Adriano
3688611a40
Rimuove sidebar acquario (slider max-pesci, toggle label): la dimensione popolazione è già definita dal GA, le label sono ridondanti col pannello di ispezione. Mostra tutti i pesci della generazione selezionata. Aggiunge `build_lineage_index` (mappa ogni genome_id della run ai suoi attributi) e `trace_ancestors` (BFS sui parent_ids fino a max_levels, guardia su cicli). `build_fish_dataset` accetta ora il lineage_index e allega il campo `ancestors` ad ogni pesce; conserva la firma legacy per compat con i fixture di test esistenti. `build_aquarium_html` perde `show_labels`. Embedda click handler con hit-test in canvas pixel space (account per CSS scaling) + pannello info top-right con stile, fitness/DSR/Sharpe/maxDD/trades, prompt e albero discendenza colorato per cognitive_style. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
311 lines
8.9 KiB
Python
311 lines
8.9 KiB
Python
import importlib
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import pandas as pd
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def test_streamlit_app_imports():
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importlib.import_module("multi_swarm.dashboard.data")
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def test_dashboard_data_helpers_signatures():
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from multi_swarm.dashboard import data
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assert hasattr(data, "list_runs_df")
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assert hasattr(data, "generations_df")
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assert hasattr(data, "evaluations_df")
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assert hasattr(data, "genomes_df")
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def test_aquarium_helper_builds_html_with_click_handler():
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from multi_swarm.dashboard.aquarium import build_aquarium_html
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fish = [
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{
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"id": "abc123",
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"fitness": 0.8,
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"cognitive_style": "physicist",
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"n_trades": 30,
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"dsr": 0.7,
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"sharpe": 1.2,
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"max_dd": 0.1,
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"system_prompt": "test",
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"temperature": 0.9,
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"lookback_window": 200,
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"feature_access": ["close"],
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"model_tier": "C",
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"generation": 1,
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"parent_ids": [],
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"ancestors": [],
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}
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]
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html = build_aquarium_html(fish, canvas_w=800, canvas_h=400)
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assert "canvas" in html
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assert "abc123" in html # fish id present in JSON payload
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assert "addEventListener('click'" in html
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assert "fish-info-panel" in html
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assert "showFishInfo" in html
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assert "Discendenza" in html
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assert "requestAnimationFrame" in html
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def test_aquarium_build_fish_dataset_legacy_path():
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from multi_swarm.dashboard.aquarium import build_fish_dataset
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df = pd.DataFrame(
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[
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{
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"genome_id": "low",
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"fitness": 0.1,
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"cognitive_style": "physicist",
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"n_trades": 1,
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"dsr": 0.0,
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},
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{
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"genome_id": "high",
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"fitness": 0.9,
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"cognitive_style": "biologist",
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"n_trades": 10,
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"dsr": 0.5,
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},
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]
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)
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out = build_fish_dataset(df)
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ids = {f["id"] for f in out}
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assert ids == {"low", "high"}
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high = next(f for f in out if f["id"] == "high")
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assert high["cognitive_style"] == "biologist"
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assert high["ancestors"] == []
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def test_aquarium_build_fish_dataset_drops_nan_fitness():
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from multi_swarm.dashboard.aquarium import build_fish_dataset
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df = pd.DataFrame(
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[
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{
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"genome_id": "ok",
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"fitness": 0.4,
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"cognitive_style": "historian",
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"n_trades": 2,
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"dsr": 0.1,
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},
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{
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"genome_id": "bad",
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"fitness": float("nan"),
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"cognitive_style": "ecologist",
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"n_trades": 0,
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"dsr": 0.0,
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},
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]
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)
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out = build_fish_dataset(df)
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assert len(out) == 1
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assert out[0]["id"] == "ok"
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def test_aquarium_empty_input_returns_empty():
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from multi_swarm.dashboard.aquarium import build_aquarium_html, build_fish_dataset
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assert build_fish_dataset(pd.DataFrame()) == []
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html = build_aquarium_html([], canvas_w=400, canvas_h=200)
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assert "canvas" in html
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assert "Acquario vuoto" in html
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def test_build_lineage_index_returns_dict_keyed_by_id():
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from multi_swarm.dashboard.aquarium import build_lineage_index
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genomes = pd.DataFrame(
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[
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{
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"id": "g1",
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"generation_idx": 0,
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"generation": 0,
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"system_prompt": "x",
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"feature_access": ["close"],
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"temperature": 0.9,
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"top_p": 0.95,
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"model_tier": "C",
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"lookback_window": 100,
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"cognitive_style": "physicist",
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"parent_ids": [],
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},
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{
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"id": "g2",
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"generation_idx": 1,
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"generation": 1,
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"system_prompt": "y",
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"feature_access": ["close", "volume"],
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"temperature": 1.0,
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"top_p": 0.95,
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"model_tier": "C",
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"lookback_window": 200,
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"cognitive_style": "biologist",
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"parent_ids": ["g1"],
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},
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]
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)
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evals = pd.DataFrame(
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[
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{
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"genome_id": "g1",
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"fitness": 0.5,
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"dsr": 0.6,
