# Copyright 2017 the GPflow authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.from __future__ import print_function

import six
import tensorflow as tf

import numpy as np
from numpy.testing import assert_allclose

import gpflow
from gpflow import settings
from gpflow.test_util import GPflowTestCase

import pytest


class LikelihoodSetup(object):
    def __init__(self, likelihood, Y, tolerance):
        self.likelihood, self.Y, self.tolerance = likelihood, Y, tolerance
        self.is_analytic = six.get_unbound_function(likelihood.predict_density) is not\
            six.get_unbound_function(gpflow.likelihoods.Likelihood.predict_density)


def getLikelihoodSetups(includeMultiClass=True, addNonStandardLinks=False):
    test_setups = []
    rng = np.random.RandomState(1)
    for likelihoodClass in gpflow.likelihoods.Likelihood.__subclasses__():
        if likelihoodClass == gpflow.likelihoods.Ordinal:
            test_setups.append(
                LikelihoodSetup(likelihoodClass(np.array([-1, 1])),
                          rng.randint(0, 3, (10, 2)), 1e-6))
        elif likelihoodClass == gpflow.likelihoods.SwitchedLikelihood:
            continue  # switched likelihood tested separately
        elif likelihoodClass == gpflow.likelihoods.MultiClass:
            if includeMultiClass:
                sample = rng.randn(10, 2)
                # Multiclass needs a less tight tolerance due to presence of clipping.
                tolerance = 1e-3
                test_setups.append(
                    LikelihoodSetup(likelihoodClass(2),
                              np.argmax(sample, 1).reshape(-1, 1), tolerance))
        else:
            # most likelihoods follow this standard:
            test_setups.append(
                LikelihoodSetup(likelihoodClass(),
                          rng.rand(10, 2).astype(settings.float_type), 1e-6))

    if addNonStandardLinks:
        test_setups.append(LikelihoodSetup(gpflow.likelihoods.Poisson(invlink=tf.square),
                                     rng.rand(10, 2).astype(settings.float_type), 1e-6))
        test_setups.append(LikelihoodSetup(gpflow.likelihoods.Exponential(invlink=tf.square),
                                     rng.rand(10, 2).astype(settings.float_type), 1e-6))
        test_setups.append(LikelihoodSetup(gpflow.likelihoods.Gamma(invlink=tf.square),
                                     rng.rand(10, 2).astype(settings.float_type), 1e-6))

        def sigmoid(x):
            return 1./(1 + tf.exp(-x))
        test_setups.append(LikelihoodSetup(gpflow.likelihoods.Bernoulli(invlink=sigmoid),
                                     rng.rand(10, 2).astype(settings.float_type), 1e-6))
    return test_setups


class TestPredictConditional(GPflowTestCase):
    """
    Here we make sure that the conditional_mean and contitional_var functions
    give the same result as the predict_mean_and_var function if the prediction
    has no uncertainty.
    """
    def setUp(self):
        self.test_graph = tf.Graph()

    def prepare(self):
        test_setups = getLikelihoodSetups(addNonStandardLinks=True)
        rng = np.random.RandomState(0)
        F = tf.placeholder(settings.float_type)
        F_data = rng.randn(10, 2).astype(settings.float_type)
        feed = {F: F_data}
        return test_setups, F, feed

    def test_mean(self):
        with self.test_context() as session:
            test_setups, F, feed = self.prepare()
            for test_setup in test_setups:
                l = test_setup.likelihood
                l.compile()
                mu1 = session.run(l.conditional_mean(F), feed_dict=feed)
                zero = F * 0.
                mu2, _ = session.run(l.predict_mean_and_var(F, zero), feed_dict=feed)
                assert_allclose(mu1, mu2, test_setup.tolerance, test_setup.tolerance)

    def test_variance(self):
        with self.test_context() as session:
            test_setups, F, feed = self.prepare()
            for test_setup in test_setups:
                l = test_setup.likelihood
                l.compile()
                zero = F * 0.
                v1 = session.run(l.conditional_variance(F), feed_dict=feed)
                v2 = session.run(l.predict_mean_and_var(F, zero)[1], feed_dict=feed)
                assert_allclose(v1, v2, atol=test_setup.tolerance)

    def test_var_exp(self):
        """
        Here we make sure that the variational_expectations gives the same result
        as logp if the latent function has no uncertainty.
        """
        with self.test_context() as session:
            test_setups, F, feed = self.prepare()
            for test_setup in test_setups:
                l = test_setup.likelihood
                y = test_setup.Y
                l.compile()
                r1 = session.run(l.logp(F, y), feed_dict=feed)
                zero = F * 0.
                r2 = session.run(
                    l.variational_expectations(F, zero, test_setup.Y), feed_dict=feed)
                assert_allclose(r1, r2, atol=test_setup.tolerance, rtol=test_setup.tolerance)


