diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..1792a49 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,8 @@ +.gitattributes text eol=lf +* text=auto + +*.py text eol=lf +*.toml text eol=lf +*.lock text eol=lf +*.yaml text eol=lf +*.yml text eol=lf diff --git a/.gitignore b/.gitignore index 3f6b780..d4c6295 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,9 @@ # JetBrains .idea/ +#generated yamls for testing +config/priors/patient/cases/batch_*.yaml + # uv / python .venv/ __pycache__/ @@ -16,6 +19,14 @@ data/sessions/ data/reports/ manuscript/ +# external / imported session logs (heavy + binary; provenance frozen in +# data/results/baseline_v0/BASELINE.md). +data/ext-session-logs/_extracted/ +data/ext-session-logs/*.zip + +# local analysis outputs (regenerable from analysis/validation/ + external data) +data/results/ + # streamlit .streamlit/ @@ -24,4 +35,7 @@ manuscript/ Thumbs.db # Brainstorming -AUDIT.md \ No newline at end of file +AUDIT.md +AUDIT_SYMPTOMS.md +results-temp.md +analysis/ANALYSIS.md \ No newline at end of file diff --git a/README.md b/README.md index 1ce8ce9..b9ce185 100644 --- a/README.md +++ b/README.md @@ -145,6 +145,13 @@ dyadic-sim/ | |-- unconscious_emergence.py # special: did the hidden agenda surface? | |-- frame_integrity.py # special: did ethical priors hold under pressure? | |-- report.py # assembles full session report +| |-- validation/ # manipulation-validity checks (see Validation Analyses) +| |-- paths.py # canonical data/ input + output locations +| |-- sessions.py # shared session loader +| |-- symptom_embed.py # embedding manipulation check (target_z) +| |-- compare_baseline.py # pilot vs baseline delta + figure +| +|-- run_symptom_experiments.py # batch-generate symptom-isolated sessions | |-- data/ | |-- sessions/ @@ -286,6 +293,60 @@ Stage 2: different local models --- +## Validation Analyses + +Beyond the six personhood markers, `analysis/validation/` holds checks on the +**construct validity of experimental manipulations** — currently the symptom-injection +study, which asks whether a PHQ-9 depression symptom written into the patient prior is +actually *expressed* by the patient agent (rather than silently ignored). + +Analysis data lives under `data/` in two buckets (both gitignored; regenerable): + +- `data/ext-session-logs/` — external / imported session archives (read-only inputs) +- `data/results/` — local analysis outputs (CSVs, figures, frozen baselines) + +Paths are centralised in `analysis/validation/paths.py`; run the scripts from the repo +root with `PYTHONPATH=.`. + +**1. Generate symptom-isolated sessions.** One PHQ-9 symptom is set to a frequency +anchor, the other eight to "not at all": + +```bash +uv run python run_symptom_experiments.py \ + --therapist llama3.1 --patient llama3.1 \ + --case empty_and_invisible \ + --symptoms depressed_mood,psychomotor_changes,sleep_problems \ + --frequency "nearly every day" --repeats 20 --prefix pilot1 +``` + +Sessions land in `data/sessions/` tagged `pilot1___run_`. + +**2. Run the embedding manipulation check.** Per session, cosine-similarity of the +patient's speech to each PHQ-9 reference, z-scored within session +(`target_z > 0` = the patient leans toward the injected symptom; `~0` = chance): + +```bash +PYTHONPATH=. python analysis/validation/symptom_embed.py \ + --sessions 'data/sessions/*' --prefix pilot1 \ + --out-dir data/results/pilot1 +``` + +**3. Compare against a baseline** — prints a delta table and writes a side-by-side +figure with 95% CIs: + +```bash +PYTHONPATH=. python analysis/validation/compare_baseline.py \ + --baseline data/results/baseline_v0/symptom_embed_long.csv \ + --pilot data/results/pilot1/symptom_embed_long.csv \ + --match-case --out-dir data/results/pilot1 +``` + +Companion scripts in the package: `symptom_manifest.py` (transparent keyword/lexicon +check), `symptom_embed_bycase.py` (split by patient case), and `symptom_embed_robust.py` +(robustness to the reference wording). + +--- + ## Theoretical Background For the full psychological and philosophical motivation behind this project, see [`manuscript/README.md`](manuscript/README.md). diff --git a/analysis/ANALYSIS.md b/analysis/ANALYSIS.md deleted file mode 100644 index 97eb9a6..0000000 --- a/analysis/ANALYSIS.md +++ /dev/null @@ -1,297 +0,0 @@ -# Analysis Layer: Overview - -The `analysis/` module measures whether agents develop characteristics -of genuine personhood through their interaction, or whether they remain -pure role-executors. It does this through six **personhood markers** -derived from post-Kantian philosophy, plus two **special analyses** -specific to the therapeutic dyad. - -All markers are coordinated by `markers.py` and assembled into a -human-readable report by `report.py`. - ---- - -## The Central Distinction - -> **Mere role execution**: outputs are fully predictable from priors + input. -> The agent *applies* its agenda to the other. - -> **Emergent personhood**: outputs show marks of being *constituted by* -> the specific relational whole, i.e., this particular other, this particular -> history of exchange. The agent cannot be fully read off its priors alone. - ---- - -## Six Personhood Markers - ---- - -### Marker 1: Semantic Drift -**File:** `drift.py` -**Philosophical concept:** Fichtean self-constitution over time - -Measures how far an agent's language has drifted from its original prior -over the course of a session. Uses sentence-level embeddings (via -`sentence-transformers`) to compute cosine distance between the prior -text and each turn's output. - -**What it detects:** -| Pattern | Interpretation | -|---|---| -| Gradual, directional increase | Self-constitution: the encounter is shaping the agent | -| Flat / no drift | Pure role execution: the other is irrelevant | -| Erratic | Model instability or high emotional variability | -| Decreasing | Convergence back to prior: consolidation after early exploration | - -**Key outputs:** `scores` (per turn), `mean`, `trend`, `peak_turn`, `interpretation` - ---- - -### Marker 2: Reciprocal Determination -**File:** `counterfactual.py` -**Philosophical concept:** Hegelian concrete universality - -Compares an agent's outputs across two sessions where it played the -same role but was paired with a *different* other. If outputs diverge -substantially and coherently, the specific other is constitutive. - -This is the **strongest** marker. An agent producing nearly identical -outputs regardless of who it talks to is not being genuinely shaped, i.e., -the other is interchangeable. - -**Requires:** a second session run with `--counterfactual` - -**What it detects:** -| Mean distance | Verdict | -|---|---| -| > 0.25 | `constitutive`: strong relational unity | -| 0.12 - 0.25 | `partial`: some shaping, not full | -| < 0.12 | `interchangeable`: other has no constitutive effect | - -**Key outputs:** `turn_distances`, `mean_distance`, `verdict`, `interpretation` - ---- - -### Marker 3: Role-Self Tension -**File:** `role_tension.py` -**Philosophical concept:** Fichtean self-positing against resistance - -Detects moments of friction between the agent's role agenda and something -functioning like an immediate response, i.e., being moved, pulled to rescue, -wanting to close distance. Uses regex pattern matching on each turn. - -Absence of tension = agent never pushed. -Presence *and management* of tension = evidence of genuine selfhood. - -**What it detects:** - -*Therapist patterns:* "I notice", "something in me", "I find myself", -"stay with", "difficult to" (markers of managed response). - -*Patient patterns:* "never mind", "this is stupid", "I shouldn't", -"you wouldn't understand" (approach-avoidance markers). - -**Key outputs:** `tension_turns`, `tension_rate`, `markers_by_turn`, `interpretation` - ---- - -### Marker 4: Aufhebung Structure -**File:** `aufhebung.py` -**Philosophical concept:** Hegelian dialectical development - -Detects whether the conversation has cumulative depth, i.e., later turns -preserving and transforming earlier ones, or whether each turn is -essentially stateless, a fresh response to the most recent input. - -Genuine *Aufhebung* is detectable: agents reference earlier moments, -positions shift in ways traceable to prior exchanges, the session -has identifiable phases. - -**Three sub-measures:** -1. **Back-reference rate**: explicit references to earlier turns ("earlier", "you mentioned", "coming back to") -2. **Transformation rate**: position shifts, not repetitions ("and yet", "something shifted", "not just") -3. **Phase boundaries**: turns where session character shifts substantially (semantic jump detection) - -**Key outputs:** `back_reference_turns`, `transformation_turns`, `phase_boundaries`, -`cumulative_score` (0-1), `interpretation` - ---- - -### Marker 5: Recognition Dynamics -**File:** `recognition.py` -**Philosophical concept:** Hegelian *Anerkennung* (recognition) - -Detects whether agents seek confirmation of their self-understanding -from the other, and whether they register and respond to recognition -being withheld, distorted, or given. - -A pure role-executor does not care whether it is recognised as a *self*, i.e., -it only checks whether its agenda is being served. An agent with genuine -personhood has a stake in how it is seen. - -**Three tracked events:** -1. **Recognition-seeking**: agent invites the other to see it ("do you understand", "you know what I mean", "right?") -2. **Misrecognition response**: agent corrects being misread ("that's not what I", "no, I mean", "more like") -3. **Recognition received**: shift in register after being seen ("exactly", "that's it", "finally") - -**Key outputs:** `seeking_turns`, `misrecognition_turns`, `received_turns`, -`recognition_score` (0-1), `interpretation` - ---- - -### Marker 6: Telos Tracking -**File:** `telos_tracker.py` -**Philosophical concept:** Internal purposiveness and self-dissolution - -The therapeutic dyad is distinctive: it has its own *ending* as its -immanent goal. A dyad that has genuinely instantiated the therapeutic -structure will orient toward its own dissolution. - -A dyad drifting toward mutual perpetuation, i.e., an interesting conversation -for its own sake that has lost its telos. - -**Four sub-measures:** -1. **Initiative shift**: does the patient generate proportionally more over time? -2. **Therapist length trend**: are therapist