"""
Test functions for models.tools
"""
from statsmodels.compat.pandas import assert_frame_equal, assert_series_equal
from statsmodels.compat.python import lrange

import string

import numpy as np
from numpy.random import standard_normal
from numpy.testing import (
    assert_almost_equal,
    assert_array_equal,
    assert_equal,
    assert_string_equal,
)
import pandas as pd
import pytest

from statsmodels.datasets import longley
from statsmodels.tools import tools
from statsmodels.tools.tools import pinv_extended

# Ignore future warnings from test code
pytestmark = pytest.mark.filterwarnings(
    "ignore:categorical is deprecated:FutureWarning"
)


@pytest.fixture(scope="module")
def string_var():
    string_var = [
        string.ascii_lowercase[0:5],
        string.ascii_lowercase[5:10],
        string.ascii_lowercase[10:15],
        string.ascii_lowercase[15:20],
        string.ascii_lowercase[20:25],
    ]
    string_var *= 5
    string_var = np.asarray(sorted(string_var))
    series = pd.Series(string_var, name="string_var")
    return series


class TestTools(object):
    def test_add_constant_list(self):
        x = lrange(1, 5)
        x = tools.add_constant(x)
        y = np.asarray([[1, 1, 1, 1], [1, 2, 3, 4.0]]).T
        assert_equal(x, y)

    def test_add_constant_1d(self):
        x = np.arange(1, 5)
        x = tools.add_constant(x)
        y = np.asarray([[1, 1, 1, 1], [1, 2, 3, 4.0]]).T
        assert_equal(x, y)

    def test_add_constant_has_constant1d(self):
        x = np.ones(5)
        x = tools.add_constant(x, has_constant="skip")
        assert_equal(x, np.ones((5, 1)))

        with pytest.raises(ValueError):
            tools.add_constant(x, has_constant="raise")

        assert_equal(
            tools.add_constant(x, has_constant="add"), np.ones((5, 2))
        )

    def test_add_constant_has_constant2d(self):
        x = np.asarray([[1, 1, 1, 1], [1, 2, 3, 4.0]]).T
        y = tools.add_constant(x, has_constant="skip")
        assert_equal(x, y)

        with pytest.raises(ValueError):
            tools.add_constant(x, has_constant="raise")

        assert_equal(
            tools.add_constant(x, has_constant="add"),
            np.column_stack((np.ones(4), x)),
        )

    def test_add_constant_series(self):
        s = pd.Series([1.0, 2.0, 3.0])
        output = tools.add_constant(s)
        expected = pd.Series([1.0, 1.0, 1.0], name="const")
        assert_series_equal(expected, output["const"])

    def test_add_constant_dataframe(self):
        df = pd.DataFrame([[1.0, "a", 4], [2.0, "bc", 9], [3.0, "def", 16]])
        output = tools.add_constant(df)
        expected = pd.Series([1.0, 1.0, 1.0], name="const")
        assert_series_equal(expected, output["const"])
        dfc = df.copy()
        dfc.insert(0, "const", np.ones(3))
        assert_frame_equal(dfc, output)

    def test_add_constant_zeros(self):
        a = np.zeros(100)
        output = tools.add_constant(a)
        assert_equal(output[:, 0], np.ones(100))

        s = pd.Series([0.0, 0.0, 0.0])
        output = tools.add_constant(s)
        expected = pd.Series([1.0, 1.0, 1.0], name="const")
        assert_series_equal(expected, output["const"])

        df = pd.DataFrame([[0.0, "a", 4], [0.0, "bc", 9], [0.0, "def", 16]])
        output = tools.add_constant(df)
        dfc = df.copy()
        dfc.insert(0, "const", np.ones(3))
        assert_frame_equal(dfc, output)

        df = pd.DataFrame([[1.0, "a", 0], [0.0, "bc", 0], [0.0, "def", 0]])
        output = tools.add_constant(df)
        dfc = df.copy()
        dfc.insert(0, "const", np.ones(3))
        assert_frame_equal(dfc, output)

    def test_recipr(self):
        X = np.array([[2, 1], [-1, 0]])
        Y = tools.recipr(X)
        assert_almost_equal(Y, np.array([[0.5, 1], [0, 0]]))

    def test_recipr0(self):
        X = np.array([[2, 1], [-4, 0]])
        Y = tools.recipr0(X)
        assert_almost_equal(Y, np.array([[0.5, 1], [-0.25, 0]]))

