update exceptions

.readthedocs.yml

@@ -1,9 +1,64 @@
-# Read the Docs configuration file
+# Read the Docs configuration file for Sphinx projects
+
 # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
 
-conda:
-    file: docs/environment.yml
+
+# Required
+
+version: 2
+
+
+# Set the OS, Python version and other tools you might need
+
+build:
+
+  os: ubuntu-22.04
+
+  tools:
+
+    python: "3.8"
+
+    # 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/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:
-    version: 3.8
-    setup_py_install: true
+
+  install:
+
+    - requirements: docs/requirements.txt

README.rst

@@ -4,7 +4,7 @@
      :align: center
      :width: 200px
 
-.. image:: https://github.com/cog-imperial/OMLT/workflows/CI/badge.svg?branch=main
+.. image:: https://github.com/cog-imperial/OMLT/actions/workflows/main.yml/badge.svg
      :target: https://github.com/cog-imperial/OMLT/actions?workflow=CI
      :alt: CI Status
 

docs/requirements.txt

@@ -0,0 +1,10 @@
+# Required dependencies for Sphinx documentation
+sphinx
+sphinx-rtd-theme
+numpy
+pyomo
+networkx
+onnx
+tensorflow
+linear-tree
+importlib-metadata

src/omlt/gbt/gbt_formulation.py

@@ -197,13 +197,23 @@ def _branching_y(tree_id, branch_node_id):
         node_mask = (nodes_tree_ids == tree_id) & (nodes_node_ids == branch_node_id)
         feature_id = nodes_feature_ids[node_mask]
         branch_value = nodes_values[node_mask]
-        assert len(feature_id) == 1 and len(branch_value) == 1
+        if len(branch_value) != 1:
+            raise ValueError(
+                f"The given tree_id and branch_node_id do not uniquely identify a branch value."
+            )
+        if len(feature_id) != 1:
+            raise ValueError(
+                f"The given tree_id and branch_node_id do not uniquely identify a feature."
+            )
         feature_id = feature_id[0]
         branch_value = branch_value[0]
         (branch_y_idx,) = np.where(
             branch_value_by_feature_id[feature_id] == branch_value
         )
-        assert len(branch_y_idx) == 1
+        if len(branch_y_idx) != 1:
+            raise ValueError(
+                f"The given tree_id and branch_node_id do not uniquely identify a branch index."
+            )
         return block.y[feature_id, branch_y_idx[0]]
 
     def _sum_of_z_l(tree_id, start_node_id):

src/omlt/gbt/model.py

@@ -56,24 +56,36 @@ def scaling_object(self, scaling_object):
 def _model_num_inputs(model):
     """Returns the number of input variables"""
     graph = model.graph
-    assert len(graph.input) == 1
+    if len(graph.input) != 1:
+        raise ValueError(
+            f"Model graph input field is multi-valued {graph.input}. A single value is required."
+        )
     return _tensor_size(graph.input[0])
 
 
 def _model_num_outputs(model):
     """Returns the number of output variables"""
     graph = model.graph
-    assert len(graph.output) == 1
+    if len(graph.output) != 1:
+        raise ValueError(
+            f"Model graph output field is multi-valued {graph.output}. A single value is required."
+        )
     return _tensor_size(graph.output[0])
 
 
 def _tensor_size(tensor):
     """Returns the size of an input tensor"""
     tensor_type = tensor.type.tensor_type
     size = None
-    for dim in tensor_type.shape.dim:
-        if dim.dim_value is not None and dim.dim_value > 0:
-            assert size is None
-            size = dim.dim_value
-    assert size is not None
+    dim_values = [
+        dim.dim_value
+        for dim in tensor_type.shape.dim
+        if dim.dim_value is not None and dim.dim_value > 0
+    ]
+    if len(dim_values) == 1:
+        size = dim_values[0]
+    elif dim_values == []:
+        raise ValueError(f"Tensor {tensor} has no positive dimensions.")
+    else:
+        raise ValueError(f"Tensor {tensor} has multiple positive dimensions.")
     return size