"""Testing OLS robust covariance matrices against STATA

Created on Mon Oct 28 15:25:14 2013

Author: Josef Perktold
"""

import warnings

import numpy as np
from numpy.testing import assert_allclose, assert_equal
import pytest
from scipy import stats

from statsmodels.datasets import macrodata
from statsmodels.regression.linear_model import OLS, WLS
import statsmodels.stats.sandwich_covariance as sw
from statsmodels.tools.sm_exceptions import InvalidTestWarning
from statsmodels.tools.tools import add_constant

from .results import (
    results_grunfeld_ols_robust_cluster as res2,
    results_macro_ols_robust as res,
)

# TODO: implement test_hac_simple


class CheckOLSRobust:
    def test_basic(self):
        res1 = self.res1
        res2 = self.res2
        rtol = getattr(self, "rtol", 1e-10)
        assert_allclose(res1.params, res2.params, rtol=rtol)
        assert_allclose(self.bse_robust, res2.bse, rtol=rtol)
        assert_allclose(self.cov_robust, res2.cov, rtol=rtol)

    @pytest.mark.smoke
    def test_t_test_summary(self):
        res1 = self.res1
        mat = np.eye(len(res1.params))
        # TODO: if the t_test call is expensive, possibly make it a fixture?
        tt = res1.t_test(mat, cov_p=self.cov_robust)

        tt.summary()

    @pytest.mark.smoke
    def test_t_test_summary_frame(self):
        res1 = self.res1
        mat = np.eye(len(res1.params))
        tt = res1.t_test(mat, cov_p=self.cov_robust)

        tt.summary_frame()

    @pytest.mark.smoke
    def test_f_test_summary(self):
        res1 = self.res1
        mat = np.eye(len(res1.params))
        ft = res1.f_test(mat[:-1], cov_p=self.cov_robust)

        ft.summary()

    def test_tests(self):  # TODO: break into well-scoped tests
        # Note: differences between small (t-distribution, ddof) and large (normal)
        # F statistic has no ddof correction in large, but uses F distribution (?)
        res1 = self.res1
        res2 = self.res2
        rtol = getattr(self, "rtol", 1e-10)
        getattr(self, "rtolh", 1e-12)
        mat = np.eye(len(res1.params))
        tt = res1.t_test(mat, cov_p=self.cov_robust)
        # has 'effect', 'pvalue', 'sd', 'tvalue'
        # TODO confint missing
        assert_allclose(tt.effect, res2.params, rtol=rtol)
        assert_allclose(tt.sd, res2.bse, rtol=rtol)
        assert_allclose(tt.tvalue, res2.tvalues, rtol=rtol)
        if self.small:
            assert_allclose(tt.pvalue, res2.pvalues, rtol=5 * rtol)
        else:
            pval = stats.norm.sf(np.abs(tt.tvalue)) * 2
            assert_allclose(pval, res2.pvalues, rtol=5 * rtol, atol=1e-25)

        ft = res1.f_test(mat[:-1], cov_p=self.cov_robust)
        if self.small:
            # 'df_denom', 'df_num', 'fvalue', 'pvalue'
            assert_allclose(ft.fvalue, res2.F, rtol=rtol)
            # f-pvalue is not directly available in Stata results, but is in ivreg2
            if hasattr(res2, "Fp"):
                assert_allclose(ft.pvalue, res2.Fp, rtol=rtol)
        elif not getattr(self, "skip_f", False):
            dof_corr = res1.df_resid * 1.0 / res1.nobs
            assert_allclose(ft.fvalue * dof_corr, res2.F, rtol=rtol)

        if hasattr(res2, "df_r"):
            assert_equal(ft.df_num, res2.df_m)
            assert_equal(ft.df_denom, res2.df_r)
        else:
            # ivreg2
            assert_equal(ft.df_num, res2.Fdf1)
            assert_equal(ft.df_denom, res2.Fdf2)


class TestOLSRobust1(CheckOLSRobust):
    # compare with regress robust

    def setup_method(self):
        res_ols = self.res1
        self.bse_robust = res_ols.HC1_se
        self.cov_robust = res_ols.cov_HC1
        self.small = True
        self.res2 = res.results_hc0

