import pandas as pd
import pytest

from delta_barth.analysis import forecast, parse
from delta_barth.errors import FeaturesMissingError


def test_check_needed_features():
    target_features = set(("feat1", "feat2", "feat3"))
    data = pd.DataFrame(
        data=[[1, 2, 3, 4, 5]], columns=["feat1", "feat2", "feat3", "feat4", "feat5"]
    )
    parse._check_needed_features(data, target_features)
    data = pd.DataFrame(
        data=[[1, 2, 3, 4, 5]], columns=["featX", "feat2", "feat3", "feat4", "feat5"]
    )
    with pytest.raises(FeaturesMissingError):
        parse._check_needed_features(data, target_features)


def test_map_features_to_targets():
    feature_map = dict(feat1="feat10", feat2="feat20", feat5="feat50")
    data = pd.DataFrame(
        data=[[1, 2, 3, 4, 5]], columns=["feat1", "feat2", "feat3", "feat4", "feat5"]
    )
    data = parse._map_features_to_targets(data, feature_map)
    assert "feat10" in data.columns
    assert "feat20" in data.columns
    assert "feat50" in data.columns
    assert "feat3" in data.columns
    assert "feat4" in data.columns
    assert "feat1" not in data.columns
    assert "feat2" not in data.columns
    assert "feat5" not in data.columns


def test_preprocess_features(exmpl_api_sales_prognosis_resp):
    resp = exmpl_api_sales_prognosis_resp
    df = forecast._parse_api_resp_to_df(resp)
    feat_mapping: dict[str, str] = {
        "artikelId": "artikel_refid",
        "firmaId": "firma_refid",
        "betrag": "betrag",
        "menge": "menge",
        "buchungsDatum": "buchungs_datum",
    }
    target_features: frozenset[str] = frozenset(
        (
            "firma_refid",
            "betrag",
            "buchungs_datum",
        )
    )

    assert all(feat in df.columns for feat in feat_mapping.keys())
    data = parse.process_features(df, feat_mapping, target_features)
    assert len(data.columns) == len(df.columns)
    assert (data.columns != df.columns).any()
    assert any(feat not in data.columns for feat in feat_mapping.keys())