模型怎麼變成可呼叫的服務？Pipeline 與 API

原始四欄 → 已 fit 的 StandardScaler → 已 fit 的 LogisticRegression

z_j = (x_j − train_mean_j) / train_scale_j
logit = Σ_j coefficient_j × z_j + intercept
p(class 1) = 1 / (1 + exp(−logit))

{"rows": [[0, 1, -2, 3]]}

{"model_version":"synthetic-four-feature-v1","positive_class":1,"predictions":[{"class":1,"probability_class_1":0.7971203905962692}]}

{"detail":[{"type":"finite_number","loc":["body","rows",0,0],"msg":"Input should be a finite number"}]}

python3 verify_http.py > stdout.json

python3 -m venv .venv
. .venv/bin/activate
python -m pip install -r requirements.txt
python verify_http.py > stdout.json

numpy==1.26.3
scipy==1.14.0
scikit-learn==1.5.1
joblib==1.4.2
threadpoolctl==3.5.0
fastapi==0.128.7
pydantic==2.12.5
uvicorn==0.41.0
httpx==0.28.1

"""Original synthetic train-only Pipeline; writes only this demo's own trusted artifact."""
import hashlib
import json
import platform
from pathlib import Path

import fastapi
import httpx
import joblib
import numpy as np
import pydantic
import sklearn
import uvicorn
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

BASE = Path(__file__).resolve().parent
ARTIFACTS = BASE / "artifacts"
FEATURE_ORDER = ["signal_a", "signal_b", "signal_c", "signal_d"]
MODEL_VERSION = "synthetic-four-feature-v1"


def runtime_versions():
    return {"python": platform.python_version(), "numpy": np.__version__,
        "scikit_learn": sklearn.__version__, "joblib": joblib.__version__,
        "fastapi": fastapi.__version__, "pydantic": pydantic.__version__,
        "uvicorn": uvicorn.__version__, "httpx": httpx.__version__}


def train():
    x, y = make_classification(n_samples=240, n_features=4, n_informative=3,
        n_redundant=0, n_clusters_per_class=1, flip_y=.04, random_state=42)
    train_ids, test_ids = train_test_split(np.arange(len(y)), test_size=.25,
        stratify=y, random_state=17)
    pipeline = Pipeline([("scale", StandardScaler()),
        ("classifier", LogisticRegression(max_iter=1000, random_state=0))])
    pipeline.fit(x[train_ids], y[train_ids])  # No test fit or refit after evaluation.
    probability = pipeline.predict_proba(x[test_ids])
    prediction = pipeline.predict(x[test_ids])
    ARTIFACTS.mkdir(exist_ok=True)
    model_path = ARTIFACTS / "pipeline.joblib"
    joblib.dump(pipeline, model_path, compress=0, protocol=5)
    metadata = {"model_version": MODEL_VERSION, "feature_order": FEATURE_ORDER,
        "label_map": {"0": "synthetic_negative", "1": "synthetic_positive"},
        "probability_semantics": "probability_class_1 is model predict_proba for class1, not calibrated assurance",
        "classes": pipeline.classes_.tolist(), "runtime_versions": runtime_versions(),
        "artifact_sha256": hashlib.sha256(model_path.read_bytes()).hexdigest(),
        "training_recipe": {"n_samples": 240, "data_seed": 42, "split_seed": 17,
            "train_count": len(train_ids), "test_count": len(test_ids),
            "scaler_fit_scope": "180trainingrows only; population variance, no testfit",
            "pipeline": "StandardScaler -> LogisticRegression(max_iter1000,random_state0)",
            "train_ids": train_ids.tolist(), "test_ids": test_ids.tolist()},
        "heldout_toy_metrics": {"accuracy": float(accuracy_score(y[test_ids], prediction)),
            "log_loss": float(log_loss(y[test_ids], probability, labels=pipeline.classes_))},
        "fitted_values_for_checking": {"scaler_mean": pipeline.named_steps["scale"].mean_.tolist(),
            "scaler_scale": pipeline.named_steps["scale"].scale_.tolist(),
            "classifier_coef": pipeline.named_steps["classifier"].coef_.tolist(),
            "classifier_intercept": pipeline.named_steps["classifier"].intercept_.tolist()},
        "limits": "Handset recipe and one fixed random split; no CV/tuning/real-world accuracy claim"}
    (ARTIFACTS / "metadata.json").write_text(json.dumps(metadata, indent=2) + "\n")
    fixture = {"rows": x[test_ids[:3]].tolist(), "labels": y[test_ids[:3]].tolist()}
    (ARTIFACTS / "client_fixture.json").write_text(json.dumps(fixture, indent=2) + "\n")
    return metadata


if __name__ == "__main__":
    print(json.dumps(train(), indent=2))