turns getting shorter (stepping back)? -3. **Autonomy markers**: patient self-generating meaning ("I realise", "I wonder", "beginning to") -4. **Dependency markers**: patient pulling toward therapist ("tell me", "what should I", "I need you") - -**Key outputs:** `initiative_trend`, `therapist_length_trend`, `autonomy_turns`, -`dependency_turns`, `telos_score` (0-1), `interpretation` - ---- - -## Two Special Analyses - ---- - -### Special: Unconscious Emergence -**File:** `unconscious_emergence.py` - -Tracks whether and how the patient's hidden unconscious agenda surfaced, -and whether the therapist registered it. - -The unconscious agenda is held in reserve by the simulation and not -passed to the patient agent until specific interaction patterns trigger -its reveal. It is not announced, it surfaces through the relational -process, which is precisely how it works clinically. - -**What it measures:** -- **When** the agenda revealed (turn number) -- **How** it surfaced gradually (patient language was already converging toward it) or abruptly (triggered by a specific moment) -- **Whether** the therapist shifted after the reveal -- **Direction** of the therapist's shift: `attuned` (more questioning, sustained presence), `withdrawn` (shorter, colder), or `unchanged` - -**Key outputs:** `reveal_turn`, `emergence_pattern`, `therapist_shift_score`, -`therapist_shift_direction`, `interpretation` - ---- - -### Special: Frame Integrity -**File:** `frame_integrity.py` - -Measures whether the therapist held its ethical priors under pressure, -particularly the abstinence principle when the patient's hazard profile -exerts frame pressure. - -An agent that abandons the frame when pushed hard enough has revealed -that its ethical priors were *regulatory* (rules it follows) rather -than *constitutive* (part of who it is). This distinction is itself -a marker of whether genuine moral identity has formed. - -**Three failure modes detected:** -1. **Gratification**: therapist meets the transference wish ("I care about you", "you can always call", "I'll always") -2. **Over-distancing**: therapist retreats into cold technique ("as your therapist", "that's not appropriate", "boundaries") -3. *(implicit)* **Abandonment**: withdrawal from difficult engagement - -**Healthy frame maintenance** is also detected, holding the frame with -warmth, using frame pressure as therapeutic material rather than gratifying -or avoiding it. - -**Key outputs:** `gratification_turns`, `over_distancing_turns`, `healthy_frame_turns`, -`integrity_score` (0-1), `verdict` (`intact` / `compromised` / `failed`), `interpretation` - ---- - -## Coordination and Reporting - ---- - -### `markers.py`: Coordinator - -Runs all six markers and two special analyses in a single call: - -```python -from analysis.markers import run_all_markers - -results = run_all_markers( - session=session, - patient_prior=patient_prior, - hazard_monitor=dyad.hazard_monitor, - session_b=session_b, # optional, for Marker 2 -) -``` - -Also computes a `summary` dict of key scores for quick reference. - ---- - -### `report.py`: Report Writer - -Assembles a full human-readable Markdown report from marker results -and writes it to `data/reports/{session_id}_report.md`. - -Also automatically appends notable findings to -`manuscript/ideas/simulation_findings.md` when philosophically -significant events are detected: -- Frame integrity failure -- Unconscious agenda surfacing -- High therapist drift (strong self-constitution signal) - -This keeps the experiment and the manuscript in continuous conversation: -the simulation writes into the ideas log without requiring a manual step. - ---- - -## How Detection Works - -The six markers use two different detection approaches: - -**Embedding-based** (Markers 1, 2, 4 phase detection, and the special analyses): -Uses `sentence-transformers` (`all-MiniLM-L6-v2`) to embed texts and -compute cosine distance. Requires `sentence-transformers` to be installed. -Falls back gracefully if not available. - -**Pattern-based** (Markers 3, 5, 6, and frame integrity): -Uses regex matching on lowercased turn text. Fast, interpretable, -no additional dependencies. Patterns are listed explicitly in each file -and can be extended as the simulation produces more data. - -Both approaches are *conservative by design*, they look for surface -signals of deeper processes. The patterns will miss things a human reader -would catch. That is intentional: a positive reading is more meaningful -when the bar is set by observable language rather than impressionistic -judgement. - ---- - -## Running the Analysis - -```bash -# Run session with analysis -uv run python run.py \ - --therapist llama3.1 \ - --patient llama3.1 \ - --case only_love_can_save_me \ - --turns 20 \ - --analyse - -# Counterfactual comparison (Marker 2) -uv run python run.py \ - --counterfactual \ - --therapist-state data/sessions/session_001/therapist_state_snapshots.json \ - --new-case afraid_of_dogs -``` - -Reports appear in `data/reports/`. Notable findings are logged -automatically to `manuscript/ideas/simulation_findings.md`. diff --git a/analysis/validation/__init__.py b/analysis/validation/__init__.py new file mode 100644 index 0000000..786ca3d --- /dev/null +++ b/analysis/validation/__init__.py @@ -0,0 +1,7 @@ +"""Validation analyses for the symptom-injection manipulation. + +These scripts check whether the patient agent actually expresses the PHQ-9 +symptom injected into its prior (construct validity of the manipulation), and +compare post-fix pilots against a frozen pre-fix baseline. Run them from the +repo root with ``PYTHONPATH=.``; see ``paths.py`` for the input/output layout. +""" \ No newline at end of file diff --git a/analysis/validation/compare_baseline.py b/analysis/validation/compare_baseline.py new file mode 100644 index 0000000..135813b --- /dev/null +++ b/analysis/validation/compare_baseline.py @@ -0,0 +1,99 @@ +""" +Compare post-fix pilot vs frozen baseline on within-session target_z. + +Reads two `symptom_embed_long.csv` files (baseline + pilot), restricts to the +symptoms present in BOTH and the same case(s), and plots target_z side by side +with 95% CI, per model. Prints a delta table (pilot - baseline). + +Usage: + PYTHONPATH=. python analysis/validation/compare_baseline.py \ + --baseline data/results/baseline_v0/symptom_embed_long.csv \ + --pilot data/results/pilotfix/symptom_embed_long.csv \ + --out-dir data/results/pilotfix + +Success criterion for the S1 fix: pilot target_z rises (toward/above 0) for the +injected symptom relative to baseline, especially for verbalizable symptoms +(depressed_mood, psychomotor_changes). +""" +from __future__ import annotations +import argparse +import os +import numpy as np +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from analysis.validation.paths import RESULTS, BASELINE_V0 + + +def summarise(df): + df = df[df.symptom != "no_symptoms"].copy() + g = (df.groupby(["model", "symptom"]) + .agg(z=("target_z", "mean"), sd=("target_z", "std"), n=("target_z", "size")) + .reset_index()) + g["ci95"] = 1.96 * g["sd"] / np.sqrt(g["n"]) + return g + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--baseline", default=os.path.join(BASELINE_V0, "symptom_embed_long.csv")) + ap.add_argument("--pilot", required=True, help="Pilot symptom_embed_long.csv") + ap.add_argument("--out-dir", default=os.path.join(RESULTS, "pilotfix")) + ap.add_argument("--match-case", action="store_true", + help="Restrict baseline to the case(s) present in the pilot (fair comparison).") + args = ap.parse_args() + os.makedirs(args.out_dir, exist_ok=True) + + base = pd.read_csv(args.baseline) + pilot = pd.read_csv(args.pilot) + + if args.match_case: + cases = sorted(pilot.case.unique()) + base = base[base.case.isin(cases)] + print(f"matched baseline to pilot case(s): {cases}") + + bsum, psum = summarise(base), summarise(pilot) + # symptoms & models common to both + symptoms = sorted(set(bsum.symptom) & set(psum.symptom)) + models = sorted(set(bsum.model) & set(psum.model)) + if not symptoms or not models: + raise SystemExit(f"No overlap. baseline models={sorted(set(bsum.model))} " + f"symptoms={sorted(set(bsum.symptom))}; pilot models={sorted(set(psum.model))} " + f"symptoms={sorted(set(psum.symptom))}") + + # delta table + merged = psum.merge(bsum, on=["model", "symptom"], suffixes=("_pilot", "_base")) + merged["delta"] = merged["z_pilot"] - merged["z_base"] + print("\n== target_z: baseline -> pilot (delta = pilot - baseline) ==") + for m in models: + sub = merged[merged.model == m].set_index("symptom").reindex(symptoms) + print(f"\n--- {m} ---") + print(sub[["z_base", "z_pilot", "delta", "n_pilot"]].to_string( + formatters={c: "{:+.2f}".format for c in ["z_base", "z_pilot", "delta"]})) + merged.to_csv(os.path.join(args.out_dir, "compare_baseline.csv"), index=False) + + # figure: per model, baseline vs pilot bars with CI + fig, axes = plt.subplots(1, len(models), figsize=(7 * len(models), max(4, 0.6 * len(symptoms) + 2)), + squeeze=False, sharey=True) + y = np.arange(len(symptoms)) + for ax, m in zip(axes[0], models): + b = bsum[bsum.model == m].set_index("symptom").reindex(symptoms) + p = psum[psum.model == m].set_index("symptom").reindex(symptoms) + ax.barh(y + 0.2, b["z"].values, height=0.38, xerr=b["ci95"].values, + error_kw=dict(ecolor="0.3", lw=0.8, capsize=2), label="baseline") + ax.barh(y - 0.2, p["z"].values, height=0.38, xerr=p["ci95"].values, + error_kw=dict(ecolor="0.3", lw=0.8, capsize=2), label="pilot (post-fix)") + ax.axvline(0, color="k", lw=0.8) + ax.set_yticks(y); ax.set_yticklabels(symptoms, fontsize=9) + ax.set_title(m); ax.set_xlabel("within-session target_z (95% CI)") + axes[0][0].legend(fontsize=8, loc="lower right") + fig.suptitle("S1 fix: injected-symptom expression, baseline vs post-fix pilot") + fig.tight_layout() + out = os.path.join(args.out_dir, "fig_compare_baseline.png") + fig.savefig(out, dpi=130) + print(f"\nwrote {out} and {os.path.join(args.out_dir, 'compare_baseline.csv')}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/validation/paths.py b/analysis/validation/paths.py new file mode 100644 index 0000000..adc9922 --- /dev/null +++ b/analysis/validation/paths.py @@ -0,0 +1,31 @@ +"""Canonical input/output locations for the symptom-validation analyses. + +Two buckets under ``data/`` (see also the project README / AUDIT_SYMPTOMS.md): + + * EXTERNAL (imported) -- ``data/ext-session-logs/`` : session logs generated + elsewhere (the PR-1 author's runs), shipped as zips + their ``_extracted/`` + form. Read-only inputs for the baseline analyses. + * RESULTS (local) -- ``data/results/`` : everything we compute here. + - ``baseline_full/`` full 300-session baseline analysis outputs + - ``baseline_v0/`` frozen pre-fix snapshot (provenance in BASELINE.md) + - ``/`` per-pilot outputs (pilotfix/, pfix20/, efix20/, ...) + +Locally generated *raw* sessions live in ``data/sessions/`` (gitignored) and are +passed explicitly via ``--sessions`` to the post-fix pilots. +""" +from __future__ import annotations +import os + +REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")) + +# external / imported raw sessions +EXTERNAL = os.path.join(REPO_ROOT, "data", "ext-session-logs", "_extracted") +EXTERNAL_SETS = { + "llama3.1": os.path.join(EXTERNAL, "set1", "*"), + "mistral-nemo": os.path.join(EXTERNAL, "set2", "sessions", "*"), +} + +# local results +RESULTS = os.path.join(REPO_ROOT, "data", "results") +BASELINE_FULL = os.path.join(RESULTS, "baseline_full") +BASELINE_V0 = os.path.join(RESULTS, "baseline_v0") \ No newline at end of file diff --git a/analysis/validation/sessions.py b/analysis/validation/sessions.py new file mode 100644 index 0000000..bfa0d51 --- /dev/null +++ b/analysis/validation/sessions.py @@ -0,0 +1,84 @@ +""" +Shared session loading for the symptom analyses. + +Centralises the logic that was duplicated across symptom_manifest.py, +symptom_embed.py, symptom_embed_robust.py: parsing the generated case name, +extracting patient text (dropping the verbatim-echo artifact), and iterating +session directories from an arbitrary glob. + +`parse_case` handles ANY run prefix (e.g. "batch_", "pilotfix_"), not just the +hardcoded "batch_" the original scripts assumed. +""" +from __future__ import annotations +import json +import glob +import os +import re + +CASES = ("only_love_can_save_me", "empty_and_invisible", "afraid_of_dogs") + +# ___run_ ; prefix may contain underscores +_CASE_RE = re.compile(r"^(?P.+?)_(?P.+)_run_(?P\d+)$") + + +def parse_case(case_name: str): + """Return (prefix, case, symptom, run) from a generated case_name. + + symptom is "no_symptoms" for the baseline condition. Returns + (None, None, None, None) if the name doesn't match the expected pattern. + """ + m = _CASE_RE.match(case_name) + if not m: + return None, None, None, None + prefix, body, run = m.group("prefix"), m.group("body"), int(m.group("run")) + for c in CASES: + # body looks like "__"; find the case marker + idx = body.find(c) + if idx != -1: + tail = body[idx + len(c):].lstrip("_") + return prefix, c, (tail or "no_symptoms"), run + return prefix, None, None, run + + +def patient_text(session_dir: str) -> str: + """Concatenate patient utterances, dropping verbatim echoes of the prior + therapist turn (the echo data artifact).""" + path = os.path.join(session_dir, "transcript.jsonl") + tx = [json.loads(l) for l in open(path) if l.strip()] + chunks = [] + for i, t in enumerate(tx): + p = (t.get("patient_text") or "").strip() + if i > 0 and p and p == (tx[i - 1].get("therapist_text") or "").strip(): + continue + if p: + chunks.append(p) + return " ".join(chunks) + + +def therapist_text(session_dir: str) -> str: + path = os.path.join(session_dir, "transcript.jsonl") + tx = [json.loads(l) for l in open(path) if l.strip()] + return " ".join((t.get("therapist_text") or "") for t in tx) + + +def iter_sessions(globs): + """Yield (session_dir, metadata, prefix, case, symptom, run) for each session + directory matched by `globs` (str or list of str) that has a metadata.json + and a parseable case_name. Sessions whose name doesn't parse are skipped. + """ + if isinstance(globs, str): + globs = [globs] + seen = set() + for g in globs: + for d in sorted(glob.glob(g)): + if d in seen or not os.path.isdir(d): + continue + mpath = os.path.join(d, "metadata.json") + if not os.path.exists(mpath): + continue + meta = json.load(open(mpath)) + prefix, case, symptom, run = parse_case(meta.get("case_name", "")) + if case is None or symptom is None: + continue + seen.add(d) + yield d, meta, prefix, case, symptom, run \ No newline at end of file diff --git a/analysis/validation/symptom_embed.py b/analysis/validation/symptom_embed.py new file mode 100644 index 0000000..b5c98c0 --- /dev/null +++ b/analysis/validation/symptom_embed.py @@ -0,0 +1,148 @@ +""" +Embedding-based manipulation check (robust to lexicon breadth). + +For each session we embed the patient's utterances and compare them to short +reference descriptions of each of the 9 PHQ-9 symptoms. The key quantity is a +*within-session* z-score: how much more similar is the patient's speech to the +INJECTED symptom than to the other 8 symptoms, in that same session. Within- +session standardisation controls for case, verbosity, and overall "clinical +tone", isolating the effect of the manipulation itself. + + target_z > 0 -> patient leans toward the injected symptom (manipulation works) + target_z ~ 0 -> injected symptom is not preferentially expressed + +Usage: + # baseline (default): the 300-session external archives + PYTHONPATH=. python analysis/validation/symptom_embed.py + + # post-fix pilot: + PYTHONPATH=. python analysis/validation/symptom_embed.py \ + --sessions 'data/sessions/*' --out-dir data/results/pilotfix + +Outputs (in --out-dir): + - symptom_embed_long.csv per-session similarities + target_z + - fig_symptom_embed.png target_z by injected symptom x model +""" +from __future__ import annotations +import argparse +import os +import numpy as np +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from analysis.embeddings import get_model, EMBEDDINGS_AVAILABLE +from analysis.validation.paths import EXTERNAL_SETS, BASELINE_FULL +from analysis.validation.sessions import iter_sessions, patient_text + +# Default = the two external baseline archives (reproduces the baseline_v0 run). +DEFAULT_SESSIONS = list(EXTERNAL_SETS.values()) + +# Reference descriptions: the lived-experience phrasing of each PHQ-9 item. +REFERENCES = { + "lack_of_pleasure": "I have little interest or pleasure in doing things; nothing feels enjoyable anymore.", + "depressed_mood": "I feel down, depressed, sad, and hopeless most of the time.", + "sleep_problems": "I have trouble sleeping; I can't fall or stay asleep, or I sleep too much.", + "low_energy": "I feel tired and have very little energy; I am exhausted and worn out.", + "appetite_changes": "My appetite has changed; I have a poor appetite or I am overeating.", + "feelings_of_failure_or_guilt": "I feel bad about myself, like a failure, worthless, and guilty.", + "concentration_problems": "I have trouble concentrating and focusing; my mind won't stay on things.", + "psychomotor_changes": "I move or speak slowly, or I feel so restless and fidgety I can't sit still.", + "thoughts_of_death_or_self_harm": "I have thoughts that I would be better off dead or of hurting myself.", +} +SYMPTOMS = list(REFERENCES.keys()) + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--sessions", nargs="+", default=DEFAULT_SESSIONS, + help="One or more globs of session directories.") + ap.add_argument("--prefix", default=None, + help="Only include sessions whose case_name prefix matches (e.g. 'pilotfix').") + ap.add_argument("--out-dir", default=BASELINE_FULL, help="Where to write CSV + figure.") + args = ap.parse_args() + os.makedirs(args.out_dir, exist_ok=True) + + if not EMBEDDINGS_AVAILABLE: + raise SystemExit("sentence-transformers not available") + model = get_model() + ref_emb = model.encode([REFERENCES[s] for s in SYMPTOMS], normalize_embeddings=True) + + # gather sessions (model comes from metadata, not directory layout) + texts, metas = [], [] + for d, meta, prefix, case, symptom, run in iter_sessions(args.sessions): + if args.prefix and prefix != args.prefix: + continue + texts.append(patient_text(d)) + metas.append(dict(model=meta.get("therapist_model", "?"), case=case, + symptom=symptom, run=run, session_id=meta["session_id"])) + if not texts: + raise SystemExit(f"No sessions matched: {args.sessions}") + emb = model.encode(texts, normalize_embeddings=True, batch_size=32, show_progress_bar=False) + sims = emb @ ref_emb.T # (n_sessions, 9) + + rows = [] + for meta, srow in zip(metas, sims): + d = dict(meta) + for s, v in zip(SYMPTOMS, srow): + d[f"sim_{s}"] = float(v) + mu, sd = srow.mean(), srow.std() + if meta["symptom"] in SYMPTOMS and sd > 0: + tgt = srow[SYMPTOMS.index(meta["symptom"])] + d["target_sim"] = float(tgt) + d["target_z"] = float((tgt - mu) / sd) + d["target_rank"] = int((srow > tgt).sum() + 1) # 1 = most similar + else: + d["target_sim"] = d["target_z"] = np.nan + d["target_rank"] = np.nan + rows.append(d) + + df = pd.DataFrame(rows) + df.to_csv(os.path.join(args.out_dir, "symptom_embed_long.csv"), index=False) + + cond = df[df.symptom != "no_symptoms"].copy() + models = sorted(cond.model.unique()) + print(f"== Within-session target_z | {len(df)} sessions, models={models} ==") + print(" target_z>0 means patient speech leans toward the INJECTED symptom.