UI review; 24.1a6 (#81)

* start 24.1a6.dev

* set max of 8 degree on multitumor model w/ auto degree set

* remove fixed_lsc from excel report

* add back fixed_lsc

* begin refactor of input data

* aggregate summary

* nd outputs

* add mscombo results

* make results optional

* change version to 24.1a6

* update todo

src/bmdscore/bmds_helper.cpp

@@ -15,7 +15,7 @@
 #include "bmds_helper.h"
 
 // calendar versioning; see https://peps.python.org/pep-0440/#pre-releases
-std::string BMDS_VERSION = "24.1a4";
+std::string BMDS_VERSION = "24.1a6";
 
 double python_dichotomous_model_result::getSRAtDose(double targetDose, std::vector<double> doses) {
   std::vector<double> diff;

src/pybmds/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "24.1a5"  # see docs/development for versioning
+__version__ = "24.1a6"  # see docs/development for versioning
 
 from .batch import BatchResponse, BatchSession  # noqa: F401
 from .constants import DistType as ContinuousDistType  # noqa: F401

src/pybmds/batch.py

@@ -277,11 +277,7 @@ def to_docx(
             report = Report.build_default()
 
         for session in self.sessions:
-            session.to_docx(
-                report,
-                header_level=header_level,
-                citation=False,
-            )
+            session.to_docx(report, header_level=header_level, citation=False, **kw)
 
         if citation and len(self.sessions) > 0:
             write_citation(report, header_level=header_level)

src/pybmds/models/multi_tumor.py

@@ -23,6 +23,7 @@
 from ..types.multi_tumor import MultitumorAnalysis, MultitumorResult, MultitumorSettings
 from ..types.priors import multistage_cancer_prior
 from ..types.session import VersionSchema
+from ..utils import unique_items
 from .dichotomous import MultistageCancer
 
 
@@ -102,9 +103,15 @@ def write_docx_inputs_table(report: Report, session):
     hdr = report.styles.tbl_header
     body = report.styles.tbl_body
 
-    rows = session.models[0][0].settings.docx_table_data()
+    settings = [models[len(models) - 1].settings for models in session.models]
+    rows = {
+        "Setting": "Value",
+        "BMR": unique_items(settings, "bmr_text"),
+        "Confidence Level (one sided)": unique_items(settings, "confidence_level"),
+        "Maximum Degree": unique_items(settings, "degree"),
+    }
     tbl = report.document.add_table(len(rows), 2, style=styles.table)
-    for idx, (key, value) in enumerate(rows):
+    for idx, (key, value) in enumerate(rows.items()):
         write_cell(tbl.cell(idx, 0), key, style=hdr)
         write_cell(tbl.cell(idx, 1), value, style=hdr if idx == 0 else body)
 
@@ -177,7 +184,9 @@ def _build_model_settings(self) -> list[list[DichotomousModelSettings]]:
             ds_settings = []
             degree_i = self.degrees[i]
             degrees_i = (
-                range(degree_i, degree_i + 1) if degree_i > 0 else range(1, dataset.num_dose_groups)
+                range(degree_i, degree_i + 1)
+                if degree_i > 0
+                else range(1, min(dataset.num_dose_groups, 9))  # max of 8 if degree is 0 (auto)
             )
             for degree in degrees_i:
                 model_settings = self.settings.model_copy(
@@ -454,6 +463,7 @@ def to_docx(
         dataset_format_long: bool = True,
         all_models: bool = False,
         bmd_cdf_table: bool = False,
+        **kw,
     ):
         """Return a Document object with the session executed
 
