"""GraphLasso: sparse inverse covariance estimation with an l1-penalized
estimator.
"""

# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD Style
# Copyright: INRIA
import warnings
import operator
import sys
import time

import numpy as np
from scipy import linalg

from .empirical_covariance_ import empirical_covariance, \
                EmpiricalCovariance, log_likelihood

from ..utils import ConvergenceWarning
from ..linear_model import lars_path
from ..linear_model import cd_fast
from ..cross_validation import check_cv, cross_val_score
from ..externals.joblib import Parallel, delayed


###############################################################################
# Helper functions to compute the objective and dual objective functions
# of the l1-penalized estimator
def _objective(mle, precision_, alpha):
    cost = -log_likelihood(mle, precision_)
    cost += alpha * (np.abs(precision_).sum()
                     - np.abs(np.diag(precision_)).sum())
    return cost


def _dual_gap(emp_cov, precision_, alpha):
    """Expression of the dual gap convergence criterion

    The specific definition is given in Duchi "Projected Subgradient Methods
    for Learning Sparse Gaussians".
    """
    gap = np.sum(emp_cov * precision_)
    gap -= precision_.shape[0]
    gap += alpha * (np.abs(precision_).sum()
                    - np.abs(np.diag(precision_)).sum())
    return gap


def alpha_max(emp_cov):
    """Find the maximum alpha for which there are some non-zeros off-diagonal.

    Parameters
    ----------
    emp_cov: 2D array, (n_features, n_features)
        The sample covariance matrix

    Notes
    -----

    This results from the bound for the all the Lasso that are solved
    in GraphLasso: each time, the row of cov corresponds to Xy. As the
    bound for alpha is given by max(abs(Xy)), the result follows.
    """
    A = np.copy(emp_cov)
    A.flat[::A.shape[0] + 1] = 0
    return np.max(np.abs(A))


###############################################################################
# The g-lasso algorithm

def graph_lasso(emp_cov, alpha, cov_init=None, mode='cd', tol=1e-4,
                max_iter=100, verbose=False, return_costs=False,
                eps=np.finfo(np.float).eps):
    """l1-penalized covariance estimator

    Parameters
    ----------
    emp_cov: 2D ndarray, shape (n_features, n_features)
        Empirical covariance from which to compute the covariance estimate
    alpha: positive float
        The regularization parameter: the higher alpha, the more
        regularization, the sparser the inverse covariance
    cov_init: 2D array (n_features, n_features), optional
        The initial guess for the covariance
    mode: {'cd', 'lars'}
        The Lasso solver to use: coordinate descent or LARS. Use LARS for
        very sparse underlying graphs, where p > n. Elsewhere prefer cd
        which is more numerically stable.
    tol: positive float, optional
        The tolerance to declare convergence: if the dual gap goes below
        this value, iterations are stopped
    max_iter: integer, optional
        The maximum number of iterations
    verbose: boolean, optional
        If verbose is True, the objective function and dual gap are
        printed at each iteration
    return_costs: boolean, optional
        If return_costs is True, the objective function and dual gap
        at each iteration are returned
    eps: float, optional
        The machine-precision regularization in the computation of the
        Cholesky diagonal factors. Increase this for very ill-conditioned
        systems.

    Returns
    -------
    covariance : 2D ndarray, shape (n_features, n_features)
        The estimated covariance matrix
    precision : 2D ndarray, shape (n_features, n_features)
        The estimated (sparse) precision matrix
    costs : list of (objective, dual_gap) pairs
        The list of values of the objective function and the dual gap at
        each iteration. Returned only if return_costs is True

    See Also
    --------
    GraphLasso, GraphLassoCV

    Notes
    -----

    The algorithm employed to solve this problem is the GLasso algorithm,
    from the Friedman 2008 Biostatistics paper. It is the same algorithm
    as in the R `glasso` package.

    One possible difference with the `glasso` R package is that the
    diagonal coefficients are not penalized.
    """
    _, n_features = emp_cov.shape
    if alpha == 0:
        return emp_cov, linalg.inv(emp_cov)
    if cov_init is None:
        covariance_ = emp_cov.copy()
    else:
        covariance_ = cov_init.copy()
    # As a trivial regularization (Tikhonov like), we scale down the
    # off-diagonal coefficients of our starting point: This is needed, as
    # in the cross-validation the cov_init can easily be
    # ill-conditioned, and the CV loop blows. Beside, this takes
    # conservative stand-point on the initial conditions, and it tends to
    # make the convergence go faster.
    covariance_ *= 0.95
    diagonal = emp_cov.flat[::n_features + 1]
    covariance_.flat[::n_features + 1] = diagonal
    precision_ = linalg.pinv(covariance_)