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"sharpe": 1.2,
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"max_dd": 0.1,
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"n_trades": 30,
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"parse_error": None,
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"raw_text": "",
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},
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{
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"genome_id": "g2",
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"fitness": 0.7,
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"dsr": 0.8,
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"sharpe": 1.5,
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"max_dd": 0.05,
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"n_trades": 40,
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"parse_error": None,
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"raw_text": "",
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},
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]
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)
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idx = build_lineage_index(genomes, evals)
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assert "g1" in idx and "g2" in idx
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assert idx["g2"]["parent_ids"] == ["g1"]
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assert idx["g2"]["fitness"] == 0.7
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assert idx["g1"]["cognitive_style"] == "physicist"
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assert idx["g2"]["feature_access"] == ["close", "volume"]
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def test_trace_ancestors_walks_levels():
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from multi_swarm.dashboard.aquarium import trace_ancestors
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idx = {
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"child": {
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"id": "child",
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"parent_ids": ["p1", "p2"],
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"fitness": 0.8,
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"generation": 2,
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"cognitive_style": "physicist",
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},
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"p1": {
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"id": "p1",
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"parent_ids": ["gp1"],
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"fitness": 0.5,
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"generation": 1,
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"cognitive_style": "biologist",
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},
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"p2": {
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"id": "p2",
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"parent_ids": [],
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"fitness": 0.3,
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"generation": 1,
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"cognitive_style": "engineer",
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},
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"gp1": {
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"id": "gp1",
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"parent_ids": [],
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"fitness": 0.2,
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"generation": 0,
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"cognitive_style": "historian",
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},
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}
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levels = trace_ancestors("child", idx, max_levels=5)
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assert len(levels) == 2
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assert {a["id"] for a in levels[0]} == {"p1", "p2"}
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assert {a["id"] for a in levels[1]} == {"gp1"}
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def test_trace_ancestors_handles_cycles():
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from multi_swarm.dashboard.aquarium import trace_ancestors
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# Pathological cycle: a <-> b. Should terminate cleanly.
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idx = {
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"a": {
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"id": "a",
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"parent_ids": ["b"],
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"fitness": 0.1,
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"generation": 1,
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"cognitive_style": "physicist",
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},
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"b": {
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"id": "b",
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"parent_ids": ["a"],
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"fitness": 0.2,
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"generation": 0,
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"cognitive_style": "biologist",
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},
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}
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levels = trace_ancestors("a", idx, max_levels=5)
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# a -> b at level 0; b's only parent is a, already seen -> stop.
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assert len(levels) == 1
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assert levels[0][0]["id"] == "b"
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def test_trace_ancestors_no_parents_returns_empty():
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from multi_swarm.dashboard.aquarium import trace_ancestors
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idx = {
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"solo": {
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"id": "solo",
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"parent_ids": [],
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"fitness": 0.4,
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"generation": 0,
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"cognitive_style": "engineer",
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},
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}
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assert trace_ancestors("solo", idx) == []
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def test_build_fish_dataset_attaches_ancestors():
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from multi_swarm.dashboard.aquarium import build_fish_dataset, build_lineage_index
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genomes = pd.DataFrame(
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[
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{
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"id": "p",
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"generation_idx": 0,
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"generation": 0,
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"system_prompt": "p",
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"feature_access": ["close"],
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"temperature": 0.8,
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"top_p": 0.9,
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"model_tier": "C",
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"lookback_window": 100,
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"cognitive_style": "physicist",
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"parent_ids": [],
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},
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{
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"id": "c",
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"generation_idx": 1,
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"generation": 1,
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"system_prompt": "c",
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"feature_access": ["close"],
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"temperature": 0.8,
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"top_p": 0.9,
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"model_tier": "C",
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"lookback_window": 120,
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"cognitive_style": "biologist",
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"parent_ids": ["p"],
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},
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]
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)
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evals = pd.DataFrame(
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[
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{"genome_id": "p", "fitness": 0.3, "dsr": 0.0, "sharpe": 0.0,
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"max_dd": 0.0, "n_trades": 0},
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{"genome_id": "c", "fitness": 0.6, "dsr": 0.0, "sharpe": 0.0,
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"max_dd": 0.0, "n_trades": 0},
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]
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)
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lineage = build_lineage_index(genomes, evals)
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active = genomes[genomes["generation_idx"] == 1].merge(
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evals, left_on="id", right_on="genome_id", how="left"
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)
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fish = build_fish_dataset(active, lineage)
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assert len(fish) == 1
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assert fish[0]["id"] == "c"
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assert len(fish[0]["ancestors"]) == 1
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assert fish[0]["ancestors"][0][0]["id"] == "p"
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