from collections import OrderedDict

import numpy as np
from numpy.testing import assert_array_equal
import pandas as pd
import pandas.testing as tm
import pytest

from sksurv.preprocessing import OneHotEncoder


def _encoded_data(data):
    expected = []
    for nam, col in data.items():
        if hasattr(col, "cat"):
            for cat in col.cat.categories[1:]:
                name = f"{nam}={cat}"
                s = pd.Series(col == cat, dtype=np.float64)
                expected.append((name, s))
        else:
            expected.append((nam, col))

    expected_data = pd.DataFrame.from_dict(OrderedDict(expected))
    return expected_data


@pytest.fixture()
def create_data():
    def _create_data(n_samples=117):
        rnd = np.random.RandomState(51365192)
        data_num = pd.DataFrame(rnd.rand(n_samples, 5), columns=[f"N{i}" for i in range(5)])

        dat_cat = pd.DataFrame(
            OrderedDict(
                [
                    ("binary_1", pd.Categorical.from_codes(rnd.binomial(1, 0.6, n_samples), ["Yes", "No"])),
                    ("binary_2", pd.Categorical.from_codes(rnd.binomial(1, 0.376, n_samples), ["East", "West"])),
                    ("trinary", pd.Categorical.from_codes(rnd.binomial(2, 0.76, n_samples), ["Green", "Blue", "Red"])),
                    (
                        "many",
                        pd.Categorical.from_codes(
                            rnd.binomial(5, 0.47, n_samples), ["One", "Two", "Three", "Four", "Five", "Six"]
                        ),
                    ),
                ]
            )
        )
        data = pd.concat((data_num, dat_cat), axis=1)
        return data, _encoded_data(data)

    return _create_data


class TestOneHotEncoder:
    @staticmethod
    def test_fit(create_data):
        data, expected_data = create_data()

        t = OneHotEncoder().fit(data)

        assert t.feature_names_.tolist() == ["binary_1", "binary_2", "trinary", "many"]
        assert set(t.encoded_columns_) == set(expected_data.columns)

        assert t.categories_ == {k: data[k].cat.categories for k in ["binary_1", "binary_2", "trinary", "many"]}

    @staticmethod
    def test_fit_transform(create_data):
        data, expected_data = create_data()

        actual_data = OneHotEncoder().fit_transform(data)
        tm.assert_frame_equal(actual_data, expected_data)

    @staticmethod
    def test_transform(create_data):
        data, _ = create_data()

        t = OneHotEncoder().fit(data)
        data, expected_data = create_data(165)
        actual_data = t.transform(data)
        tm.assert_frame_equal(actual_data, expected_data)

        data = pd.concat((data.iloc[:, :2], data.iloc[:, 5:], data.iloc[:, 2:5]), axis=1)
        actual_data = t.transform(data)
        tm.assert_frame_equal(actual_data, expected_data)

    @staticmethod
    def test_get_feature_names_out(create_data):
        data, expected_data = create_data()

        t = OneHotEncoder()
        t.fit(data)

        out_names = t.get_feature_names_out()
        assert_array_equal(out_names, expected_data.columns.values)

    @staticmethod
    def test_get_feature_names_out_shuffled(create_data):
        data, _ = create_data()
        order = np.array(["binary_1", "N0", "N3", "trinary", "binary_2", "N1", "N2", "many"])
        expected_columns = np.array(
            [
                "binary_1=No",
                "N0",
                "N3",
                "trinary=Blue",
                "trinary=Red",
                "binary_2=West",
                "N1",
                "N2",
                "many=Two",
                "many=Three",
                "many=Four",
                "many=Five",
                "many=Six",
            ]
        )

        t = OneHotEncoder()
        t.fit(data.loc[:, order])

        out_names = t.get_feature_names_out()
        assert_array_equal(out_names, expected_columns)

        with pytest.raises(ValueError, match="input_features is not equal to feature_names_in_"):
            t.get_feature_names_out(data.columns.tolist())

    @staticmethod
    def test_transform_other_columns(create_data):
        data, _ = create_data()

        t = OneHotEncoder().fit(data)
        data, _ = create_data(125)

        data_renamed = data.rename(columns={"binary_1": "renamed_1"})
        with pytest.raises(ValueError, match=r"1 features are missing from data: \['binary_1'\]"):
            t.transform(data_renamed)

        data_dropped = data.drop("trinary", axis=1)
        error_msg = "X has 8 features, but OneHotEncoder is expecting 9 features as input"
        with pytest.raises(ValueError, match=error_msg):
            t.transform(data_dropped)

        data_renamed = data.rename(columns={"binary_1": "renamed_1", "many": "too_many"})
        with pytest.raises(ValueError, match=r"2 features are missing from data: \['binary_1', 'many'\]"):
            t.transform(data_renamed)
