79 lines
3.3 KiB
Python
79 lines
3.3 KiB
Python
from pathlib import Path
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import numpy as np
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from app.services.interviews.base import PersonaRecord, MemoryDigest
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from app.services.interviews.diversity import (
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DiversitySubagent, run_typology,
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)
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class _Mem:
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def get_digest(self, agent_id, max_chars=2000):
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return MemoryDigest(text="x", available=True)
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class _CannedLLM:
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def chat_json(self, messages, temperature=0.0, max_tokens=None, **kw):
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# Place all 24 statements into legal buckets per the forced distribution
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placements = {}
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buckets = [-3]*2 + [-2]*3 + [-1]*4 + [0]*6 + [1]*4 + [2]*3 + [3]*2
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for i in range(24):
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placements[f"st_{i+1:02d}"] = buckets[i]
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return {
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"placements": placements,
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"likert_axes": {"ax_pres_extr": 5, "ax_loc_eu": 3, "ax_sci_trad": 4,
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"ax_ind_col": 4, "ax_short_long": 5, "ax_mkt_reg": 3},
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}
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INSTRUMENT = Path(__file__).resolve().parents[2] / "scripts" / "instruments" / "diversity_v1.yaml"
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def test_diversity_administer():
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sub = DiversitySubagent(llm=_CannedLLM(), memory=_Mem(), instrument_path=INSTRUMENT)
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persona = PersonaRecord(agent_id=1, name="A", persona="p")
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resp = sub.administer(persona)
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assert len(resp.placements) == 24
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assert set(resp.likert_axes.keys()) == {
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"ax_pres_extr","ax_loc_eu","ax_sci_trad","ax_ind_col","ax_short_long","ax_mkt_reg"
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}
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def test_typology_runs_pca_kmeans():
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from app.models.interview import QSortResponse
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rng = np.random.default_rng(42)
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responses = []
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for aid in range(20):
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placements = {f"st_{i+1:02d}": int(rng.integers(-3, 4)) for i in range(24)}
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axes = {f"ax_{j}": int(rng.integers(1, 8)) for j in range(6)}
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responses.append(QSortResponse(agent_id=aid, placements=placements, likert_axes=axes))
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result = run_typology(responses, n_clusters=3)
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assert "clusters" in result
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assert len(result["clusters"]) == 3
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assert "pca" in result
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assert len(result["pca"]["components"]) >= 2
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def test_diversity_accepts_string_likert_values():
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"""Diversity placements + axes should accept stringified ints."""
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from app.services.interviews.base import PersonaRecord, MemoryDigest
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from app.services.interviews.diversity import DiversitySubagent
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from pathlib import Path as _P
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class _Mem:
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def get_digest(self, agent_id, max_chars=2000):
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return MemoryDigest(text="x", available=True)
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buckets = [-3]*2 + [-2]*3 + [-1]*4 + [0]*6 + [1]*4 + [2]*3 + [3]*2
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class _StringLLM:
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def chat_json(self, messages, temperature=0.0, max_tokens=None, **kw):
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return {
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"placements": {f"st_{i+1:02d}": str(buckets[i]) for i in range(24)},
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"likert_axes": {a: "4" for a in (
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"ax_pres_extr","ax_loc_eu","ax_sci_trad",
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"ax_ind_col","ax_short_long","ax_mkt_reg")},
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}
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inst = _P(__file__).resolve().parents[2] / "scripts" / "instruments" / "diversity_v1.yaml"
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sub = DiversitySubagent(llm=_StringLLM(), memory=_Mem(), instrument_path=inst)
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persona = PersonaRecord(agent_id=7, name="A", persona="p")
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resp = sub.administer(persona)
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assert isinstance(resp.placements["st_01"], int)
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assert isinstance(resp.likert_axes["ax_pres_extr"], int)
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assert resp.likert_axes["ax_pres_extr"] == 4
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