    def test_extendedpinv(self):
        X = standard_normal((40, 10))
        np_inv = np.linalg.pinv(X)
        np_sing_vals = np.linalg.svd(X, 0, 0)
        sm_inv, sing_vals = pinv_extended(X)
        assert_almost_equal(np_inv, sm_inv)
        assert_almost_equal(np_sing_vals, sing_vals)

    def test_extendedpinv_singular(self):
        X = standard_normal((40, 10))
        X[:, 5] = X[:, 1] + X[:, 3]
        np_inv = np.linalg.pinv(X)
        np_sing_vals = np.linalg.svd(X, 0, 0)
        sm_inv, sing_vals = pinv_extended(X)
        assert_almost_equal(np_inv, sm_inv)
        assert_almost_equal(np_sing_vals, sing_vals)

    def test_fullrank(self):
        import warnings

        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            X = standard_normal((40, 10))
            X[:, 0] = X[:, 1] + X[:, 2]

            Y = tools.fullrank(X)
            assert_equal(Y.shape, (40, 9))

            X[:, 5] = X[:, 3] + X[:, 4]
            Y = tools.fullrank(X)
            assert_equal(Y.shape, (40, 8))
            warnings.simplefilter("ignore")


def test_estimable():
    rng = np.random.RandomState(20120713)
    N, P = (40, 10)
    X = rng.normal(size=(N, P))
    C = rng.normal(size=(1, P))
    isestimable = tools.isestimable
    assert isestimable(C, X)
    assert isestimable(np.eye(P), X)
    for row in np.eye(P):
        assert isestimable(row, X)
    X = np.ones((40, 2))
    assert isestimable([1, 1], X)
    assert not isestimable([1, 0], X)
    assert not isestimable([0, 1], X)
    assert not isestimable(np.eye(2), X)
    halfX = rng.normal(size=(N, 5))
    X = np.hstack([halfX, halfX])
    assert not isestimable(np.hstack([np.eye(5), np.zeros((5, 5))]), X)
    assert not isestimable(np.hstack([np.zeros((5, 5)), np.eye(5)]), X)
    assert isestimable(np.hstack([np.eye(5), np.eye(5)]), X)
    # Test array_like for design
    XL = X.tolist()
    assert isestimable(np.hstack([np.eye(5), np.eye(5)]), XL)
    # Test ValueError for incorrect number of columns
    X = rng.normal(size=(N, 5))
    for n in range(1, 4):
        with pytest.raises(ValueError):
            isestimable(np.ones((n,)), X)
    with pytest.raises(ValueError):
        isestimable(np.eye(4), X)


class TestCategoricalNumerical(object):
    # TODO: use assert_raises to check that bad inputs are taken care of
    @classmethod
    def setup_class(cls):
        # import string
        stringabc = "abcdefghijklmnopqrstuvwxy"
        cls.des = np.random.randn(25, 2)
        cls.instr = np.floor(np.arange(10, 60, step=2) / 10)
        x = np.zeros((25, 5))
        x[:5, 0] = 1
        x[5:10, 1] = 1
        x[10:15, 2] = 1
        x[15:20, 3] = 1
        x[20:25, 4] = 1
        cls.dummy = x
        structdes = pd.DataFrame(index=np.arange(25))
        structdes["var1"] = cls.des[:, 0]
        structdes["var2"] = cls.des[:, 1]
        structdes["instrument"] = cls.instr
        string_var = [
            stringabc[0:5],
            stringabc[5:10],
            stringabc[10:15],
            stringabc[15:20],
            stringabc[20:25],
        ]
        string_var *= 5
        cls.string_var = np.array(sorted(string_var))
        structdes["str_instr"] = cls.string_var
        cls.recdes = cls.structdes = structdes

    def test_array2d(self):
        des = np.column_stack((self.des, self.instr, self.des))
        des = tools.categorical(des, col=2)
        assert_array_equal(des[:, -5:], self.dummy)
        assert_equal(des.shape[1], 10)

    def test_array1d(self):
        des = tools.categorical(self.instr)
        assert_array_equal(des[:, -5:], self.dummy)
        assert_equal(des.shape[1], 6)

    def test_array1d_col_error(self):
        with pytest.raises(TypeError, match="col must be a str, int or None"):
            tools.categorical(self.instr, col={"a": 1})

    def test_array2d_drop(self):
        des = np.column_stack((self.des, self.instr, self.des))
        des = tools.categorical(des, col=2, drop=True)
        assert_array_equal(des[:, -5:], self.dummy)
        assert_equal(des.shape[1], 9)

    def test_array1d_drop(self):
        des = tools.categorical(self.instr, drop=True)
        assert_array_equal(des, self.dummy)
        assert_equal(des.shape[1], 5)

