Release 1.2.1

CHANGELOG.md

@@ -2,6 +2,50 @@
 
 All notable changes to this project will be documented in this file.
 
+
+<a name="1.2.1"></a>
+## [1.2.1] - 2024-05-28
+
+### Bug Fixes
+
+- (**uni**) `load_patient_data` should accept `None`.
+- (**mid**) Correct type hint of `marginalize`.
+- (**graph**) Wrong dict when trinary.\
+  The `to_dict()` method returned a wrong graph dictionary when trinary
+  due to growth edges. This is fixed now.
+- Skip `marginalize` only when safe.\
+  The marginalization should only be skipped (and 1 returned), when the
+  entire disease state of interest is `None`. In the midline case, this
+  disease state includes the midline extension.\
+  Previously, only the involvement pattern was checked. Now, the model is
+  more careful about when to take shortcuts.
+
+
+### Features
+
+- (**graph**) Modify mermaid graph.\
+  The `get_mermaid()` and `get_mermaid_url()` methods now accept arguments
+  that allow some modifications of the output.
+- (**uni**) Add `__repr__()`.
+
+### Refactor
+
+- (**uni**) Use pandas `map` instead of `apply`.\
+  This saves us a couple of lines in the `load_patient_data` method and is
+  more readable.
+
+
+### Merge
+
+- Branch 'main' into 'dev'.
+
+### Remove
+
+- Remains of callbacks.\
+  Some callback functionality that was tested in a pre-release has been
+  forgotten in the code base and is now deleted.
+
+
 <a name="1.2.0"></a>
 ## [1.2.0] - 2024-03-29
 
@@ -668,7 +712,8 @@ Almost the entire API has changed. I'd therefore recommend to have a look at the
 - add pre-commit hook to check commit msg
 
 
-[Unreleased]: https://github.com/rmnldwg/lymph/compare/1.2.0...HEAD
+[Unreleased]: https://github.com/rmnldwg/lymph/compare/1.2.1...HEAD
+[1.2.1]: https://github.com/rmnldwg/lymph/compare/1.1.0...1.2.1
 [1.2.0]: https://github.com/rmnldwg/lymph/compare/1.1.0...1.2.0
 [1.1.0]: https://github.com/rmnldwg/lymph/compare/1.0.0...1.1.0
 [1.0.0]: https://github.com/rmnldwg/lymph/compare/1.0.0.rc2...1.0.0

lymph/graph.py

@@ -14,16 +14,17 @@
 import base64
 import warnings
 from itertools import product
+from typing import Literal
 
 import numpy as np
 
+from lymph import types
 from lymph.utils import (
     check_unique_names,
     comp_transition_tensor,
     flatten,
     popfirst,
     set_params_for,
-    trigger,
 )
 
 
@@ -224,7 +225,6 @@ def __init__(
         child: LymphNodeLevel,
         spread_prob: float = 0.,
         micro_mod: float = 1.,
-        callbacks: list[callable] | None = None,
     ) -> None:
         """Create a new edge between two nodes.
 
@@ -235,10 +235,6 @@ def __init__(
         spread to the next LNL. The ``micro_mod`` parameter is a modifier for the spread
         probability in case of only a microscopic node involvement.
         """
-        self.trigger_callbacks = []
-        if callbacks is not None:
-            self.trigger_callbacks += callbacks
-
         self.parent: Tumor | LymphNodeLevel = parent
         self.child: LymphNodeLevel = child
 
@@ -353,7 +349,6 @@ def get_micro_mod(self) -> float:
             self._micro_mod = 1.
         return self._micro_mod
 
-    @trigger
     def set_micro_mod(self, new_micro_mod: float | None) -> None:
         """Set the spread modifier for LNLs with microscopic involvement."""
         if new_micro_mod is None:
@@ -380,7 +375,6 @@ def get_spread_prob(self) -> float:
             self._spread_prob = 0.
         return self._spread_prob
 
-    @trigger
     def set_spread_prob(self, new_spread_prob: float | None) -> None:
         """Set the spread probability of the edge."""
         if new_spread_prob is None:
@@ -493,7 +487,6 @@ def __init__(
         graph_dict: dict[tuple[str], list[str]],
         tumor_state: int | None = None,
         allowed_states: list[int] | None = None,
-        on_edge_change: list[callable] | None = None,
     ) -> None:
         """Create a new graph representation of nodes and edges.
 