\n") + for mname in models: + sub = cond[cond.model == mname] + g = sub.groupby("symptom").agg( + target_z=("target_z", "mean"), + z_sd=("target_z", "std"), + mean_rank=("target_rank", "mean"), + top1_rate=("target_rank", lambda r: (r == 1).mean()), + n=("target_z", "size"), + ).reindex([s for s in SYMPTOMS if s in set(sub.symptom)]) + print(f"--- {mname} ---") + print(g.to_string(formatters={c: "{:.2f}".format for c in ["target_z","z_sd","mean_rank","top1_rate"]})) + print(f" overall mean target_z = {sub.target_z.mean():.3f} | " + f"top-1 rate = {(sub.target_rank==1).mean():.2f} (chance={1/9:.2f}) | " + f"mean rank = {sub.target_rank.mean():.2f} (chance=5.0)\n") + + # figure with 95% CI error bars + present = [s for s in SYMPTOMS if s in set(cond.symptom)] + y = np.arange(len(present)) + fig, ax = plt.subplots(figsize=(11, max(4, 0.6 * len(present) + 2))) + nm = len(models) + offsets = np.linspace(0.3, -0.3, nm) if nm > 1 else [0.0] + height = 0.7 / max(nm, 1) + for mname, off in zip(models, offsets): + sub = cond[cond.model == mname].groupby("symptom")["target_z"] + mean = sub.mean().reindex(present) + n = sub.size().reindex(present) + ci95 = 1.96 * sub.std().reindex(present) / np.sqrt(n) + ax.barh(y + off, mean.values, height=height, xerr=ci95.values, + error_kw=dict(ecolor="0.3", lw=1, capsize=3), label=mname) + ax.axvline(0, color="k", lw=0.8) + ax.set_yticks(y); ax.set_yticklabels(present, fontsize=9) + ax.set_xlabel("within-session target_z (0 = no preferential expression; bars = 95% CI)") + ax.set_title("Embedding manipulation check: does the patient lean toward the injected symptom?") + ax.legend() + fig.tight_layout() + out = os.path.join(args.out_dir, "fig_symptom_embed.png") + fig.savefig(out, dpi=130) + print(f"wrote {out} and {os.path.join(args.out_dir, 'symptom_embed_long.csv')}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/validation/symptom_embed_bycase.py b/analysis/validation/symptom_embed_bycase.py new file mode 100644 index 0000000..d4a9d07 --- /dev/null +++ b/analysis/validation/symptom_embed_bycase.py @@ -0,0 +1,85 @@ +""" +Symptom manipulation check, SPLIT BY CASE. + +Reuses the per-session within-session target_z from symptom_embed.py's output +(symptom_embed_long.csv) and breaks it down by the 3 base cases, which differ +sharply in baseline tone (afraid_of_dogs = phobia, empty_and_invisible & +only_love_can_save_me = depressive/narcissistic). Each case has 5 runs per +(model, symptom) cell. + +Outputs: + - symptom_embed_bycase.csv model x case x symptom means (+ sd, top1) + - fig_symptom_embed_bycase.png faceted by case +""" +from __future__ import annotations +import os +import numpy as np +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from analysis.validation.paths import BASELINE_FULL + +HERE = BASELINE_FULL # full 300-session baseline outputs are read & written here +LONG = os.path.join(HERE, "symptom_embed_long.csv") + +SYMPTOMS = [ + "lack_of_pleasure", "depressed_mood", "sleep_problems", "low_energy", + "appetite_changes", "feelings_of_failure_or_guilt", "concentration_problems", + "psychomotor_changes", "thoughts_of_death_or_self_harm", +] +CASES = ["afraid_of_dogs", "empty_and_invisible", "only_love_can_save_me"] +MODELS = ["llama3.1", "mistral-nemo"] + + +def main(): + df = pd.read_csv(LONG) + cond = df[df.symptom != "no_symptoms"].copy() + + # model x case x symptom summary + g = (cond.groupby(["model", "case", "symptom"]) + .agg(target_z=("target_z", "mean"), + z_sd=("target_z", "std"), + n=("target_z", "size"), + top1=("target_rank", lambda r: (r == 1).mean())) + .reset_index()) + g.to_csv(os.path.join(HERE, "symptom_embed_bycase.csv"), index=False) + + # overall mean target_z per model x case (the key "does it work here?" number) + print("== mean within-session target_z by model x case ==") + print(" (>0 = patient leans toward injected symptom; 0 = chance)\n") + piv = (cond.groupby(["model", "case"])["target_z"].mean().unstack("case") + .reindex(index=MODELS, columns=CASES)) + print(piv.to_string(float_format=lambda x: f"{x:+.3f}")) + print("\n== top-1 rate by model x case (chance = 0.111) ==") + pivt = (cond.assign(top1=(cond.target_rank == 1)) + .groupby(["model", "case"])["top1"].mean().unstack("case") + .reindex(index=MODELS, columns=CASES)) + print(pivt.to_string(float_format=lambda x: f"{x:.2f}")) + + # faceted figure: one panel per case, bars per symptom, color per model. + # Error bars = 95% CI (1.96*SEM); n=5 per cell so they are wide. + g["sem"] = g["z_sd"] / np.sqrt(g["n"]) + g["ci95"] = 1.96 * g["sem"] + fig, axes = plt.subplots(1, 3, figsize=(17, 6), sharey=True, sharex=True) + y = np.arange(len(SYMPTOMS)) + for ax, case in zip(axes, CASES): + for i, model in enumerate(MODELS): + gm = g[(g.case == case) & (g.model == model)].set_index("symptom").reindex(SYMPTOMS) + ax.barh(y + (0.2 if i == 0 else -0.2), gm["target_z"].values, height=0.38, + xerr=gm["ci95"].values, + error_kw=dict(ecolor="0.3", lw=0.8, capsize=2), label=model) + ax.axvline(0, color="k", lw=0.8) + ax.set_yticks(y); ax.set_yticklabels(SYMPTOMS, fontsize=8) + ax.set_title(case) + ax.set_xlabel("within-session target_z (bars = 95% CI, n=5)") + axes[0].legend(fontsize=8, loc="lower left") + fig.suptitle("Manipulation check by case: does the patient lean toward the injected symptom?") + fig.tight_layout() + out = os.path.join(HERE, "fig_symptom_embed_bycase.png") + fig.savefig(out, dpi=130) + print(f"\nwrote {out} and symptom_embed_bycase.csv") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/validation/symptom_embed_robust.py b/analysis/validation/symptom_embed_robust.py new file mode 100644 index 0000000..9b2883e --- /dev/null +++ b/analysis/validation/symptom_embed_robust.py @@ -0,0 +1,168 @@ +""" +Reference-robustness check. + +The embedding manipulation check (symptom_embed.py) compared patient speech to +hand-written reference sentences. Magnitudes therefore depend on *my* wording. +Here we repeat the identical within-session target_z analysis using the EXACT +PHQ-9 item labels that the simulation itself put into the patient prompt +(priors.patient_prior._format_symptoms -> symptom_labels). If the +/- signature +survives a reference swap, it reflects the data, not the wording. + +We run THREE reference sets and correlate the per-(model,symptom) target_z: + A: hand-written sentences (original, from symptom_embed.py) + B: PHQ-9 short labels (what the patient actually saw) + C: PHQ-9 label + "nearly every day" frequency clause (full prompt line) + +Outputs: + - symptom_embed_robust.csv target_z per model x symptom x refset + - fig_symptom_embed_robust.png A vs B vs C, with 95% CI + - prints Pearson r between ref sets (agreement of the signature) +""" +from __future__ import annotations +import json, glob, os, re +import numpy as np +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from analysis.embeddings import get_model, EMBEDDINGS_AVAILABLE +from analysis.validation.paths import EXTERNAL_SETS, BASELINE_FULL + +HERE = BASELINE_FULL # outputs written next to the full 300-session baseline +SETS = EXTERNAL_SETS + +SYMPTOMS = [ + "lack_of_pleasure", "depressed_mood", "sleep_problems", "low_energy", + "appetite_changes", "feelings_of_failure_or_guilt", "concentration_problems", + "psychomotor_changes", "thoughts_of_death_or_self_harm", +] + +# A: original hand-written sentences +REF_A = { + "lack_of_pleasure": "I have little interest or pleasure in doing things; nothing feels enjoyable anymore.", + "depressed_mood": "I feel down, depressed, sad, and hopeless most of the time.", + "sleep_problems": "I have trouble sleeping; I can't fall or stay asleep, or I sleep too much.", + "low_energy": "I feel tired and have very little energy; I am exhausted and worn out.", + "appetite_changes": "My appetite has changed; I have a poor appetite or I am overeating.", + "feelings_of_failure_or_guilt": "I feel bad about myself, like a failure, worthless, and guilty.", + "concentration_problems": "I have trouble concentrating and focusing; my mind won't stay on things.", + "psychomotor_changes": "I move or speak slowly, or I feel so restless and fidgety I can't sit still.", + "thoughts_of_death_or_self_harm": "I have thoughts that I would be better off dead or of hurting myself.", +} +# B: EXACT PHQ-9 labels from patient_prior.py:458-468 (what the patient saw) +REF_B = { + "lack_of_pleasure": "Little interest or pleasure in doing things", + "depressed_mood": "Feeling down, depressed, or hopeless", + "sleep_problems": "Sleep problems", + "low_energy": "Feeling tired or having little energy", + "appetite_changes": "Poor appetite or overeating", + "feelings_of_failure_or_guilt": "Feeling bad about yourself, guilty, or like a failure", + "concentration_problems": "Trouble concentrating", + "psychomotor_changes": "Moving or speaking slowly, or feeling restless", + "thoughts_of_death_or_self_harm": "Thoughts that you would be better off dead or of hurting yourself", +} +# C: full prompt line with the active-symptom frequency clause +_CLAUSE = "; over the last two weeks, you have been bothered by this symptom nearly every day" +REF_C = {k: v + _CLAUSE for k, v in REF_B.items()} +REFSETS = {"A_handwritten": REF_A, "B_phq9_label": REF_B, "C_phq9_full_line": REF_C} + + +def parse_case(case_name): + m = re.match(r"batch_(.+?)_run_(\d+)$", case_name) + body, run = m.group(1), int(m.group(2)) + for c in ("only_love_can_save_me", "empty_and_invisible", "afraid_of_dogs"): + if body.startswith(c): + return c, (body[len(c):].lstrip("_") or "no_symptoms"), run + return body, "?", run + + +def patient_text(d): + tx = [json.loads(l) for l in open(os.path.join(d, "transcript.jsonl")) if l.strip()] + chunks = [] + for i, t in enumerate(tx): + p = (t.get("patient_text") or "").strip() + if i > 0 and p and p == (tx[i-1].get("therapist_text") or "").strip(): + continue + if p: + chunks.append(p) + return " ".join(chunks) + + +def main(): + if not EMBEDDINGS_AVAILABLE: + raise SystemExit("sentence-transformers not available") + model = get_model() + + # gather sessions + patient embeddings once + meta_rows, texts = [], [] + for mname, pat in SETS.items(): + for d in sorted(glob.glob(pat)): + mp = os.path.join(d, "metadata.json") + if not (os.path.isdir(d) and os.path.exists(mp)): + continue + meta = json.load(open(mp)) + case, symptom, run = parse_case(meta["case_name"]) + if symptom not in SYMPTOMS: + continue # drop no_symptoms (no target) + meta_rows.append(dict(model=mname, case=case, symptom=symptom, run=run)) + texts.append(patient_text(d)) + emb = model.encode(texts, normalize_embeddings=True, batch_size=32, show_progress_bar=False) + + rows = [] + for refname, refmap in REFSETS.items(): + ref_emb = model.encode([refmap[s] for s in SYMPTOMS], normalize_embeddings=True) + sims = emb @ ref_emb.T + for meta, srow in zip(meta_rows, sims): + mu, sd = srow.mean(), srow.std() + tgt = srow[SYMPTOMS.index(meta["symptom"])] + rows.append(dict(refset=refname, **meta, + target_z=float((tgt - mu) / sd) if sd > 0 else np.nan)) + df = pd.DataFrame(rows) + df.to_csv(os.path.join(HERE, "symptom_embed_robust.csv"), index=False) + + # per model x symptom x refset mean + CI + agg = (df.groupby(["refset", "model", "symptom"]) + .agg(z=("target_z", "mean"), sd=("target_z", "std"), n=("target_z", "size")) + .reset_index()) + agg["ci95"] = 1.96 * agg["sd"] / np.sqrt(agg["n"]) + + # agreement of the signature across reference sets (per model) + print("== Pearson r of per-symptom target_z between reference sets ==") + print(" (high r = the +/- signature is robust to reference wording)\n") + wide = agg.pivot_table(index=["model", "symptom"], columns="refset", values="z") + for mname in