class TestQuadrature(GPflowTestCase):
    """
    Where quadratre methods have been overwritten, make sure the new code
     does something close to the quadrature
    """
    def setUp(self):
        self.test_graph = tf.Graph()
        self.rng = np.random.RandomState()
        self.Fmu, self.Fvar, self.Y = self.rng.randn(3, 10, 2).astype(settings.float_type)
        self.Fvar = 0.01 * (self.Fvar ** 2)
        with self.test_context():
            self.test_setups = getLikelihoodSetups(includeMultiClass=False)

    def test_var_exp(self):
        for test_setup in self.test_setups:
            with self.test_context() as session:
                # get all the likelihoods where variational expectations has been overwritten
                if not test_setup.is_analytic:
                    continue
                l = test_setup.likelihood
                y = test_setup.Y
                # 'build' the functions
                l.compile()
                F1 = l.variational_expectations(self.Fmu, self.Fvar, y)
                F2 = gpflow.likelihoods.Likelihood.variational_expectations(
                    l, self.Fmu, self.Fvar, y)
                # compile and run the functions:
                F1 = session.run(F1)
                F2 = session.run(F2)
                self.assertTrue(np.allclose(F1, F2, test_setup.tolerance, test_setup.tolerance))

    def test_pred_density(self):
        # get all the likelihoods where predict_density  has been overwritten.
        for test_setup in self.test_setups:
            with self.test_context() as session:
                if not test_setup.is_analytic:
                    continue
                l = test_setup.likelihood
                y = test_setup.Y
                l.compile()
                # 'build' the functions
                F1 = l.predict_density(self.Fmu, self.Fvar, y)
                F2 = gpflow.likelihoods.Likelihood.predict_density(l, self.Fmu, self.Fvar, y)
                # compile and run the functions:
                F1 = session.run(F1)
                F2 = session.run(F2)
                self.assertTrue(np.allclose(F1, F2, test_setup.tolerance, test_setup.tolerance))


class TestRobustMaxMulticlass(GPflowTestCase):
    """
    Some specialized tests to the multiclass likelihood with RobustMax inverse link function.
    """
    def setUp(self):
        self.test_graph = tf.Graph()

    def testSymmetric(self):
        """
        This test is based on the observation that for
        symmetric inputs the class predictions must have equal probability.
        """
        with self.test_context() as session:
            nClasses = 5
            nPoints = 10
            tolerance = 1e-4
            epsilon = 1e-3
            F = tf.placeholder(settings.float_type)
            F_data = np.ones((nPoints, nClasses))
            feed = {F: F_data}
            rng = np.random.RandomState(1)
            Y = rng.randint(nClasses, size=(nPoints, 1))

            l = gpflow.likelihoods.MultiClass(nClasses)
            l.invlink.epsilon = epsilon
            l.compile()

            mu, _ = session.run(l.predict_mean_and_var(F, F), feed_dict=feed)
            pred = session.run(l.predict_density(F, F, Y), feed_dict=feed)
            variational_expectations = session.run(
                l.variational_expectations(F, F, Y), feed_dict=feed)
            expected_mu = (1./nClasses * (1. - epsilon) + (1. - 1. / nClasses) *\
                           epsilon / (nClasses - 1)) * np.ones((nPoints, 1))

            self.assertTrue(np.allclose(mu, expected_mu, tolerance, tolerance))
            expected_log_denisty = np.log(expected_mu)
            self.assertTrue(np.allclose(pred, expected_log_denisty, 1e-3, 1e-3))
            validation_variational_expectation = 1./nClasses * np.log(1. - epsilon) + \
                (1. - 1./nClasses) * np.log(epsilon / (nClasses - 1))
            self.assertTrue(
                np.allclose(
                    variational_expectations,
                    np.ones((nPoints, 1)) * validation_variational_expectation,
                    tolerance, tolerance))

    def testPredictDensity(self):
        tol = 1e-4
        num_points = 100
        mock_prob = 0.73

        class MockRobustMax(gpflow.likelihoods.RobustMax):
            def prob_is_largest(self, Y, Fmu, Fvar, gh_x, gh_w):
                return tf.ones((num_points, 1)) * mock_prob

        with self.test_context() as session:
            epsilon = 0.231
            num_classes = 5
            l = gpflow.likelihoods.MultiClass(
                num_classes, invlink=MockRobustMax(num_classes, epsilon))
            l.compile()