@@ -490,6 +500,8 @@ def to_docx(
         report.document.add_paragraph("Maximum Likelihood Approach", h2)
         write_docx_frequentist_table(report, self)
         report.document.add_paragraph(add_mpl_figure(report.document, self.plot(), 6))
+        report.document.add_paragraph(self.results.ms_combo_text(), report.styles.fixed_width)
+
         report.document.add_paragraph("Individual Model Results", h2)
 
         for dataset, selected_idx, models in zip(

src/pybmds/models/nested_dichotomous.py

@@ -88,7 +88,7 @@ def get_priors_list(self) -> list[list]:
         return self.settings.priors.priors_list(nphi=self.dataset.num_dose_groups)
 
     def model_settings_text(self) -> str:
-        input_tbl = self.settings.tbl()
+        input_tbl = self.settings.tbl(self.results)
         return multi_lstrip(
             f"""
         Input Summary:
@@ -142,13 +142,16 @@ class Nctr(BmdModelNestedDichotomous):
     bmd_model_class = NestedDichotomousModelChoices.nctr.value
     model_class = bmdscore.nested_model.nctr
 
+    def execute(self) -> NestedDichotomousResult:
+        raise NotImplementedError("TODO - future release")
+
     def get_param_names(self) -> list[str]:
         return ["a", "b", "theta1", "theta2", "rho"] + [
             f"phi{i}" for i in range(1, self.dataset.num_dose_groups + 1)
         ]
 
     def dr_curve(self, doses: np.ndarray, params: dict, fixed_lsc: float) -> np.ndarray:
-        raise NotImplementedError("TODO - update formula")
+        raise NotImplementedError("TODO - future release")
 
     def get_default_prior_class(self) -> PriorClass:
         return PriorClass.frequentist_restricted

src/pybmds/reporting/styling.py

@@ -306,13 +306,11 @@ def write_inputs_table(report: Report, session: Session):
     hdr = report.styles.tbl_header
     body = report.styles.tbl_body
 
-    model_index = 0
-    if hasattr(session.models[0].settings, "degree"):
-        degrees = [model.settings.degree for model in session.models]
-        model_index = degrees.index(max(degrees))
-    rows = session.models[model_index].settings.docx_table_data()
-    tbl = report.document.add_table(len(rows), 2, style=styles.table)
-    for idx, (key, value) in enumerate(rows):
+    results = session.models[0].results if len(session.models) > 0 else None
+    settings = [model.settings for model in session.models]
+    content = session.models[0].settings.docx_table_data(settings, results)
+    tbl = report.document.add_table(len(content), 2, style=styles.table)
+    for idx, (key, value) in enumerate(content.items()):
         write_cell(tbl.cell(idx, 0), key, style=hdr)
         write_cell(tbl.cell(idx, 1), value, style=hdr if idx == 0 else body)
 

src/pybmds/types/continuous.py

@@ -7,7 +7,7 @@
 from .. import bmdscore, constants
 from ..constants import BOOL_YES_NO, ContinuousModelChoices, Dtype
 from ..datasets.continuous import ContinuousDatasets
-from ..utils import multi_lstrip, pretty_table
+from ..utils import multi_lstrip, pretty_table, unique_items
 from .common import (
     BOUND_FOOTNOTE,
     CONTINUOUS_TEST_FOOTNOTES,
@@ -100,17 +100,18 @@ def tbl(self, show_degree: bool = True) -> str:
 
         return pretty_table(data, "")
 
-    def docx_table_data(self) -> list:
-        data = [
-            ["Setting", "Value"],
-            ["BMR", self.bmr_text],
-            ["Distribution", self.distribution],
-            ["Adverse Direction", self.direction],
-            ["Maximum Polynomial Degree", self.degree],
-            ["Confidence Level (one sided)", self.confidence_level],
-        ]
-        if self.is_hybrid:
-            data.append(["Tail Probability", self.tail_prob])
+    @classmethod
+    def docx_table_data(cls, settings: list[Self], results) -> dict:
+        data = {
+            "Setting": "Value",
+            "BMR": unique_items(settings, "bmr_text"),
+            "Distribution": unique_items(settings, "distribution"),
+            "Adverse Direction": unique_items(settings, "direction"),
+            "Maximum Polynomial Degree": str(max(setting.degree for setting in settings)),
+            "Confidence Level (one sided)": unique_items(settings, "confidence_level"),
+        }
+        if settings[0].is_hybrid:
+            data["Tail Probability"] = unique_items(settings, "tail_prob")
         return data
 