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_arraylike2d(self):
        des = tools.categorical(self.structdes.tolist(), col=2)
        test_des = des[:, -5:]
        assert_array_equal(test_des, self.dummy)
        assert_equal(des.shape[1], 9)

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_arraylike1d(self):
        instr = self.structdes["instrument"].tolist()
        dum = tools.categorical(instr)
        test_dum = dum[:, -5:]
        assert_array_equal(test_dum, self.dummy)
        assert_equal(dum.shape[1], 6)

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_arraylike2d_drop(self):
        des = tools.categorical(self.structdes.tolist(), col=2, drop=True)
        test_des = des[:, -5:]
        assert_array_equal(test_des, self.dummy)
        assert_equal(des.shape[1], 8)

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_arraylike1d_drop(self):
        instr = self.structdes["instrument"].tolist()
        dum = tools.categorical(instr, drop=True)
        assert_array_equal(dum, self.dummy)
        assert_equal(dum.shape[1], 5)


class TestCategoricalString(TestCategoricalNumerical):
    @pytest.mark.skip(reason="categorical is deprecated")
    def test_array2d(self):
        des = np.column_stack((self.des, self.instr, self.des))
        des = tools.categorical(des, col=2)
        assert_array_equal(des[:, -5:], self.dummy)
        assert_equal(des.shape[1], 10)

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_array1d(self):
        des = tools.categorical(self.instr)
        assert_array_equal(des[:, -5:], self.dummy)
        assert_equal(des.shape[1], 6)

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_array2d_drop(self):
        des = np.column_stack((self.des, self.instr, self.des))
        des = tools.categorical(des, col=2, drop=True)
        assert_array_equal(des[:, -5:], self.dummy)
        assert_equal(des.shape[1], 9)

    def test_array1d_drop(self):
        des = tools.categorical(self.string_var, drop=True)
        assert_array_equal(des, self.dummy)
        assert_equal(des.shape[1], 5)

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_arraylike2d(self):
        raise NotImplementedError

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_arraylike1d(self):
        raise NotImplementedError

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_arraylike2d_drop(self):
        raise NotImplementedError

    @pytest.mark.skip(reason="categorical is deprecated")
    def test_arraylike1d_drop(self):
        raise NotImplementedError


def test_pandas_const_series():
    dta = longley.load_pandas()
    series = dta.exog["GNP"]
    series = tools.add_constant(series, prepend=False)
    assert_string_equal("const", series.columns[1])
    assert_equal(series.var(0)[1], 0)


def test_pandas_const_series_prepend():
    dta = longley.load_pandas()
    series = dta.exog["GNP"]
    series = tools.add_constant(series, prepend=True)
    assert_string_equal("const", series.columns[0])
    assert_equal(series.var(0)[0], 0)


def test_pandas_const_df():
    dta = longley.load_pandas().exog
    dta = tools.add_constant(dta, prepend=False)
    assert_string_equal("const", dta.columns[-1])
    assert_equal(dta.var(0)[-1], 0)


def test_pandas_const_df_prepend():
    dta = longley.load_pandas().exog
    # regression test for #1025
    dta["UNEMP"] /= dta["UNEMP"].std()
    dta = tools.add_constant(dta, prepend=True)
    assert_string_equal("const", dta.columns[0])
    assert_equal(dta.var(0)[0], 0)


class TestNanDot(object):
    @classmethod
    def setup_class(cls):
        nan = np.nan
        cls.mx_1 = np.array([[nan, 1.0], [2.0, 3.0]])
        cls.mx_2 = np.array([[nan, nan], [2.0, 3.0]])
        cls.mx_3 = np.array([[0.0, 0.0], [0.0, 0.0]])
        cls.mx_4 = np.array([[1.0, 0.0], [1.0, 0.0]])
        cls.mx_5 = np.array([[0.0, 1.0], [0.0, 1.0]])
        cls.mx_6 = np.array([[1.0, 2.0], [3.0, 4.0]])

    def test_11(self):
        test_res = tools.nan_dot(self.mx_1, self.mx_1)
        expected_res = np.array([[np.nan, np.nan], [np.nan, 11.0]])
        assert_array_equal(test_res, expected_res)

    def test_12(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_1, self.mx_2)
        expected_res = np.array([[nan, nan], [nan, nan]])
        assert_array_equal(test_res, expected_res)