    indices = np.arange(n_features)
    costs = list()
    # The different l1 regression solver have different numerical errors
    if mode == 'cd':
        errors = dict(over='raise', invalid='ignore')
    else:
        errors = dict(invalid='raise')
    try:
        for i in xrange(max_iter):
            for idx in xrange(n_features):
                sub_covariance = covariance_[indices != idx].T[indices != idx]
                row = emp_cov[idx, indices != idx]
                with np.errstate(**errors):
                    if mode == 'cd':
                        # Use coordinate descent
                        coefs = -(precision_[indices != idx, idx]
                                    / (precision_[idx, idx] + 1000 * eps))
                        coefs, _, _ = cd_fast.enet_coordinate_descent_gram(
                                            coefs, alpha, 0, sub_covariance,
                                            row, row, max_iter, tol)
                    else:
                        # Use LARS
                        _, _, coefs = lars_path(sub_covariance, row,
                                            Xy=row, Gram=sub_covariance,
                                            alpha_min=alpha / (n_features - 1),
                                            copy_Gram=True,
                                            method='lars')
                        coefs = coefs[:, -1]
                # Update the precision matrix
                precision_[idx, idx] = 1. / (covariance_[idx, idx] -
                            np.dot(covariance_[indices != idx, idx], coefs))
                precision_[indices != idx, idx] = \
                                            - precision_[idx, idx] * coefs
                precision_[idx, indices != idx] = \
                                            - precision_[idx, idx] * coefs
                coefs = np.dot(sub_covariance, coefs)
                covariance_[idx, indices != idx] = coefs
                covariance_[indices != idx, idx] = coefs
            d_gap = _dual_gap(emp_cov, precision_, alpha)
            cost = _objective(emp_cov, precision_, alpha)
            if verbose:
                print (
                    '[graph_lasso] Iteration % 3i, cost % 3.2e, dual gap %.3e'
                                                % (i, cost, d_gap))
            if return_costs:
                costs.append((cost, d_gap))
            if np.abs(d_gap) < tol:
                break
            if not np.isfinite(cost) and i > 0:
                raise FloatingPointError('Non SPD result: the system is '
                                    'too ill-conditioned for this solver')
        else:
            warnings.warn('graph_lasso: did not converge after %i iteration:'
                            'dual gap: %.3e' % (max_iter, d_gap),
                            ConvergenceWarning)
    except FloatingPointError as e:
        e.args = (e.args[0]
                  + '. The system is too ill-conditioned for this solver',
                 )
        raise e
    if return_costs:
        return covariance_, precision_, costs
    return covariance_, precision_


class GraphLasso(EmpiricalCovariance):
    """Sparse inverse covariance estimation with an l1-penalized estimator.

    Parameters
    ----------
    alpha: positive float, optional
        The regularization parameter: the higher alpha, the more
        regularization, the sparser the inverse covariance
    cov_init: 2D array (n_features, n_features), optional
        The initial guess for the covariance
    mode: {'cd', 'lars'}
        The Lasso solver to use: coordinate descent or LARS. Use LARS for
        very sparse underlying graphs, where p > n. Elsewhere prefer cd
        which is more numerically stable.
    tol: positive float, optional
        The tolerance to declare convergence: if the dual gap goes below
        this value, iterations are stopped
    max_iter: integer, optional
        The maximum number of iterations
    verbose: boolean, optional
        If verbose is True, the objective function and dual gap are
        plotted at each iteration

    Attributes
    ----------
    `covariance_` : array-like, shape (n_features, n_features)
        Estimated covariance matrix

    `precision_` : array-like, shape (n_features, n_features)
        Estimated pseudo inverse matrix.

    See Also
    --------
    graph_lasso, GraphLassoCV
    """

    def __init__(self, alpha=.01, mode='cd', tol=1e-4, max_iter=100,
                 verbose=False):
        self.alpha = alpha
        self.mode = mode
        self.tol = tol
        self.max_iter = max_iter
        self.verbose = verbose
        # The base class needs this for the score method
        self.store_precision = True

    def fit(self, X, y=None):
        emp_cov = empirical_covariance(X)
        self.covariance_, self.precision_ = graph_lasso(emp_cov,
                                        alpha=self.alpha, mode=self.mode,
                                        tol=self.tol, max_iter=self.max_iter,
                                        verbose=self.verbose,
                                        )
        return self


###############################################################################
# Cross-validation with GraphLasso
def graph_lasso_path(X, alphas, cov_init=None, X_test=None, mode='cd',
                 tol=1e-4, max_iter=100, verbose=False):
    """l1-penalized covariance estimator along a path of decreasing alphas

