def demo()

in hiplot/fetchers_demo.py [0:0]

• 61 lines of code
• 13 McCabe index (conditional complexity)

def demo(n: int = 100) -> hip.Experiment:
    xp = hip.Experiment()
    xp.display_data(hip.Displays.XY).update({
        'axis_x': 'time',
        'axis_y': 'exp_metric',
    })

    # Some fake PBT-ish data
    def fake_params() -> t.Dict[str, hip.DisplayableType]:
        r = random.random()
        p: t.Dict[str, hip.DisplayableType] = {
            "lr": 10 ** random.uniform(-5, 0),
            "seed": random.uniform(0, 10),
            "name": uuid.uuid4().hex[:6],
            "optimizer": random.choice(["sgd", "adam", "adamw"]),
            "r": r,
            "c": random.choice(["red", "green", "black"]),
        }
        if r < 0.1:
            del p['optimizer']
        if r > 0.3:
            p["optionA"] = random.uniform(1, 5)
        else:
            p["optionB"] = random.uniform(1, 5)

        if r < 0.2:
            p["pctile"] = -1.0
        elif r < 0.5:
            p["pctile"] = random.uniform(-1.0, 10.0)
        elif r < 0.8:
            p["pctile"] = 10 ** random.uniform(1, 2)
        else:
            p["pctile"] = random.uniform(100, 101)

        if random.random() > 0.3:
            p["special_values"] = random.uniform(1, 5)
        else:
            p["special_values"] = random.choice([math.inf, -math.inf, math.nan])
        return p

    def fake_metrics(tm: float) -> t.Dict[str, hip.DisplayableType]:
        return {
            "exp_metric": 10 ** random.uniform(-5, 0),
            "pct_success": random.uniform(10, 90),
            "chkpt": uuid.uuid4().hex[:6],
            "time": tm + random.uniform(-0.2, 0.2),
            "force_numericlog": random.uniform(1, 100),
            'timestamp': int(time.time() + (task_idx * 2000)),
        }

    current_pop: t.List[t.Dict[str, t.Any]] = [dict(uid=f"init{i}", params=fake_params(), last_ckpt_uid=None) for i in range(10)]
    continue_num = 0
    for task_idx in range(n):
        # All drop checkpoints
        for p in current_pop:
            ckpt_uid = f"{p['uid']}_{uuid.uuid4().hex[:6]}"
            xp.datapoints.append(hip.Datapoint(uid=ckpt_uid, from_uid=p['last_ckpt_uid'], values={**p['params'], **fake_metrics(task_idx)}))
            p['last_ckpt_uid'] = ckpt_uid

        # Randomly drop some
        current_pop = [p for p in current_pop if random.random() > 0.3]

        # Respawn as needed
        for _ in range(10 - len(current_pop)):
            continue_num += 1
            parent = random.choice(xp.datapoints[-10:])
            current_pop.append(dict(uid=f"continue{continue_num}", params=fake_params(), last_ckpt_uid=parent.uid))
    xp.parameters_definition["c"].colors = {"red": "rgb(255, 0, 0)", "green": "rgb(0, 255, 0)", "black": "rgb(0, 0, 0)"}
    xp.parameters_definition["force_numericlog"].type = hip.ValueType.NUMERIC_LOG
    xp.parameters_definition["pctile"].type = hip.ValueType.NUMERIC_PERCENTILE
    xp.parameters_definition["timestamp"].type = hip.ValueType.TIMESTAMP
    return xp