def test_weight_matrix_k_8(self):
problem = ToyProblem()
model = KnnModel(problem, k=8)
nn_weights = scipy.io.loadmat("tests/matlab_variables/weights_8nn.mat")
nn_weights = nn_weights['weights']
#check nnz equal
assert model.weight_matrix.nnz == nn_weights.nnz
# remove edge points, messes w/ nearest neighbor comparison
# first get indices of edge points
edge_indices_x_max = np.where(problem.points[:, 0] >= 49, 0, 1)
edge_indices_x_min = np.where(problem.points[:, 0] <= 2, 0, 1)
edge_indices_x = np.multiply(edge_indices_x_min, edge_indices_x_max)
edge_indices_y_max = np.where(problem.points[:, 1] >= 49, 0, 1)
edge_indices_y_min = np.where(problem.points[:, 1] <= 2, 0, 1)
edge_indices_y = np.multiply(edge_indices_y_min, edge_indices_y_max)
edge_indices = np.multiply(edge_indices_x, edge_indices_y)
# next, remove entries from both nearest neighbors
nn_weights = nn_weights.toarray()
model.weight_matrix = model.weight_matrix.toarray()
nn_weights = np.multiply(nn_weights, edge_indices[:, None])
model.weight_matrix = np.multiply(model.weight_matrix,
edge_indices[:, None])
assert np.allclose(nn_weights, model.weight_matrix)
def test_weight_matrix_k_4_jitter(self):
problem = ToyProblem(jitter=True)
model = KnnModel(problem, k=4)
nn_weights = scipy.io.loadmat(
"tests/matlab_variables/weights_4nn_jitter.mat")
nn_weights = nn_weights['weights']
#check nnz equal
assert model.weight_matrix.nnz == nn_weights.nnz
nn_weights = nn_weights.toarray()
model.weight_matrix = model.weight_matrix.toarray()
#differences = np.abs(nn_weights-model.weight_matrix)
differences = nn_weights - model.weight_matrix
print(differences[1165, 1266])
print(differences[1266, 1165])
print(model.weight_matrix[1165, 1266])
print(np.amax(differences))
print(np.argmax(differences[1165]))
assert np.allclose(nn_weights, model.weight_matrix, atol=1e-02)
def test_nearest_neighbors_k_48(self):
problem = ToyProblem()
model = KnnModel(problem, k=48)
nn_values = scipy.io.loadmat(
"tests/matlab_variables/nearest_neighbors_48nn.mat")
nn_values = nn_values['nearest_neighbors']
nn_values = nn_values.T - 1
# remove edge points, messes w/ nearest neighbor comparison
# first get indices of edge points
edge_indices_x_max = np.where(problem.points[:, 0] >= 47, 0, 1)
edge_indices_x_min = np.where(problem.points[:, 0] <= 4, 0, 1)
edge_indices_x = np.multiply(edge_indices_x_min, edge_indices_x_max)
edge_indices_y_max = np.where(problem.points[:, 1] >= 47, 0, 1)
edge_indices_y_min = np.where(problem.points[:, 1] <= 4, 0, 1)
edge_indices_y = np.multiply(edge_indices_y_min, edge_indices_y_max)
edge_indices = np.multiply(edge_indices_x, edge_indices_y)
# next, remove entries from both nearest neighbors
nn_values = np.multiply(nn_values, edge_indices[:, None])
model.ind = np.multiply(model.ind, edge_indices[:, None])
assert np.all(np.sort(nn_values, axis=1) == np.sort(model.ind, axis=1))
def test_ENS_4nn_single_iteration(self):
budget = 99
problem = ToyProblem(jitter=True)
model = KnnModel(problem, k=4)
currentData = Data()
weight_matrix_matlab = scipy.io.loadmat(
"tests/matlab_variables/weights_4nn_jitter.mat")
weight_matrix_matlab = weight_matrix_matlab['weights']
nearest_neighbors_matlab = scipy.io.loadmat(
"tests/matlab_variables/nearest_neighbors_4nn_jitter.mat")
nearest_neighbors_matlab = nearest_neighbors_matlab[
'nearest_neighbors'] - 1
model.weight_matrix = weight_matrix_matlab
model.ind = nearest_neighbors_matlab.T
expected_scores = scipy.io.loadmat(
"tests/matlab_variables/ens_utilities_4nn.mat")
expected_scores = expected_scores['utilities']
utility = ENS()
selector = UnlabelSelector()
policy = ArgMaxPolicy(problem, model, utility)
np.random.seed(3)
positive_indices = [
i for i, x in enumerate(problem.labels_deterministic) if x > 0
]
firstObsIndex = positive_indices[0]
currentData = Data()
firstPointValue = problem.oracle_function(firstObsIndex)
#print("first point value:",self.oracle_function(firstObsIndex))
currentData.new_observation(firstObsIndex, firstPointValue)
#test_indices = np.array([444, 588, 1692, 1909, 2203, 2208, 2268])
test_indices = selector.filter(currentData, problem.points, model,
policy, problem, budget)
expected_test_indices = scipy.io.loadmat(
