"""

Test raising error when training and testing dataframes are inconsistent in

multivariate trainable models.

"""

import numpy as np

import pandas as pd

import pytest

from sklearn.cluster import KMeans

from sklearn.linear_model import LinearRegression

from sklearn.neighbors import LocalOutlierFactor

import adtk.detector as detector

import adtk.transformer as transformer

models = [

detector.MinClusterDetector(KMeans(n_clusters=2)),

detector.OutlierDetector(

LocalOutlierFactor(n_neighbors=20, contamination=0.1)

),

detector.RegressionAD(target="A", regressor=LinearRegression()),

detector.PcaAD(),

transformer.RegressionResidual(target="A", regressor=LinearRegression()),

transformer.PcaReconstructionError(),

transformer.PcaProjection(),

transformer.PcaReconstruction(),

]

df_train = pd.DataFrame(

np.arange(40).reshape(20, 2),

columns=["A", "B"],

index=pd.date_range(start="2017-1-1", periods=20, freq="D"),

)

df_test_ok = pd.DataFrame(

np.arange(0, -60, -1).reshape(20, 3),

columns=["C", "B", "A"],

index=pd.date_range(start="2017-1-1", periods=20, freq="D"),

)

df_test_not_ok = pd.DataFrame(

np.arange(0, -60, -1).reshape(20, 3),

columns=["C", "D", "A"],

index=pd.date_range(start="2017-1-1", periods=20, freq="D"),

)

@pytest.mark.parametrize("model", models)

def test_inconsistent_train_test(model):

model.fit(df_train)

model.predict(df_test_ok)

with pytest.raises(

ValueError,

match="The model was trained by a pandas DataFrame with columns",

):

model.predict(df_test_not_ok)