"""Loopback teaching API; loads the locally trained trusted artifact once, never fits."""
import hashlib
import json
from contextlib import asynccontextmanager
from typing import Annotated

import joblib
import numpy as np
from fastapi import FastAPI, HTTPException, Request
from fastapi.exceptions import RequestValidationError
from fastapi.responses import JSONResponse
from pydantic import BaseModel, ConfigDict, Field

from train_model import ARTIFACTS, FEATURE_ORDER, MODEL_VERSION, runtime_versions

Number = Annotated[float, Field(strict=True, allow_inf_nan=False)]
Row = Annotated[list[Number], Field(min_length=4, max_length=4)]


class PredictInput(BaseModel):
    model_config = ConfigDict(extra="forbid")
    rows: Annotated[list[Row], Field(min_length=1, max_length=32)]


@asynccontextmanager
async def lifespan(app):
    metadata = json.loads((ARTIFACTS / "metadata.json").read_text())
    if metadata["runtime_versions"] != runtime_versions():
        raise RuntimeError("Use the recorded training environment for this own artifact")
    if metadata["feature_order"] != FEATURE_ORDER or metadata["model_version"] != MODEL_VERSION:
        raise RuntimeError("Artifact schema/version does not match this demo API")
    path = ARTIFACTS / "pipeline.joblib"
    digest = hashlib.sha256(path.read_bytes()).hexdigest()
    if digest != metadata["artifact_sha256"]:
        raise RuntimeError("Local artifact digest does not match local metadata")
    # Only our own just-trained local artifact. A digest is NOT proof an untrusted pickle is safe.
    app.state.pipeline = joblib.load(path)
    app.state.metadata = metadata
    app.state.load_count = 1
    yield


app = FastAPI(title="Synthetic Pipeline Local Demo", lifespan=lifespan)


@app.exception_handler(RequestValidationError)
async def validation_error(request: Request, error: RequestValidationError):
    # Do not echo input/ctx: NaN/Infinity may be present and cannot be serialized as strict JSON.
    safe = [{"type": item["type"], "loc": list(item["loc"]), "msg": item["msg"]}
            for item in error.errors()]
    return JSONResponse(status_code=422, content={"detail": safe})


@app.get("/health")
def health():
    return {"ready": True, "model_version": app.state.metadata["model_version"],
        "feature_order": app.state.metadata["feature_order"],
        "artifact_sha256": app.state.metadata["artifact_sha256"],
        "model_load_count": app.state.load_count}


@app.post("/predict")
def predict(body: PredictInput):
    rows = np.asarray(body.rows, dtype=np.float64)
    try:
        # Some finite but extreme numbers can overflow preprocessing; do not turn that into500.
        with np.errstate(over="raise", invalid="raise", divide="raise"):
            classes = app.state.pipeline.predict(rows)
            probability = app.state.pipeline.predict_proba(rows)[:, 1]
        if not np.isfinite(probability).all():
            raise FloatingPointError("Nonfinite model output")
    except (ValueError, FloatingPointError, OverflowError) as error:
        raise HTTPException(status_code=422, detail={"code": "numerical_range",
            "message": "Feature magnitudes exceed this model's numerical range"}) from error
    return {"model_version": app.state.metadata["model_version"],
        "positive_class": 1, "predictions": [
            {"class": int(label), "probability_class_1": float(p)}
            for label, p in zip(classes, probability)]}