    @classmethod
    def setup_class(cls):
        d2 = macrodata.load_pandas().data
        g_gdp = 400 * np.diff(np.log(d2["realgdp"].values))
        g_inv = 400 * np.diff(np.log(d2["realinv"].values))
        exogg = add_constant(np.c_[g_gdp, d2["realint"][:-1].values], prepend=False)

        cls.res1 = OLS(g_inv, exogg).fit()

    def test_qr_equiv(self):
        # GH8157
        res2 = self.res1.model.fit(method="qr")
        assert_allclose(self.res1.HC0_se, res2.HC0_se)


class TestOLSRobust2(TestOLSRobust1):
    # compare with ivreg robust small

    def setup_method(self):
        res_ols = self.res1
        self.bse_robust = res_ols.HC1_se
        self.cov_robust = res_ols.cov_HC1
        self.small = True

        self.res2 = res.results_ivhc0_small


class TestOLSRobust3(TestOLSRobust1):
    # compare with ivreg robust   (not small)

    def setup_method(self):
        res_ols = self.res1
        self.bse_robust = res_ols.HC0_se
        self.cov_robust = res_ols.cov_HC0
        self.small = False

        self.res2 = res.results_ivhc0_large


class TestOLSRobustHacSmall(TestOLSRobust1):
    # compare with ivreg robust small

    def setup_method(self):
        res_ols = self.res1
        cov1 = sw.cov_hac_simple(res_ols, nlags=4, use_correction=True)
        se1 = sw.se_cov(cov1)
        self.bse_robust = se1
        self.cov_robust = cov1
        self.small = True

        self.res2 = res.results_ivhac4_small


class TestOLSRobustHacLarge(TestOLSRobust1):
    # compare with ivreg robust (not small)

    def setup_method(self):
        res_ols = self.res1
        cov1 = sw.cov_hac_simple(res_ols, nlags=4, use_correction=False)
        se1 = sw.se_cov(cov1)
        self.bse_robust = se1
        self.cov_robust = cov1
        self.small = False

        self.res2 = res.results_ivhac4_large


class CheckOLSRobustNewMixin:
    # This uses the robust covariance as default covariance

    def test_compare(self):
        rtol = getattr(self, "rtol", 1e-10)
        assert_allclose(self.cov_robust, self.cov_robust2, rtol=rtol)
        assert_allclose(self.bse_robust, self.bse_robust2, rtol=rtol)

    def test_fvalue(self):
        if not getattr(self, "skip_f", False):
            rtol = getattr(self, "rtol", 1e-10)
            assert_allclose(self.res1.fvalue, self.res2.F, rtol=rtol)
            if hasattr(self.res2, "Fp"):
                # only available with ivreg2
                assert_allclose(self.res1.f_pvalue, self.res2.Fp, rtol=rtol)
        else:
            raise pytest.skip("TODO: document why this test is skipped")

    def test_confint(self):
        rtol = getattr(self, "rtol", 1e-10)
        ci1 = self.res1.conf_int()
        ci2 = self.res2.params_table[:, 4:6]
        assert_allclose(ci1, ci2, rtol=rtol)

        # check critical value
        crit1 = np.diff(ci1, 1).ravel() / 2 / self.res1.bse
        crit2 = np.diff(ci1, 1).ravel() / 2 / self.res1.bse
        assert_allclose(crit1, crit2, rtol=12)

    def test_ttest(self):
        res1 = self.res1
        res2 = self.res2
        rtol = getattr(self, "rtol", 1e-10)
        rtolh = getattr(self, "rtol", 1e-12)