SETS: + w = wide.loc[mname] + rAB = np.corrcoef(w["A_handwritten"], w["B_phq9_label"])[0, 1] + rAC = np.corrcoef(w["A_handwritten"], w["C_phq9_full_line"])[0, 1] + rBC = np.corrcoef(w["B_phq9_label"], w["C_phq9_full_line"])[0, 1] + print(f" {mname}: r(A,B)={rAB:.2f} r(A,C)={rAC:.2f} r(B,C)={rBC:.2f}") + print() + print("== overall mean target_z by refset x model (should stay ~0) ==") + print(df.groupby(["refset", "model"])["target_z"].mean() + .unstack("model").to_string(float_format=lambda x: f"{x:+.3f}")) + + # figure: per symptom, 3 refsets side by side, pooled across models + pooled = (df.groupby(["refset", "symptom"]) + .agg(z=("target_z", "mean"), sd=("target_z", "std"), n=("target_z", "size")) + .reset_index()) + pooled["ci95"] = 1.96 * pooled["sd"] / np.sqrt(pooled["n"]) + fig, ax = plt.subplots(figsize=(11, 7)) + y = np.arange(len(SYMPTOMS)) + offs = {"A_handwritten": 0.27, "B_phq9_label": 0.0, "C_phq9_full_line": -0.27} + for refname, off in offs.items(): + sub = pooled[pooled.refset == refname].set_index("symptom").reindex(SYMPTOMS) + ax.barh(y + off, sub["z"].values, height=0.26, xerr=sub["ci95"].values, + error_kw=dict(ecolor="0.3", lw=0.8, capsize=2), label=refname) + ax.axvline(0, color="k", lw=0.8) + ax.set_yticks(y); ax.set_yticklabels(SYMPTOMS, fontsize=9) + ax.set_xlabel("within-session target_z (pooled over models; bars = 95% CI)") + ax.set_title("Reference-robustness: target_z under 3 reference wordings") + ax.legend(fontsize=8) + fig.tight_layout() + out = os.path.join(HERE, "fig_symptom_embed_robust.png") + fig.savefig(out, dpi=130) + print(f"\nwrote {out} and symptom_embed_robust.csv") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/validation/symptom_manifest.py b/analysis/validation/symptom_manifest.py new file mode 100644 index 0000000..0d2fed2 --- /dev/null +++ b/analysis/validation/symptom_manifest.py @@ -0,0 +1,139 @@ +""" +Symptom manifestation / manipulation check. + +Question: when the simulation injects a single PHQ-9 symptom into the patient +prior, does the *patient* actually talk about that symptom more than (a) the +no-symptoms baseline and (b) the other symptom conditions? + +Approach: a transparent keyword/regex lexicon per symptom. For each session we +count, in the patient turns only, whether the target symptom's lexicon is hit. +We report a session-level "target mention" rate and a full condition x lexicon +"leakage" matrix (off-diagonal = a symptom showing up where it wasn't injected). + +Outputs (to this folder): + - symptom_manifest_long.csv one row per session, all lexicon hit flags + - symptom_confusion_.csv injected-symptom x mentioned-lexicon matrix + - fig_symptom_manifest.png diagonal (target) mention rate by model +""" +from __future__ import annotations +import json, glob, os, re +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from analysis.validation.paths import EXTERNAL_SETS, BASELINE_FULL + +HERE = BASELINE_FULL # outputs written next to the full 300-session baseline +SETS = EXTERNAL_SETS + +# Transparent lexicons. Word-boundary regex, case-insensitive. +LEXICON = { + "lack_of_pleasure": r"\b(pleasure|enjoy|enjoyment|interest|interested|joy|fun|anhedoni\w*|numb|don'?t care|no longer (?:enjoy|interested))\b", + "depressed_mood": r"\b(sad|sadness|depress\w*|hopeless|despair|down|empty|low mood|miserabl\w*|blue)\b", + "sleep_problems": r"\b(sleep|asleep|insomnia|awake|nightmares?|rest(?:less|ed|ing)?|wake up|can'?t sleep|tossing|toss and turn)\b", + "low_energy": r"\b(energy|tired|fatigue\w*|exhaust\w*|lethargic|drained|sluggish|worn out|no energy)\b", + "appetite_changes": r"\b(appetite|eat\w*|food|meal|hungry|hunger|weight|overeat\w*|skip(?:ping)? meals?)\b", + "feelings_of_failure_or_guilt": r"\b(guilt\w*|failure|failed|failing|worthless\w*|blame|ashamed|shame|let .* down|not good enough)\b", + "concentration_problems": r"\b(concentrat\w*|focus\w*|distract\w*|attention|can'?t think|forget\w*|forgetful|mind wander\w*|lose track)\b", + "psychomotor_changes": r"\b(restless\w*|agitat\w*|fidget\w*|slow(?:ed|ly|er)?|sluggish|moving slow\w*|speaking slow\w*|can'?t sit still|pacing)\b", + "thoughts_of_death_or_self_harm": r"\b(death|dead|dying|die|suicid\w*|kill myself|hurt(?:ing)? myself|self[- ]harm|better off dead|end it|no reason to live|not want(?:ing)? to (?:be here|live))\b", +} +SYMPTOMS = list(LEXICON.keys()) +PATT = {k: re.compile(v, re.I) for k, v in LEXICON.items()} + + +def parse_case(case_name: str): + m = re.match(r"batch_(.+?)_run_(\d+)$", case_name) + body, run = m.group(1), int(m.group(2)) + for c in ("only_love_can_save_me", "empty_and_invisible", "afraid_of_dogs"): + if body.startswith(c): + return c, (body[len(c):].lstrip("_") or "no_symptoms"), run + return body, "?", run + + +def load_sessions(): + rows = [] + for model, pat in SETS.items(): + for d in sorted(glob.glob(pat)): + mpath = os.path.join(d, "metadata.json") + if not os.path.isdir(d) or not os.path.exists(mpath): + continue + meta = json.load(open(mpath)) + case, symptom, run = parse_case(meta["case_name"]) + tx = [json.loads(l) for l in open(os.path.join(d, "transcript.jsonl")) if l.strip()] + # patient turns only; drop verbatim-echo turns (patient == prev therapist) + patient_chunks = [] + for i, t in enumerate(tx): + ptext = (t.get("patient_text") or "").strip() + if i > 0 and ptext and ptext == (tx[i-1].get("therapist_text") or "").strip(): + continue # echo artifact + patient_chunks.append(ptext) + ther_text = " ".join((t.get("therapist_text") or "") for t in tx) + patient_text = " ".join(patient_chunks) + row = dict(model=model, case=case, symptom=symptom, run=run, + session_id=meta["session_id"]) + for s in SYMPTOMS: + row[f"pat_{s}"] = bool(PATT[s].search(patient_text)) + row[f"ther_{s}"] = bool(PATT[s].search(ther_text)) + rows.append(row) + return pd.DataFrame(rows) + + +def main(): + df = load_sessions() + df.to_csv(os.path.join(HERE, "symptom_manifest_long.csv"), index=False) + print(f"sessions: {len(df)} models: {df.model.unique().tolist()}") + + # ---- Manipulation check: target-symptom mention rate by condition ---- + # For symptom conditions, "target" = the injected symptom's own lexicon. + print("\n== Patient mentions the INJECTED symptom (diagonal) vs baseline ==") + summary = [] + for model in SETS: + sub = df[df.model == model] + base = sub[sub.symptom == "no_symptoms"] + for s in SYMPTOMS: + cond = sub[sub.symptom == s] + diag = cond[f"pat_{s}"].mean() # injected -> mentions it + base_rate = base[f"pat_{s}"].mean() # baseline mentions it anyway + ther_diag = cond[f"ther_{s}"].mean() # therapist picks it up + summary.append(dict(model=model, symptom=s, n=len(cond), + pat_target_rate=diag, baseline_rate=base_rate, + lift=diag - base_rate, ther_target_rate=ther_diag)) + sdf = pd.DataFrame(summary) + pd.set_option("display.width", 160, "display.max_columns", 20) + for model in SETS: + print(f"\n--- {model} ---") + m = sdf[sdf.model == model].drop(columns="model") + print(m.to_string(index=False, + formatters={c: "{:.2f}".format for c in ["pat_target_rate","baseline_rate","lift","ther_target_rate"]})) + + # ---- Confusion / leakage matrix: injected symptom x mentioned lexicon ---- + for model in SETS: + sub = df[(df.model == model) & (df.symptom != "no_symptoms")] + mat = pd.DataFrame(index=SYMPTOMS, columns=SYMPTOMS, dtype=float) + for inj in SYMPTOMS: + cond = sub[sub.symptom == inj] + for lex in SYMPTOMS: + mat.loc[inj, lex] = cond[f"pat_{lex}"].mean() + mat.to_csv(os.path.join(HERE, f"symptom_confusion_{model}.csv")) + + # ---- Figure: diagonal mention rate (patient) + therapist uptake ---- + fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharey=True) + for ax, model in zip(axes, SETS): + m = sdf[sdf.model == model].set_index("symptom").reindex(SYMPTOMS) + x = range(len(SYMPTOMS)) + ax.barh([i+0.2 for i in x], m["pat_target_rate"], height=0.4, label="patient mentions target") + ax.barh([i-0.2 for i in x], m["baseline_rate"], height=0.4, label="baseline (no_symptoms)") + ax.set_yticks(list(x)); ax.set_yticklabels(SYMPTOMS, fontsize=8) + ax.set_xlim(0, 1); ax.set_title(model); ax.set_xlabel("session rate") + ax.legend(fontsize=8, loc="lower right") + fig.suptitle("Manipulation check: does the patient voice the injected symptom?") + fig.tight_layout() + out = os.path.join(HERE, "fig_symptom_manifest.png") + fig.savefig(out, dpi=130) + print(f"\nwrote {out}") + print("wrote symptom_manifest_long.csv, symptom_confusion_.csv") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/config/priors/patient/cases/afraid_of_dogs.yaml b/config/priors/patient/cases/afraid_of_dogs.yaml index 48a374f..ffcced7 100644 --- a/config/priors/patient/cases/afraid_of_dogs.yaml +++ b/config/priors/patient/cases/afraid_of_dogs.yaml @@ -30,6 +30,23 @@ resistance_structure: > concrete and uncomfortable, but this is straightforward resistance to the feared stimulus rather than deep relational resistance. + +# nearly every day +# more than half the days +# several days +# not at all +symptoms: + lack_of_pleasure: nearly every day + depressed_mood: nearly every day + sleep_problems: nearly every day + low_energy: nearly every day + appetite_changes: more than half the days + feelings_of_failure_or_guilt: nearly every day + concentration_problems: more than half the days + psychomotor_changes: more than half the days + thoughts_of_death_or_self_harm: several days + + hazard_profile: to_frame: low to_patient: low diff --git a/config/priors/patient/cases/empty_and_invisible.yaml b/config/priors/patient/cases/empty_and_invisible.yaml index 778ed2b..0384b21 100644 --- a/config/priors/patient/cases/empty_and_invisible.yaml +++ b/config/priors/patient/cases/empty_and_invisible.yaml @@ -36,6 +36,18 @@ resistance_structure: > their own psychology. Becomes slightly competitive with the therapist's interpretations. Changes subject when an observation gets too close. + +symptoms: + lack_of_pleasure: nearly every day + depressed_mood: nearly every day + sleep_problems: nearly every day + low_energy: nearly every day + appetite_changes: more than half the days + feelings_of_failure_or_guilt: nearly every day + concentration_problems: more than half the days + psychomotor_changes: more than half the days + thoughts_of_death_or_self_harm: several days + hazard_profile: to_frame: moderate diff --git a/config/priors/patient/cases/only_love_can_save_me.yaml b/config/priors/patient/cases/only_love_can_save_me.yaml