@@ -512,7 +505,7 @@ def __init__(
 
         check_unique_names(graph_dict)
         self._init_nodes(graph_dict, tumor_state, allowed_states)
-        self._init_edges(graph_dict, on_edge_change)
+        self._init_edges(graph_dict)
 
 
     def _init_nodes(self, graph, tumor_state, allowed_lnl_states):
@@ -585,7 +578,6 @@ def is_trinary(self) -> bool:
     def _init_edges(
         self,
         graph: dict[tuple[str, str], list[str]],
-        on_edge_change: list[callable]
     ) -> None:
         """Initialize the edges of the ``graph``.
 
@@ -602,12 +594,12 @@ def _init_edges(
         for (_, start_name), end_names in graph.items():
             start = self.nodes[start_name]
             if isinstance(start, LymphNodeLevel) and start.is_trinary:
-                growth_edge = Edge(parent=start, child=start, callbacks=on_edge_change)
+                growth_edge = Edge(parent=start, child=start)
                 self._edges[growth_edge.get_name()] = growth_edge
 
             for end_name in end_names:
                 end = self.nodes[end_name]
-                new_edge = Edge(parent=start, child=end, callbacks=on_edge_change)
+                new_edge = Edge(parent=start, child=end)
                 self._edges[new_edge.get_name()] = new_edge
 
 
@@ -669,11 +661,19 @@ def to_dict(self) -> dict[tuple[str, str], set[str]]:
         res = {}
         for node in self.nodes.values():
             node_type = "tumor" if isinstance(node, Tumor) else "lnl"
-            res[(node_type, node.name)] = [o.child.name for o in node.out]
+            res[(node_type, node.name)] = [
+                o.child.name
+                for o in node.out
+                if not o.is_growth
+            ]
         return res
 
 
-    def get_mermaid(self) -> str:
+    def get_mermaid(
+        self,
+        with_params: bool = True,
+        direction: Literal["TD", "LR"] = "TD",
+    ) -> str:
         """Prints the graph in mermaid format.
 
         >>> graph_dict = {
@@ -691,19 +691,29 @@ def get_mermaid(self) -> str:
             T-->|20%| III
             II-->|30%| III
         <BLANKLINE>
+        >>> print(graph.get_mermaid(with_params=False)) # doctest: +NORMALIZE_WHITESPACE
+        flowchart TD
+            T--> II
+            T--> III
+            II--> III
+        <BLANKLINE>
         """
-        mermaid_graph = "flowchart TD\n"
+        mermaid_graph = f"flowchart {direction}\n"
 
         for node in self.nodes.values():
             for edge in node.out:
-                mermaid_graph += f"\t{node.name}-->|{edge.spread_prob:.0%}| {edge.child.name}\n"
+                param_str = f"|{edge.spread_prob:.0%}|" if with_params else ""
+                mermaid_graph += f"\t{node.name}-->{param_str} {edge.child.name}\n"
 
         return mermaid_graph
 
 
-    def get_mermaid_url(self) -> str:
-        """Returns the URL to the rendered graph."""
-        mermaid_graph = self.get_mermaid()
+    def get_mermaid_url(self, **mermaid_kwargs) -> str:
+        """Returns the URL to the rendered graph.
+
+        Keyword arguments are passed to :py:meth:`~Representation.get_mermaid`.
+        """
+        mermaid_graph = self.get_mermaid(**mermaid_kwargs)
         graphbytes = mermaid_graph.encode("ascii")
         base64_bytes = base64.b64encode(graphbytes)
         base64_string = base64_bytes.decode("ascii")

lymph/models/bilateral.py

@@ -626,7 +626,7 @@ def marginalize(
         are ignored if ``given_state_dist`` is provided.
         """
         if involvement is None:
-            return 1.
+            involvement = {}
 
         if given_state_dist is None:
             given_state_dist = self.state_dist(t_stage=t_stage, mode=mode)

lymph/models/midline.py

@@ -753,7 +753,7 @@ def posterior_state_dist(
 
     def marginalize(
         self,
-        involvement: types.PatternType | None = None,
+        involvement: dict[str, types.PatternType] | None = None,
         given_state_dist: np.ndarray | None = None,
         t_stage: str = "early",
         mode: Literal["HMM", "BN"] = "HMM",
@@ -770,7 +770,7 @@ def marginalize(
         :py:meth:`.state_dist` method.
         """
         if involvement is None:
-            return 1.
+            involvement = {}
 
         if given_state_dist is None:
             given_state_dist = self.state_dist(t_stage=t_stage, mode=mode, central=central)
@@ -787,7 +787,7 @@ def marginalize(
             given_state_dist = given_state_dist[int(midext)]
             # I think I don't need to normalize here, since I am not computing a
             # probability of something *given* midext, but only sum up all states that
-            # match the involvement pattern (which includes the midext status).
+            # match the disease state of interest (which includes the midext status).
 
