def event_context_pairs(
sequence: TYPE_TENSOR,
window_size: int,
event_size: int,
) -> TYPE_TENSOR:
"""Create (event, context) pairs from a sequence
For a sequence (a, b, c, d, e) of window_size 3 and event_size 1.
(event, context) = [
event=b, context=(a, c),
event=c, context=(b, d),
event=d, context=(e, e)
]
Args:
sequence: 1 dimensional tensor
window_size: size of the context including the event
event_size: size of the event which can be > 1
Returns: (event, context) pairs of shape:(N, E+C) where N=num_windows.
[
[b, a, c],
[c, b, d],
[d, e, e]
] where the first column(s) is event and the rest are context
"""
length: TYPE_INT = TYPE_INT(len(sequence))
stride: TYPE_INT = TYPE_INT((window_size - event_size) / 2)
assert \
super(Function, Function).is_tensor(sequence) and \
super(Function, Function).tensor_rank(sequence) == 1 and \
length >= window_size > event_size > 0, \
f"Expected a sequence of length >= {window_size} but {sequence}"
assert (window_size -
event_size) % 2 == 0, "Need stride as integer > 0"
# --------------------------------------------------------------------------------
# The result is a matrix in which windows are stacked up.
# The result shape is (length-window_size+1, window_size).
# --------------------------------------------------------------------------------
num_windows = length - window_size + 1
context_windows = np.array([
sequence[slice(index, index + window_size)]
for index in range(0, num_windows)
],
dtype=TYPE_INT)
assert context_windows.shape == (num_windows, window_size)
event_context_paris = np.c_[context_windows[ # Labels
::, slice(stride,
(stride + event_size))], context_windows[ # Left context
::, slice(0, stride)],
context_windows[ # Right context
::,
slice((event_size + stride), None)]]
assert event_context_paris.shape == (num_windows, window_size)
return event_context_paris
def test_020_event_context_instance_properties(caplog):
"""
Objective:
Verify the layer class validates the parameters have been initialized before accessed.
Expected:
Initialization detects the access to the non-initialized parameters and fails.
"""
caplog.set_level(logging.DEBUG)
name = "test_020_event_context_instance_properties"
msg = "Accessing uninitialized property of the layer must fail."
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
stride: TYPE_INT = TYPE_INT(np.random.randint(1, 100))
event_size: TYPE_INT = TYPE_INT(np.random.randint(1, 100))
window_size: TYPE_INT = 2 * stride + event_size
name = random_string(np.random.randint(1, 10))
event_context = _must_succeed(name=name,
num_nodes=TYPE_INT(1),
window_size=window_size,
event_size=event_size,
msg=msg)
# --------------------------------------------------------------------------------
# To pass
# --------------------------------------------------------------------------------
try:
if not event_context.name == name:
raise RuntimeError("event_context.name == name should be true")
except AssertionError as e:
raise RuntimeError(
"Access to name should be allowed as already initialized."
) from e
try:
if not isinstance(event_context.logger, logging.Logger):
raise RuntimeError(
"isinstance(event_context.logger, logging.Logger) should be true"
)
except AssertionError as e:
raise RuntimeError(
"Access to logger should be allowed as already initialized."
) from e
assert event_context.window_size == window_size
assert event_context.event_size == event_size
profiler.disable()
profiler.print_stats(sort="cumtime")
def _instantiate(
name: str = __name__,
num_nodes: TYPE_INT = 1,
target_size: TYPE_INT = 1,
context_size: TYPE_INT = 4,
negative_sample_size: TYPE_INT = 10,
event_vector_size: TYPE_INT = 20,
dictionary: EventIndexing = None,
W: TYPE_TENSOR = None,
log_level: int = logging.ERROR
) -> Tuple[Embedding, EventContext]:
event_context: EventContext = _instantiate_event_context(
name=name,
num_nodes=TYPE_INT(1),
window_size=(context_size+target_size),
event_size=target_size
)
embedding: Embedding = Embedding(
name=name,
num_nodes=num_nodes,
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=log_level
)
return embedding, event_context
def test_020_embedding_function_multi_lines(caplog):
"""
Objective:
Verify the EventIndexing function can handle multi line sentences
Expected:
"""
caplog.set_level(logging.DEBUG)
name = "test_020_embedding_function_multi_lines"
sentences = """
Verify the EventIndexing function can handle multi line sentences
the asbestos fiber <unk> is unusually <unk> once it enters the <unk>
with even brief exposures to it causing symptoms that show up decades later researchers said
"""
dictionary: EventIndexing = _instantiate_event_indexing()
