import unittest
import pysal.lib
from scipy import sparse
import numpy as np
from ..ml_lag import ML_Lag
from pysal.lib.common import RTOL
from warnings import filterwarnings
filterwarnings('ignore', category=sparse.SparseEfficiencyWarning)

class TestMLError(unittest.TestCase):
    def setUp(self):
        db =  pysal.lib.io.open(pysal.lib.examples.get_path("baltim.dbf"),'r')
        self.ds_name = "baltim.dbf"
        self.y_name = "PRICE"
        self.y = np.array(db.by_col(self.y_name)).T
        self.y.shape = (len(self.y),1)
        self.x_names = ["NROOM","AGE","SQFT"]
        self.x = np.array([db.by_col(var) for var in self.x_names]).T
        ww = pysal.lib.io.open(pysal.lib.examples.get_path("baltim_q.gal"))
        self.w = ww.read()
        ww.close()
        self.w_name = "baltim_q.gal"
        self.w.transform = 'r'

    def _estimate_and_compare(self, **kwargs):
        reg = ML_Lag(self.y, self.x, w=self.w,
                     name_y=self.y_name, name_x=self.x_names,
                     name_w=self.w_name,name_ds=self.ds_name,
                     **kwargs)
        betas = np.array([[-6.04040164],
       [ 3.48995114],
       [-0.20103955],
       [ 0.65462382],
       [ 0.62351143]])
        np.testing.assert_allclose(reg.betas,betas,RTOL)
        u = np.array([ 47.51218398])
        np.testing.assert_allclose(reg.u[0],u,RTOL)
        predy = np.array([-0.51218398])
        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
        n = 211
        np.testing.assert_allclose(reg.n,n,RTOL)
        k = 5
        np.testing.assert_allclose(reg.k,k,RTOL)
        y = np.array([ 47.])
        np.testing.assert_allclose(reg.y[0],y,RTOL)
        x = np.array([   1.  ,    4.  ,  148.  ,   11.25])
        np.testing.assert_allclose(reg.x[0],x,RTOL)
        e = np.array([ 41.99251608])
        np.testing.assert_allclose(reg.e_pred[0],e,RTOL)
        my = 44.307180094786695
        np.testing.assert_allclose(reg.mean_y,my)
        sy = 23.606076835380495
        np.testing.assert_allclose(reg.std_y,sy)
        vm = np.array([ 28.57288755,   1.42341656,   0.00288068,   0.02956392,   0.00332139])
        np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL)
        sig2 = 216.27525647243797
        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
        pr2 = 0.6133020721559487
        np.testing.assert_allclose(reg.pr2,pr2)
        std_err = np.array([ 5.34536131,  1.19307022,  0.05367198,  0.17194162,  0.05763147])
        np.testing.assert_allclose(reg.std_err,std_err,RTOL)
        logll = -875.92771143484833
        np.testing.assert_allclose(reg.logll,logll,RTOL)
        aic = 1761.8554228696967
        np.testing.assert_allclose(reg.aic,aic,RTOL)
        schwarz = 1778.614713537077
        np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)

    def test_dense(self):
        self._estimate_and_compare(method='FULL')

    def test_ord(self):
        reg = ML_Lag(self.y, self.x, w=self.w,
                     name_y=self.y_name, name_x=self.x_names,
                     name_w=self.w_name,name_ds=self.ds_name,
                     method='ORD')
        betas = np.array([[-6.04040164],
       [ 3.48995114],
       [-0.20103955],
       [ 0.65462382],
       [ 0.62351143]])
        np.testing.assert_allclose(reg.betas,betas,RTOL)
        u = np.array([ 47.51218398])
        np.testing.assert_allclose(reg.u[0],u,RTOL)
        predy = np.array([-0.51218398])
        np.testing.assert_allclose(reg.predy[0],predy,RTOL)
        n = 211
        np.testing.assert_allclose(reg.n,n,RTOL)
        k = 5
        np.testing.assert_allclose(reg.k,k,RTOL)
        y = np.array([ 47.])
        np.testing.assert_allclose(reg.y[0],y,RTOL)
        x = np.array([   1.  ,    4.  ,  148.  ,   11.25])
        np.testing.assert_allclose(reg.x[0],x,RTOL)
        e = np.array([ 41.99251608])
        np.testing.assert_allclose(reg.e_pred[0],e,RTOL)
        my = 44.307180094786695
        np.testing.assert_allclose(reg.mean_y,my)
        sy = 23.606076835380495
        np.testing.assert_allclose(reg.std_y,sy)
        vm = np.array([ 28.63404,   1.423698,   0.002884738,   0.02957845,
            0.003379166])
        np.testing.assert_allclose(reg.vm.diagonal(),vm,RTOL)
        sig2 = 216.27525647243797
        np.testing.assert_allclose(reg.sig2,sig2,RTOL)
        pr2 = 0.6133020721559487
        np.testing.assert_allclose(reg.pr2,pr2)
        std_err = np.array([ 5.351078, 1.193188, 0.05371, 0.171984, 0.058131])
        np.testing.assert_allclose(reg.std_err,std_err,RTOL)
        logll = -875.92771143484833
        np.testing.assert_allclose(reg.logll,logll,RTOL)
        aic = 1761.8554228696967
        np.testing.assert_allclose(reg.aic,aic,RTOL)
        schwarz = 1778.614713537077
        np.testing.assert_allclose(reg.schwarz,schwarz,RTOL)

    def test_LU(self):
        self._estimate_and_compare(method='LU')

if __name__ == '__main__':
    unittest.main()