        mat = np.eye(len(res1.params))
        tt = res1.t_test(mat, cov_p=self.cov_robust)
        # has 'effect', 'pvalue', 'sd', 'tvalue'
        # TODO confint missing
        assert_allclose(tt.effect, res2.params, rtol=rtolh)
        assert_allclose(tt.sd, res2.bse, rtol=rtol)
        assert_allclose(tt.tvalue, res2.tvalues, rtol=rtolh)
        assert_allclose(tt.pvalue, res2.pvalues, rtol=5 * rtol)
        ci1 = tt.conf_int()
        ci2 = self.res2.params_table[:, 4:6]
        assert_allclose(ci1, ci2, rtol=rtol)

    def test_scale(self):
        res1 = self.res1
        res2 = self.res2
        rtol = 1e-5
        # Note we always use df_resid for scale
        # Stata uses nobs or df_resid for rmse, not always available in Stata
        # assert_allclose(res1.scale, res2.rmse**2 * res2.N / (res2.N - res2.df_m - 1), rtol=rtol)
        skip = False
        if hasattr(res2, "rss"):
            scale = res2.rss / (res2.N - res2.df_m - 1)
        elif hasattr(res2, "rmse"):
            scale = res2.rmse**2
        else:
            skip = True

        if isinstance(res1.model, WLS):
            skip = True
            # Stata uses different scaling and using unweighted resid for rmse

        if not skip:
            assert_allclose(res1.scale, scale, rtol=rtol)

        if not res2.vcetype == "Newey-West":
            # no rsquared in Stata
            r2 = res2.r2 if hasattr(res2, "r2") else res2.r2c
            assert_allclose(res1.rsquared, r2, rtol=rtol, err_msg=str(skip))

        # consistency checks, not against Stata
        df_resid = res1.nobs - res1.df_model - 1
        assert_equal(res1.df_resid, df_resid)
        # variance of resid_pearson is 1, with ddof, and loc=0
        psum = (res1.resid_pearson**2).sum()
        assert_allclose(psum, df_resid, rtol=1e-13)

    @pytest.mark.smoke
    def test_summary(self):
        self.res1.summary()


class TestOLSRobust2SmallNew(TestOLSRobust1, CheckOLSRobustNewMixin):
    # compare with ivreg robust small

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results("HC1", use_t=True)
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        self.bse_robust2 = res_ols.HC1_se
        self.cov_robust2 = res_ols.cov_HC1
        self.small = True
        self.res2 = res.results_ivhc0_small

    def test_compare(self):
        # check that we get a warning using the nested compare methods
        res1 = self.res1
        endog = res1.model.endog
        exog = res1.model.exog[:, [0, 2]]  # drop one variable
        res_ols2 = OLS(endog, exog).fit()
        # results from Stata
        r_pval = 0.0307306938402991
        r_chi2 = 4.667944083588736
        r_df = 1
        with pytest.warns(InvalidTestWarning):
            res1.compare_lr_test(res_ols2)

        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            chi2, pval, df = res1.compare_lr_test(res_ols2)
        assert_allclose(chi2, r_chi2, rtol=1e-11)
        assert_allclose(pval, r_pval, rtol=1e-11)
        assert_equal(df, r_df)

        with pytest.warns(InvalidTestWarning):
            res1.compare_f_test(res_ols2)
        # fva, pval, df = res1.compare_f_test(res_ols2)


class TestOLSRobustHACSmallNew(TestOLSRobust1, CheckOLSRobustNewMixin):
    # compare with ivreg robust small

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "HAC", maxlags=4, use_correction=True, use_t=True
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_hac_simple(res_ols, nlags=4, use_correction=True)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res.results_ivhac4_small


class TestOLSRobust2LargeNew(TestOLSRobust1, CheckOLSRobustNewMixin):
    # compare with ivreg robust small

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results("HC0")
        res_ols.use_t = False
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        self.bse_robust2 = res_ols.HC0_se
        self.cov_robust2 = res_ols.cov_HC0
        self.small = False
        self.res2 = res.results_ivhc0_large

    @pytest.mark.skip(reason="not refactored yet for `large`")
    def test_fvalue(self):
        super().test_fvalue()