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
# First validate the correct configuration, then change parameter one by one.
target_size = TYPE_INT(np.random.randint(1, 3))
context_size = TYPE_INT(2 * np.random.randint(1, 5))
negative_sample_size = TYPE_INT(np.random.randint(1, 5))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 20))
W: TYPE_TENSOR = np.random.randn(dictionary.vocabulary_size, event_vector_size)
embedding, event_context = _must_succeed(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
msg="must succeed"
)
sequences = dictionary.function(sentences)
target_context_pairs = event_context.function(sequences)
embedding.function(target_context_pairs)
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_010_standardize_sd_is_zero_eps():
"""
Objective:
Verify the standardize() function when SD is zero.
Expected:
standardized == (X-mean)/sqrt(eps)
"""
name = "test_010_standardize"
keepdims = True
u = TYPE_FLOAT(1e-6)
eps = TYPE_FLOAT(1e-8)
for _ in range(NUM_MAX_TEST_TIMES):
N: int = np.random.randint(1, NUM_MAX_BATCH_SIZE)
M: int = np.random.randint(2, NUM_MAX_NODES)
row = np.random.uniform(-MAX_ACTIVATION_VALUE, MAX_ACTIVATION_VALUE,
M).astype(TYPE_FLOAT)
X = np.ones(shape=(N, M), dtype=TYPE_FLOAT) * row
Logger.debug("%s: X \n%s\n", name, X)
# Constraint: standardize(X) == (X - np.mean(A)) / np.std(X)
ddof = TYPE_INT(1) if N > 1 else TYPE_INT(0)
sd = np.std(X, axis=0, keepdims=keepdims, ddof=ddof)
assert np.allclose(sd, TYPE_FLOAT(0), atol=u, rtol=0)
# Expected
mean = np.mean(X, axis=0, dtype=TYPE_FLOAT)
E = (X - mean) / np.sqrt(eps, dtype=TYPE_FLOAT)
# Actual
A, __mean, __sd, _ = standardize(X, keepdims=keepdims, eps=eps)
# Constraint. mean/sd should be same
assert np.allclose(mean, __mean, atol=u, rtol=TYPE_FLOAT(0))
assert np.allclose(__sd, np.sqrt(eps), atol=u, rtol=TYPE_FLOAT(0))
assert np.all(np.abs(E-A) < u), \
f"X\n{X}\nstandardized\n{E}\nneeds\n{A}\n"
def test_020_event_context_function_event_size_2(caplog):
"""
Objective:
Verify the layer function with event_size = 2
Expected:
function generates expected event_context pairs
"""
caplog.set_level(logging.DEBUG)
name = "test_020_event_context_instance_properties"
msg = "Accessing uninitialized property of the layer must fail."
profiler = cProfile.Profile()
profiler.enable()
stride: TYPE_INT = 2
event_size: TYPE_INT = 2
window_size: TYPE_INT = 2 * stride + event_size
num_nodes = TYPE_INT(1)
event_context = _must_succeed(name=name,
num_nodes=num_nodes,
window_size=window_size,
event_size=event_size,
msg=msg)
# 0, 1, 2, 3, 4, 5
# expected event_context pair: [
# [2, 3, 0, 1, 4, 5],
X1 = np.arange(6)
expected1 = np.array([[2, 3, 0, 1, 4, 5]])
Y1 = event_context.function(X1)
assert \
np.array_equal(Y1, expected1), \
"Expected\n%s\nActual\n%s\n" % (expected1, Y1)
# 0, 1, 2, 3, 4, 5, 6
# expected event_context pair: [
# [2, 3, 0, 1, 4, 5],
X2 = np.arange(7)
expected2 = np.array([[2, 3, 0, 1, 4, 5], [3, 4, 1, 2, 5, 6]])
Y2 = event_context.function(X2)
assert np.array_equal(Y2, expected2), \
"Expected\n%s\nActual\n%s\n" % (expected2, Y2)
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_std_method_function_to_succeed():
"""
Objective:
Verify the _layer class instance function method
Expected:
Layer method calculate expected values.