"""Train our artifact, run our own127.0.0.1 child, send real HTTP, terminate only that child."""
import hashlib
import json
import math
import socket
import subprocess
import sys
import time

import httpx
import joblib
import numpy as np

from train_model import ARTIFACTS, BASE, FEATURE_ORDER, MODEL_VERSION

HOST, PORT = "127.0.0.1", 8769


def main():
    train_run = subprocess.run([sys.executable, str(BASE / "train_model.py")],
                               capture_output=True, check=True)
    metadata = json.loads(train_run.stdout)
    fixture = json.loads((ARTIFACTS / "client_fixture.json").read_text())
    own_pipeline = joblib.load(ARTIFACTS / "pipeline.joblib")
    artifact_before = hashlib.sha256((ARTIFACTS / "pipeline.joblib").read_bytes()).hexdigest()
    # Refuse a busy port. Never discover/kill another process or reuse its server.
    with socket.socket() as sock:
        sock.bind((HOST, PORT))
    records = []
    proc = None
    shutdown = None
    log_path = BASE / "server-run.log"
    with log_path.open("w") as log, httpx.Client(base_url=f"http://{HOST}:{PORT}",
            timeout=5, trust_env=False) as client:
        try:
            proc = subprocess.Popen([sys.executable, "-m", "uvicorn", "api_app:app",
                "--host", HOST, "--port", str(PORT), "--log-level", "warning"],
                cwd=BASE, stdout=log, stderr=log)
            for _ in range(100):
                if proc.poll() is not None:
                    raise RuntimeError("Our server exited before readiness; inspect local server-run.log")
                try:
                    response = client.get("/health")
                    if response.status_code == 200:
                        ready = response.json()
                        assert ready["model_version"] == MODEL_VERSION
                        assert ready["artifact_sha256"] == artifact_before
                        break
                except httpx.TransportError:
                    pass
                time.sleep(.05)
            else:
                raise RuntimeError("Our server did not become ready")
            records.append({"case": "health_after_startup", "status": response.status_code, "body": ready})
            valid = [("single", {"rows": fixture["rows"][:1]}),
                ("batch3", {"rows": fixture["rows"]}),
                ("integer_numbers", {"rows": [[0, 1, -2, 3]]}),
                ("maximum_batch32", {"rows": [fixture["rows"][0]] * 32})]
            probability_errors = []
            for name, payload in valid:
                response = client.post("/predict", json=payload)
                assert response.status_code == 200, response.text
                body = response.json()
                x = np.asarray(payload["rows"], dtype=float)
                expected_class = own_pipeline.predict(x).tolist()
                expected_prob = own_pipeline.predict_proba(x)[:, 1]
                scale = own_pipeline.named_steps["scale"]
                classifier = own_pipeline.named_steps["classifier"]
                # Independent explicit transform + dot + sigmoid; do not use Pipeline.transform.
                z = (x - scale.mean_) / scale.scale_
                logits = z @ classifier.coef_[0] + classifier.intercept_[0]
                manual_prob = np.array([1 / (1 + math.exp(-float(v))) for v in logits])
                got = body["predictions"]
                assert [v["class"] for v in got] == expected_class
                error = max(abs(np.array([v["probability_class_1"] for v in got]) - manual_prob))
                probability_errors.append(float(error))
                assert np.allclose(expected_prob, manual_prob, atol=1e-14, rtol=0)
                assert error < 1e-14 and body["model_version"] == MODEL_VERSION
                records.append({"case": name, "status": response.status_code,
                    "request": payload, "body": body})
            invalid = [("empty_batch", {"rows": []}),
                ("too_many_rows", {"rows": [[0, 0, 0, 0]] * 33}),
                ("row3", {"rows": [[0, 0, 0]]}), ("row5", {"rows": [[0, 0, 0, 0, 0]]}),
                ("numeric_string", {"rows": [["1.0", 0, 0, 0]]}),
                ("bool", {"rows": [[True, 0, 0, 0]]}), ("null", {"rows": [[None, 0, 0, 0]]}),
                ("extra_field", {"rows": [[0, 0, 0, 0]], "debug": True}),
                ("missing_rows", {}), ("rows_not_matrix", {"rows": [0, 1, 2, 3]}),
                ("finite_but_overflowing_magnitude", {"rows": [[1e308, 1e308, 1e308, 1e308]]})]
            for name, payload in invalid:
                response = client.post("/predict", json=payload)
                assert response.status_code == 422, (name, response.text)
                assert response.headers["content-type"].startswith("application/json")
                records.append({"case": name, "status": response.status_code,
                    "request": payload, "body": response.json()})
            raw_cases = [("raw_NaN", '{"rows":[[NaN,0,0,0]]}'),
                ("raw_Infinity", '{"rows":[[Infinity,0,0,0]]}'),
                ("raw_negative_Infinity", '{"rows":[[-Infinity,0,0,0]]}'),
                ("exponent_overflow_1e400", '{"rows":[[1e400,0,0,0]]}'),
                ("malformed_json", '{"rows":')]
            for name, raw in raw_cases:
                response = client.post("/predict", content=raw, headers={"Content-Type": "application/json"})
                assert response.status_code == 422, (name, response.text)
                body = response.json()
                assert all(set(error) == {"type", "loc", "msg"} for error in body["detail"])
                records.append({"case": name, "status": response.status_code, "raw_request": raw, "body": body})
            response = client.get("/health")
            assert response.status_code == 200 and response.json()["model_load_count"] == 1
            records.append({"case": "health_after_all_requests", "status": 200, "body": response.json()})
        finally:
            if proc is not None:
                if proc.poll() is None:
                    proc.terminate()
                    try:
                        proc.wait(timeout=5)
                    except subprocess.TimeoutExpired:
                        proc.kill()
                        proc.wait(timeout=5)
                shutdown = {"owned_child_reaped": proc.poll() is not None,
                            "exit_code": proc.returncode, "host": HOST, "port": PORT}
    artifact_after = hashlib.sha256((ARTIFACTS / "pipeline.joblib").read_bytes()).hexdigest()
    assert artifact_before == artifact_after
    print(json.dumps({"scope": "Original localhost-only trained toy Pipeline API, actual HTTP, no public deployment",
        "metadata": metadata, "request_results": records,
        "checks": {"manual_pipeline_probability_max_error": max(probability_errors),
            "artifact_unchanged_by_requests": artifact_before == artifact_after,
            "model_load_count_after_requests": 1, "feature_order": FEATURE_ORDER,
            "probability_semantics": "class1 probability; not assurance the predicted class is calibrated",
            "validation_errors_do_not_echo_input_or_ctx": True},
        "server_shutdown": shutdown,
        "limits": ["Synthetic fixed split only, no CV/hyperparameter tuning or real-world model quality.",
            "All four values are positional according to metadata; numeric column swaps cannot be semantically detected.",
            "Strict input accepts JSON ints/floats, rejects strings/bools/null/nonfinite; finite numeric overflow gets422.",
            "NaN/Infinity tokens are nonstandard JSON; current parser accepts them before finite validation rejects them.",
            "Custom validation errors expose type/loc/msg only, preventing nonfinite input echo serialization failures.",
            "joblib loading only own trusted local artifact; matching digest alone does not make hostile pickle safe.",
            "Model/environment metadata is checked at startup; no arbitrary upload/load endpoint or request fit.",
            "No auth, public serving, TLS, concurrency/load test, monitoring, calibration or production deployment claim.",
            "Only the Popen child created here is terminated/reaped; no process discovery or unrelated termination."]}, indent=2, allow_nan=False))