    Parameters
    ----------
    X: 2D ndarray, shape (n_samples, n_features)
        Data from which to compute the covariance estimate
    alphas: list of positive floats
        The list of regularization parameters, decreasing order
    X_test: 2D array, shape (n_test_samples, n_features), optional
        Optional test matrix to measure generalisation error
    mode: {'cd', 'lars'}
        The Lasso solver to use: coordinate descent or LARS. Use LARS for
        very sparse underlying graphs, where p > n. Elsewhere prefer cd
        which is more numerically stable.
    tol: positive float, optional
        The tolerance to declare convergence: if the dual gap goes below
        this value, iterations are stopped
    max_iter: integer, optional
        The maximum number of iterations
    verbose: integer, optional
        The higher the verbosity flag, the more information is printed
        during the fitting.

    Returns
    -------
    covariances_: List of 2D ndarray, shape (n_features, n_features)
        The estimated covariance matrices
    precisions_: List of 2D ndarray, shape (n_features, n_features)
        The estimated (sparse) precision matrices
    scores_: List of float
        The generalisation error (log-likelihood) on the test data.
        Returned only if test data is passed.
    """
    inner_verbose = max(0, verbose - 1)
    emp_cov = empirical_covariance(X)
    if cov_init is None:
        covariance_ = emp_cov.copy()
    else:
        covariance_ = cov_init
    covariances_ = list()
    precisions_ = list()
    scores_ = list()
    if X_test is not None:
        test_emp_cov = empirical_covariance(X_test)
    for alpha in alphas:
        try:
            # Capture the errors, and move on
            covariance_, precision_ = graph_lasso(emp_cov, alpha=alpha,
                                    cov_init=covariance_, mode=mode, tol=tol,
                                    max_iter=max_iter,
                                    verbose=inner_verbose)
            covariances_.append(covariance_)
            precisions_.append(precision_)
            if X_test is not None:
                this_score = log_likelihood(test_emp_cov, precision_)
        except FloatingPointError:
            this_score = -np.inf
            covariances_.append(np.nan)
            precisions_.append(np.nan)
        if X_test is not None:
            if not np.isfinite(this_score):
                this_score = -np.inf
            scores_.append(this_score)
        if verbose == 1:
            sys.stderr.write('.')
        elif verbose:
            if X_test is not None:
                print '[graph_lasso_path] alpha: %.2e, score: %.2e' % (alpha,
                                                            this_score)
            else:
                print '[graph_lasso_path] alpha: %.2e' % alpha
    if X_test is not None:
        return covariances_, precisions_, scores_
    return covariances_, precisions_


class GraphLassoCV(GraphLasso):
    """Sparse inverse covariance w/ cross-validated choice of the l1 penality

    Parameters
    ----------
    alphas: integer, or list positive float, optional
        If an integer is given, it fixes the number of points on the
        grids of alpha to be used. If a list is given, it gives the
        grid to be used. See the notes in the class docstring for
        more details.
    n_refinements: strictly positive integer
        The number of time the grid is refined. Not used if explicit
        values of alphas are passed.
    cv : crossvalidation generator, optional
        see sklearn.cross_validation module. If None is passed, default to
        a 3-fold strategy
    tol: positive float, optional
        The tolerance to declare convergence: if the dual gap goes below
        this value, iterations are stopped
    max_iter: integer, optional
        The maximum number of iterations
    mode: {'cd', 'lars'}
        The Lasso solver to use: coordinate descent or LARS. Use LARS for
        very sparse underlying graphs, where p > n. Elsewhere prefer cd
        which is more numerically stable.
    n_jobs: int, optional
        number of jobs to run in parallel (default 1)
    verbose: boolean, optional
        If verbose is True, the objective function and dual gap are
        print at each iteration

    Attributes
    ----------
    `covariance_` : array-like, shape (n_features, n_features)
        Estimated covariance matrix

    `precision_` : array-like, shape (n_features, n_features)
        Estimated precision matrix (inverse covariance).

    `alpha_`: float
        Penalization parameter selected

    `cv_alphas_`: list of float
        All the penalization parameters explored

    `cv_scores`: 2D array (n_alphas, n_folds)
        The log-likelihood score on left-out data across the folds.

    See Also
    --------
    graph_lasso, GraphLasso

    Notes
    -----
    The search for the optimal alpha is done on an iteratively refined
    grid: first the cross-validated scores on a grid are computed, then
    a new refined grid is center around the maximum...