    @pytest.mark.skip(reason="not refactored yet for `large`")
    def test_confint(self):
        super().test_confint()


#
#    cluster robust standard errors
#


class CheckOLSRobustCluster(CheckOLSRobust):
    # compare with regress robust

    @classmethod
    def setup_class(cls):
        # import pandas as pa
        from statsmodels.datasets import grunfeld

        dtapa = grunfeld.data.load_pandas()
        # Stata example/data seems to miss last firm
        dtapa_endog = dtapa.endog[:200]
        dtapa_exog = dtapa.exog[:200]
        exog = add_constant(dtapa_exog[["value", "capital"]], prepend=False)
        # asserts do not work for pandas
        cls.res1 = OLS(dtapa_endog, exog).fit()

        firm_names, firm_id = np.unique(
            np.asarray(dtapa_exog[["firm"]], "S20"), return_inverse=True
        )
        cls.groups = firm_id
        # time indicator in range(max Ti)
        time = np.require(dtapa_exog[["year"]], requirements="W")
        time -= time.min()
        cls.time = np.squeeze(time).astype(int)
        # nw_panel function requires interval bounds
        cls.tidx = [(i * 20, 20 * (i + 1)) for i in range(10)]


class TestOLSRobustCluster2(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "cluster", groups=self.groups, use_correction=True, use_t=True
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_cluster

        self.rtol = 1e-6
        self.rtolh = 1e-10


class TestOLSRobustCluster2Input(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        import pandas as pd

        fat_array = self.groups.reshape(-1, 1)
        fat_groups = pd.DataFrame(fat_array)

        res_ols = self.res1.get_robustcov_results(
            "cluster", groups=fat_groups, use_correction=True, use_t=True
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_cluster

        self.rtol = 1e-6
        self.rtolh = 1e-10

    def test_too_many_groups(self):
        long_groups = self.groups.reshape(-1, 1)
        groups3 = np.hstack((long_groups, long_groups, long_groups))
        with pytest.raises(ValueError):
            self.res1.get_robustcov_results(
                "cluster",
                groups=groups3,
                use_correction=True,
                use_t=True,
            )

    def test_2way_dataframe(self):
        import pandas as pd

        long_groups = self.groups.reshape(-1, 1)
        groups2 = pd.DataFrame(np.hstack((long_groups, long_groups)))
        self.res1.get_robustcov_results(
            "cluster", groups=groups2, use_correction=True, use_t=True
        )


class TestOLSRobustCluster2Fit(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # copy, past uses fit method
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.model.fit(
            cov_type="cluster",
            cov_kwds=dict(groups=self.groups, use_correction=True, use_t=True),
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_cluster

        self.rtol = 1e-6
        self.rtolh = 1e-10

    def test_basic_inference(self):
        res1 = self.res1
        res2 = self.res2
        rtol = 1e-7
        assert_allclose(res1.params, res2.params, rtol=1e-8)
        assert_allclose(res1.bse, res2.bse, rtol=rtol)
        assert_allclose(res1.pvalues, res2.pvalues, rtol=rtol, atol=1e-20)
        ci = res2.params_table[:, 4:6]
        assert_allclose(res1.conf_int(), ci, rtol=5e-7, atol=1e-20)


class TestOLSRobustCluster2Large(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "cluster",
            groups=self.groups,
            use_correction=False,
            use_t=False,
            df_correction=True,
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = False
        self.res2 = res2.results_cluster_large

        self.skip_f = True
        self.rtol = 1e-6
        self.rtolh = 1e-10

    @pytest.mark.skip(reason="GH#1189 issuecomment-29141741")
    def test_f_value(self):
        super().test_fvalue()


class TestOLSRobustCluster2LargeFit(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        model = OLS(self.res1.model.endog, self.res1.model.exog)
        # res_ols = self.res1.model.fit(cov_type='cluster',
        res_ols = model.fit(
            cov_type="cluster",
            cov_kwds=dict(
                groups=self.groups,
                use_correction=False,
                use_t=False,
                df_correction=True,
            ),
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = False
        self.res2 = res2.results_cluster_large

