"""
ML Estimation of Spatial Lag Model
"""

__author__ = "Luc Anselin luc.anselin@asu.edu, \
              Serge Rey srey@asu.edu, \
              Levi Wolf levi.john.wolf@gmail.com"

import numpy as np
import numpy.linalg as la
from scipy import sparse as sp
from scipy.sparse.linalg import splu as SuperLU
from .utils import RegressionPropsY, RegressionPropsVM, inverse_prod
from .sputils import spdot, spfill_diagonal, spinv, spbroadcast
from . import diagnostics as DIAG
from . import user_output as USER
from . import summary_output as SUMMARY
from .w_utils import symmetrize
from pysal.lib import weights
try:
    from scipy.optimize import minimize_scalar
    minimize_scalar_available = True
except ImportError:
    minimize_scalar_available = False

__all__ = ["ML_Lag"]


class BaseML_Lag(RegressionPropsY, RegressionPropsVM):

    """
    ML estimation of the spatial lag model (note no consistency
    checks, diagnostics or constants added) :cite:`Anselin1988` 

    Parameters
    ----------
    y            : array
                   nx1 array for dependent variable
    x            : array
                   Two dimensional array with n rows and one column for each
                   independent (exogenous) variable, excluding the constant
    w            : pysal W object
                   Spatial weights object
    method       : string
                   if 'full', brute force calculation (full matrix expressions)
                   if 'ord', Ord eigenvalue method
                   if 'LU', LU sparse matrix decomposition
    epsilon      : float
                   tolerance criterion in mimimize_scalar function and inverse_product

    Attributes
    ----------
    betas        : array
                   (k+1)x1 array of estimated coefficients (rho first)
    rho          : float
                   estimate of spatial autoregressive coefficient
    u            : array
                   nx1 array of residuals
    predy        : array
                   nx1 array of predicted y values
    n            : integer
                   Number of observations
    k            : integer
                   Number of variables for which coefficients are estimated
                   (including the constant, excluding the rho)
    y            : array
                   nx1 array for dependent variable
    x            : array
                   Two dimensional array with n rows and one column for each
                   independent (exogenous) variable, including the constant
    method       : string
                   log Jacobian method
                   if 'full': brute force (full matrix computations)
                   if 'ord' : Ord eigenvalue method
    epsilon      : float
                   tolerance criterion used in minimize_scalar function and inverse_product
    mean_y       : float
                   Mean of dependent variable
    std_y        : float
                   Standard deviation of dependent variable
    vm           : array
                   Variance covariance matrix (k+1 x k+1)
    vm1          : array
                   Variance covariance matrix (k+2 x k+2) includes sigma2
    sig2         : float
                   Sigma squared used in computations
    logll        : float
                   maximized log-likelihood (including constant terms)
    predy_e      : array
                   predicted values from reduced form
    e_pred       : array
                   prediction errors using reduced form predicted values


