"""Tests for the kernel sub-module."""
# Author: Ioannis Siglidis <y.siglidis@gmail.com>
# License: BSD 3 clause
import os
import numpy as np

from time import time
from warnings import warn

from numpy.testing import assert_array_less

from sklearn.model_selection import train_test_split

from grakel.datasets import fetch_dataset
from grakel.datasets import get_dataset_info
from grakel.datasets.base import read_data

from grakel.kernels import GraphletSampling
from grakel.kernels import RandomWalk
from grakel.kernels import RandomWalkLabeled
from grakel.kernels import ShortestPath
from grakel.kernels import ShortestPathAttr
from grakel.kernels import WeisfeilerLehman
from grakel.kernels import NeighborhoodHash
from grakel.kernels import PyramidMatch
from grakel.kernels import SubgraphMatching
from grakel.kernels import NeighborhoodSubgraphPairwiseDistance
from grakel.kernels import LovaszTheta
from grakel.kernels import SvmTheta
from grakel.kernels import OddSth
from grakel.kernels import Propagation
from grakel.kernels import PropagationAttr
from grakel.kernels import HadamardCode
from grakel.kernels import MultiscaleLaplacian
from grakel.kernels import MultiscaleLaplacianFast
from grakel.kernels import VertexHistogram
from grakel.kernels import EdgeHistogram
from grakel.kernels import GraphHopper
from grakel.kernels import CoreFramework

global verbose, main, development

fdir = os.path.dirname(__file__)

default_eigvalue_precision = float("-1e-5")

# Mark optional dependencies
cvxopt = True
try:
    import cvxopt
except ImportError:
    cvxopt = False


if __name__ == '__main__':
    import argparse
    # Create an argument parser for the installer of pynauty
    parser = argparse.ArgumentParser(description='A test file for all kernels')

    parser.add_argument(
        '--verbose',
        help='print kernels with their outputs on stdout',
        action="store_true")
    parser.add_argument(
        '--problematic',
        help='allow execution of problematic test cases in development',
        action="store_true")
    parser.add_argument(
        '--slow',
        help='allow execution of slow test cases in development',
        action="store_true")
    parser.add_argument(
        '--ignore_warnings',
        help='ignore warnings produced by kernel executions',
        action="store_true")

    parser.add_argument(
        '--time',
        help='time the kernel computation (has effect only on verbose)',
        action="store_true")
    parser.add_argument(
        '--dataset',
        help='choose the dataset for tests requiring node/edge labels',
        type=str,
        default="MUTAG"
    )

    parser.add_argument(
        '--dataset_attr',
        help='choose the dataset for tests requiring node attributes',
        type=str,
        default="Cuneiform"
    )

    parser.add_argument('--normalize', help='normalize the kernel output',
                        action="store_true")

    meg = parser.add_mutually_exclusive_group()
    meg.add_argument(
        '--develop',
        help='execute only tests connected with current development',
        action="store_true")
    meg.add_argument(
        '--all',
        help='execute all tests',
        action="store_true")
    meg.add_argument(
        '--main',
        help='execute the main tests [default]',
        action="store_true")

    args = parser.parse_args()

    verbose = bool(args.verbose)
    if args.all:
        main, develop = True, True
    elif args.develop:
        main, develop = False, True
    else:
        main, develop = True, False
    problematic = bool(args.problematic)
    slow = bool(args.slow)

    if bool(args.ignore_warnings):
        import warnings
        warnings.filterwarnings('ignore', category=UserWarning)

    normalize = bool(args.normalize)
    time_kernel = bool(args.time)
    dataset_name = args.dataset
    dataset_attr_name = args.dataset_attr

else:
    import warnings
    warnings.filterwarnings('ignore', category=UserWarning)
    main, develop, problematic, slow = True, False, False, False
    normalize, verbose, time_kernel = False, False, False
    dataset_name = "MUTAG"
    dataset_attr_name = "Cuneiform"

# consistency check for the dataset
dinfo = get_dataset_info(dataset_name)
if dinfo is None:
    raise TypeError('dataset not found')
elif not dinfo["nl"] and not dinfo["el"]:
    raise TypeError('dataset must have either node and edge labels')

# consistency check for the attribute dataset
dinfo_attr = get_dataset_info(dataset_attr_name)
if dinfo is None:
    raise TypeError('dataset for attributes not found')
elif not dinfo_attr["nl"] and not dinfo_attr["el"]:
    raise TypeError('dataset must have node attributes')


# The baseline dataset for node, edge_labels
global dataset, dataset_tr, dataset_te

try:
    dataset = fetch_dataset(dataset_name, with_classes=False, verbose=verbose).data
except Exception:
    # Offline testing
    warn('There was a problem fetching dataset for attributes: [' + dataset_name + ']')
    if dataset_name != 'MUTAG':
        warn('Switching back to baseline dataset MUTAG')
    warn('Using an offline version..')
    cwd = os.getcwd()
    os.chdir(os.path.join(fdir, 'data'))
    dataset = read_data('MUTAG', with_classes=False).data
    os.chdir(cwd)

dataset_tr, dataset_te = train_test_split(dataset,
                                          test_size=0.2,
                                          random_state=42)