        self.skip_f = True
        self.rtol = 1e-6
        self.rtolh = 1e-10

    @pytest.mark.skip(reason="GH#1189 issuecomment-29141741")
    def test_fvalue(self):
        super().test_fvalue()


class TestOLSRobustClusterGS(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "nw-groupsum",
            time=self.time,
            maxlags=4,
            use_correction=False,
            use_t=True,
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_nw_groupsum(self.res1, 4, self.time, use_correction=False)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_nw_groupsum4

        self.skip_f = True
        self.rtol = 1e-6
        self.rtolh = 1e-10


class TestOLSRobustClusterGSFit(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.model.fit(
            cov_type="nw-groupsum",
            cov_kwds=dict(time=self.time, maxlags=4, use_correction=False, use_t=True),
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_nw_groupsum(self.res1, 4, self.time, use_correction=False)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_nw_groupsum4

        self.skip_f = True
        self.rtol = 1e-6
        self.rtolh = 1e-10


class TestOLSRobustClusterNWP(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "nw-panel",
            time=self.time,
            maxlags=4,
            use_correction="hac",
            use_t=True,
            df_correction=False,
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_nw_panel(self.res1, 4, self.tidx)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_nw_panel4

        self.skip_f = True
        self.rtol = 1e-6
        self.rtolh = 1e-10

    def test_keyword(self):
        # check corrected keyword
        res_ols = self.res1.get_robustcov_results(
            "hac-panel",
            time=self.time,
            maxlags=4,
            use_correction="hac",
            use_t=True,
            df_correction=False,
        )
        assert_allclose(res_ols.bse, self.res1.bse, rtol=1e-12)


class TestOLSRobustClusterNWPGroupsFit(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.model.fit(
            cov_type="nw-panel",
            cov_kwds=dict(
                groups=self.groups,
                maxlags=4,
                use_correction="hac",
                use_t=True,
                df_correction=False,
            ),
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_nw_panel(self.res1, 4, self.tidx)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_nw_panel4

        self.skip_f = True
        self.rtol = 1e-6
        self.rtolh = 1e-10


# TODO: low precision/agreement
class TestOLSRobustCluster2G(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "cluster",
            groups=(self.groups, self.time),
            use_correction=True,
            use_t=True,
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster_2groups(
            self.res1, self.groups, group2=self.time, use_correction=True
        )[0]
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_cluster_2groups_small

        self.rtol = 0.35  # only f_pvalue and confint for constant differ >rtol=0.05
        self.rtolh = 1e-10


class TestOLSRobustCluster2GLarge(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "cluster",
            groups=(self.groups, self.time),
            use_correction=False,  # True,
            use_t=False,
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster_2groups(
            self.res1, self.groups, group2=self.time, use_correction=False
        )[0]
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = False
        self.res2 = res2.results_cluster_2groups_large

        self.skip_f = True
        self.rtol = 1e-7
        self.rtolh = 1e-10


#
#                 WLS
#


class CheckWLSRobustCluster(CheckOLSRobust):
    # compare with regress robust

    @classmethod
    def setup_class(cls):
        # import pandas as pa
        from statsmodels.datasets import grunfeld

        dtapa = grunfeld.data.load_pandas()
        # Stata example/data seems to miss last firm
        dtapa_endog = dtapa.endog[:200]
        dtapa_exog = dtapa.exog[:200]
        exog = add_constant(dtapa_exog[["value", "capital"]], prepend=False)
        # asserts do not work for pandas
        cls.res1 = WLS(dtapa_endog, exog, weights=1 / dtapa_exog["value"]).fit()

        firm_names, firm_id = np.unique(
            np.asarray(dtapa_exog[["firm"]], "S20"), return_inverse=True
        )
        cls.groups = firm_id
        # time indicator in range(max Ti)
        time = np.require(dtapa_exog[["year"]], requirements="W")
        time -= time.min()
        cls.time = np.squeeze(time).astype(int)
        # nw_panel function requires interval bounds
        cls.tidx = [(i * 20, 20 * (i + 1)) for i in range(10)]