    Examples
    --------

    >>> import numpy as np
    >>> import pysal.lib
    >>> db =  pysal.lib.io.open(pysal.lib.examples.get_path("baltim.dbf"),'r')
    >>> ds_name = "baltim.dbf"
    >>> y_name = "PRICE"
    >>> y = np.array(db.by_col(y_name)).T
    >>> y.shape = (len(y),1)
    >>> x_names = ["NROOM","NBATH","PATIO","FIREPL","AC","GAR","AGE","LOTSZ","SQFT"]
    >>> x = np.array([db.by_col(var) for var in x_names]).T
    >>> x = np.hstack((np.ones((len(y),1)),x))
    >>> ww = ps.open(ps.examples.get_path("baltim_q.gal"))
    >>> w = ww.read()
    >>> ww.close()
    >>> w.transform = 'r'
    >>> w_name = "baltim_q.gal"
    >>> mllag = BaseML_Lag(y,x,w,method='ord') #doctest: +SKIP
    >>> "{0:.6f}".format(mllag.rho) #doctest: +SKIP
    '0.425885'
    >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP
    array([[ 4.3675],
           [ 0.7502],
           [ 5.6116],
           [ 7.0497],
           [ 7.7246],
           [ 6.1231],
           [ 4.6375],
           [-0.1107],
           [ 0.0679],
           [ 0.0794],
           [ 0.4259]])
    >>> "{0:.6f}".format(mllag.mean_y) #doctest: +SKIP
    '44.307180'
    >>> "{0:.6f}".format(mllag.std_y) #doctest: +SKIP
    '23.606077'
    >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP
    array([  23.8716,    1.1222,    3.0593,    7.3416,    5.6695,    5.4698,
              2.8684,    0.0026,    0.0002,    0.0266,    0.0032,  220.1292])
    >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP
    array([ 23.8716,   1.1222,   3.0593,   7.3416,   5.6695,   5.4698,
             2.8684,   0.0026,   0.0002,   0.0266,   0.0032])
    >>> "{0:.6f}".format(mllag.sig2) #doctest: +SKIP
    '151.458698'
    >>> "{0:.6f}".format(mllag.logll) #doctest: +SKIP
    '-832.937174'
    >>> mllag = BaseML_Lag(y,x,w) #doctest: +SKIP
    >>> "{0:.6f}".format(mllag.rho) #doctest: +SKIP
    '0.425885'
    >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP
    array([[ 4.3675],
           [ 0.7502],
           [ 5.6116],
           [ 7.0497],
           [ 7.7246],
           [ 6.1231],
           [ 4.6375],
           [-0.1107],
           [ 0.0679],
           [ 0.0794],
           [ 0.4259]])
    >>> "{0:.6f}".format(mllag.mean_y) #doctest: +SKIP
    '44.307180'
    >>> "{0:.6f}".format(mllag.std_y) #doctest: +SKIP
    '23.606077'
    >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP
    array([  23.8716,    1.1222,    3.0593,    7.3416,    5.6695,    5.4698,
              2.8684,    0.0026,    0.0002,    0.0266,    0.0032,  220.1292])
    >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP
    array([ 23.8716,   1.1222,   3.0593,   7.3416,   5.6695,   5.4698,
             2.8684,   0.0026,   0.0002,   0.0266,   0.0032])
    >>> "{0:.6f}".format(mllag.sig2) #doctest: +SKIP
    '151.458698'
    >>> "{0:.6f}".format(mllag.logll) #doctest: +SKIP
    '-832.937174'


    """

    def __init__(self, y, x, w, method='full', epsilon=0.0000001):
        # set up main regression variables and spatial filters
        self.y = y
        self.x = x
        self.n, self.k = self.x.shape
        self.method = method
        self.epsilon = epsilon
        #W = w.full()[0]
        #Wsp = w.sparse
        ylag = weights.lag_spatial(w, y)
        # b0, b1, e0 and e1
        xtx = spdot(self.x.T, self.x)
        xtxi = la.inv(xtx)
        xty = spdot(self.x.T, self.y)
        xtyl = spdot(self.x.T, ylag)
        b0 = spdot(xtxi, xty)
        b1 = spdot(xtxi, xtyl)
        e0 = self.y - spdot(x, b0)
        e1 = ylag - spdot(x, b1)
        methodML = method.upper()
        # call minimizer using concentrated log-likelihood to get rho
        if methodML in ['FULL', 'LU', 'ORD']:
            if methodML == 'FULL':
                W = w.full()[0]     # moved here
                res = minimize_scalar(lag_c_loglik, 0.0, bounds=(-1.0, 1.0),
                                      args=(
                                          self.n, e0, e1, W), method='bounded',
                                      tol=epsilon)
            elif methodML == 'LU':
                I = sp.identity(w.n)
                Wsp = w.sparse  # moved here
                W = Wsp
                res = minimize_scalar(lag_c_loglik_sp, 0.0, bounds=(-1.0,1.0),
                                      args=(self.n, e0, e1, I, Wsp),
                                      method='bounded', tol=epsilon)
            elif methodML == 'ORD':
                # check on symmetry structure
                if w.asymmetry(intrinsic=False) == []:
                    ww = symmetrize(w)
                    WW = np.array(ww.todense())
                    evals = la.eigvalsh(WW)
                    W = WW
                else:
                    W = w.full()[0]     # moved here
                    evals = la.eigvals(W)
                res = minimize_scalar(lag_c_loglik_ord, 0.0, bounds=(-1.0, 1.0),
                                      args=(
                                          self.n, e0, e1, evals), method='bounded',
                                      tol=epsilon)
        else:
            # program will crash, need to catch
            print(("{0} is an unsupported method".format(methodML)))
            self = None
            return

        self.rho = res.x[0][0]