"""
def objective(x: TYPE_TENSOR):
"""Dummy objective function"""
return np.sum(x, dtype=TYPE_FLOAT)
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
name = random_string(np.random.randint(1, 10))
numexpr_enabled = bool(np.random.randint(0, 2))
numba_enabled = bool(np.random.randint(0, 2))
# For which works on statistics on per-feature basis,
# no sense if M = 1 or N = 1.
N: int = np.random.randint(1, NUM_MAX_BATCH_SIZE)
M: int = np.random.randint(2, NUM_MAX_NODES)
X = np.random.randn(N, M).astype(TYPE_FLOAT)
momentum = TYPE_FLOAT(np.random.uniform(0.7, 0.99))
eps = TYPE_FLOAT(np.random.uniform(1e-12, 1e-10)) \
if np.random.uniform() < 0.5 else TYPE_FLOAT(0)
_layer: Standardization = \
_instance(name=name, num_nodes=M, momentum=momentum, eps=eps)
_layer.objective = objective
# ********************************************************************************
# Constraint: total_rows_processed = times_of_invocations * N
# ********************************************************************************
assert _layer.total_rows_processed == 0
ru = _layer.RU
rsd = _layer.RSD
_layer.function(
X,
numexpr_enabled=numexpr_enabled,
)
_validate_layer_values(_layer, X, eps=eps)
_validate_layer_running_statistics(
_layer=_layer, previous_ru=ru, previous_rsd=rsd, X=X, eps=eps
)
# ********************************************************************************
# Constraint:
# _layer.N provides the latest X.shape[0]
# X related arrays should have its storage allocated and has the X.shape.
# * dX
# * dXmd01
# * dXmd02
# ********************************************************************************
assert _layer.N == X.shape[0]
assert \
_layer.dX.dtype == TYPE_FLOAT and \
_layer.dX.shape == (N, M)
assert \
_layer.dXmd01.dtype == TYPE_FLOAT and \
_layer.dXmd01.shape == (N, M)
assert \
_layer.dXmd02.dtype == TYPE_FLOAT and \
_layer.dXmd02.shape == (N, M)
assert _layer.total_rows_processed == N
# ********************************************************************************
# Constraint: total_rows_processed = times_of_invocations * N
# ********************************************************************************
for i in range(np.random.randint(1, 100)):
_layer.function(
X,
numexpr_enabled=numexpr_enabled,
)
assert _layer.total_rows_processed == TYPE_INT(N * (i + 2))
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_010_base_instantiation():
"""Test case for layer base class
"""
# --------------------------------------------------------------------------------
# name, num_nodes, log_level _init_ properties.
# Logging debug outputs.
# X setter/getter
# T setter/getter
# objective function setter/getter
# function(x) repeats x.
# gradient(dL/dY) repeats dL/dY,
# gradient_numerical() returns 1
# --------------------------------------------------------------------------------
def objective(X: np.ndarray) -> Union[TYPE_FLOAT, np.ndarray]:
"""Dummy objective function"""
return np.sum(X)
N: int = np.random.randint(1, NUM_MAX_BATCH_SIZE)
M: int = np.random.randint(1, NUM_MAX_NODES)
name = "test_010_base"
_layer: Layer = Layer(name=name, num_nodes=M, log_level=logging.DEBUG)
# --------------------------------------------------------------------------------
# Properties
# --------------------------------------------------------------------------------
assert _layer.name == name
assert _layer.num_nodes == _layer.M == M
_layer._D = 1
assert _layer.D == 1
X = np.random.randn(N, M).astype(TYPE_FLOAT)
_layer.X = X
assert np.array_equal(_layer.X, X), \
"Expected:\n%s\nDiff\n%s\n" % (X, (_layer.X-X))
assert _layer.N == N
_layer._dX = X
assert np.array_equal(_layer.dX, X)
T = np.random.randint(0, M, N).astype(TYPE_LABEL)
_layer.T = T
assert np.array_equal(_layer.T, T)
_layer._Y = np.dot(X, X.T)
assert np.array_equal(_layer.Y, np.dot(X, X.T))
_layer._dY = np.array(0.9, dtype=TYPE_FLOAT)
assert _layer._dY == np.array(0.9, dtype=TYPE_FLOAT)
_layer.logger.debug("This is a pytest")
# --------------------------------------------------------------------------------
# Methods
# --------------------------------------------------------------------------------
try:
# pylint: disable=not-callable
_layer.function(TYPE_INT(1))
raise RuntimeError("Invoke layer.function(int(1)) must fail.")
