Add expanded scenario filters

app.py

@@ -39,64 +39,125 @@ st.info(
 
 with st.sidebar:
     st.header("Scenario")
-    base_population_text = st.text_input(
-        "Baseline population",
-        value=format_count(BASELINE.total_reference_population),
-        help=f"Reference population before filters, formatted with commas. Demo default: {format_count(BASELINE.total_reference_population)}.",
-    )
-    try:
-        base_population = parse_count(base_population_text, BASELINE.total_reference_population)
-    except ValueError:
-        st.warning("Use digits with optional commas, for example 10,000,000.")
-        base_population = BASELINE.total_reference_population
-    if not 10_000 <= base_population <= 50_000_000:
-        st.warning("Baseline population should stay between 10,000 and 50,000,000 for this demo.")
-        base_population = max(10_000, min(base_population, 50_000_000))
-
-    target_population = st.selectbox(
-        "Target population",
-        ["all_adults", "women", "men"],
-        format_func=title_label,
-        help="Applies a demo sex-share coefficient before the other filters. Women: 53%, Men: 47%, All adults: 100%.",
-    )
-    age_min, age_max = st.slider(
-        "Age range",
-        18,
-        70,
-        (28, 42),
-        help="Narrows the pool by the selected age-band overlap.",
-    )
-    region_scope = st.selectbox(
-        "Region scope",
-        ["all_ukraine", "large_cities", "kyiv_region", "western_regions"],
-        format_func=title_label,
-        help="Applies the selected regional scope coefficient.",
-    )
-    relationship_status = st.selectbox(
-        "Relationship status",
-        ["any", "not_married", "single_or_divorced"],
-        format_func=title_label,
-        help="Demo availability proxy. Official marital status is not the same as real availability.",
-    )
-    min_height = st.slider(
-        "Minimum height, cm",
-        150,
-        205,
-        175,
-        help="Interpolates a demo height-distribution coefficient.",
-    )
-    income_level = st.selectbox(
-        "Income threshold",
-        ["any", "above_median", "top_25", "top_10"],
-        format_func=title_label,
-        help="Applies an estimated income threshold coefficient.",
-    )
-    education_level = st.selectbox(
-        "Education filter",
-        ["any", "higher_education", "graduate_plus"],
-        format_func=title_label,
-        help="Applies an estimated education-level coefficient.",
-    )
+    with st.expander("Core demographics", expanded=True):
+        base_population_text = st.text_input(
+            "Baseline population",
+            value=format_count(BASELINE.total_reference_population),
+            help=f"Reference population before filters, formatted with commas. Demo default: {format_count(BASELINE.total_reference_population)}.",
+        )
+        try:
+            base_population = parse_count(base_population_text, BASELINE.total_reference_population)
+        except ValueError:
+            st.warning("Use digits with optional commas, for example 10,000,000.")
+            base_population = BASELINE.total_reference_population
+        if not 10_000 <= base_population <= 50_000_000:
+            st.warning("Baseline population should stay between 10,000 and 50,000,000 for this demo.")
+            base_population = max(10_000, min(base_population, 50_000_000))
+
+        target_population = st.selectbox(
+            "Target population",
+            ["all_adults", "women", "men"],
+            format_func=title_label,
+            help="Applies a demo sex-share coefficient before the other filters. Women: 53%, Men: 47%, All adults: 100%.",
+        )
+        age_min, age_max = st.slider(
+            "Age range",
+            18,
+            70,
+            (28, 42),