"tests/matlab_variables/ens_test_indices_4nn.mat")
expected_test_indices = expected_test_indices['test_ind'] - 1
expected_test_indices = np.sort(expected_test_indices, axis=0)
#compare test_indices
for index, expected_index in zip(test_indices, expected_test_indices):
assert index == expected_index
scores = utility.get_scores(model, currentData, test_indices, budget,
problem.points)
print(problem.points)
for score, expected in zip(scores, expected_scores):
assert score == pytest.approx(expected)
def __init__(self, problem, model=None, utility=None):
if not model:
model = KnnModel(problem)
if not utility:
utility = OneStep()
self.model = model
self.utility = utility
def __init__(self, problem, model=None, utility=None, do_pruning = True):
if not model:
model = KnnModel(problem)
if not utility:
utility = ENS()
self.model = model
self.utility = utility
self.do_pruning = do_pruning
def test_nearest_neighbors_k_4_jitter(self):
problem = ToyProblem(jitter=True)
model = KnnModel(problem, k=4)
nn_values = scipy.io.loadmat(
"tests/matlab_variables/nearest_neighbors_4nn_jitter.mat")
nn_values = nn_values['nearest_neighbors']
nn_values = nn_values.T - 1
assert np.all(np.sort(nn_values, axis=1) == np.sort(model.ind, axis=1))
def test_two_step_48nn(self):
budget = 100
problem = ToyProblem()
model = KnnModel(problem, k=48)
currentData = Data()
#two_step_scores = scipy.io.loadmat("tests/matlab_variables/two_step_scores48nn.mat")
#nn_weights = two_step_scores['expected_utilities']
utility = TwoStep()
selector = TwoStepPruningSelector()
policy = ArgMaxPolicy(problem, model, utility)
np.random.seed(3)
positive_indices = [
i for i, x in enumerate(problem.labels_deterministic) if x > 0
]
#firstObsIndex = np.random.choice(positive_indices)
is24 = np.where(positive_indices == 24)
firstObsIndex = positive_indices[0]
currentData = Data()
print("K-nearest neighbors indices of first point:",
model.ind[firstObsIndex] + 1)
print("selected point is index:", firstObsIndex)
firstPointValue = problem.oracle_function(firstObsIndex)
#print("first point value:",self.oracle_function(firstObsIndex))
currentData.new_observation(firstObsIndex, firstPointValue)
#test_indices = np.array([444, 588, 1692, 1909, 2203, 2208, 2268])
test_indices = selector.filter(currentData, problem.points, model,
policy, problem, budget)
scores = utility.get_scores(model, currentData, test_indices, budget,
problem.points)
expected_scores = np.array([
2.11514795905353, 2.11514795905353, 2.11514795905353,
2.11514795905353
])
for score, expected in zip(scores, expected_scores):
assert score == pytest.approx(expected)
def test_ENS_4nn_every_iteration_with_pruning(self):
budget = 99
problem = ToyProblem(jitter=True)
model = KnnModel(problem, k=4)
currentData = Data()
weight_matrix_matlab = scipy.io.loadmat(
"tests/matlab_variables/weights_4nn_jitter.mat")
weight_matrix_matlab = weight_matrix_matlab['weights']
nearest_neighbors_matlab = scipy.io.loadmat(
"tests/matlab_variables/nearest_neighbors_4nn_jitter.mat")
nearest_neighbors_matlab = nearest_neighbors_matlab[
'nearest_neighbors'] - 1
model.weight_matrix = weight_matrix_matlab
model.ind = nearest_neighbors_matlab.T
#declare 2 instances of selectors, ENS_no_pruning and ENS_pruning
utility = ENS()
selector = UnlabelSelector()
policy = ENSPolicy(problem, model, utility)
np.random.seed(3)
positive_indices = [
i for i, x in enumerate(problem.labels_deterministic) if x > 0
]
firstObsIndex = positive_indices[0]
currentData = Data()
firstPointValue = problem.oracle_function(firstObsIndex)
#print("first point value:",self.oracle_function(firstObsIndex))
currentData.new_observation(firstObsIndex, firstPointValue)
#test_indices = np.array([444, 588, 1692, 1909, 2203, 2208, 2268])
while budget > 0:
test_indices = selector.filter(currentData, problem.points, model,
policy, problem, budget)
budget_string = 'budget' + str(budget)
probabilities = policy.model.predict(currentData, test_indices)
argsort_ind = (-probabilities).argsort(axis=0)
probabilities = probabilities[argsort_ind[:, 0]]
test_indices = test_indices[argsort_ind[:, 0]]
#indices_argsorter = np.argsort(test_indices)
scores = utility.get_scores(model, currentData, test_indices,