if __name__ == "__main__":
    main()

DS69 image attributions and license texts

Figure 1 and cover: scikit-learn developers, official scikit-learn 1.5.2 documentation, “Displaying Pipelines”. Source: https://scikit-learn.org/1.5/auto_examples/miscellaneous/plot_pipeline_display.html . The graph is a documentation example showing Pipeline -> StandardScaler -> LogisticRegression; it is not the local fitted artifact. Image crop and proportional white-canvas cover fit are modifications. BSD 3-Clause license text follows.

Figures 2–3 and their mobile views: Sunny Data Science local synthetic API screenshots, captured from an isolated copy of the DS69 practice app. Swagger UI interface distributed under Apache License 2.0; full license text follows. FastAPI was used by the local application (MIT text included below for reference to the supporting project); the MIT license is not asserted as the screenshot's UI image grant. The screenshots were cropped to retain the request and actual response. The capture used FastAPI's default Swagger UI CDN major alias @5. The exact patch bytes were not pinned at capture time; the later package metadata reports 5.34.1 and is not a claim about the exact captured patch. The upstream swagger-ui-dist 5.34.1 distribution and matching official repository tag include the NOTICE reproduced below. This preserves the upstream attribution and does not identify the exact browser-loaded @5 patch.

scikit-learn BSD 3-Clause License (full text)
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   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
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   See the License for the specific language governing permissions and
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FastAPI MIT License (supporting project copy)
The MIT License (MIT)

Copyright (c) 2018 Sebastián Ramírez

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of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Swagger UI upstream NOTICE (verbatim from official swagger-ui-dist 5.34.1 package and matching tag)
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