+            help="Narrows the pool by the selected age-band overlap.",
+        )
+        region_scope = st.selectbox(
+            "Region scope",
+            ["all_ukraine", "large_cities", "kyiv_region", "western_regions"],
+            format_func=title_label,
+            help="Applies the selected regional scope coefficient.",
+        )
+        relationship_status = st.selectbox(
+            "Relationship status",
+            ["any", "not_married", "single_or_divorced"],
+            format_func=title_label,
+            help="Demo availability proxy. Official marital status is not the same as real availability.",
+        )
+        min_height = st.slider(
+            "Minimum height, cm",
+            150,
+            205,
+            175,
+            help="Interpolates a demo height-distribution coefficient.",
+        )
+        income_level = st.selectbox(
+            "Income threshold",
+            ["any", "above_median", "top_25", "top_10"],
+            format_func=title_label,
+            help="Applies an estimated income threshold coefficient.",
+        )
+        education_level = st.selectbox(
+            "Education filter",
+            ["any", "higher_education", "graduate_plus"],
+            format_func=title_label,
+            help="Applies an estimated education-level coefficient.",
+        )
+
+    with st.expander("Family context"):
+        children_status = st.selectbox(
+            "Children status",
+            ["any", "no_children", "has_children", "co_parenting_ready"],
+            format_func=title_label,
+            help="Scenario preference around existing children. These are demo assumptions, not value judgments.",
+        )
+        future_children = st.selectbox(
+            "Future children",
+            ["any", "wants_children", "does_not_want_children", "open_or_undecided"],
+            format_func=title_label,
+            help="Scenario preference around future children.",
+        )
+
+    with st.expander("War and mobility"):
+        military_status = st.selectbox(
+            "Military status",
+            ["any", "civilian_or_not_serving", "active_service", "veteran_or_service_history"],
+            format_func=title_label,
+            help="War-related scenario filter. Active service and veteran/service-history shares are placeholders until sourced.",
+        )
+        relocation = st.selectbox(
+            "Relocation",
+            ["any", "same_city_only", "open_to_relocation", "remote_or_long_distance_ok"],
+            format_func=title_label,
+            help="Mobility and distance preference filter.",
+        )
+
+    with st.expander("Lifestyle and compatibility"):
+        housing = st.selectbox(
+            "Housing",
+            ["any", "independent_living", "own_or_stable_housing"],
+            format_func=title_label,
+            help="Scenario proxy for independent or stable living setup.",
+        )
+        smoking = st.selectbox(
+            "Smoking",
+            ["any", "non_smoker", "ok_with_smoking"],
+            format_func=title_label,
+            help="Lifestyle preference around smoking.",
+        )
+        alcohol = st.selectbox(
+            "Alcohol",
+            ["any", "rare_or_none", "moderate_ok"],
+            format_func=title_label,
+            help="Lifestyle preference around alcohol use.",
+        )
+        language = st.selectbox(
+            "Language comfort",
+            ["any", "ukrainian_comfortable", "english_comfortable", "ukrainian_and_english"],
+            format_func=title_label,
+            help="Communication comfort filter.",
+        )
+        pets = st.selectbox(
+            "Pets",
+            ["any", "pet_friendly", "no_pets_preferred"],
+            format_func=title_label,
+            help="Household compatibility preference around pets.",
+        )
 