except AssertionError:
pass
x = np.array(1.0, dtype=TYPE_FLOAT)
assert np.array_equal(_layer.function(x), x)
try:
Y = _layer.function(X)
assert Y.ndim > 0
_layer.gradient(int(1))
raise RuntimeError("Invoke layer.gradient(int(1)) must fail.")
except AssertionError:
pass
Y = _layer.function(X)
assert np.array_equal(_layer.gradient(Y), Y)
_layer.objective = objective
# pylint: disable=not-callable
assert np.array_equal(_layer.objective(X), objective(X))
def stride(self) -> TYPE_INT:
"""Length of preceding and succeeding context"""
return TYPE_INT((self.window_size - self.event_size) / 2)
def vocabulary_size(self) -> TYPE_INT:
"""Number of unique events in the vocabulary"""
return TYPE_INT(len(self.vocabulary))
def test_word2vec():
USE_TEXT8 = False
USE_PTB = not USE_TEXT8
# --------------------------------------------------------------------------------
# text8 is one gigantic line having millions of words, which cannot fit in a vector.
# Need to split into N words per line.
# cat text8 | xargs -n $N > text8_$N
# --------------------------------------------------------------------------------
CORPUS_FILE = "text8_512" if USE_TEXT8 else "ptb"
CORPUS_URL = "https://data.deepai.org/text8.zip" \
if USE_TEXT8 else 'https://raw.githubusercontent.com/tomsercu/lstm/master/data/ptb.test.txt'
TARGET_SIZE = TYPE_INT(1) # Size of the target event (word)
CONTEXT_SIZE = TYPE_INT(
6) # Size of the context in which the target event occurs.
WINDOW_SIZE = TARGET_SIZE + CONTEXT_SIZE
SAMPLE_SIZE = TYPE_INT(6) # Size of the negative samples
VECTOR_SIZE = TYPE_INT(100) # Number of features in the event vector.
WEIGHT_SCHEME = "normal"
WEIGHT_PARAMS = {"std": 0.01}
LR = TYPE_FLOAT(20.0)
# For text8_512 where one line has 512 words, the number of sentences to take in is small.
# For ptb_train where one line may have only a few words, take in more lines to avoid a
# (event, context) pair where event is an invalid padding word.
# Because of padding, when there are only a few sentence provided,
# the 'event' in (event,context) can result in 0.
# [[562 58 117 0 0 0 0 0 0 0 0]]
NUM_SENTENCES = 1 if USE_TEXT8 else 10
STATE_FILE = \
"/home/oonisim/home/repository/git/oonisim/python_programs/nlp/models/" \
"word2vec_sgram_%s_E%s_C%s_S%s_W%s_%s_%s_V%s_LR%s_N%s.pkl" % (
CORPUS_FILE,
TARGET_SIZE,
CONTEXT_SIZE,
SAMPLE_SIZE,
WEIGHT_SCHEME,
"std",
WEIGHT_PARAMS["std"],
VECTOR_SIZE,
LR,
NUM_SENTENCES,
)
MAX_ITERATIONS = 30
# --------------------------------------------------------------------------------
# Corpus text
# --------------------------------------------------------------------------------
path_to_corpus = f"{str(Path.home())}/.keras/datasets/{CORPUS_FILE}"
if fileio.Function.is_file(path_to_corpus):
pass
else:
path_to_corpus = tf.keras.utils.get_file(fname=CORPUS_FILE,
origin=CORPUS_URL,
extract=True)
corpus = fileio.Function.read_file(path_to_corpus)
# --------------------------------------------------------------------------------
# Logistic Log Loss
# --------------------------------------------------------------------------------
loss = CrossEntropyLogLoss(
name="loss",
num_nodes=1, # Logistic log loss
log_loss_function=sigmoid_cross_entropy_log_loss)
# --------------------------------------------------------------------------------
# Event indexing
# --------------------------------------------------------------------------------
word_indexing = EventIndexing(name="word_indexing",
corpus=corpus,
min_sequence_length=WINDOW_SIZE)
del corpus
# --------------------------------------------------------------------------------
# Event Context
# --------------------------------------------------------------------------------
event_context = EventContext(name="ev",
window_size=WINDOW_SIZE,
event_size=TARGET_SIZE)
# --------------------------------------------------------------------------------
# Event Embedding
# --------------------------------------------------------------------------------
embedding: Embedding = Embedding(
name="embedding",
num_nodes=WINDOW_SIZE,
target_size=TARGET_SIZE,
context_size=CONTEXT_SIZE,
negative_sample_size=SAMPLE_SIZE,
event_vector_size=VECTOR_SIZE,
optimizer=SGD(lr=LR),
dictionary=word_indexing,
weight_initialization_scheme=WEIGHT_SCHEME,
weight_initialization_parameters=WEIGHT_PARAMS)