         return self.ext.marginalize(
             involvement=involvement,

lymph/models/unilateral.py

@@ -2,7 +2,7 @@
 
 import warnings
 from itertools import product
-from typing import Any, Iterable, Literal
+from typing import Any, Callable, Iterable, Literal
 
 import numpy as np
 import pandas as pd
@@ -117,6 +117,17 @@ def trinary(cls, graph_dict: types.GraphDictType, **kwargs) -> Unilateral:
         return cls(graph_dict, allowed_states=[0, 1, 2], **kwargs)
 
 
+    def __repr__(self) -> str:
+        """Return a string representation of the instance."""
+        return (
+            f"{type(self).__name__}("
+            f"graph_dict={self.graph.to_dict()}, "
+            f"tumor_state={list(self.graph.tumors.values())[0].state}, "
+            f"allowed_states={self.graph.allowed_states}, "
+            f"max_time={self.max_time})"
+        )
+
+
     def __str__(self) -> str:
         """Print info about the instance."""
         return f"Unilateral with {len(self.graph.tumors)} tumors and {len(self.graph.lnls)} LNLs"
@@ -489,7 +500,7 @@ def load_patient_data(
         self,
         patient_data: pd.DataFrame,
         side: str = "ipsi",
-        mapping: callable | dict[int, Any] | None = None,
+        mapping: Callable[[int], Any] | dict[int, Any] | None = None,
     ) -> None:
         """Load patient data in `LyProX`_ format into the model.
 
@@ -512,7 +523,6 @@ def load_patient_data(
         if mapping is None:
             mapping = early_late_mapping
 
-        # pylint: disable=unnecessary-lambda-assignment
         patient_data = (
             patient_data
             .copy()
@@ -545,15 +555,7 @@ def load_patient_data(
 
                 patient_data["_model", modality, lnl] = column
 
-        if len(patient_data) == 0:
-            patient_data[MAP_T_COL] = None
-        else:
-            mapping = dict_to_func(mapping) if isinstance(mapping, dict) else mapping
-            patient_data[MAP_T_COL] = patient_data.apply(
-                lambda row: mapping(row[RAW_T_COL]),
-                axis=1,
-            )
-
+        patient_data[MAP_T_COL] = patient_data[RAW_T_COL].map(mapping)
         self._patient_data = patient_data
         self._cache_version += 1
 
@@ -833,7 +835,11 @@ def marginalize(
         :py:meth:`.state_dist` with the given ``t_stage`` and ``mode``. These arguments
         are ignored if ``given_state_dist`` is provided.
         """
-        if involvement is None:
+        if (
+            involvement is None
+            or not involvement     # empty dict is falsey
+            or all(value is None for value in involvement.values())
+        ):
             return 1.
 
         if given_state_dist is None:

tests/edge_test.py

@@ -13,12 +13,7 @@ def setUp(self) -> None:
         super().setUp()
         parent = graph.LymphNodeLevel("parent")
         child = graph.LymphNodeLevel("child")
-        self.was_called = False
-        self.edge = graph.Edge(parent, child, callbacks=[self.callback])
-
-    def callback(self) -> None:
-        """Callback function for the edge."""
-        self.was_called = True
+        self.edge = graph.Edge(parent, child)
 
     def test_str(self) -> None:
         """Test the string representation of the edge."""
@@ -41,17 +36,11 @@ def test_repr(self) -> None:
         self.assertEqual(self.edge.spread_prob, recreated_edge.spread_prob)
         self.assertEqual(self.edge.micro_mod, recreated_edge.micro_mod)
 
-    def test_callback_on_param_change(self) -> None:
-        """Test if the callback function is called."""
-        self.edge.spread_prob = 0.5
-        self.assertTrue(self.was_called)
-
     def test_graph_change(self) -> None:
         """Check if the callback also works when parent/child nodes are changed."""
         old_child = self.edge.child
         new_child = graph.LymphNodeLevel("new_child")
         self.edge.child = new_child
-        self.assertTrue(self.was_called)
         self.assertNotIn(self.edge, old_child.inc)
 
     def test_transition_tensor_row_sums(self) -> None:

tests/graph_representation_test.py

@@ -34,15 +34,10 @@ def setUp(self) -> None:
         self.graph_repr = graph.Representation(
             graph_dict=self.graph_dict,
             allowed_states=[0, 1],
-            on_edge_change=[self.callback],
         )
         self.was_called = False
         self.rng = np.random.default_rng(42)
 
-    def callback(self) -> None:
-        """Callback function for the graph."""
-        self.was_called = True
-
     def test_nodes(self) -> None:
         """Test the number of nodes."""
         self.assertEqual(len(self.graph_repr.nodes), len(self.graph_dict))