from statsmodels.compat.python import iteritems, StringIO
import warnings

from statsmodels.formula.api import ols
from statsmodels.formula.formulatools import make_hypotheses_matrices
from statsmodels.tools import add_constant
from statsmodels.datasets.longley import load, load_pandas
from statsmodels.datasets import cpunish

import numpy.testing as npt
from statsmodels.tools.testing import assert_equal
import numpy as np


longley_formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'

class CheckFormulaOLS(object):

    @classmethod
    def setup_class(cls):
        cls.data = load()

    def test_endog_names(self):
        assert self.model.endog_names == 'TOTEMP'

    def test_exog_names(self):
        assert self.model.exog_names == ['Intercept', 'GNPDEFL', 'GNP',
                                             'UNEMP', 'ARMED', 'POP', 'YEAR']
    def test_design(self):
        npt.assert_equal(self.model.exog,
                         add_constant(self.data.exog, prepend=True))

    def test_endog(self):
        npt.assert_equal(self.model.endog, self.data.endog)

    def test_summary(self):
        # smoke test
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore",
                                    "kurtosistest only valid for n>=20")
            self.model.fit().summary()


class TestFormulaPandas(CheckFormulaOLS):
    @classmethod
    def setup_class(cls):
        data = load_pandas().data
        cls.model = ols(longley_formula, data)
        super(TestFormulaPandas, cls).setup_class()


class TestFormulaDict(CheckFormulaOLS):
    @classmethod
    def setup_class(cls):
        data = dict((k, v.tolist()) for k, v in iteritems(load_pandas().data))
        cls.model = ols(longley_formula, data)
        super(TestFormulaDict, cls).setup_class()


class TestFormulaRecArray(CheckFormulaOLS):
    @classmethod
    def setup_class(cls):
        data = load().data
        cls.model = ols(longley_formula, data)
        super(TestFormulaRecArray, cls).setup_class()


def test_tests():
    formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
    dta = load_pandas().data
    results = ols(formula, dta).fit()
    test_formula = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)'
    LC = make_hypotheses_matrices(results, test_formula)
    R = LC.coefs
    Q = LC.constants
    npt.assert_almost_equal(R, [[0, 1, -1, 0, 0, 0, 0],
                               [0, 0 , 0, 1, 0, 0, 0],
                               [0, 0, 0, 0, 0, 0, 1./1829]], 8)
    npt.assert_array_equal(Q, [[0],[2],[1]])


def test_formula_labels():
    # make sure labels pass through patsy as expected
    # data(Duncan) from car in R
    dta = StringIO(""""type" "income" "education" "prestige"\n"accountant" "prof" 62 86 82\n"pilot" "prof" 72 76 83\n"architect" "prof" 75 92 90\n"author" "prof" 55 90 76\n"chemist" "prof" 64 86 90\n"minister" "prof" 21 84 87\n"professor" "prof" 64 93 93\n"dentist" "prof" 80 100 90\n"reporter" "wc" 67 87 52\n"engineer" "prof" 72 86 88\n"undertaker" "prof" 42 74 57\n"lawyer" "prof" 76 98 89\n"physician" "prof" 76 97 97\n"welfare.worker" "prof" 41 84 59\n"teacher" "prof" 48 91 73\n"conductor" "wc" 76 34 38\n"contractor" "prof" 53 45 76\n"factory.owner" "prof" 60 56 81\n"store.manager" "prof" 42 44 45\n"banker" "prof" 78 82 92\n"bookkeeper" "wc" 29 72 39\n"mail.carrier" "wc" 48 55 34\n"insurance.agent" "wc" 55 71 41\n"store.clerk" "wc" 29 50 16\n"carpenter" "bc" 21 23 33\n"electrician" "bc" 47 39 53\n"RR.engineer" "bc" 81 28 67\n"machinist" "bc" 36 32 57\n"auto.repairman" "bc" 22 22 26\n"plumber" "bc" 44 25 29\n"gas.stn.attendant" "bc" 15 29 10\n"coal.miner" "bc" 7 7 15\n"streetcar.motorman" "bc" 42 26 19\n"taxi.driver" "bc" 9 19 10\n"truck.driver" "bc" 21 15 13\n"machine.operator" "bc" 21 20 24\n"barber" "bc" 16 26 20\n"bartender" "bc" 16 28 7\n"shoe.shiner" "bc" 9 17 3\n"cook" "bc" 14 22 16\n"soda.clerk" "bc" 12 30 6\n"watchman" "bc" 17 25 11\n"janitor" "bc" 7 20 8\n"policeman" "bc" 34 47 41\n"waiter" "bc" 8 32 10""")
    from pandas import read_table
    dta = read_table(dta, sep=" ")
    model = ols("prestige ~ income + education", dta).fit()
    assert_equal(model.fittedvalues.index, dta.index)


def test_formula_predict():
    from numpy import log
    formula = """TOTEMP ~ log(GNPDEFL) + log(GNP) + UNEMP + ARMED +
                    POP + YEAR"""
    data = load_pandas()
    dta = load_pandas().data
    results = ols(formula, dta).fit()
    npt.assert_almost_equal(results.fittedvalues.values,
                            results.predict(data.exog), 8)


def test_formula_predict_series():
    import pandas as pd
    import pandas.util.testing as tm
    data = pd.DataFrame({"y": [1, 2, 3], "x": [1, 2, 3]}, index=[5, 3, 1])
    results = ols('y ~ x', data).fit()

    result = results.predict(data)
    expected = pd.Series([1., 2., 3.], index=[5, 3, 1])
    tm.assert_series_equal(result, expected)

    result = results.predict(data.x)
    tm.assert_series_equal(result, expected)

    result = results.predict(pd.Series([1, 2, 3], index=[1, 2, 3], name='x'))
    expected = pd.Series([1., 2., 3.], index=[1, 2, 3])
    tm.assert_series_equal(result, expected)

    result = results.predict({"x": [1, 2, 3]})
    expected = pd.Series([1., 2., 3.], index=[0, 1, 2])
    tm.assert_series_equal(result, expected)


def test_patsy_lazy_dict():
    class LazyDict(dict):
        def __init__(self, data):
            self.data = data

        def __missing__(self, key):
            return np.array(self.data[key])

    data = cpunish.load_pandas().data
    data = LazyDict(data)
    res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()

    res2 = res.predict(data)
    npt.assert_allclose(res.fittedvalues, res2)

    data = cpunish.load_pandas().data
    data['INCOME'].loc[0] = None

    data = LazyDict(data)
    data.index = cpunish.load_pandas().data.index
    res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()

    res2 = res.predict(data)
    assert_equal(res.fittedvalues, res2)  # Should lose a record
    assert_equal(len(res2) + 1, len(cpunish.load_pandas().data))


def test_patsy_missing_data():
    # Test pandas-style first
    data = cpunish.load_pandas().data
    data['INCOME'].loc[0] = None
    res = ols('EXECUTIONS ~ SOUTH + INCOME', data=data).fit()
    res2 = res.predict(data)
    # First record will be dropped during fit, but not during predict
    assert_equal(res.fittedvalues, res2[1:])

    # Non-pandas version
    data = cpunish.load_pandas().data
    data['INCOME'].loc[0] = None
    data = data.to_records(index=False)
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        res2 = res.predict(data)
        assert_equal(repr(w[-1].message),
                     "ValueWarning('nan values have been dropped',)")
    # Frist record will be dropped in both cases
    assert_equal(res.fittedvalues, res2)