# --------------------------------------------------------------------------------
# Adapter between Embedding and Log Loss
# --------------------------------------------------------------------------------
adapter_function = embedding.adapt_function_to_logistic_log_loss(loss=loss)
adapter_gradient = embedding.adapt_gradient_to_logistic_log_loss()
adapter: Adapter = Adapter(
name="adapter",
num_nodes=TYPE_INT(1), # Number of output M=1
function=adapter_function,
gradient=adapter_gradient)
# --------------------------------------------------------------------------------
# Network
# --------------------------------------------------------------------------------
network = SequentialNetwork(
name="word2vec",
num_nodes=1,
inference_layers=[word_indexing, event_context, embedding, adapter],
objective_layers=[loss])
def sentences_generator(path_to_file, num_sentences):
stream = fileio.Function.file_line_stream(path_to_file)
try:
while True:
yield np.array(fileio.Function.take(num_sentences, stream))
finally:
stream.close()
# Restore the state if exists.
if fileio.Function.is_file(STATE_FILE):
state = embedding.load(STATE_FILE)
fmt = """Model loaded.
event_size %s
context_size: %s
event_vector_size: %s
"""
print(fmt % (state["target_size"], state["context_size"],
state["event_vector_size"]))
else:
print("State file does not exist. Saving the initial model %s." %
STATE_FILE)
embedding.save(STATE_FILE)
# Continue training
profiler = cProfile.Profile()
profiler.enable()
total_sentences = 0
epochs = 0
source = sentences_generator(path_to_file=path_to_corpus,
num_sentences=NUM_SENTENCES)
for i in range(MAX_ITERATIONS):
try:
sentences = next(source)
total_sentences += len(sentences)
start = time.time()
network.train(X=sentences, T=np.array([0]))
if i % 100 == 0:
print(f"Batch {i:05d} of {NUM_SENTENCES} sentences: "
f"Average Loss: {np.mean(network.history):10f} "
f"Duration {time.time() - start:3f}")
if i % 10000 == 0:
embedding.save(STATE_FILE)
pass
except fileio.Function.GenearatorHasNoMore as e:
source.close()
embedding.save(STATE_FILE)
# Next epoch
print(f"epoch {epochs} batches {i:05d} done")
epochs += 1
source = sentences_generator(path_to_file=path_to_corpus,
num_sentences=NUM_SENTENCES)
except Exception as e:
print("Unexpected error:", sys.exc_info()[0])
source.close()
raise e
embedding.save(STATE_FILE)
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_embedding_save_load(caplog):
"""
Objective:
Verify the save/load function of EventIndexing
Expected:
"""
caplog.set_level(logging.DEBUG)
name = "test_020_embedding_save_load"
sentences = """
Verify the gradient descent, especially np.ufunc.at, is working as expected
"""
dictionary: EventIndexing = _instantiate_event_indexing()
profiler = cProfile.Profile()
profiler.enable()
for _ in range(10):
# First validate the correct configuration, then change parameter one by one.
target_size = TYPE_INT(np.random.randint(1, 2))
context_size = TYPE_INT(2 * np.random.randint(1, 2))
negative_sample_size = TYPE_INT(np.random.randint(1, 2))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(2, 3))
W: TYPE_TENSOR = np.random.randn(dictionary.vocabulary_size, event_vector_size)
embedding, event_context = _must_succeed(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
msg="must succeed"
)
# --------------------------------------------------------------------------------
# Run methods and save the results to compare later
# --------------------------------------------------------------------------------
sequences = dictionary.function(sentences)
target_context_pairs = event_context.function(sequences)
Y1 = embedding.function(target_context_pairs)
# --------------------------------------------------------------------------------
# Save the layer state and invalidate the state variables
# --------------------------------------------------------------------------------
tester = testing.layer.Function(instance=embedding)
tester.test_save()
backup_W = copy.deepcopy(embedding.W)
embedding._W = np.empty(0)
# --------------------------------------------------------------------------------
# Confirm the layer does not function anymore
# --------------------------------------------------------------------------------
try:
embedding.function(target_context_pairs)
raise RuntimeError("Must fail with state deleted")
except Exception as e:
