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import joblib | |
import numpy as np | |
from pathlib import Path | |
from sklearn.ensemble import RandomForestRegressor | |
from sklearn.datasets import load_boston | |
class Model: | |
def __init__(self, model_path: str = None): |
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class Model: | |
def train(self, X, y): | |
pass | |
def predict(self, X): | |
pass | |
def save(self): | |
pass |
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from typing import List | |
from fastapi import FastAPI | |
from pydantic import BaseModel | |
class PredictRequest(BaseModel): | |
data: List[List[float]] | |
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import numpy as np | |
from fastapi import Depends | |
from .ml.model import get_model | |
@app.post("/predict", response_model=PredictResponse) | |
def predict(input: PredictRequest, model: Model = Depends(get_model)): | |
X = np.array(input.data) | |
y_pred = model.predict(X) |
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@app.post("/predict", response_model=PredictResponse) | |
def predict(input: PredictRequest): | |
return PredictResponse(data=[0.0]) |
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from pydantic import BaseModel, ValidationError, validator | |
from .ml.model import n_features | |
class PredictRequest(BaseModel): | |
data: List[List[float]] | |
@validator("data") | |
def check_dimensionality(cls, v): |
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import numpy as np | |
class MockModel: | |
def __init__(self, model_path: str = None): | |
self._model_path = None | |
self._model = None | |
def predict(self, X: np.ndarray) -> np.ndarray: | |
n_instances = len(X) |
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import pytest | |
from starlette.testclient import TestClient | |
from ..main import app | |
from ..ml.model import get_model | |
from .mocks import MockModel | |
def get_model_override(): |
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import pytest | |
import random | |
from starlette.testclient import TestClient | |
from starlette.status import HTTP_200_OK | |
from api.ml.model import n_features | |
@pytest.mark.parametrize("n_instances", range(1, 10)) |
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from itertools import product | |
from starlette.status import HTTP_422_UNPROCESSABLE_ENTITY | |
@pytest.mark.parametrize( | |
"n_instances, test_data_n_features", | |
product(range(1, 10), [n for n in range(1, 20) if n != n_features]), | |
) | |
def test_predict_with_wrong_input( |
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