# ==============================================================================
# scclust for R -- R wrapper for the scclust library
# https://github.com/fsavje/scclust-R
#
# Copyright (C) 2016-2017  Fredrik Savje -- http://fredriksavje.com
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see http://www.gnu.org/licenses/
# ==============================================================================

library(scclust)
context("utilities.R")


# ==============================================================================
# check_clustering
# ==============================================================================

cl1 <- scclust(c(1, 1, 2, 3, 2, 3, 3, 1, 2, 2))
cl2 <- scclust(c(1, 1, 2, 3, 2, 3, 3, 1, 2, 2), 1)
cl3 <- scclust(c(1, 1, 2, 3, 2, 3, 3, 1, 2, 2), ids = letters[1:10])
cl4 <- scclust(c(1, 1, 2, 3, 2, 3, 3, 1, 2, 2), 1, ids = letters[1:10])
cl5 <- scclust(c(1, 1, 2, NA, 2, 3, 3, 1, NA, 2))
dp_types <- factor(c("x", "y", "y", "z", "z", "x", "y", "z", "x", "x"))

test_that("`check_clustering` returns correct output", {
  expect_equal(check_clustering(cl1, 2), TRUE)
  expect_equal(check_clustering(cl1, 3), TRUE)
  expect_equal(check_clustering(cl1, 4), FALSE)
  expect_equal(check_clustering(cl2, 2), TRUE)
  expect_equal(check_clustering(cl2, 3), TRUE)
  expect_equal(check_clustering(cl2, 4), FALSE)
  expect_equal(check_clustering(cl3, 2), TRUE)
  expect_equal(check_clustering(cl3, 3), TRUE)
  expect_equal(check_clustering(cl3, 4), FALSE)
  expect_equal(check_clustering(cl4, 2), TRUE)
  expect_equal(check_clustering(cl4, 3), TRUE)
  expect_equal(check_clustering(cl4, 4), FALSE)

  expect_equal(check_clustering(cl1, NULL, dp_types, c("x" = 1, "y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl1, 3, dp_types, c("x" = 1, "y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl1, NULL, dp_types, c("x" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl1, 3, dp_types, c("y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl1, 4, dp_types, c("x" = 1, "y" = 1, "z" = 1)), FALSE)
  expect_equal(check_clustering(cl1, NULL, dp_types, c("x" = 3, "y" = 1, "z" = 1)), FALSE)

  expect_equal(check_clustering(cl2, NULL, dp_types, c("x" = 1, "y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl2, 3, dp_types, c("x" = 1, "y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl2, NULL, dp_types, c("x" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl2, 3, dp_types, c("y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl2, 4, dp_types, c("x" = 1, "y" = 1, "z" = 1)), FALSE)
  expect_equal(check_clustering(cl2, NULL, dp_types, c("x" = 3, "y" = 1, "z" = 1)), FALSE)

  expect_equal(check_clustering(cl3, NULL, dp_types, c("x" = 1, "y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl3, 3, dp_types, c("x" = 1, "y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl3, NULL, dp_types, c("x" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl3, 3, dp_types, c("y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl3, 4, dp_types, c("x" = 1, "y" = 1, "z" = 1)), FALSE)
  expect_equal(check_clustering(cl3, NULL, dp_types, c("x" = 3, "y" = 1, "z" = 1)), FALSE)

  expect_equal(check_clustering(cl4, NULL, dp_types, c("x" = 1, "y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl4, 3, dp_types, c("x" = 1, "y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl4, NULL, dp_types, c("x" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl4, 3, dp_types, c("y" = 1, "z" = 1)), TRUE)
  expect_equal(check_clustering(cl4, 4, dp_types, c("x" = 1, "y" = 1, "z" = 1)), FALSE)
  expect_equal(check_clustering(cl4, NULL, dp_types, c("x" = 3, "y" = 1, "z" = 1)), FALSE)

  expect_equal(check_clustering(cl5, 2), TRUE)
  expect_equal(check_clustering(cl5, 2, primary_data_points = c(1, 2, 3, 5, 6, 7, 8, 10)), TRUE)
})


# ==============================================================================
# get_clustering_stats
# ==============================================================================

cl1 <- scclust(c(1, 1, 2, 3, 2, 3, 3, 1, 2, 2))
cl2 <- scclust(c(1, 1, 2, 3, 2, 3, 3, 1, 2, 2), 1)
cl3 <- scclust(c(1, 1, 2, 3, 2, 3, 3, 1, 2, 2), ids = letters[1:10])
cl4 <- scclust(c(1, 1, 2, 3, 2, 3, 3, 1, 2, 2), 1, ids = letters[1:10])
cl5 <- make_scclust(c(1L, 1L, 3L, 4L, 3L, 4L, 4L, 1L, 3L, 3L),
                    5L, NULL)
cl6 <- make_scclust(c(NA, NA, 3L, 4L, 3L, 4L, 4L, NA, 3L, 3L),
                    5L, NULL)
cl7 <- make_scclust(c(1L, 1L, 3L, 4L, 3L, 4L, 4L, 1L, 3L, 3L),
                    5L, ids = letters[1:10])
cl8 <- make_scclust(c(NA, NA, 3L, 4L, 3L, 4L, 4L, NA, 3L, 3L),
                    5L, ids = letters[1:10])
dp_data <- matrix(c(1.43247031975424, -0.0743079629128504, 0.664288699457021, 0.041257931075698, -0.745804833010271, -0.603411650921577, -0.705441137672159, 1.1906572821574, 1.25508544074284, -0.732099707199829, 1.18248873752832, 0.0593032947441447, 0.547530248329181, -1.30988291210263, -1.92018121606691, 0.212216066820278, -1.32505024286282, -1.27119614395428, -0.207545316189576, -1.51865610892823),
                  ncol = 2)