    def test_13(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_1, self.mx_3)
        expected_res = np.array([[0.0, 0.0], [0.0, 0.0]])
        assert_array_equal(test_res, expected_res)

    def test_14(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_1, self.mx_4)
        expected_res = np.array([[nan, 0.0], [5.0, 0.0]])
        assert_array_equal(test_res, expected_res)

    def test_41(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_4, self.mx_1)
        expected_res = np.array([[nan, 1.0], [nan, 1.0]])
        assert_array_equal(test_res, expected_res)

    def test_23(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_2, self.mx_3)
        expected_res = np.array([[0.0, 0.0], [0.0, 0.0]])
        assert_array_equal(test_res, expected_res)

    def test_32(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_3, self.mx_2)
        expected_res = np.array([[0.0, 0.0], [0.0, 0.0]])
        assert_array_equal(test_res, expected_res)

    def test_24(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_2, self.mx_4)
        expected_res = np.array([[nan, 0.0], [5.0, 0.0]])
        assert_array_equal(test_res, expected_res)

    def test_25(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_2, self.mx_5)
        expected_res = np.array([[0.0, nan], [0.0, 5.0]])
        assert_array_equal(test_res, expected_res)

    def test_66(self):
        nan = np.nan
        test_res = tools.nan_dot(self.mx_6, self.mx_6)
        expected_res = np.array([[7.0, 10.0], [15.0, 22.0]])
        assert_array_equal(test_res, expected_res)


class TestEnsure2d(object):
    @classmethod
    def setup_class(cls):
        x = np.arange(400.0).reshape((100, 4))
        cls.df = pd.DataFrame(x, columns=["a", "b", "c", "d"])
        cls.series = cls.df.iloc[:, 0]
        cls.ndarray = x

    def test_enfore_numpy(self):
        results = tools._ensure_2d(self.df, True)
        assert_array_equal(results[0], self.ndarray)
        assert_array_equal(results[1], self.df.columns)
        results = tools._ensure_2d(self.series, True)
        assert_array_equal(results[0], self.ndarray[:, [0]])
        assert_array_equal(results[1], self.df.columns[0])

    def test_pandas(self):
        results = tools._ensure_2d(self.df, False)
        assert_frame_equal(results[0], self.df)
        assert_array_equal(results[1], self.df.columns)

        results = tools._ensure_2d(self.series, False)
        assert_frame_equal(results[0], self.df.iloc[:, [0]])
        assert_equal(results[1], self.df.columns[0])

    def test_numpy(self):
        results = tools._ensure_2d(self.ndarray)
        assert_array_equal(results[0], self.ndarray)
        assert_equal(results[1], None)

        results = tools._ensure_2d(self.ndarray[:, 0])
        assert_array_equal(results[0], self.ndarray[:, [0]])
        assert_equal(results[1], None)


def test_categorical_pandas_errors(string_var):
    with pytest.raises(ValueError, match="data.name does not match col"):
        tools.categorical(string_var, "unknown")

    df = pd.DataFrame(string_var)
    with pytest.raises(TypeError, match="col must be a str or int"):
        tools.categorical(df, None)
    with pytest.raises(
        ValueError, match="Column 'unknown' not found in " "data"
    ):
        tools.categorical(df, "unknown")


def test_categorical_series(string_var):
    design = tools.categorical(string_var, drop=True)
    dummies = pd.get_dummies(pd.Categorical(string_var))
    assert_frame_equal(design, dummies)
    design = tools.categorical(string_var, drop=False)
    dummies.columns = list(dummies.columns)
    assert_frame_equal(design.iloc[:, :5], dummies)
    assert_series_equal(design.iloc[:, 5], string_var)
    _, dictnames = tools.categorical(string_var, drop=False, dictnames=True)
    for i, c in enumerate(pd.Categorical(string_var).categories):
        assert i in dictnames
        assert dictnames[i] == c


def test_categorical_dataframe(string_var):
    df = pd.DataFrame(string_var)
    design = tools.categorical(df, "string_var", drop=True)
    dummies = pd.get_dummies(pd.Categorical(string_var))
    assert_frame_equal(design, dummies)

    df = pd.DataFrame({"apple": string_var, "ban": string_var})
    design = tools.categorical(df, "apple", drop=True)
    assert_frame_equal(design, dummies)


def test_categorical_errors(string_var):
    with pytest.raises(ValueError, match="Can only convert one column"):
        tools.categorical(string_var, (0, 1))
    with pytest.raises(ValueError, match="data.name does not match col"):
        tools.categorical(string_var, {"a": 1})