    One of the challenges that we have to face is that the solvers can
    fail to converge to a well-conditioned estimate. The corresponding
    values of alpha then come out as missing values, but the optimum may
    be close to these missing values.
    """

    def __init__(self, alphas=4, n_refinements=4, cv=None, tol=1e-4,
                 max_iter=100, mode='cd', n_jobs=1, verbose=False):
        self.alphas = alphas
        self.n_refinements = n_refinements
        self.mode = mode
        self.tol = tol
        self.max_iter = max_iter
        self.verbose = verbose
        self.cv = cv
        self.n_jobs = n_jobs
        # The base class needs this for the score method
        self.store_precision = True

    def fit(self, X, y=None):
        X = np.asarray(X)
        emp_cov = empirical_covariance(X)

        cv = check_cv(self.cv, X, y, classifier=False)

        # List of (alpha, scores, covs)
        path = list()
        n_alphas = self.alphas
        inner_verbose = max(0, self.verbose - 1)

        if operator.isSequenceType(n_alphas):
            alphas = self.alphas
            n_refinements = 1
        else:
            n_refinements = self.n_refinements
            alpha_1 = alpha_max(emp_cov)
            alpha_0 = 1e-2 * alpha_1
            alphas = np.logspace(np.log10(alpha_0),
                                            np.log10(alpha_1),
                                            n_alphas)[::-1]
        covs_init = (None, None, None)

        t0 = time.time()
        for i in range(n_refinements):
            with warnings.catch_warnings():
                # No need to see the convergence warnings on this grid:
                # they will always be points that will not converge
                # during the cross-validation
                warnings.simplefilter('ignore',  ConvergenceWarning)
                # Compute the cross-validated loss on the current grid
                this_path = Parallel(
                    n_jobs=self.n_jobs,
                    verbose=self.verbose)(
                        delayed(graph_lasso_path)(
                            X[train], alphas=alphas,
                            X_test=X[test], mode=self.mode,
                            tol=self.tol,
                            max_iter=int(.1 * self.max_iter),
                            verbose=inner_verbose)
                        for (train, test), cov_init in zip(cv, covs_init))

            # Little danse to transform the list in what we need
            covs, _, scores = zip(*this_path)
            covs = zip(*covs)
            scores = zip(*scores)
            path.extend(zip(alphas, scores, covs))
            path = sorted(path, key=operator.itemgetter(0), reverse=True)

            # Find the maximum (we avoid using built in 'max' function to
            # have a fully-reproducible selection of the smallest alpha
            # is case of equality)
            best_score = -np.inf
            last_finite_idx = 0
            for index, (alpha, scores, _) in enumerate(path):
                this_score = np.mean(scores)
                if this_score >= .1 / np.finfo(np.float).eps:
                    this_score = np.nan
                if np.isfinite(this_score):
                    last_finite_idx = index
                if this_score >= best_score:
                    best_score = this_score
                    best_index = index

            # Refine our grid
            if best_index == 0:
                # We do not need to go back: we have choosen
                # the highest value of alpha for which there are
                # non-zero coefficients
                alpha_1 = path[0][0]
                alpha_0 = path[1][0]
                covs_init = path[0][-1]
            elif (best_index == last_finite_idx
                        and not best_index == len(path) - 1):
                # We have non-converged models on the upper bound of the
                # grid, we need to refine the grid there
                alpha_1 = path[best_index][0]
                alpha_0 = path[best_index + 1][0]
                covs_init = path[best_index][-1]
            elif best_index == len(path) - 1:
                alpha_1 = path[best_index][0]
                alpha_0 = 0.01 * path[best_index][0]
                covs_init = path[best_index][-1]
            else:
                alpha_1 = path[best_index - 1][0]
                alpha_0 = path[best_index + 1][0]
                covs_init = path[best_index - 1][-1]
            alphas = np.logspace(np.log10(alpha_1), np.log10(alpha_0),
                                 n_alphas + 2)
            alphas = alphas[1:-1]
            if self.verbose and n_refinements > 1:
                print '[GraphLassoCV] Done refinement % 2i out of %i: % 3is'\
                        % (i + 1, n_refinements, time.time() - t0)

        path = zip(*path)
        cv_scores = list(path[1])
        alphas = list(path[0])
        # Finally, compute the score with alpha = 0
        alphas.append(0)
        cv_scores.append(cross_val_score(EmpiricalCovariance(), X,
                                         cv=cv, n_jobs=self.n_jobs,
                                         verbose=inner_verbose))
        self.cv_scores = np.array(cv_scores)
        best_alpha = alphas[best_index]
        self.alpha_ = best_alpha
        self.cv_alphas_ = alphas

        # Finally fit the model with the selected alpha
        self.covariance_, self.precision_ = graph_lasso(emp_cov,
                        alpha=best_alpha, mode=self.mode, tol=self.tol,
                        max_iter=self.max_iter, verbose=inner_verbose)
        return self