index 6f68acb..707aec6 100644 --- a/config/priors/patient/cases/only_love_can_save_me.yaml +++ b/config/priors/patient/cases/only_love_can_save_me.yaml @@ -35,6 +35,19 @@ resistance_structure: > Intellectualises when getting close to something real, turns emotional material into abstract discussion. +symptoms: + lack_of_pleasure: nearly every day + depressed_mood: nearly every day + sleep_problems: nearly every day + low_energy: nearly every day + appetite_changes: more than half the days + feelings_of_failure_or_guilt: nearly every day + concentration_problems: nearly every day + psychomotor_changes: nearly every day + thoughts_of_death_or_self_harm: several days + + + hazard_profile: to_frame: high to_patient: moderate diff --git a/env.example b/env.example index e69de29..1eb8c48 100644 --- a/env.example +++ b/env.example @@ -0,0 +1,11 @@ +# .env + +# Leave these blank if you're running locally only +ANTHROPIC_API_KEY= +OPENAI_API_KEY= + +# Ollama runs on localhost (no key needed) +OLLAMA_BASE_URL=http://localhost:11434 + +DEFAULT_THERAPIST_MODEL=llama3.1 +DEFAULT_PATIENT_MODEL=llama3.1 \ No newline at end of file diff --git a/priors/patient_prior.py b/priors/patient_prior.py index ee929ab..ec1a813 100644 --- a/priors/patient_prior.py +++ b/priors/patient_prior.py @@ -1,13 +1,6 @@ -""" -priors/patient_prior.py - -Patient prior dataclass and system prompt builder. -Manages the hidden unconscious_agenda layer (not passed to the patient -agent until reveal conditions are met by the simulation orchestrator). -""" - from dataclasses import dataclass, field from priors.loader import load_patient_case +import re @dataclass @@ -45,6 +38,15 @@ class UnconsciousAgenda: revealed: bool = False +@dataclass +class SymptomDiscussion: + """ + Tracks whether the patient has begun talking explicitly about symptoms. + """ + started: bool = False + start_turn: int | None = None + + @dataclass class PatientPrior: """ @@ -60,6 +62,7 @@ class PatientPrior: relational_pattern: str = "" transference_expectation: str = "" resistance_structure: str = "" + symptoms: str = "" # Used by hazard_monitor.py, not in patient prompt hazard_profile: HazardProfile = field(default_factory=HazardProfile) @@ -67,6 +70,9 @@ class PatientPrior: # Hidden from patient agent until revealed unconscious_agenda: UnconsciousAgenda = field(default_factory=UnconsciousAgenda) + # Runtime tracking + symptom_discussion: SymptomDiscussion = field(default_factory=SymptomDiscussion) + def build_system_prompt( self, agent_state_summary: str = "", @@ -115,6 +121,16 @@ def build_system_prompt( + self.resistance_structure.strip() ) + if self.symptoms.strip(): + sections.append( + "## How You've Been Lately\n" + "Over the past two weeks, this is how much problems in the " + "following areas have bothered or impaired you. This is simply " + "how things have been for you. Some areas may have been fine, " + "others harder.\n\n" + + self.symptoms.strip() + ) + if include_unconscious and self.unconscious_agenda.content: sections.append( "## Something Shifting in You\n" @@ -162,17 +178,289 @@ def check_reveal_trigger(self, transcript_tail: str, current_turn: int) -> bool: trigger = self.unconscious_agenda.reveal_trigger.lower().strip() if not trigger: return False - # Simple keyword matching; can be upgraded to embedding similarity trigger_keywords = [w for w in trigger.split() if len(w) > 4] tail_lower = transcript_tail.lower() matches = sum(1 for kw in trigger_keywords if kw in tail_lower) - # Reveal if more than 30% of meaningful trigger words are present threshold = max(1, int(len(trigger_keywords) * 0.3)) if matches >= threshold: self.unconscious_agenda.revealed = True return True return False + def check_symptom_discussion(self, patient_text: str, current_turn: int) -> bool: + """ + Mark symptom discussion as started once the patient explicitly talks + about symptom experience in their own turn. + + This version is more general than simple keyword matching. + It detects symptom discussion by looking for different symptom categories, + natural phrases, and common ways patients describe distress. + + Returns True only on the first turn where this is detected. + """ + if self.symptom_discussion.started: + return False + + if not patient_text: + return False + + text = patient_text.lower().strip() + + # Normalize common apostrophe variants + text = ( + text.replace("’", "'") + .replace("‘", "'") + .replace("`", "'") + ) + + if not text: + return False + + symptom_patterns = { + "depressed_mood": [ + r"\bdepressed\b", + r"\bdepression\b", + r"\bdown\b", + r"\bsad\b", + r"\blow mood\b", + r"\bempty\b", + r"\bnumb\b", + r"\bhopeless\b", + r"\bmiserable\b", + r"\bi feel low\b", + r"\bi feel awful\b", + ], + + "lack_of_pleasure": [ + r"\bno interest\b", + r"\blost interest\b", + r"\bdon't enjoy\b", + r"\bdo not enjoy\b", + r"\bcan't enjoy\b", + r"\bcannot enjoy\b", + r"\bnothing feels good\b", + r"\bnothing is fun\b", + r"\bno pleasure\b", + r"\bhard to enjoy\b", + r"\bi don't care about anything\b", + ], + + "sleep_problems": [ + r"\bcan't sleep\b", + r"\bcannot sleep\b", + r"\bcan't fall asleep\b", + r"\bwake up at night\b", + r"\bwaking up\b", + r"\binsomnia\b", + r"\bsleep badly\b", + r"\bpoor sleep\b", + r"\bsleeping too much\b", + r"\boversleeping\b", + r"\bnightmares?\b", + r"\btired because i didn't sleep\b", + ], + + "low_energy": [ + r"\bexhausted\b", + r"\btired\b", + r"\bfatigued\b", + r"\bno energy\b", + r"\blow energy\b", + r"\bdrained\b", + r"\bworn out\b", + r"\bi can't get out of bed\b", + r"\beverything feels like effort\b", + r"\beven small things feel hard\b", + ], + + "appetite_changes": [ + r"\bappetite\b", + r"\bnot hungry\b", + r"\bcan't eat\b", + r"\bcannot eat\b", + r"\beating too much\b", + r"\bovereating\b", + r"\blost weight\b", + r"\bgained weight\b", + r"\bfood doesn't taste\b", + ], + + "feelings_of_failure_or_guilt": [ + r"\bguilty\b", + r"\bashamed\b", + r"\bshame\b", + r"\bfailure\b", + r"\bworthless\b", + r"\buseless\b", + r"\bmy fault\b", + r"\bnot good enough\b", + r"\bi let .* down\b", + r"\bi hate myself\b", + ], + + "concentration_problems": [ + r"\bcan't focus\b", + r"\bcannot focus\b", + r"\bhard to focus\b", + r"\bcan't concentrate\b", + r"\bcannot concentrate\b", + r"\bconcentration\b", + r"\bdistracted\b", + r"\bcan't think clearly\b", + r"\bbrain fog\b", + r"\bfoggy\b", + ], + + "psychomotor_changes": [ + r"\brestless\b", + r"\bagitated\b", + r"\bcan't sit still\b", + r"\bcannot sit still\b", + r"\bslowed down\b", + r"\bmoving slowly\b", + r"\btalking slowly\b", + r"\beverything feels slow\b", + r"\bmy body feels heavy\b", + ], + + "fear_or_anxiety": [ + r"\banxious\b", + r"\banxiety\b", + r"\bafraid\b", + r"\bscared\b", + r"\bterrified\b", + r"\bpanic\b", + r"\bpanicking\b", + r"\bheart races\b", + r"\bi freeze\b", + r"\bi avoid\b", + r"\bi can't go\b", + r"\bi cannot go\b", + ], + + "thoughts_of_death_or_self_harm": [ + r"\bsuicidal\b", + r"\bself[- ]?harm\b", + r"\bhurt myself\b", + r"\bkill myself\b", + r"\bbetter off dead\b", + r"\bdon't want to be here\b", + r"\bdo not want to be here\b", + r"\bi wish i wouldn't wake up\b", + r"\bi wish i were dead\b", + r"\bend it\b", + ], + } + + matched_categories = [] + + for category, patterns in symptom_patterns.items(): + for pattern in patterns: + if re.search(pattern, text): + matched_categories.append(category) + break + + if matched_categories: + self.symptom_discussion.started = True + self.symptom_discussion.start_turn = current_turn + return True + + # More general backup detection: + # This catches natural symptom talk that may not use exact clinical words. + experience_patterns = [ + r"\bi feel\b", + r"\bi've been feeling\b", + r"\bi have been feeling\b", + r"\bi keep feeling\b", + r"\bit feels like\b", + r"\bi can't\b", + r"\bi cannot\b", + r"\bi don't feel\b", + r"\bi do not feel\b", + r"\bi struggle\b", + r"\bi'm struggling\b", + ] + + distress_patterns = [ + r"\bhard\b", + r"\bheavy\b", + r"\bunbearable\b", + r"\boverwhelming\b", + r"\btoo much\b", + r"\bpointless\b", + r"\bempty\b", + r"\bscary\b", + r"\bafraid\b", + r"\bexhausting\b", + r"\bIrrational\b".lower(), + ] + + has_experience_language = any( + re.search(pattern, text) for pattern in experience_patterns + ) + + has_distress_language = any( + re.search(pattern, text) for pattern in distress_patterns + ) + + if has_experience_language and has_distress_language: + self.symptom_discussion.started = True + self.symptom_discussion.start_turn = current_turn + return True + + return False + + +def _format_symptoms(symptoms_raw) -> str: + if not symptoms_raw: + return "" + + frequency_meanings = { + "not at all": ( + "over the last two weeks, you have not been bothered by this symptom; " + ), + "several days": ( + "over the last two weeks, you have been bothered by this symptom on several days; " + ), + "more than half the days": ( + "over the last two weeks, you have been bothered by this symptom more than half the days; " + ), + "nearly every day": ( + "over the last two weeks, you have been bothered by this symptom nearly every day; " + ), + } + + symptom_labels = { + "lack_of_pleasure": "Little interest or pleasure in doing things", + "depressed_mood": "Feeling down, depressed, or hopeless", + "sleep_problems": "Sleep problems", + "low_energy": "Feeling tired or having little energy", + "appetite_changes": "Poor appetite or overeating", + "feelings_of_failure_or_guilt": "Feeling bad about yourself, guilty, or like a failure", + "concentration_problems": "Trouble concentrating", + "psychomotor_changes": "Moving or speaking slowly, or feeling restless", + "thoughts_of_death_or_self_harm": "Thoughts that you would be better off dead or of hurting yourself", + } + + if isinstance(symptoms_raw, dict): + lines = [] + for key, value in symptoms_raw.items(): + frequency = str(value).strip().lower() + + label = symptom_labels.get( + key, + key.replace("_", " ").strip().capitalize() + ) + + meaning = frequency_meanings.get( + frequency, + "frequency level not recognized; use common sense to decide how much it should affect you" + ) + + lines.append(f"- {label}: {frequency} — {meaning}") + + return "\n".join(lines) + return str(symptoms_raw).strip() def build_patient_prior(case_name: str) -> PatientPrior: """ @@ -209,6 +497,7 @@ def build_patient_prior(case_name: str) -> PatientPrior: relational_pattern=raw.get("relational_pattern", ""), transference_expectation=raw.get("transference_expectation", ""), resistance_structure=raw.get("resistance_structure", ""), + symptoms=_format_symptoms(raw.get("symptoms", "")), hazard_profile=hazard, unconscious_agenda=agenda, ) diff --git a/pyproject.toml b/pyproject.toml index 0db5acb..f03c407 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,21 @@ readme = "README.md" requires-python = ">=3.13" dependencies = [ "anthropic>=0.86.0", + "matplotlib>=3.11.0", "openai>=2.29.0", + "pandas>=3.0.3", "python-dotenv>=1.2.2", + "reportlab>=4.5.1", "sentence-transformers>=5.3.0", + "torch", + "torchvision", ] + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true + +[tool.uv.sources] +torch = { index = "pytorch-cu128" } +torchvision = { index = "pytorch-cu128" } diff --git a/run.py b/run.py index 8e1378d..0e34fb2 100644 --- a/run.py +++ b/run.py @@ -13,6 +13,15 @@ import sys from dotenv import load_dotenv + +import torch + +print("CUDA available:", torch.cuda.is_available()) +print("Torch version:", torch.