# not available yet for WLS
class TestWLSRobustCluster2(CheckWLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "cluster", groups=self.groups, use_correction=True, use_t=True
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_cluster_wls_small

        self.rtol = 1e-6
        self.rtolh = 1e-10


# not available yet for WLS
class TestWLSRobustCluster2Large(CheckWLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results(
            "cluster",
            groups=self.groups,
            use_correction=False,
            use_t=False,
            df_correction=True,
        )
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False)
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = False
        self.res2 = res2.results_cluster_wls_large

        self.skip_f = True
        self.rtol = 1e-6
        self.rtolh = 1e-10


class TestWLSRobustSmall(CheckWLSRobustCluster, CheckOLSRobustNewMixin):
    # compare with `reg cluster`

    def setup_method(self):
        res_ols = self.res1.get_robustcov_results("HC1", use_t=True)
        self.res3 = self.res1
        self.res1 = res_ols
        self.bse_robust = res_ols.bse
        self.cov_robust = res_ols.cov_params()
        # TODO: check standalone function
        # cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False)
        cov1 = res_ols.cov_HC1
        se1 = sw.se_cov(cov1)
        self.bse_robust2 = se1
        self.cov_robust2 = cov1
        self.small = True
        self.res2 = res2.results_hc1_wls_small

        self.skip_f = True
        self.rtol = 1e-6
        self.rtolh = 1e-10


class TestWLSOLSRobustSmall:
    @classmethod
    def setup_class(cls):
        # import pandas as pa
        from statsmodels.datasets import grunfeld

        dtapa = grunfeld.data.load_pandas()
        # Stata example/data seems to miss last firm
        dtapa_endog = dtapa.endog[:200]
        dtapa_exog = dtapa.exog[:200]
        exog = add_constant(dtapa_exog[["value", "capital"]], prepend=False)
        # asserts do not work for pandas
        cls.res_wls = WLS(dtapa_endog, exog, weights=1 / dtapa_exog["value"]).fit()
        w_sqrt = 1 / np.sqrt(np.asarray(dtapa_exog["value"]))
        cls.res_ols = OLS(
            dtapa_endog * w_sqrt, np.asarray(exog) * w_sqrt[:, None]
        ).fit()
        ids = np.asarray(dtapa_exog[["firm"]], "S20")
        firm_names, firm_id = np.unique(ids, return_inverse=True)
        cls.groups = firm_id
        # time indicator in range(max Ti)
        time = np.require(dtapa_exog[["year"]], requirements="W")
        time -= time.min()
        cls.time = np.squeeze(time).astype(int)
        # nw_panel function requires interval bounds
        cls.tidx = [(i * 20, 20 * (i + 1)) for i in range(10)]

    def test_all(self):
        all_cov = [
            ("HC0", dict(use_t=True)),
            ("HC1", dict(use_t=True)),
            ("HC2", dict(use_t=True)),
            ("HC3", dict(use_t=True)),
        ]

        for cov_type, kwds in all_cov:
            res1 = self.res_ols.get_robustcov_results(cov_type, **kwds)
            res2 = self.res_wls.get_robustcov_results(cov_type, **kwds)
            assert_allclose(res1.params, res2.params, rtol=1e-13)
            assert_allclose(res1.cov_params(), res2.cov_params(), rtol=1e-13)
            assert_allclose(res1.bse, res2.bse, rtol=1e-13)
            assert_allclose(res1.pvalues, res2.pvalues, rtol=1e-13)
            mat = np.eye(len(res1.params))
            ft1 = res1.f_test(mat)
            ft2 = res2.f_test(mat)
            assert_allclose(ft1.fvalue, ft2.fvalue, rtol=1e-12)
            assert_allclose(ft1.pvalue, ft2.pvalue, rtol=5e-11)

