337
-    NLLH: -4.137257028152152
+    AICc: -0.27451438069575573
+    NLLH: -4.137257190347878
   estimated_parameters:
-    k2: 0.10147827639089564
-    sigma_x2: 0.12142256779953603
-  model_hash: M_2-000
-  model_id: M_2-000
-  model_subspace_id: M_2
+    k2: 0.10147824307890803
+    sigma_x2: 0.12142219599557078
+  iteration: 2
+  model_hash: M-010
+  model_id: M-010
+  model_subspace_id: M
   model_subspace_indices:
   - 0
-  - 0
+  - 1
   - 0
   parameters:
     k1: 0.2
     k2: estimate
     k3: 0
-  petab_yaml: petab_problem.yaml
-  predecessor_model_hash: M_0-000
+  model_subspace_petab_yaml: petab_problem.yaml
+  predecessor_model_hash: M-000
 - criteria:
-    AICc: -0.7053270517931587
-    NLLH: -4.352663525896579
+    AICc: -0.7053270766271886
+    NLLH: -4.352663538313594
   estimated_parameters:
-    k1: 0.20160888007873565
-    sigma_x2: 0.11713858557052499
-  model_hash: M_3-000
-  model_id: M_3-000
-  model_subspace_id: M_3
+    k1: 0.20160925279667963
+    sigma_x2: 0.11714017664827497
+  iteration: 2
+  model_hash: M-100
+  model_id: M-100
+  model_subspace_id: M
   model_subspace_indices:
-  - 0
+  - 1
   - 0
   - 0
   parameters:
     k1: estimate
     k2: 0.1
     k3: 0
-  petab_yaml: petab_problem.yaml
-  predecessor_model_hash: M_0-000
+  model_subspace_petab_yaml: petab_problem.yaml
+  predecessor_model_hash: M-000
 - criteria:
-    AICc: 9.294672948206841
-    NLLH: -4.352663525896579
+    AICc: 9.294672923372811
+    NLLH: -4.352663538313594
   estimated_parameters:
-    k1: 0.20160888007873565
+    k1: 0.20160925279667963
     k3: 0.0
-    sigma_x2: 0.11713858557052499
-  model_hash: M_5-000
-  model_id: M_5-000
-  model_subspace_id: M_5
+    sigma_x2: 0.11714017664827497
+  iteration: 3
+  model_hash: M-101
+  model_id: M-101
+  model_subspace_id: M
   model_subspace_indices:
+  - 1
   - 0
-  - 0
-  - 0
+  - 1
   parameters:
     k1: estimate
     k2: 0.1
     k3: estimate
-  petab_yaml: petab_problem.yaml
-  predecessor_model_hash: M_3-000
+  model_subspace_petab_yaml: petab_problem.yaml
+  predecessor_model_hash: M-100
 - criteria:
-    AICc: 7.852170288089528
-    NLLH: -5.073914855955236
+    AICc: 7.8521704398854
+    NLLH: -5.0739147800573
   estimated_parameters:
-    k1: 0.20924739987621038
-    k2: 0.0859065470362628
-    sigma_x2: 0.1038731029818225
-  model_hash: M_6-000
-  model_id: M_6-000
-  model_subspace_id: M_6
+    k1: 0.20924804320838675
+    k2: 0.0859052351446815
+    sigma_x2: 0.10386846319370771
+  iteration: 3
+  model_hash: M-110
+  model_id: M-110
+  model_subspace_id: M
   model_subspace_indices:
-  - 0
-  - 0
+  - 1
+  - 1
   - 0
   parameters:
     k1: estimate
     k2: estimate
     k3: 0
-  petab_yaml: petab_problem.yaml
-  predecessor_model_hash: M_3-000
+  model_subspace_petab_yaml: petab_problem.yaml
+  predecessor_model_hash: M-100
+- criteria:
+    AICc: 35.94352968170024
+    NLLH: -6.028235159149878
+  estimated_parameters:
+    k1: 0.6228488917665873
+    k2: 0.020189424009226256
+    k3: 0.0010850434974038557
+    sigma_x2: 0.08859278245811462
+  iteration: 4
+  model_hash: M-111
+  model_id: M-111
+  model_subspace_id: M
+  model_subspace_indices:
+  - 1
+  - 1
+  - 1
+  parameters:
+    k1: estimate
+    k2: estimate
+    k3: estimate
+  model_subspace_petab_yaml: petab_problem.yaml
+  predecessor_model_hash: M-100