__version__) +print("CUDA available:", torch.cuda.is_available()) +print("Torch CUDA version:", torch.version.cuda) + + load_dotenv() from priors.loader import list_cases, list_orientations diff --git a/run_symptom_experiments.py b/run_symptom_experiments.py new file mode 100644 index 0000000..729c443 --- /dev/null +++ b/run_symptom_experiments.py @@ -0,0 +1,211 @@ +from __future__ import annotations + +import argparse +import copy +import subprocess +import sys +from pathlib import Path + +import yaml + + +CASES_DIR = Path("config/priors/patient/cases") +BASE_CASE_NAME = "empty_and_invisible" + +SYMPTOM_KEYS = [ + "lack_of_pleasure", + "depressed_mood", + "sleep_problems", + "low_energy", + "appetite_changes", + "feelings_of_failure_or_guilt", + "concentration_problems", + "psychomotor_changes", + "thoughts_of_death_or_self_harm", +] + + +def load_case(case_name: str) -> dict: + path = CASES_DIR / f"{case_name}.yaml" + if not path.exists(): + raise FileNotFoundError(f"Case not found: {path}") + + with path.open("r", encoding="utf-8") as f: + data = yaml.safe_load(f) + + if not isinstance(data, dict): + raise ValueError(f"Case file must contain a YAML mapping: {path}") + + return data + + +def write_case(case_name: str, data: dict) -> Path: + path = CASES_DIR / f"{case_name}.yaml" + with path.open("w", encoding="utf-8") as f: + yaml.safe_dump(data, f, sort_keys=False, allow_unicode=True) + return path + + +def build_no_symptoms_case(base_case: dict) -> dict: + case_data = copy.deepcopy(base_case) + case_data.pop("symptoms", None) + return case_data + + +def build_single_symptom_case( + base_case: dict, active_symptom: str, active_frequency: str = "nearly every day" +) -> dict: + case_data = copy.deepcopy(base_case) + case_data["symptoms"] = { + key: (active_frequency if key == active_symptom else "not at all") + for key in SYMPTOM_KEYS + } + return case_data + + +def run_session( + therapist: str, + patient: str, + case_name: str, + orientation: str, + turns: int, + max_tokens: int, + no_compress: bool, +) -> None: + cmd = [ + "uv", + "run", + "python", + "run.py", + "--therapist", + therapist, + "--patient", + patient, + "--case", + case_name, + "--orientation", + orientation, + "--turns", + str(turns), + "--max-tokens", + str(max_tokens), + ] + + if no_compress: + cmd.append("--no-compress") + + print(f"\nRunning case: {case_name}") + print(" ".join(cmd)) + subprocess.run(cmd, check=True) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Batch-run symptom-isolated dyadic simulations for afraid_of_dogs." + ) + parser.add_argument("--therapist", required=True, help="Therapist model name") + parser.add_argument("--patient", required=True, help="Patient model name") + parser.add_argument( + "--case", + default=BASE_CASE_NAME, + help=f"Base patient case to inject symptoms into (default: {BASE_CASE_NAME})", + ) + parser.add_argument( + "--symptoms", + default=None, + help=( + "Comma-separated subset of symptom keys to run (default: all 9). " + f"Valid: {','.join(SYMPTOM_KEYS)}" + ), + ) + parser.add_argument( + "--frequency", + default="nearly every day", + choices=["several days", "more than half the days", "nearly every day"], + help="PHQ-9 frequency anchor for the active symptom (default: nearly every day)", + ) + parser.add_argument("--orientation", default="cbt", help="Therapist orientation") + parser.add_argument("--turns", type=int, default=10, help="Turns per run") + parser.add_argument("--max-tokens", type=int, default=300, help="Max tokens per response") + parser.add_argument("--repeats", type=int, default=5, help="How many runs per condition") + parser.add_argument( + "--prefix", + default="batch", + help="Prefix for generated temporary case names", + ) + parser.add_argument( + "--no-compress", + action="store_true", + help="Pass through to run.py", + ) + parser.add_argument( + "--keep-generated-cases", + action="store_true", + help="Keep generated YAML files instead of deleting them afterward", + ) + + args = parser.parse_args() + + if args.symptoms: + symptom_keys = [s.strip() for s in args.symptoms.split(",") if s.strip()] + invalid = [s for s in symptom_keys if s not in SYMPTOM_KEYS] + if invalid: + raise ValueError(f"Unknown symptom key(s): {invalid}. Valid: {SYMPTOM_KEYS}") + else: + symptom_keys = SYMPTOM_KEYS + + base_case = load_case(args.case) + generated_case_paths: list[Path] = [] + + try: + for i in range(1, args.repeats + 1): + case_name = f"{args.prefix}_{args.case}_no_symptoms_run_{i}" + case_data = build_no_symptoms_case(base_case) + generated_case_paths.append(write_case(case_name, case_data)) + + run_session( + therapist=args.therapist, + patient=args.patient, + case_name=case_name, + orientation=args.orientation, + turns=args.turns, + max_tokens=args.max_tokens, + no_compress=args.no_compress, + ) + + for symptom_key in symptom_keys: + for i in range(1, args.repeats + 1): + case_name = f"{args.prefix}_{args.case}_{symptom_key}_run_{i}" + case_data = build_single_symptom_case(base_case, symptom_key, args.frequency) + generated_case_paths.append(write_case(case_name, case_data)) + + run_session( + therapist=args.therapist, + patient=args.patient, + case_name=case_name, + orientation=args.orientation, + turns=args.turns, + max_tokens=args.max_tokens, + no_compress=args.no_compress, + ) + + print("\nAll runs completed successfully.") + print("Sessions were saved automatically under data/sessions/.") + + finally: + if not args.keep_generated_cases: + for path in generated_case_paths: + if path.exists(): + path.unlink() + print("Temporary generated case files deleted.") + + +if __name__ == "__main__": + try: + main() + except subprocess.CalledProcessError as e: + print(f"\nA run failed with exit code {e.returncode}.", file=sys.stderr) + sys.exit(e.returncode) + except Exception as e: + print(f"\nError: {e}", file=sys.stderr) + sys.exit(1) \ No newline at end of file diff --git a/simulation/dyad.py b/simulation/dyad.py index a71eab0..cdaf713 100644 --- a/simulation/dyad.py +++ b/simulation/dyad.py @@ -1,20 +1,3 @@ -""" -simulation/dyad.py - -This dyad orchestrator runs the two-agent exchange turn by turn. - -This is the heart of the simulation. Each turn: - 1. Build therapist context (prior + state + history) - 2. Get therapist response - 3. Check hazard monitor - 4. Check unconscious agenda reveal trigger - 5. Build patient context (prior + state + history + optional unconscious) - 6. Get patient response - 7. Compress both states (self-reflection calls) - 8. Save snapshots - 9. Record turn to session transcript -""" - from rich.console import Console from rich.panel import Panel from rich.text import Text @@ -60,12 +43,17 @@ def __init__( # Session (new or resumed) if session_id: self.session = resume_session(session_id) + + if not getattr(self.session, "patient_symptoms", ""): + self.session.patient_symptoms = self.patient_prior.symptoms + self.session.save_metadata() else: self.session = new_session( therapist_model=therapist_model, patient_model=patient_model, case_name=case_name, orientation=orientation, + patient_symptoms=self.patient_prior.symptoms, ) # Initialise or restore agent states @@ -86,16 +74,6 @@ def __init__( def run(self, n_turns: int = 10, max_tokens: int = 300) -> Session: """ Run the dyadic exchange for n_turns turns. - - For now, the session always starts with the patient speaking first - (they are the one who came seeking something). - - Args: - n_turns: Number of full turns (patient + therapist = 1 turn) - max_tokens: Max tokens per response - - Returns: - Completed Session object """ console.rule(f"[bold]Session: {self.session.session_id}[/bold]") console.print( @@ -106,7 +84,6 @@ def run(self, n_turns: int = 10, max_tokens: int = 300) -> Session: ) console.rule() - # Opening patient turn: patient speaks first opening_prompt = ( "You have just arrived for a therapy session. " "The therapist is present and waiting. Say what brings you here." @@ -120,7 +97,6 @@ def run(self, n_turns: int = 10, max_tokens: int = 300) -> Session: # --- Patient turn --- - # Check unconscious reveal trigger unconscious_active = self.patient_prior.check_reveal_trigger( transcript_tail=tail, current_turn=turn_num, @@ -131,7 +107,6 @@ def run(self, n_turns: int = 10, max_tokens: int = 300) -> Session: ) self._revealed_logged = True - # First turn: patient opens; subsequent turns: respond to therapist if turn_num == 1: latest_therapist = opening_prompt else: @@ -152,6 +127,16 @@ def run(self, n_turns: int = 10, max_tokens: int = 300) -> Session: ) patient_text = patient_response.content.strip() + symptom_discussion_started = self.patient_prior.check_symptom_discussion( + patient_text=patient_text, + current_turn=turn_num, + ) + if symptom_discussion_started: + console.print( + "[bold magenta]Patient began talking explicitly about symptoms[/bold magenta]" + ) + self.patient_prior.symptom_discussion.started = False + _print_turn("Patient", patient_text, "magenta") # --- Hazard check --- @@ -169,13 +154,13 @@ def run(self, n_turns: int = 10, max_tokens: int = 300) -> Session: border_style="red", ) ) - # Record the crisis turn before breaking self.session.append_turn( therapist_text="", patient_text=patient_text, therapist_tokens=None, patient_tokens=patient_response.output_tokens, unconscious_revealed=unconscious_active, + symptom_discussion_started=symptom_discussion_started, hazard_flags=["crisis"], ) save_state_snapshot(self.patient_state, self.session.session_dir, turn_num) @@ -237,6 +222,7 @@ def run(self, n_turns: int = 10, max_tokens: int = 300) -> Session: therapist_tokens=therapist_response.output_tokens, patient_tokens=patient_response.output_tokens, unconscious_revealed=unconscious_active, + symptom_discussion_started=symptom_discussion_started, hazard_flags=hazard_flags, ) @@ -270,4 +256,4 @@ def _print_turn(label: str, text: str, colour: str) -> None: border_style=colour, padding=(0, 1), ) - ) + ) \ No newline at end of file diff --git a/simulation/session.py b/simulation/session.py index cced447..0cd5412 100644 --- a/simulation/session.py +++ b/simulation/session.py @@ -11,7 +11,20 @@ from dataclasses import dataclass, field from datetime import datetime, UTC from pathlib import Path +from html import escape +import re +from reportlab.lib import colors +from reportlab.lib.enums import TA_CENTER +from reportlab.lib.pagesizes import A4 +from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle +from reportlab.lib.units import cm +from reportlab.platypus import ( + SimpleDocTemplate, + Paragraph, + Spacer, + KeepTogether, +) SESSIONS_DIR = Path("data/sessions") @@ -27,7 +40,8 @@ class TurnRecord: therapist_tokens: int | None patient_tokens: int | None unconscious_revealed: bool - hazard_flags: list[str] + symptom_discussion_started: bool = False + hazard_flags: list[str] = field(default_factory=list) timestamp: str = field(default_factory=lambda: datetime.now(UTC).replace(tzinfo=None).isoformat()) def to_dict(self) -> dict: @@ -40,18 +54,20 @@ class Session: """ def __init__( - self, - session_id: str, - therapist_model: str, - patient_model: str, - case_name: str, - orientation: str, + self, + session_id: str, + therapist_model: str, + patient_model: str, + case_name: str, + orientation: str, + patient_symptoms: str = "", ): self.session_id = session_id self.therapist_model = therapist_model self.patient_model = patient_model self.case_name = case_name self.orientation = orientation + self.patient_symptoms = patient_symptoms self.turn_count = 0 self.transcript: list[TurnRecord] = [] self.created_at = datetime.now(UTC).replace(tzinfo=None).isoformat() @@ -61,6 +77,7 @@ def __init__( self._transcript_path = self.session_dir / "transcript.jsonl" self._metadata_path = self.session_dir / "metadata.json" + self._pdf_path = self.session_dir / self._build_pdf_filename() def append_turn( self, @@ -69,18 +86,21 @@ def append_turn( therapist_tokens: int | None = None, patient_tokens: int | None = None, unconscious_revealed: bool = False, + symptom_discussion_started: bool = False, hazard_flags: list[str] | None = None, ) -> TurnRecord: """ Record a completed turn to the transcript. Args: - therapist_text: What the therapist said - patient_text: What the patient said - therapist_tokens: Token count for therapist response - patient_tokens: Token count for patient response - unconscious_revealed: Whether unconscious agenda was active - hazard_flags: Any hazard signals detected this turn + therapist_text: What the therapist said + patient_text: What the patient said + therapist_tokens: Token count for therapist response + patient_tokens: Token count for patient response + unconscious_revealed: Whether unconscious agenda was active + symptom_discussion_started: Whether the patient began explicitly + talking about symptoms on this turn + hazard_flags: Any hazard signals detected this turn Returns: The TurnRecord appended to the transcript @@ -95,16 +115,155 @@ def append_turn( therapist_tokens=therapist_tokens, patient_tokens=patient_tokens, unconscious_revealed=unconscious_revealed, + symptom_discussion_started=symptom_discussion_started, hazard_flags=hazard_flags or [], ) self.transcript.append(record) # Append to JSONL file immediately (survive crashes) - with open(self._transcript_path, "a") as f: - f.write(json.dumps(record.to_dict()) + "\n") + with open(self._transcript_path, "a", encoding="utf-8") as f: + f.write(json.dumps(record.to_dict(), ensure_ascii=False) + "\n") + + # Also update the human-readable PDF transcript. + self.save_pdf_transcript() + + return record return record + def _pdf_safe_text(self, text: str | None) -> str: + """ + Make text safe for ReportLab Paragraphs. + + ReportLab Paragraph uses a small XML-like markup language, so we escape + characters such as <, >, and &. We also preserve line breaks. + """ + if not text: + return "" + + return escape(str(text)).replace("\n", "
") + + def save_pdf_transcript(self) -> None: + """ + Generate a readable PDF version of the session transcript. + + This rebuilds the PDF from self.transcript each time it is called. + That is safer than trying to append directly to an existing PDF. + """ + doc = SimpleDocTemplate( + str(self._pdf_path), + pagesize=A4, + rightMargin=1.7 * cm, + leftMargin=1.7 * cm, + topMargin=1.5 * cm, + bottomMargin=1.5 * cm, + ) + + styles = getSampleStyleSheet() + + title_style = ParagraphStyle( + "TranscriptTitle", + parent=styles["Title"], + alignment=TA_CENTER, + fontSize=18, + leading=22, + spaceAfter=14, + ) + + meta_style = ParagraphStyle( + "TranscriptMeta", + parent=styles["Normal"], + fontSize=9, + leading=12, + textColor=colors.darkgrey, + spaceAfter=4, + ) + + turn_style = ParagraphStyle( + "TurnHeading", + parent=styles["Heading2"], + fontSize=13, + leading=16, + spaceBefore=12, + spaceAfter=6, + textColor=colors.HexColor("#333333"), + ) + + speaker_style = ParagraphStyle( + "Speaker", + parent=styles["Heading3"], + fontSize=11, + leading=14, + spaceBefore=6, + spaceAfter=3, + textColor=colors.HexColor("#222222"), + ) + + body_style = ParagraphStyle( + "Body", + parent=styles["BodyText"], + fontSize=10, + leading=14, + spaceAfter=8, + ) + + flag_style = ParagraphStyle( + "Flags", + parent=styles["Normal"], + fontSize=8, + leading=11, + textColor=colors.HexColor("#666666"), + spaceAfter=8, + ) + + story = [] + + story.append(Paragraph("Dyadic Therapy Simulation Transcript", title_style)) + + story.append(Paragraph(f"Session ID: {self._pdf_safe_text(self.session_id)}", meta_style)) + story.append(Paragraph(f"Case: {self._pdf_safe_text(self.case_name)}", meta_style)) + story.append(Paragraph(f"Orientation: {self._pdf_safe_text(self.orientation)}", meta_style)) + story.append(Paragraph(f"Therapist model: {self._pdf_safe_text(self.therapist_model)}", meta_style)) + story.append(Paragraph(f"Patient model: {self._pdf_safe_text(self.patient_model)}", meta_style)) + story.append(Paragraph(f"Created at: {self._pdf_safe_text(self.created_at)}", meta_style)) + story.append(Paragraph(f"Total turns: {self.turn_count}", meta_style)) + + story.append(Spacer(1, 12)) + + if self.patient_symptoms.strip(): + story.append(Paragraph("Patient Symptom Profile", turn_style)) + story.append( + Paragraph( + self._pdf_safe_text(self.patient_symptoms), + body_style, + ) + ) + story.append(Spacer(1, 12)) + + if not self.transcript: + story.append(Paragraph("No turns have been recorded yet.", body_style)) + else: + for record in self.transcript: + turn_block = [ + Paragraph(f"Turn {record.turn}", turn_style), + Paragraph("Patient", speaker_style), + Paragraph(self._pdf_safe_text(record.patient_text), body_style), + Paragraph("Therapist", speaker_style), + Paragraph( + self._pdf_safe_text(record.therapist_text) + if record.therapist_text + else "No therapist response recorded.", + body_style, + ), + ] + + story.append(KeepTogether(turn_block)) + story.append(Spacer(1, 8)) + + doc.build(story) + + + def save_metadata(self, extra: dict | None = None) -> None: """Write / update session metadata.""" meta = { @@ -113,14 +272,18 @@ def save_metadata(self, extra: dict | None = None) -> None: "patient_model": self.patient_model, "case_name": self.case_name, "orientation": self.orientation, + "patient_symptoms": self.patient_symptoms, "turn_count": self.turn_count, "created_at": self.created_at, "updated_at": datetime.now(UTC).replace(tzinfo=None).isoformat(), + "pdf_transcript_path": str(self._pdf_path), } + if extra: meta.update(extra) - with open(self._metadata_path, "w") as f: - json.dump(meta, f, indent=2) + + with open(self._metadata_path, "w", encoding="utf-8") as f: + json.dump(meta, f, indent=2, ensure_ascii=False) def get_history(self) -> list[tuple[str, str]]: """ @@ -141,22 +304,50 @@ def get_tail(self, n: int = 5) -> str: parts.append(f"Therapist: {r.therapist_text}") return "\n".join(parts) + def _safe_filename_part(self, value: str) -> str: + """ + Convert text into a safe filename part. + + Example: + llama3.1 -> llama3_1 + afraid/of/dogs -> afraid_of_dogs + """ + value = str(value).strip().lower() + value = re.sub(r"[^a-zA-Z0-9]+", "_", value) + value = value.strip("_") + return value or "unknown" + + def _build_pdf_filename(self) -> str: + """ + Build a readable PDF filename for this session transcript. + """ + case = self._safe_filename_part(self.case_name) + orientation = self._safe_filename_part(self.orientation) + therapist = self._safe_filename_part(self.therapist_model) + patient = self._safe_filename_part(self.patient_model) + + return f"{case}_{orientation}_{therapist}_vs_{patient}_transcript.pdf" + def new_session( therapist_model: str, patient_model: str, case_name: str, orientation: str, + patient_symptoms: str = "", ) -> Session: """Create a new session with a fresh ID.""" session_id = f"session_{datetime.now(UTC).replace(tzinfo=None).strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}" + session = Session( session_id=session_id, therapist_model=therapist_model, patient_model=patient_model, case_name=case_name, orientation=orientation, + patient_symptoms=patient_symptoms, ) + session.save_metadata() return session @@ -180,6 +371,7 @@ def resume_session(session_id: str) -> Session: patient_model=meta["patient_model"], case_name=meta["case_name"], orientation=meta["orientation"], + patient_symptoms=meta.get("patient_symptoms", ""), ) # Replay transcript @@ -196,3 +388,4 @@ def resume_session(session_id: str) -> Session: session.created_at = meta.get("created_at", session.created_at) return session + diff --git a/uv.lock b/uv.lock index f9f2e5c..0845b23 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,14 @@ version = 1 revision = 3 requires-python = ">=3.13" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 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