pass
# --------------------------------------------------------------------------------
# Restore the state and confirm the layer functions as expected.
# Because of random negative sampling, the result of context part differs every time.
# Hence only true label (target) part can be the same.
# --------------------------------------------------------------------------------
try:
# Constraint:
# Layer works after state reloaded
tester.test_load()
assert np.allclose(backup_W, embedding.W)
Y2 = embedding.function(target_context_pairs)
assert \
np.array_equal(Y1[::, 0], Y2[::,0]), \
"Expected Y\n%s\nActual\n%s\ndiff\n%s\n" \
% (Y1[::, 0], Y2[::,0], (Y1[::,0]-Y2[::,0]))
except Exception as e:
raise e
finally:
tester.clean()
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_adapt_embedding_loss_adapter_function_to_fail(caplog):
"""
Objective:
Verify the Adapter function invalidate the shape other than
- Y:(N, 1+SL)
- ys:(N,SL)
- ye:(N,1)
Expected:
Adapter.function(Y) to fail when Y shape is not (N, 1+SL)
"""
caplog.set_level(logging.DEBUG)
name = "test_020_adapt_embedding_logistic_loss_function_multi_lines"
sentences = """
Verify the EventIndexing function can handle multi line sentences
the asbestos fiber <unk> is unusually <unk> once it enters the <unk>
with even brief exposures to it causing symptoms that show up decades later researchers said
"""
dictionary: EventIndexing = _instantiate_event_indexing()
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
# First validate the correct configuration, then change parameter one by one.
E = target_size = TYPE_INT(np.random.randint(1, 3))
C = context_size = TYPE_INT(2 * np.random.randint(1, 5))
SL = negative_sample_size = TYPE_INT(np.random.randint(1, 5))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 20))
W: TYPE_TENSOR = np.random.randn(dictionary.vocabulary_size,
event_vector_size)
loss, adapter, embedding, event_context = _instantiate(
name=name,
num_nodes=TYPE_INT(1),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
)
sequences = dictionary.function(sentences)
target_context_pairs = event_context.function(sequences)
Y = embedding.function(target_context_pairs)
N, _ = embedding.tensor_shape(Y)
# ********************************************************************************
# Constraint: Y in shape:(N,M) where M > SL+1 must fail
# ********************************************************************************
shape = (N, 1 + SL + np.random.randint(1, 100))
msg = "Y shape %s which is not the expected shape %s must fail" % \
(shape, (N, SL+1))
dummy_Y = np.random.uniform(size=shape).astype(TYPE_FLOAT)
_function_must_fail(adapter=adapter, Y=dummy_Y, msg=msg)
# ********************************************************************************
# Constraint: Y in shape (N+,) must fail.
# Adapter function can accept (N,) but not (N+,)
# ********************************************************************************
shape = (N + np.random.randint(1, 100), )
msg = "Y shape %s which is not the expected shape %s must fail" % \
(shape, (N,))
dummy_Y = np.random.uniform(size=shape).astype(TYPE_FLOAT)
_function_must_fail(adapter=adapter, Y=dummy_Y, msg=msg)
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_embedding_gradient_vs_autodiff(caplog):
"""
Objective:
Verify the Embedding analytical gradient with TF autodiff implemented
in the gradient_numerical() method of the layer.
Expected:
Gradients [dWe, dWs, dWc] calculated in gradient() method matches with
those calculated in the gradient_numerical().