     def update_record(self, d: dict) -> None:

src/pybmds/types/dichotomous.py

@@ -7,7 +7,7 @@
 from .. import bmdscore, constants
 from ..constants import BOOL_YES_NO, DichotomousModelChoices
 from ..datasets import DichotomousDataset
-from ..utils import multi_lstrip, pretty_table
+from ..utils import multi_lstrip, pretty_table, unique_items
 from .common import (
     BOUND_FOOTNOTE,
     NumpyFloatArray,
@@ -69,13 +69,20 @@ def tbl(self, show_degree: bool = True) -> str:
 
         return pretty_table(data, "")
 
-    def docx_table_data(self) -> list:
-        return [
-            ["Setting", "Value"],
-            ["BMR", self.bmr_text],
-            ["Confidence Level (one sided)", self.confidence_level],
-            ["Maximum Multistage Degree", self.degree],
-        ]
+    @classmethod
+    def docx_table_data(cls, settings: list[Self], results) -> dict:
+        data = {
+            "Setting": "Value",
+            "BMR": unique_items(settings, "bmr_text"),
+            "Confidence Level (one sided)": unique_items(settings, "confidence_level"),
+            "Maximum Multistage Degree": str(max(setting.degree for setting in settings)),
+        }
+        if settings[0].priors.is_bayesian:
+            data.update(
+                Samples=unique_items(settings, "samples"),
+                **{"Burn-in": unique_items(settings, "burnin")},
+            )
+        return data
 
     def update_record(self, d: dict) -> None:
         """Update data record for a tabular-friendly export"""

src/pybmds/types/multi_tumor.py

@@ -4,7 +4,7 @@
 
 from .. import bmdscore
 from ..models.dichotomous import BmdModelDichotomousSchema
-from ..utils import multi_lstrip, pretty_table
+from ..utils import get_version, multi_lstrip, pretty_table
 from .common import inspect_cpp_obj
 from .dichotomous import (
     DichotomousAnalysisCPPStructs,
@@ -95,6 +95,12 @@ def text(self, datasets, models) -> str:
         """
         )
 
+    def ms_combo_text(self) -> str:
+        title = "Multitumor MS Combo Model".center(30) + "\n══════════════════════════════"
+        version = get_version()
+        version = f"Version: pybmds {version.python} (bmdscore {version.dll})"
+        return "\n\n".join([title, version, self.tbl()]) + "\n"
+
     def tbl(self) -> str:
         data = [
             ["BMD", self.bmd],

src/pybmds/types/nested_dichotomous.py

@@ -7,7 +7,7 @@
 
 from .. import bmdscore, constants
 from ..datasets import NestedDichotomousDataset
-from ..utils import camel_to_title, multi_lstrip, pretty_table
+from ..utils import camel_to_title, multi_lstrip, pretty_table, unique_items
 from .common import NumpyFloatArray, clean_array, inspect_cpp_obj
 from .priors import ModelPriors, PriorClass
 
@@ -22,6 +22,13 @@ class LitterSpecificCovariate(IntEnum):
     OverallMean = 1
     ControlGroupMean = 2
 
+    def label(self, fixed_lsc: float | None) -> str:
+        text = camel_to_title(self.name)
+        if self == self.Unused:
+            return text
+        lsc = f" ({fixed_lsc:.3f})" if fixed_lsc else ""
+        return f"{text}{lsc}"
+
     @property
     def text(self) -> str:
         return "lsc-" if self == self.Unused else "lsc+"
@@ -31,6 +38,10 @@ class IntralitterCorrelation(IntEnum):
     Zero = 0
     Estimate = 1
 
+    @property
+    def label(self) -> str:
+        return camel_to_title(self.name)
+
     @property
     def text(self) -> str:
         return "ilc+" if self == self.Estimate else "ilc-"
@@ -69,25 +80,47 @@ def confidence_level(self) -> float:
     def modeling_approach(self) -> str:
         return "MLE"
 