# The baseline dataset for node/edge-attributes
global dataset_attr, dataset_attr_tr, dataset_attr_te

try:
    dataset_attr = fetch_dataset(dataset_attr_name, with_classes=False,
                                 prefer_attr_nodes=True,
                                 prefer_attr_edges=True,
                                 verbose=verbose).data
except Exception:
    # Offline testing
    warn('There was a problem fetching dataset for attributes: [' + dataset_attr_name + ']')
    if dataset_attr_name != 'Cuneiform':
        warn('Switching back to baseline dataset Cuneiform')
    warn('Using an offline version..')
    cwd = os.getcwd()
    os.chdir(os.path.join(fdir, 'data'))
    dataset_attr = read_data('Cuneiform', with_classes=False, prefer_attr_nodes=True,
                             prefer_attr_edges=True).data
    os.chdir(cwd)

dataset_attr_tr, dataset_attr_te = train_test_split(dataset_attr,
                                                    test_size=0.2,
                                                    random_state=42)


def test_random_walk():
    """Eigenvalue test for the Simple, Labelled Random Walk kernel."""
    rw_kernel = RandomWalk(verbose=verbose, normalize=normalize, lamda=0.01)
    if verbose:
        print_kernel("Random Walk", rw_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(rw_kernel, dataset)

    rw_kernel_lab = RandomWalkLabeled(verbose=verbose, normalize=normalize, lamda=0.0001)
    if verbose:
        print_kernel("Random Walk Labelled", rw_kernel_lab, dataset_tr, dataset_te)
    else:
        positive_eig(rw_kernel_lab, dataset)


def test_shortest_path():
    """Eigenvalue test for the Shortest Path kernel."""
    sp_kernel = ShortestPath(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Shortest Path", sp_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(sp_kernel, dataset)

    sp_kernel_attr = ShortestPathAttr(verbose=verbose, normalize=normalize)
    if verbose and slow:
        print_kernel("Shortest Path Attr", sp_kernel_attr, dataset_attr_tr, dataset_attr_te)
    elif not verbose and slow:
        positive_eig(sp_kernel_attr, dataset_attr)


def test_graphlet_sampling():
    """Eigenvalue test for the Graphlet Sampling Kernel."""
    gs_kernel = GraphletSampling(verbose=verbose, normalize=normalize, sampling=dict(n_samples=150))
    if verbose:
        print_kernel("Graphlet Sampling", gs_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(gs_kernel, dataset)


def test_weisfeiler_lehman():
    """Eigenvalue test for the Weisfeiler Lehman kernel."""
    wl_st_kernel = WeisfeilerLehman(verbose=verbose, normalize=normalize,
                                    base_kernel=VertexHistogram)
    if verbose:
        print_kernel("WL/Subtree", wl_st_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(wl_st_kernel, dataset)


def test_pyramid_match():
    """Eigenvalue test for the Pyramid Match kernel."""
    pm_kernel = PyramidMatch(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Pyramid Match", pm_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(pm_kernel, dataset)


def test_neighborhood_hash():
    """Eigenvalue test for the Neighborhood Hash kernel."""
    nh_kernel = NeighborhoodHash(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Neighborhood Hash", nh_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(nh_kernel, dataset)


def test_subgraph_matching():
    """Eigenvalue test for the subgraph_matching kernel."""
    sm_kernel = SubgraphMatching(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Subgraph Matching", sm_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(sm_kernel, dataset)


def test_neighborhood_subgraph_pairwise_distance():
    """Eigenvalue test for the neighborhood subgraph pairwise distance kernel."""
    nspd_kernel = NeighborhoodSubgraphPairwiseDistance(
        verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("NSPD", nspd_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(nspd_kernel, dataset)


if cvxopt:
    def test_lovasz_theta():
        """Eigenvalue test for the Lovasz-theta distance kernel."""
        lt_kernel = LovaszTheta(verbose=verbose, normalize=normalize)

        if verbose:
            print_kernel("Lovasz-theta", lt_kernel, dataset_tr, dataset_te)
        else:
            positive_eig(lt_kernel, dataset)


def test_svm_theta():
    """Eigenvalue test for the SVM-theta distance kernel."""
    svm_kernel = SvmTheta(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("SVM-theta", svm_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(svm_kernel, dataset)


def test_odd_sth():
    """Eigenvalue test for the ODD-STh kernel."""
    odd_sth_kernel = OddSth(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("ODD-STh", odd_sth_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(odd_sth_kernel, dataset)


def test_propagation():
    """Eigenvalue test for the Propagation kernel."""
    propagation_kernel = Propagation(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Propagation", propagation_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(propagation_kernel, dataset)

    propagation_kernel_attr = PropagationAttr(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Propagation", propagation_kernel_attr, dataset_attr_tr, dataset_attr_te)
    else:
        positive_eig(propagation_kernel_attr, dataset_attr)