"""
caplog.set_level(logging.DEBUG)
name = "test_020_embedding_gradient_multi_lines"
dictionary: EventIndexing = _instantiate_event_indexing()
from function import text
from . test_020_embedding_sample_sentences import (
bbc_world_us_canada_56988381 as sentences
)
max_sentence_length = TYPE_INT(text.Function.max_sentence_length(sentences))
assert max_sentence_length >= 3
profiler = cProfile.Profile()
profiler.enable()
def L(x):
loss = Function.sum(
x, axis=None, keepdims=False
)
return loss
# --------------------------------------------------------------------------------
# Ye = einsum("nd,ncd->n", Bc:(N,D), We:(N,E,D))
# dL/dWe:(N,E,D) = dL/dYe * dYe/dWe = dL/dYe * Bc
#
# Ys = einsum("nd,nsd->ns", Bc:(N,D), Ws:(N,SL,D))
# dL/dWs:(N,SL,D) = dL/dYs * dYs/dWs = dL/dYs * Bc
#
# By setting
# 1. dL/dY = np.c_[dL/dYe,dL/dYs] = I and
# 2. context_size C == negative_sample_size SL
# The constraint is E * dL/dWe == dL/dWs == Bc because dL/dYe, dL/dYs are I.
# dL/dWe is normalized with E to be independent from the event (target) size.
# --------------------------------------------------------------------------------
for _ in range(NUM_MAX_TEST_TIMES):
# C must be even number
C = TYPE_INT(np.random.randint(1, max_sentence_length / 2) * 2)
assert C < max_sentence_length
# E=SL for (N,E,D) and (N,SL,D) has the same shape
E = SL = TYPE_INT(
np.random.randint(
1,
min(
Embedding.MAX_TARGET_SIZE,
Embedding.MAX_NEGATIVE_SAMPLE_SIZE,
(max_sentence_length - C)
)+1
)
)
target_size = negative_sample_size = TYPE_INT(E)
context_size = TYPE_INT(C)
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(1, 100))
W: TYPE_TENSOR = np.random.randn(dictionary.vocabulary_size, event_vector_size)
embedding, event_context = _must_succeed(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
msg="must succeed"
)
embedding.objective = L
sequences = dictionary.function(sentences)
target_context_pairs = event_context.function(sequences)
# --------------------------------------------------------------------------------
# Forward path
# --------------------------------------------------------------------------------
Y = embedding.function(target_context_pairs)
EDWe, EDWs, EDWc = embedding.gradient_numerical()
# --------------------------------------------------------------------------------
# Backward path
# --------------------------------------------------------------------------------
dY = Function.ones(shape=Function.tensor_shape(Y))
embedding.gradient(dY)
# --------------------------------------------------------------------------------
# Backward path
# --------------------------------------------------------------------------------
dWe, dWs, dWc = embedding.update()
# ********************************************************************************
# Constraint:
# - dW is close to EDW
# - dL/dWe = dL/dWs = Bc when dL/dY = I
# ********************************************************************************
assert Function.all_close(
EDWe, dWe
), "Expected (EDWe==dWe)\n%s\ndifference\n%s\n" % (EDWe, EDWe-dWe)
assert Function.all_close(
EDWs, dWs
), "Expected (EDWs==dWs)\n%s\ndifference\n%s\n" % (EDWs, EDWs-dWs)
assert Function.all_close(
EDWc, dWc
), "Expected (EWc5==W[5])\n%s\ndifference\n%s\n" % (EDWc, EDWc-dWc)
assert Function.all_close(
dWe * E, dWs
), "Expected (dWe==dWs) but dWe:\n%s\ndifference\n%s\n" % (dWe, dWe-dWs)
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_embedding_instance_properties_access_to_succeed(caplog):
"""
Objective:
Verify the layer class validates the parameters have been initialized before accessed.
Expected:
Initialization detects the access to the initialized parameters and succeeds.
"""
caplog.set_level(logging.DEBUG)
name = "test_020_embedding_instance_properties_access_to_succeed"
dictionary: EventIndexing = _instantiate_event_indexing()
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
# First validate the correct configuration, then change parameter one by one.
target_size = TYPE_INT(np.random.randint(1, 10))
context_size = TYPE_INT(2 * np.random.randint(1, 10))
negative_sample_size = TYPE_INT(np.random.randint(5, 20))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 100))
W: TYPE_TENSOR = np.random.rand(dictionary.vocabulary_size, event_vector_size).astype(TYPE_FLOAT)
embedding, event_context = _must_succeed(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
msg="must succeed"
)
# --------------------------------------------------------------------------------
# To pass
# --------------------------------------------------------------------------------
try:
if not embedding.name == name:
raise RuntimeError("embedding.name == name should be true")
except AssertionError:
raise RuntimeError("Access to name should be allowed as already initialized.")
try:
if not embedding.V == dictionary.vocabulary_size:
raise RuntimeError("embedding.V == vocabulary_size should be true")
except AssertionError:
raise RuntimeError("Access to V should be allowed as already initialized.")