        # compute full log-likelihood, including constants
        ln2pi = np.log(2.0 * np.pi)
        llik = -res.fun - self.n / 2.0 * ln2pi - self.n / 2.0
        self.logll = llik[0][0]

        # b, residuals and predicted values

        b = b0 - self.rho * b1
        self.betas = np.vstack((b, self.rho))   # rho added as last coefficient
        self.u = e0 - self.rho * e1
        self.predy = self.y - self.u

        xb = spdot(x, b)

        self.predy_e = inverse_prod(
            w.sparse, xb, self.rho, inv_method="power_exp", threshold=epsilon)
        self.e_pred = self.y - self.predy_e

        # residual variance
        self._cache = {}
        self.sig2 = self.sig2n  # no allowance for division by n-k

        # information matrix
        # if w should be kept sparse, how can we do the following:
        a = -self.rho * W
        spfill_diagonal(a, 1.0)
        ai = spinv(a)
        wai = spdot(W, ai)
        tr1 = wai.diagonal().sum() #same for sparse and dense

        wai2 = spdot(wai, wai)
        tr2 = wai2.diagonal().sum()

        waiTwai = spdot(wai.T, wai)
        tr3 = waiTwai.diagonal().sum()
        ### to here

        wpredy = weights.lag_spatial(w, self.predy_e)
        wpyTwpy = spdot(wpredy.T, wpredy)
        xTwpy = spdot(x.T, wpredy)

        # order of variables is beta, rho, sigma2

        v1 = np.vstack(
            (xtx / self.sig2, xTwpy.T / self.sig2, np.zeros((1, self.k))))
        v2 = np.vstack(
            (xTwpy / self.sig2, tr2 + tr3 + wpyTwpy / self.sig2, tr1 / self.sig2))
        v3 = np.vstack(
            (np.zeros((self.k, 1)), tr1 / self.sig2, self.n / (2.0 * self.sig2 ** 2)))

        v = np.hstack((v1, v2, v3))

        self.vm1 = la.inv(v)  # vm1 includes variance for sigma2
        self.vm = self.vm1[:-1, :-1]  # vm is for coefficients only


class ML_Lag(BaseML_Lag):

    """
    ML estimation of the spatial lag model with all results and diagnostics; :cite:`Anselin1988`

    Parameters
    ----------
    y            : array
                   nx1 array for dependent variable
    x            : array
                   Two dimensional array with n rows and one column for each
                   independent (exogenous) variable, excluding the constant
    w            : pysal W object
                   Spatial weights object
    method       : string
                   if 'full', brute force calculation (full matrix expressions)
                   if 'ord', Ord eigenvalue method
    epsilon      : float
                   tolerance criterion in mimimize_scalar function and inverse_product
    spat_diag    : boolean
                   if True, include spatial diagnostics
    vm           : boolean
                   if True, include variance-covariance matrix in summary
                   results
    name_y       : string
                   Name of dependent variable for use in output
    name_x       : list of strings
                   Names of independent variables for use in output
    name_w       : string
                   Name of weights matrix for use in output
    name_ds      : string
                   Name of dataset for use in output