def test_hadamard_code():
    """Eigenvalue test for the Hadamard Code kernel."""
    hadamard_code_kernel = HadamardCode(verbose=verbose, normalize=normalize,
                                        base_kernel=VertexHistogram)
    if verbose:
        print_kernel("Hadamard-Code/VH [Simple]",
                     hadamard_code_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(hadamard_code_kernel, dataset)


def test_multiscale_laplacian():
    """Eigenvalue test for the Multiscale Laplacian kernel."""
    ml_kernel = MultiscaleLaplacian(verbose=verbose, normalize=normalize)
    if verbose and slow:
        print_kernel("Multiscale Laplacian", ml_kernel,
                     dataset_attr_tr, dataset_attr_te)
    elif not verbose and slow:
        positive_eig(ml_kernel, dataset_attr)


def test_multiscale_laplacian_fast():
    """Eigenvalue test for the Fast Multiscale Laplacian kernel."""
    mlf_kernel = MultiscaleLaplacianFast(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Multiscale Laplacian Fast", mlf_kernel,
                     dataset_attr_tr, dataset_attr_te)
    else:
        positive_eig(mlf_kernel, dataset_attr)


def test_vertex_histogram():
    """Eigenvalue test for the Vertex Histogram Kernel."""
    vh_kernel = VertexHistogram(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Vertex Histogram", vh_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(vh_kernel, dataset)


def test_edge_histogram():
    """Eigenvalue test for the Edge Histogram Kernel."""
    eh_kernel = EdgeHistogram(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Edge Histogram", eh_kernel, dataset_tr, dataset_te)
    else:
        positive_eig(eh_kernel, dataset)


def test_graph_hopper():
    """Eigenvalue test for the Graph Hopper Kernel."""
    gh_kernel = GraphHopper(verbose=verbose, normalize=normalize)
    if verbose:
        print_kernel("Graph Hopper", gh_kernel, dataset_attr_tr, dataset_attr_te)
    else:
        positive_eig(gh_kernel, dataset_attr)


def test_core_framework():
    """Eigenvalue test for the Core kernel Framework."""
    base_kernel = (WeisfeilerLehman, dict(base_kernel=VertexHistogram))
    core_framework = CoreFramework(verbose=verbose, normalize=normalize, base_kernel=base_kernel)
    if verbose:
        print_kernel("Core Framework", core_framework, dataset_tr, dataset_te)
    else:
        positive_eig(core_framework, dataset)


def print_kernel(name, kernel, X, Y):
    """Print kernels in case of verbose execution."""
    if time_kernel:
        print(str(name) + ":\n" + (len(str(name)) * "-") + "-\n")
        print("fit_transform\n-------------")

        # [time] fit_transform
        start = time()
        Kft = kernel.fit_transform(X)
        ft_time = time() - start

        print(Kft)
        print("[TIME] fit_transform:", sec_to_time(ft_time))
        print("\ntransform\n---------")

        start = time()
        Kt = kernel.transform(Y)
        t_time = time() - start
        print(Kt)
        print("[TIME] transform:", sec_to_time(t_time))
        print("[TIME] total:", sec_to_time(ft_time+t_time))
        print("--------------------------------------" +
              "--------------------------------------\n")
    else:
        print(str(name) + ":\n" + (len(str(name)) * "-") + "-\n")
        print("fit_transform\n-------------")
        print(kernel.fit_transform(X))

        print("\ntransform\n---------")
        print(kernel.transform(Y))
        print("--------------------------------------" +
              "--------------------------------------\n")


def sec_to_time(sec):
    """Print time in a correct format."""
    dt = list()
    days = int(sec // 86400)
    if days > 0:
        sec -= 86400*days
        dt.append(str(days) + " d")

    hrs = int(sec // 3600)
    if hrs > 0:
        sec -= 3600*hrs
        dt.append(str(hrs) + " h")

    mins = int(sec // 60)
    if mins > 0:
        sec -= 60*mins
        dt.append(str(mins) + " m")

    if sec > 0:
        dt.append(str(round(sec, 2)) + " s")
    return " ".join(dt)


def positive_eig(kernel, X):
    """Assert true if the calculated kernel matrix is valid."""
    K = kernel.fit_transform(X)
    min_eig = np.real(np.min(np.linalg.eig(K)[0]))
    assert_array_less(default_eigvalue_precision, min_eig)


if verbose and main:
    test_random_walk()
    test_shortest_path()
    test_weisfeiler_lehman()
    test_neighborhood_hash()
    test_graphlet_sampling()
    test_lovasz_theta()
    test_svm_theta()
    test_odd_sth()
    test_propagation()
    test_hadamard_code()
    test_neighborhood_subgraph_pairwise_distance()
    test_pyramid_match()
    test_multiscale_laplacian_fast()
    test_edge_histogram()
    test_vertex_histogram()
    test_subgraph_matching()
    test_graph_hopper()
    test_core_framework()

if verbose and develop:
    if slow:
        test_multiscale_laplacian()
    if problematic:
        pass