 criteria = Criteria(
     base_population=base_population,
@@ -108,6 +169,15 @@ criteria = Criteria(
     min_height_cm=min_height,
     income_level=income_level,
     education_level=education_level,
+    children_status=children_status,
+    future_children=future_children,
+    military_status=military_status,
+    relocation=relocation,
+    housing=housing,
+    smoking=smoking,
+    alcohol=alcohol,
+    language=language,
+    pets=pets,
 )
 
 estimate = estimate_pool(criteria)
@@ -149,5 +219,6 @@ for note in DATA_QUALITY_NOTES:
 st.subheader("Interpretation guardrails")
 st.write(
     "This model estimates a demographic scenario, not compatibility, attraction, safety, or relationship success. "
-    "A stricter filter can make a pool smaller, but it does not define a person's real-life chances."
+    "A stricter filter can make a pool smaller, but it does not define a person's real-life chances. "
+    "War, children, housing, and lifestyle filters are sensitive context variables; treat them as transparent assumptions."
 )

src/assumptions.py

@@ -62,6 +62,65 @@ EDUCATION_FACTORS = {
     "graduate_plus": 0.16,
 }
 
+CHILDREN_STATUS_FACTORS = {
+    "any": 1.0,
+    "no_children": 0.62,
+    "has_children": 0.31,
+    "co_parenting_ready": 0.18,
+}
+
+FUTURE_CHILDREN_FACTORS = {
+    "any": 1.0,
+    "wants_children": 0.48,
+    "does_not_want_children": 0.22,
+    "open_or_undecided": 0.58,
+}
+
+MILITARY_STATUS_FACTORS = {
+    "any": 1.0,
+    "civilian_or_not_serving": 0.91,
+    "active_service": 0.07,
+    "veteran_or_service_history": 0.15,
+}
+
+RELOCATION_FACTORS = {
+    "any": 1.0,
+    "same_city_only": 0.22,
+    "open_to_relocation": 0.36,
+    "remote_or_long_distance_ok": 0.44,
+}
+
+HOUSING_FACTORS = {
+    "any": 1.0,
+    "independent_living": 0.48,
+    "own_or_stable_housing": 0.29,
+}
+
+SMOKING_FACTORS = {
+    "any": 1.0,
+    "non_smoker": 0.72,
+    "ok_with_smoking": 1.0,
+}
+
+ALCOHOL_FACTORS = {
+    "any": 1.0,
+    "rare_or_none": 0.46,
+    "moderate_ok": 0.76,
+}
+
+LANGUAGE_FACTORS = {
+    "any": 1.0,
+    "ukrainian_comfortable": 0.82,
+    "english_comfortable": 0.38,
+    "ukrainian_and_english": 0.31,
+}
+
+PETS_FACTORS = {
+    "any": 1.0,
+    "pet_friendly": 0.54,
+    "no_pets_preferred": 0.42,
+}
+
 DATA_QUALITY_NOTES = [
     {
         "label": "Population",
@@ -79,4 +138,16 @@ DATA_QUALITY_NOTES = [
         "label": "Height",
         "note": "Height currently requires proxy distribution unless a Ukraine-specific source is validated.",
     },
+    {
+        "label": "Military status",
+        "note": "War-related filters are sensitive, time-changing, and should stay scenario-only until sourced.",
+    },
+    {
+        "label": "Children",
+        "note": "Children and co-parenting filters are preference-context assumptions, not value judgments.",
+    },
+    {
+        "label": "Independence",
+        "note": "Multiplying many filters assumes independence; use results as a stress test, not a factual census.",
+    },
 ]

src/model_pool.py

@@ -4,12 +4,21 @@ from dataclasses import dataclass
 
 from .assumptions import (
     AGE_BAND_FACTORS,
+    ALCOHOL_FACTORS,
     BASELINE,
+    CHILDREN_STATUS_FACTORS,
     EDUCATION_FACTORS,
+    FUTURE_CHILDREN_FACTORS,
     HEIGHT_FACTORS,
+    HOUSING_FACTORS,
     INCOME_FACTORS,
+    LANGUAGE_FACTORS,
+    MILITARY_STATUS_FACTORS,
+    PETS_FACTORS,
     REGION_FACTORS,
     RELATIONSHIP_STATUS_FACTORS,
+    RELOCATION_FACTORS,
+    SMOKING_FACTORS,
     TARGET_POPULATION_FACTORS,
 )
 
@@ -25,6 +34,15 @@ class Criteria:
     min_height_cm: int
     income_level: str
     education_level: str
+    children_status: str
+    future_children: str
+    military_status: str
+    relocation: str
+    housing: str
+    smoking: str
+    alcohol: str
+    language: str
+    pets: str
 
 
 @dataclass(frozen=True)
@@ -78,6 +96,15 @@ def model_factors(criteria: Criteria) -> list[tuple[str, float]]:
         ("Minimum height", height_factor(criteria.min_height_cm)),
         ("Income threshold", INCOME_FACTORS[criteria.income_level]),
         ("Education filter", EDUCATION_FACTORS[criteria.education_level]),
+        ("Children status", CHILDREN_STATUS_FACTORS[criteria.children_status]),
+        ("Future children", FUTURE_CHILDREN_FACTORS[criteria.future_children]),
+        ("Military status", MILITARY_STATUS_FACTORS[criteria.military_status]),
+        ("Relocation", RELOCATION_FACTORS[criteria.relocation]),
+        ("Housing", HOUSING_FACTORS[criteria.housing]),
+        ("Smoking", SMOKING_FACTORS[criteria.smoking]),
+        ("Alcohol", ALCOHOL_FACTORS[criteria.alcohol]),
+        ("Language", LANGUAGE_FACTORS[criteria.language]),
+        ("Pets", PETS_FACTORS[criteria.pets]),
     ]