cosmic-cortex

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(