    Attributes
    ----------
    betas        : array
                   (k+1)x1 array of estimated coefficients (rho first)
    rho          : float
                   estimate of spatial autoregressive coefficient
    u            : array
                   nx1 array of residuals
    predy        : array
                   nx1 array of predicted y values
    n            : integer
                   Number of observations
    k            : integer
                   Number of variables for which coefficients are estimated
                   (including the constant, excluding the rho)
    y            : array
                   nx1 array for dependent variable
    x            : array
                   Two dimensional array with n rows and one column for each
                   independent (exogenous) variable, including the constant
    method       : string
                   log Jacobian method
                   if 'full': brute force (full matrix computations)
    epsilon      : float
                   tolerance criterion used in minimize_scalar function and inverse_product
    mean_y       : float
                   Mean of dependent variable
    std_y        : float
                   Standard deviation of dependent variable
    vm           : array
                   Variance covariance matrix (k+1 x k+1), all coefficients
    vm1          : array
                   Variance covariance matrix (k+2 x k+2), includes sig2
    sig2         : float
                   Sigma squared used in computations
    logll        : float
                   maximized log-likelihood (including constant terms)
    aic          : float
                   Akaike information criterion
    schwarz      : float
                   Schwarz criterion
    predy_e      : array
                   predicted values from reduced form
    e_pred       : array
                   prediction errors using reduced form predicted values
    pr2          : float
                   Pseudo R squared (squared correlation between y and ypred)
    pr2_e        : float
                   Pseudo R squared (squared correlation between y and ypred_e
                   (using reduced form))
    utu          : float
                   Sum of squared residuals
    std_err      : array
                   1xk array of standard errors of the betas
    z_stat       : list of tuples
                   z statistic; each tuple contains the pair (statistic,
                   p-value), where each is a float
    name_y       : string
                   Name of dependent variable for use in output
    name_x       : list of strings
                   Names of independent variables for use in output
    name_w       : string
                   Name of weights matrix for use in output
    name_ds      : string
                   Name of dataset for use in output
    title        : string
                   Name of the regression method used

