add a basic test for sciml

Author: m-philipps

petab/v2/lint.py

@@ -45,6 +45,7 @@
     "CheckUndefinedExperiments",
     "CheckInitialChangeSymbols",
     "CheckMappingTable",
+    "CheckHybridizationTable",
     "lint_problem",
     "default_validation_tasks",
 ]
@@ -1186,10 +1187,24 @@ def get_placeholders(
 
 #: Validation tasks that should be run PEtab SciML problems
 sciml_validation_tasks = [
+    CheckProblemConfig(),
+    CheckModel(),
+    CheckUniquePrimaryKeys(),
+    CheckMeasurementModelId(),
+    CheckMeasuredObservablesDefined(),
+    CheckPosLogMeasurements(),
+    CheckOverridesMatchPlaceholders(),
+    CheckValidConditionTargets(),
+    CheckExperimentTable(),
+    CheckExperimentConditionsExist(),
+    CheckUndefinedExperiments(),
+    CheckObservablesDoNotShadowModelEntities(),
+    # CheckAllParametersPresentInParameterTable(),
+    CheckValidParameterInConditionOrParameterTable(),
+    CheckUnusedExperiments(),
+    CheckUnusedConditions(),
+    CheckPriorDistribution(),
+    CheckInitialChangeSymbols(),
+    CheckMappingTable(),
     CheckHybridizationTable(),
-] + list(
-    set(default_validation_tasks)
-    - {
-        CheckAllParametersPresentInParameterTable(),
-    }
-)
+]

tests/v2/test_sciml.py

@@ -0,0 +1,138 @@
+import numpy as np
+from pydantic import ConfigDict
+
+from petab.v2.core import *
+from petab.v2.core import ModelFile, NeuralNetConfig
+from petab.v2.lint import sciml_validation_tasks
+from petab.v2.models.sbml_model import SbmlModel
+
+
+def _get_test_problem():
+    problem = Problem()
+    problem.validation_tasks = sciml_validation_tasks
+    problem.config = ProblemConfig(
+        format_version="2.0.0",
+        model_files=ConfigDict(
+            {"lv": ModelFile(location="lv.xml", language="sbml")}
+        ),
+        parameter_files=["parameters.tsv"],
+        measurement_files=["measurements.tsv"],
+        observable_files=["observables.tsv"],
+        experiment_files=["experiments.tsv"],
+        mapping_files=["mappings.tsv"],
+        extensions={
+            "sciml": {
+                "version": "0.1.0",
+                "array_files": ["net1_ps.hdf5"],
+                "hybridization_files": ["hybridizations.tsv"],
+                "neural_nets": {
+                    "net1": NeuralNetConfig(
+                        location="net1.yaml",
+                        pre_initialization=False,
+                        format="YAML",
+                    )
+                },
+            }
+        },
+    )
+    problem.model = SbmlModel.from_antimony("""
+    model lv
+      species A, B;
+      A = 0.442;
+      B = 4.63;
+      alpha = 1.3;
+      gamma_ = 0.8;
+      -> A; alpha * A;
+      B -> ; 1.8 * B;
+      A -> ; 0.9 * A * B;
+      -> B; gamma_;
+    end
+    """)
+    problem.add_experiment("e1", 0, "")
+    problem.add_mapping("net1_input1", "net1.inputs[0][0]")
+    problem.add_mapping("net1_input2", "net1.inputs[0][1]")
+    problem.add_mapping("net1_output1", "net1.outputs[0][0]")
+    problem.add_mapping("net1_ps", "net1.parameters")
+    problem.add_measurement("B_obs", time=1, measurement=1, experiment_id="e1")
+    problem.add_observable("B_obs", "B", noise_formula="0.05")
+    problem.add_parameter(
+        "alpha", estimate=True, lb=0, ub=15, nominal_value=1.3
+    )
+    problem.add_parameter(
+        "net1_ps", estimate=True, lb=-np.inf, ub=np.inf, nominal_value="array"
+    )
+    problem.add_hybridization("net1_input1", "A")
+    problem.add_hybridization("net1_input2", "B")
+    problem.add_hybridization("gamma_", "net1_output_1")
+    problem.add_neural_network_from_dict(
+        "net1",
+        nn_dict={
+            "nn_model_id": "net1",
+            "inputs": [{"input_id": "input0"}],
+            "layers": [
+                {
+                    "layer_id": "layer1",
+                    "layer_type": "Linear",
+                    "args": {
+                        "in_features": 2,
+                        "out_features": 1,
+                        "bias": True,
+                    },
+                }
+            ],
+            "forward": [
+                {
+                    "name": "net_input",
+                    "op": "placeholder",
+                    "target": "net_input",
+                },
+                {
+                    "name": "layer1",
+                    "op": "call_module",
+                    "target": "layer1",
+                    "args": ["net_input"],
+                },
+                {
+                    "name": "tanh",
+                    "op": "call_method",
+                    "target": "tanh",
+                    "args": ["layer1"],
+                },
+            ],
+        },
+    )
+
+    # array data
+    problem.add_array_data_from_dict(
+        {
+            "metadata": {"pytorch_format": True},
+            "inputs": {},
+            "parameters": {
+                "net1": {
+                    "layer1": {
+                        "bias": np.random.randn(2),
+                        "weight": np.random.randn(2),
+                    }
+                }
+            },
+        }
+    )
+
+    # set the filenames
+    problem.config.filepath = "problem.yaml"
+    problem.model.rel_path = "lv.xml"
+    problem.experiment_tables[0].rel_path = "experiments.tsv"
+    problem.mapping_tables[0].rel_path = "mappings.tsv"
+    problem.measurement_tables[0].rel_path = "measurements.tsv"
+    problem.observable_tables[0].rel_path = "observables.tsv"
+    problem.parameter_tables[0].rel_path = "parameters.tsv"
+    problem.hybridization_tables[0].rel_path = "hybridizations.tsv"
+    # problem.neural_networks[0].rel_path = "net1.yaml"
+    # problem.array_data_files[0].rel_path = "net1_ps.hdf5"
+
+    return problem
+
+
+def test_lint():
+    problem = _get_test_problem()
+    assert problem.validate() == []