budget, problem.points, probabilities)
#scores = utility.get_scores(model, currentData, this_iter_expected_test_indices,budget,problem.points)
max_index = np.argmax(scores)
this_iter_expected_scores = expected_scores[budget_string][0][0]
#print(this_iter_expected_scores)
#np.savetxt('bound.txt', this_iter_expected_scores, fmt='%10.5f', delimiter=' ')
#np.savetxt('bound2.txt', scores, fmt='%10.5f', delimiter=' ')
for score, expected in zip(scores, this_iter_expected_scores):
assert score == pytest.approx(expected, abs=1e-13)
chosen_x_index = test_indices[max_index]
#assert chosen_x_index==expected_selected_indices[100-budget]
this_expected_selected_index = expected_selected_indices[
budget_string]
if chosen_x_index != this_expected_selected_index - 1:
#import pdb; pdb.set_trace()
warnings.warn(
UserWarning(
"chosen index doesnt match up, however expected scores may match. replaced chosen index"
))
chosen_x_index = this_expected_selected_index[0][0][0][0] - 1
y = problem.oracle_function(chosen_x_index)
currentData.new_observation(chosen_x_index, y)
budget -= 1
if budget == 97:
import pdb
pdb.set_trace()
def test_one_step_4nn_every_iteration(self):
budget = 99
problem = ToyProblem(jitter=True)
model = KnnModel(problem, k=4)
currentData = Data()
weight_matrix_matlab = scipy.io.loadmat(
"tests/matlab_variables/weights_4nn_jitter.mat")
weight_matrix_matlab = weight_matrix_matlab['weights']
nearest_neighbors_matlab = scipy.io.loadmat(
"tests/matlab_variables/nearest_neighbors_4nn_jitter.mat")
nearest_neighbors_matlab = nearest_neighbors_matlab[
'nearest_neighbors'] - 1
model.weight_matrix = weight_matrix_matlab
model.ind = nearest_neighbors_matlab.T
expected_scores = scipy.io.loadmat(
"tests/matlab_variables/one_step_utilities_every_iter_4nn.mat")
expected_scores = expected_scores['test_policies_utilities']
expected_test_indices = scipy.io.loadmat(
"tests/matlab_variables/one_step_test_indices_every_iter_4nn.mat")
expected_test_indices = expected_test_indices[
'test_policies_utilities']
#expected_selected_indices = scipy.io.loadmat("tests/matlab_variables/expected_selected_indices_every_iter_4nn.mat")
#expected_selected_indices = expected_selected_indices['train_and_selected_ind']-1
utility = OneStep()
selector = UnlabelSelector()
policy = ArgMaxPolicy(problem, model, utility)
np.random.seed(3)
positive_indices = [
i for i, x in enumerate(problem.labels_deterministic) if x > 0
]
firstObsIndex = positive_indices[0]
currentData = Data()
firstPointValue = problem.oracle_function(firstObsIndex)
#print("first point value:",self.oracle_function(firstObsIndex))
currentData.new_observation(firstObsIndex, firstPointValue)
#test_indices = np.array([444, 588, 1692, 1909, 2203, 2208, 2268])
while budget > 0:
test_indices = selector.filter(currentData, problem.points, model,
policy, problem, budget)
budget_string = 'budget' + str(budget + 1)
#expected_test_indices['budget98']
this_iter_expected_test_indices = expected_test_indices[
budget_string] - 1
this_iter_expected_test_indices = this_iter_expected_test_indices[
0][0].reshape(-1, )
#print(this_iter_expected_test_indices[0][0])
#compare test_indices
for index, expected_index in zip(test_indices,
this_iter_expected_test_indices):
assert index == expected_index
#print(test_indices.shape)
#print(this_iter_expected_test_indices.reshape(-1,).shape)
scores = utility.get_scores(model, currentData,
this_iter_expected_test_indices,
budget, problem.points)
max_index = np.argmax(scores)
this_iter_expected_scores = expected_scores[budget_string][0][0]
#print(this_iter_expected_scores)
for score, expected in zip(scores, this_iter_expected_scores):
assert score == pytest.approx(expected, abs=1e-13)
chosen_x_index = this_iter_expected_test_indices[max_index]
#assert chosen_x_index==expected_selected_indices[100-budget]
#if chosen_x_index!=expected_selected_indices[100-budget]:
# warnings.warn(UserWarning("chosen index doesnt match up, however expected scores may match. replaced chosen index"))
# chosen_x_index=expected_selected_indices[100-budget][0]
y = problem.oracle_function(chosen_x_index)
currentData.new_observation(chosen_x_index, y)
budget -= 1