            F = tf.placeholder(settings.float_type)
            y = tf.placeholder(settings.float_type)
            F_data = np.ones((num_points, num_classes))
            rng = np.random.RandomState(1)
            Y_data = rng.randint(num_classes, size=(num_points, 1))
            feed = {F: F_data, y: Y_data}

            pred = session.run(l.predict_density(F, F, y), feed_dict=feed)
            expected_prediction = -0.5499780059
            # ^^^ evaluated on calculator:
            # log((1-\epsilon) * 0.73 + (1-0.73) * \epsilon/(num_classes -1))

            self.assertTrue(np.allclose(pred, expected_prediction, tol, tol))


class TestMulticlassIndexFix(GPflowTestCase):
    """
    A regression test for a bug in multiclass likelihood.
    """
    def testA(self):
        with self.test_context():
            mu = tf.placeholder(settings.float_type)
            var = tf.placeholder(settings.float_type)
            Y = tf.placeholder(tf.int32)
            lik = gpflow.likelihoods.MultiClass(3)
            ve = lik.variational_expectations(mu, var, Y)
            tf.gradients(tf.reduce_sum(ve), mu)


class TestSwitchedLikelihood(GPflowTestCase):
    """
    SwitchedLikelihood is saparately tested here.
    Here, we make sure the partition-stictch works fine.
    """
    def setUp(self):
        self.test_graph = tf.Graph()

        with self.test_context():
            rng = np.random.RandomState(1)
            self.Y_list = [rng.randn(3, 2),  rng.randn(4, 2),  rng.randn(5, 2)]
            self.F_list = [rng.randn(3, 2),  rng.randn(4, 2),  rng.randn(5, 2)]
            self.Fvar_list = [np.exp(rng.randn(3, 2)),  np.exp(rng.randn(4, 2)),  np.exp(rng.randn(5, 2))]
            self.Y_label = [np.ones((3, 1))*0, np.ones((4, 1))*1, np.ones((5, 1))*2]
            self.Y_perm = list(range(3+4+5))
            rng.shuffle(self.Y_perm)

            # shuffle the original data
            self.Y_sw = np.hstack([
                np.concatenate(self.Y_list),
                np.concatenate(self.Y_label)])[self.Y_perm, :]
            self.F_sw = np.concatenate(self.F_list)[self.Y_perm, :]
            self.Fvar_sw = np.concatenate(self.Fvar_list)[self.Y_perm, :]
            # likelihoods
            self.likelihoods = [gpflow.likelihoods.Gaussian(),
                                gpflow.likelihoods.Gaussian(),
                                gpflow.likelihoods.Gaussian()]
            for lik in self.likelihoods:
                lik.variance = np.exp(rng.randn(1)).squeeze()
            self.switched_likelihood = gpflow.likelihoods.SwitchedLikelihood(self.likelihoods)

    def test_logp(self):
        # switchedlikelihood
        with self.test_context() as session:
            self.switched_likelihood.compile()
            switched_rslt = session.run(self.switched_likelihood.logp(self.F_sw, self.Y_sw))
            rslts = []
            for lik, y, f in zip(self.likelihoods, self.Y_list, self.F_list):
                rslts.append(session.run(lik.logp(f, y)))
            self.assertTrue(np.allclose(switched_rslt, np.concatenate(rslts)[self.Y_perm, :]))

    def test_predict_density(self):
        with self.test_context() as session:
            self.switched_likelihood.compile()
            # switchedlikelihood
            switched_rslt = session.run(
                self.switched_likelihood.predict_density(self.F_sw, self.Fvar_sw, self.Y_sw))
            # likelihood
            rslts = []
            for lik, y, f, fvar in zip(self.likelihoods,
                                       self.Y_list,
                                       self.F_list,
                                       self.Fvar_list):
                rslts.append(session.run(lik.predict_density(f, fvar, y)))
            self.assertTrue(np.allclose(switched_rslt, np.concatenate(rslts)[self.Y_perm, :]))

    def test_variational_expectations(self):
        # switchedlikelihood
        with self.test_context() as session:
            self.switched_likelihood.compile()
            switched_rslt = session.run(
                self.switched_likelihood.variational_expectations(
                    self.F_sw, self.Fvar_sw, self.Y_sw))
            rslts = []
            for lik, y, f, fvar in zip(self.likelihoods,
                                       self.Y_list,
                                       self.F_list,
                                       self.Fvar_list):
                rslts.append(session.run(lik.variational_expectations(f, fvar, y)))
            self.assertTrue(np.allclose(switched_rslt, np.concatenate(rslts)[self.Y_perm, :]))


if __name__ == "__main__":
    tf.test.main()