    def test_fixed_scale(self):
        cov_type = "fixed_scale"
        kwds = {}
        res1 = self.res_ols.get_robustcov_results(cov_type, **kwds)
        res2 = self.res_wls.get_robustcov_results(cov_type, **kwds)
        assert_allclose(res1.params, res2.params, rtol=1e-13)
        assert_allclose(res1.cov_params(), res2.cov_params(), rtol=1e-13)
        assert_allclose(res1.bse, res2.bse, rtol=1e-13)
        assert_allclose(res1.pvalues, res2.pvalues, rtol=1e-12)

        tt = res2.t_test(np.eye(len(res2.params)), cov_p=res2.normalized_cov_params)
        assert_allclose(res2.cov_params(), res2.normalized_cov_params, rtol=1e-13)
        assert_allclose(res2.bse, tt.sd, rtol=1e-13)
        assert_allclose(res2.pvalues, tt.pvalue, rtol=1e-13)
        assert_allclose(res2.tvalues, tt.tvalue, rtol=1e-13)

        # using cov_type in fit
        mod = self.res_wls.model
        mod3 = WLS(mod.endog, mod.exog, weights=mod.weights)
        res3 = mod3.fit(cov_type=cov_type, cov_kwds=kwds)
        tt = res3.t_test(np.eye(len(res3.params)), cov_p=res3.normalized_cov_params)
        assert_allclose(res3.cov_params(), res3.normalized_cov_params, rtol=1e-13)
        assert_allclose(res3.bse, tt.sd, rtol=1e-13)
        assert_allclose(res3.pvalues, tt.pvalue, rtol=1e-13)
        assert_allclose(res3.tvalues, tt.tvalue, rtol=1e-13)


def test_cov_type_fixed_scale():
    # this is a unit test from scipy curvefit for `absolute_sigma` keyword
    xdata = np.array([0, 1, 2, 3, 4, 5])
    ydata = np.array([1, 1, 5, 7, 8, 12])
    sigma = np.array([1, 2, 1, 2, 1, 2])

    xdata = np.column_stack((xdata, np.ones(len(xdata))))
    weights = 1.0 / sigma**2

    res = WLS(ydata, xdata, weights=weights).fit()
    assert_allclose(res.bse, [0.20659803, 0.57204404], rtol=1e-3)

    res = WLS(ydata, xdata, weights=weights).fit()
    assert_allclose(res.bse, [0.20659803, 0.57204404], rtol=1e-3)

    res = WLS(ydata, xdata, weights=weights).fit(cov_type="fixed scale")
    assert_allclose(res.bse, [0.30714756, 0.85045308], rtol=1e-3)

    res = WLS(ydata, xdata, weights=weights / 9.0).fit(cov_type="fixed scale")
    assert_allclose(res.bse, [3 * 0.30714756, 3 * 0.85045308], rtol=1e-3)

    res = WLS(ydata, xdata, weights=weights).fit(
        cov_type="fixed scale", cov_kwds={"scale": 9}
    )
    assert_allclose(res.bse, [3 * 0.30714756, 3 * 0.85045308], rtol=1e-3)


@pytest.mark.parametrize(
    "cov_info",
    [
        ("nonrobust", {}),
        ("HC0", {}),
        ("HC1", {}),
        ("HC2", {}),
        ("HC3", {}),
        ("HAC", {"maxlags": 7}),
        ("cluster", {"groups": (np.arange(500) % 27)}),
    ],
)
def test_qr_equiv(cov_info):
    cov_type, cov_kwds = cov_info
    rs = np.random.RandomState(123498)
    x = rs.standard_normal((500, 3))
    b = np.ones(3)
    y = x @ b + rs.standard_normal(500)
    mod = OLS(y, x)
    pinv_fit = mod.fit(cov_type=cov_type, cov_kwds=cov_kwds)
    qr_fit = mod.fit(cov_type=cov_type, cov_kwds=cov_kwds, method="qr")
    assert_allclose(pinv_fit.bse, qr_fit.bse)