    Examples
    ________

    >>> import numpy as np
    >>> import pysal.lib
    >>> db =  pysal.lib.io.open(pysal.lib.examples.get_path("baltim.dbf"),'r')
    >>> ds_name = "baltim.dbf"
    >>> y_name = "PRICE"
    >>> y = np.array(db.by_col(y_name)).T
    >>> y.shape = (len(y),1)
    >>> x_names = ["NROOM","NBATH","PATIO","FIREPL","AC","GAR","AGE","LOTSZ","SQFT"]
    >>> x = np.array([db.by_col(var) for var in x_names]).T
    >>> ww = ps.open(ps.examples.get_path("baltim_q.gal"))
    >>> w = ww.read()
    >>> ww.close()
    >>> w_name = "baltim_q.gal"
    >>> w.transform = 'r'
    >>> mllag = ML_Lag(y,x,w,name_y=y_name,name_x=x_names,\
               name_w=w_name,name_ds=ds_name) #doctest: +SKIP
    >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP
    array([[ 4.3675],
           [ 0.7502],
           [ 5.6116],
           [ 7.0497],
           [ 7.7246],
           [ 6.1231],
           [ 4.6375],
           [-0.1107],
           [ 0.0679],
           [ 0.0794],
           [ 0.4259]])
    >>> "{0:.6f}".format(mllag.rho) #doctest: +SKIP
    '0.425885'
    >>> "{0:.6f}".format(mllag.mean_y) #doctest: +SKIP
    '44.307180'
    >>> "{0:.6f}".format(mllag.std_y) #doctest: +SKIP
    '23.606077'
    >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP
    array([  23.8716,    1.1222,    3.0593,    7.3416,    5.6695,    5.4698,
              2.8684,    0.0026,    0.0002,    0.0266,    0.0032,  220.1292])
    >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP
    array([ 23.8716,   1.1222,   3.0593,   7.3416,   5.6695,   5.4698,
             2.8684,   0.0026,   0.0002,   0.0266,   0.0032])
    >>> "{0:.6f}".format(mllag.sig2) #doctest: +SKIP
    '151.458698'
    >>> "{0:.6f}".format(mllag.logll) #doctest: +SKIP
    '-832.937174'
    >>> "{0:.6f}".format(mllag.aic) #doctest: +SKIP
    '1687.874348'
    >>> "{0:.6f}".format(mllag.schwarz) #doctest: +SKIP
    '1724.744787'
    >>> "{0:.6f}".format(mllag.pr2) #doctest: +SKIP
    '0.727081'
    >>> "{0:.4f}".format(mllag.pr2_e) #doctest: +SKIP
    '0.7062'
    >>> "{0:.4f}".format(mllag.utu) #doctest: +SKIP
    '31957.7853'
    >>> np.around(mllag.std_err, decimals=4) #doctest: +SKIP
    array([ 4.8859,  1.0593,  1.7491,  2.7095,  2.3811,  2.3388,  1.6936,
            0.0508,  0.0146,  0.1631,  0.057 ])
    >>> np.around(mllag.z_stat, decimals=4) #doctest: +SKIP
    array([[ 0.8939,  0.3714],
           [ 0.7082,  0.4788],
           [ 3.2083,  0.0013],
           [ 2.6018,  0.0093],
           [ 3.2442,  0.0012],
           [ 2.6181,  0.0088],
           [ 2.7382,  0.0062],
           [-2.178 ,  0.0294],
           [ 4.6487,  0.    ],
           [ 0.4866,  0.6266],
           [ 7.4775,  0.    ]])
    >>> mllag.name_y #doctest: +SKIP
    'PRICE'
    >>> mllag.name_x #doctest: +SKIP
    ['CONSTANT', 'NROOM', 'NBATH', 'PATIO', 'FIREPL', 'AC', 'GAR', 'AGE', 'LOTSZ', 'SQFT', 'W_PRICE']
    >>> mllag.name_w #doctest: +SKIP
    'baltim_q.gal'
    >>> mllag.name_ds #doctest: +SKIP
    'baltim.dbf'
    >>> mllag.title #doctest: +SKIP
    'MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)'
    >>> mllag = ML_Lag(y,x,w,method='ord',name_y=y_name,name_x=x_names,\
               name_w=w_name,name_ds=ds_name) #doctest: +SKIP
    >>> np.around(mllag.betas, decimals=4) #doctest: +SKIP
    array([[ 4.3675],
           [ 0.7502],
           [ 5.6116],
           [ 7.0497],
           [ 7.7246],
           [ 6.1231],
           [ 4.6375],
           [-0.1107],
           [ 0.0679],
           [ 0.0794],
           [ 0.4259]])
    >>> "{0:.6f}".format(mllag.rho) #doctest: +SKIP
    '0.425885'
    >>> "{0:.6f}".format(mllag.mean_y) #doctest: +SKIP
    '44.307180'
    >>> "{0:.6f}".format(mllag.std_y) #doctest: +SKIP
    '23.606077'
    >>> np.around(np.diag(mllag.vm1), decimals=4) #doctest: +SKIP
    array([  23.8716,    1.1222,    3.0593,    7.3416,    5.6695,    5.4698,
              2.8684,    0.0026,    0.0002,    0.0266,    0.0032,  220.1292])
    >>> np.around(np.diag(mllag.vm), decimals=4) #doctest: +SKIP
    array([ 23.8716,   1.1222,   3.0593,   7.3416,   5.6695,   5.4698,
             2.8684,   0.0026,   0.0002,   0.0266,   0.0032])
    >>> "{0:.6f}".format(mllag.sig2) #doctest: +SKIP
    '151.458698'
    >>> "{0:.6f}".format(mllag.logll) #doctest: +SKIP
    '-832.937174'
    >>> "{0:.6f}".format(mllag.aic) #doctest: +SKIP
    '1687.874348'
    >>> "{0:.6f}".format(mllag.schwarz) #doctest: +SKIP
    '1724.744787'
    >>> "{0:.6f}".format(mllag.pr2) #doctest: +SKIP
    '0.727081'
    >>> "{0:.6f}".format(mllag.pr2_e) #doctest: +SKIP
    '0.706198'
    >>> "{0:.4f}".format(mllag.utu) #doctest: +SKIP
    '31957.7853'
    >>> np.around(mllag.std_err, decimals=4) #doctest: +SKIP
    array([ 4.8859,  1.0593,  1.7491,  2.7095,  2.3811,  2.3388,  1.6936,
            0.0508,  0.0146,  0.1631,  0.057 ])
    >>> np.around(mllag.z_stat, decimals=4) #doctest: +SKIP
    array([[ 0.8939,  0.3714],
           [ 0.7082,  0.4788],
           [ 3.2083,  0.0013],
           [ 2.6018,  0.0093],
           [ 3.2442,  0.0012],
           [ 2.6181,  0.0088],
           [ 2.7382,  0.0062],
           [-2.178 ,  0.0294],
           [ 4.6487,  0.    ],
           [ 0.4866,  0.6266],
           [ 7.4775,  0.    ]])
    >>> mllag.name_y #doctest: +SKIP
    'PRICE'
    >>> mllag.name_x #doctest: +SKIP
    ['CONSTANT', 'NROOM', 'NBATH', 'PATIO', 'FIREPL', 'AC', 'GAR', 'AGE', 'LOTSZ', 'SQFT', 'W_PRICE']
    >>> mllag.name_w #doctest: +SKIP
    'baltim_q.gal'
    >>> mllag.name_ds #doctest: +SKIP
    'baltim.dbf'
    >>> mllag.title #doctest: +SKIP
    'MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = ORD)'