doc/examples/calibrated_models/model_space.tsv

@@ -1,9 +1,2 @@
 model_subspace_id	petab_yaml	k1	k2	k3
-M_0	petab_problem.yaml	0	0	0
-M_1	petab_problem.yaml	0.2	0.1	estimate
-M_2	petab_problem.yaml	0.2	estimate	0
-M_3	petab_problem.yaml	estimate	0.1	0
-M_4	petab_problem.yaml	0.2	estimate	estimate
-M_5	petab_problem.yaml	estimate	0.1	estimate
-M_6	petab_problem.yaml	estimate	estimate	0
-M_7	petab_problem.yaml	estimate	estimate	estimate
+M	petab_problem.yaml	0.2;estimate	0.1;estimate	0

doc/examples/example_cli_famos.ipynb

@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "1f04dce0",
    "metadata": {},
    "outputs": [],
@@ -33,17 +33,19 @@
     "\n",
     "from example_cli_famos_helpers import (\n",
     "    parse_summary_to_progress_list,\n",
+    "    petab_select_problem_yaml,  # noqa: F401\n",
     ")\n",
     "\n",
     "output_path = Path().resolve() / \"output_famos\"\n",
     "output_path_str = str(output_path)\n",
-    "shutil.rmtree(output_path_str)\n",
+    "if output_path.exists():\n",
+    "    shutil.rmtree(output_path_str)\n",
     "output_path.mkdir(exist_ok=False, parents=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "a81560e6",
    "metadata": {},
    "outputs": [],
@@ -108,72 +110,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "bb1a5144",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Executing iteration 1\n",
-      "Executing iteration 2\n",
-      "Executing iteration 3\n",
-      "Executing iteration 4\n",
-      "Executing iteration 5\n",
-      "Executing iteration 6\n",
-      "Executing iteration 7\n",
-      "Executing iteration 8\n",
-      "Executing iteration 9\n",
-      "Executing iteration 10\n",
-      "Executing iteration 11\n",
-      "Executing iteration 12\n",
-      "Executing iteration 13\n",
-      "Executing iteration 14\n",
-      "Executing iteration 15\n",
-      "Executing iteration 16\n",
-      "Executing iteration 17\n",
-      "Executing iteration 18\n",
-      "Executing iteration 19\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/dilan/Documents/future_annex/model_selection/packages/petab_select/petab_select/candidate_space.py:376: RuntimeWarning: Model `model_subspace_1-0001011010010010` has been previously excluded from the candidate space so is skipped here.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Executing iteration 20\n",
-      "Executing iteration 21\n",
-      "Executing iteration 22\n",
-      "Executing iteration 23\n",
-      "Executing iteration 24\n",
-      "Executing iteration 25\n",
-      "Executing iteration 26\n",
-      "Executing iteration 27\n",
-      "Executing iteration 28\n",
-      "Executing iteration 29\n",
-      "Executing iteration 30\n",
-      "Executing iteration 31\n",
-      "Executing iteration 32\n",
-      "Executing iteration 33\n",
-      "Executing iteration 34\n",
-      "Executing iteration 35\n",
-      "Executing iteration 36\n",
-      "Executing iteration 37\n",
-      "Model selection has terminated.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%bash -s \"$petab_select_problem_yaml\" \"$output_path_str\"\n",
-    "\n",
     "petab_select_problem_yaml=$1\n",
     "output_path_str=$2\n",
     "\n",
@@ -217,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "93caf071",
    "metadata": {},
    "outputs": [],
@@ -227,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "cb61d0f7",
    "metadata": {},
    "outputs": [],