-    def _tbl_rows(self) -> list:
-        return [
-            ["BMR", self.bmr_text],
-            ["Confidence Level (one sided)", self.confidence_level],
-            ["Litter Specific Covariate", camel_to_title(self.litter_specific_covariate.name)],
-            ["Intralitter Correlation", self.intralitter_correlation.name],
-            ["Estimate Background", self.estimate_background],
-            ["Bootstrap Runs", self.bootstrap_n],
-            ["Bootstrap Iterations", self.bootstrap_iterations],
-            ["Bootstrap Seed", self.bootstrap_seed],
-        ]
-
-    def tbl(self, degree_required: bool = False) -> str:
-        return pretty_table(self._tbl_rows(), "")
+    def tbl(self, results=None) -> str:
+        fixed_lsc = results.fixed_lsc if results else None
+        return pretty_table(
+            [
+                ["BMR", self.bmr_text],
+                ["Confidence Level (one sided)", self.confidence_level],
+                ["Litter Specific Covariate", self.litter_specific_covariate.label(fixed_lsc)],
+                ["Intralitter Correlation", self.intralitter_correlation.label],
+                ["Estimate Background", self.estimate_background],
+                ["Bootstrap Runs", self.bootstrap_n],
+                ["Bootstrap Iterations", self.bootstrap_iterations],
+                ["Bootstrap Seed", self.bootstrap_seed],
+            ],
+            "",
+        )
 
-    def docx_table_data(self) -> list:
-        rows = self._tbl_rows()
-        rows.insert(0, ["Setting", "Value"])
-        return rows
+    @classmethod
+    def docx_table_data(cls, settings: list[Self], results) -> dict:
+        fixed_lsc = results.fixed_lsc if results else None
+        lsc = [
+            setting.litter_specific_covariate.label(fixed_lsc)
+            for setting in settings
+            if setting.litter_specific_covariate != LitterSpecificCovariate.Unused
+        ]
+        ilc = [
+            setting.intralitter_correlation.label
+            for setting in settings
+            if setting.intralitter_correlation != IntralitterCorrelation.Zero
+        ]
+        data = {
+            "Setting": "Value",
+            "BMR": unique_items(settings, "bmr_text"),
+            "Confidence Level (one sided)": unique_items(settings, "confidence_level"),
+            "Litter Specific Covariate": ", ".join(sorted(set(lsc))),
+            "Intralitter Correlation": ", ".join(sorted(set(ilc))),
+            "Estimate Background": unique_items(settings, "estimate_background"),
+            "Bootstrap Runs": unique_items(settings, "bootstrap_n"),
+            "Bootstrap Seed": unique_items(settings, "bootstrap_seed"),
+            "Bootstrap Iterations": unique_items(settings, "bootstrap_iterations"),
+        }
+        return data
 
     def update_record(self, d: dict) -> None:
         """Update data record for a tabular-friendly export"""

src/pybmds/utils.py

@@ -78,3 +78,7 @@ def get_version():
 
 def camel_to_title(txt: str) -> str:
     return re.sub(r"(?<=\w)([A-Z])", r" \1", txt)
+
+
+def unique_items(settings: list, getter: str) -> str:
+    return ", ".join(sorted(list(set(str(getattr(setting, getter)) for setting in settings))))

tests/test_pybmds/test_utils.py

@@ -1,5 +1,7 @@
+from dataclasses import dataclass
+
 import pybmds
-from pybmds.utils import get_version
+from pybmds.utils import get_version, unique_items
 
 
 def test_citation():
@@ -9,3 +11,12 @@ def test_citation():
 def test_get_version():
     version = get_version()
     assert int(version.dll.split(".")[0]) >= 24  # assume dll in format "YY.MM..."
+
+
+@dataclass
+class Foo:
+    bar: str
+
+
+def test_unique_items():
+    assert unique_items([Foo(bar="b"), Foo(bar="a"), Foo(bar="b")], "bar") == "a, b"