all_assigned_stats <- structure(list(
  num_data_points = 10L,
  num_assigned = 10L,
  num_clusters = 3L,
  min_cluster_size = 3L,
  max_cluster_size = 4L,
  avg_cluster_size = 10/3,
  sum_dists = 21.8322314142849,
  min_dist = 0.401758935353289,
  max_dist = 2.84217586398484,
  avg_min_dist = 0.994822547780713,
  avg_max_dist = 2.32024663290364,
  avg_dist_weighted = 1.79285753468519,
  avg_dist_unweighted = 1.77519414590393
), class = c("clustering_stats"))

some_assigned_stats <- structure(list(
  num_data_points = 10L,
  num_assigned = 7L,
  num_clusters = 2L,
  min_cluster_size = 3,
  max_cluster_size = 4,
  avg_cluster_size = 3.5,
  sum_dists = 15.6514622631497,
  min_dist = 0.401758935353289,
  max_dist = 2.84217586398484,
  avg_min_dist = 0.57430601573908,
  avg_max_dist = 2.24758417693106,
  avg_dist_weighted = 1.67825802795953,
  avg_dist_unweighted = 1.63266302700004
), class = c("clustering_stats"))

all_assigned_emptycl_stats <- structure(list(
  num_data_points = 10L,
  num_assigned = 10L,
  num_clusters = 3L,
  min_cluster_size = 3L,
  max_cluster_size = 4L,
  avg_cluster_size = 10/3,
  sum_dists = 21.8322314142849,
  min_dist = 0.401758935353289,
  max_dist = 2.84217586398484,
  avg_min_dist = 0.994822547780713,
  avg_max_dist = 2.32024663290364,
  avg_dist_weighted = 1.79285753468519,
  avg_dist_unweighted = 1.77519414590393
), class = c("clustering_stats"))

some_assigned_emptycl_stats <- structure(list(
  num_data_points = 10L,
  num_assigned = 7L,
  num_clusters = 2L,
  min_cluster_size = 3,
  max_cluster_size = 4,
  avg_cluster_size = 3.5,
  sum_dists = 15.6514622631497,
  min_dist = 0.401758935353289,
  max_dist = 2.84217586398484,
  avg_min_dist = 0.57430601573908,
  avg_max_dist = 2.24758417693106,
  avg_dist_weighted = 1.67825802795953,
  avg_dist_unweighted = 1.63266302700004
), class = c("clustering_stats"))

test_that("`get_clustering_stats` returns correct output", {
  expect_equal(get_clustering_stats(distances::distances(dp_data), cl1), all_assigned_stats)
  expect_equal(get_clustering_stats(distances::distances(dp_data), cl2), some_assigned_stats)
  expect_equal(get_clustering_stats(distances::distances(dp_data), cl3), all_assigned_stats)
  expect_equal(get_clustering_stats(distances::distances(dp_data), cl4), some_assigned_stats)
  expect_equal(get_clustering_stats(distances::distances(dp_data), cl5), all_assigned_emptycl_stats)
  expect_equal(get_clustering_stats(distances::distances(dp_data), cl6), some_assigned_emptycl_stats)
  expect_equal(get_clustering_stats(distances::distances(dp_data), cl7), all_assigned_emptycl_stats)
  expect_equal(get_clustering_stats(distances::distances(dp_data), cl8), some_assigned_emptycl_stats)
})

test_that("`print.clustering_stats` prints correctly", {
  expect_output(print(all_assigned_stats), "num_data_points     10.0000000", fixed = TRUE)
  expect_output(print(all_assigned_stats), "num_assigned        10.0000000", fixed = TRUE)
  expect_output(print(all_assigned_stats), "num_clusters         3.0000000", fixed = TRUE)
  expect_output(print(all_assigned_stats), "min_cluster_size     3.0000000", fixed = TRUE)
  expect_output(print(all_assigned_stats), "max_cluster_size     4.0000000", fixed = TRUE)
  expect_output(print(all_assigned_stats), "avg_cluster_size     3.3333333", fixed = TRUE)
  expect_output(print(all_assigned_stats), "sum_dists           21.8322314", fixed = TRUE)
  expect_output(print(all_assigned_stats), "min_dist             0.4017589", fixed = TRUE)
  expect_output(print(all_assigned_stats), "max_dist             2.8421759", fixed = TRUE)
  expect_output(print(all_assigned_stats), "avg_min_dist         0.9948225", fixed = TRUE)
  expect_output(print(all_assigned_stats), "avg_max_dist         2.3202466", fixed = TRUE)
  expect_output(print(all_assigned_stats), "avg_dist_weighted    1.7928575", fixed = TRUE)
  expect_output(print(all_assigned_stats), "avg_dist_unweighted  1.7751941", fixed = TRUE)
})