    """

    def __init__(self, y, x, w, method='full', epsilon=0.0000001,
                 spat_diag=False, vm=False, name_y=None, name_x=None,
                 name_w=None, name_ds=None):
        n = USER.check_arrays(y, x)
        USER.check_y(y, n)
        USER.check_weights(w, y, w_required=True)
        x_constant = USER.check_constant(x)
        method = method.upper()
        BaseML_Lag.__init__(
            self, y=y, x=x_constant, w=w, method=method, epsilon=epsilon)
        # increase by 1 to have correct aic and sc, include rho in count
        self.k += 1
        self.title = "MAXIMUM LIKELIHOOD SPATIAL LAG" + \
            " (METHOD = " + method + ")"
        self.name_ds = USER.set_name_ds(name_ds)
        self.name_y = USER.set_name_y(name_y)
        self.name_x = USER.set_name_x(name_x, x)
        name_ylag = USER.set_name_yend_sp(self.name_y)
        self.name_x.append(name_ylag)  # rho changed to last position
        self.name_w = USER.set_name_w(name_w, w)
        self.aic = DIAG.akaike(reg=self)
        self.schwarz = DIAG.schwarz(reg=self)
        SUMMARY.ML_Lag(reg=self, w=w, vm=vm, spat_diag=spat_diag)

def lag_c_loglik(rho, n, e0, e1, W):
    # concentrated log-lik for lag model, no constants, brute force
    er = e0 - rho * e1
    sig2 = spdot(er.T, er) / n
    nlsig2 = (n / 2.0) * np.log(sig2)
    a = -rho * W
    spfill_diagonal(a, 1.0)
    jacob = np.log(np.linalg.det(a))
    # this is the negative of the concentrated log lik for minimization
    clik = nlsig2 - jacob
    return clik

def lag_c_loglik_sp(rho, n, e0, e1, I, Wsp):
    # concentrated log-lik for lag model, sparse algebra
    if isinstance(rho, np.ndarray):
        if rho.shape == (1,1):
            rho = rho[0][0] #why does the interior value change?
    er = e0 - rho * e1
    sig2 = spdot(er.T, er) / n
    nlsig2 = (n / 2.0) * np.log(sig2)
    a = I - rho * Wsp
    LU = SuperLU(a.tocsc())
    jacob = np.sum(np.log(np.abs(LU.U.diagonal())))
    clike = nlsig2 - jacob
    return clike

def lag_c_loglik_ord(rho, n, e0, e1, evals):
    # concentrated log-lik for lag model, no constants, Ord eigenvalue method
    er = e0 - rho * e1
    sig2 = spdot(er.T, er) / n
    nlsig2 = (n / 2.0) * np.log(sig2)
    revals = rho * evals
    jacob = np.log(1 - revals).sum()
    if isinstance(jacob, complex):
        jacob = jacob.real
    # this is the negative of the concentrated log lik for minimization
    clik = nlsig2 - jacob
    return clik

def _test():
    import doctest
    start_suppress = np.get_printoptions()['suppress']
    np.set_printoptions(suppress=True)
    doctest.testmod()
    np.set_printoptions(suppress=start_suppress)