try:
if not embedding.E == target_size:
raise RuntimeError("embedding.V == target_size should be true")
except AssertionError:
raise RuntimeError("Access to E should be allowed as already initialized.")
try:
if not embedding.C == context_size:
raise RuntimeError("embedding.C == context_size should be true")
except AssertionError:
raise RuntimeError("Access to C should be allowed as already initialized.")
try:
if not embedding.window_size == target_size+context_size:
raise RuntimeError("embedding.window_size == target_size+context_size should be true")
except AssertionError:
raise RuntimeError("Access to window_size should be allowed as already initialized.")
try:
if not embedding.SL == negative_sample_size:
raise RuntimeError("embedding.negative_sample_size == negative_sample_size should be true")
except AssertionError:
raise RuntimeError("Access to negative_sample_size should be allowed as already initialized.")
try:
if embedding.dictionary is not dictionary:
raise RuntimeError("embedding.dictionary is dictionary should be true")
except AssertionError:
raise RuntimeError("Access to dictionary should be allowed as already initialized.")
try:
# Embedding internally deepcopy W to avoid unexpected change and
# event vector for UNK and NIL are zero cleared.
if not np.array_equal(
embedding.W[len(EVENT_META_ENTITIES):],
W[len(EVENT_META_ENTITIES):]
):
raise RuntimeError("np.array_equal(embedding.W, W) should be true")
except AssertionError:
raise RuntimeError("Access to W should be allowed as already initialized.")
try:
if not isinstance(embedding.optimizer, SGD):
raise RuntimeError("isinstance(embedding.optimizer, SGD) should be true")
except AssertionError:
raise RuntimeError("Access to optimizer should be allowed as already initialized.")
try:
opt = SGD()
if not embedding.lr == opt.lr:
raise RuntimeError("embedding.lr == lr should be true")
except AssertionError:
raise RuntimeError("Access to lr should be allowed as already initialized.")
try:
opt = SGD()
if not embedding.l2 == opt.l2:
raise RuntimeError("embedding.l2 == context_size should be true")
except AssertionError:
raise RuntimeError("Access to l2 should be allowed as already initialized.")
try:
if not np.array_equal(embedding.S["target_size"], embedding.E):
raise RuntimeError("embedding.E == E should be true")
except AssertionError:
raise RuntimeError("Access to S['target_size'] should be allowed as already initialized.")
try:
if not np.array_equal(embedding.S["context_size"], embedding.C):
raise RuntimeError("embedding.C == C should be true")
except AssertionError:
raise RuntimeError("Access to S['context_size'] should be allowed as already initialized.")
try:
if not np.array_equal(embedding.S["negative_sample_size"], embedding.SL):
raise RuntimeError("embedding.SL == SL should be true")
except AssertionError:
raise RuntimeError("Access to S['negative_sample_size'] should be allowed as already initialized.")
try:
if not np.array_equal(embedding.S["W"], embedding.W):
raise RuntimeError("embedding.W == W should be true")
except AssertionError:
raise RuntimeError("Access to S['W'] should be allowed as already initialized.")
try:
if not isinstance(embedding.logger, logging.Logger):
raise RuntimeError("isinstance(embedding.logger, logging.Logger) should be true")
except AssertionError:
raise RuntimeError("Access to logger should be allowed as already initialized.")
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_adapt_embedding_logistic_loss_instantiation_to_fail():
"""
Objective:
Verify the layer class validates the initialization parameter constraints.
Expected:
Initialization detects parameter constraints not meet and fails.
"""
name = "test_020_adapt_embedding_logistic_loss_instantiation_to_fail"
# First validate the correct configuration, then change parameter one by one.
dictionary: EventIndexing = _instantiate_event_indexing()
target_size = TYPE_INT(np.random.randint(1, 10))
context_size = TYPE_INT(2 * np.random.randint(1, 10))
negative_sample_size = TYPE_INT(np.random.randint(5, 20))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 100))
W: TYPE_TENSOR = np.random.randn(dictionary.vocabulary_size,
event_vector_size)
_instantiate(
name=name,
num_nodes=TYPE_INT(1),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
)
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
target_size = TYPE_INT(np.random.randint(1, 10))
context_size = TYPE_INT(2 * np.random.randint(1, 10))
negative_sample_size = TYPE_INT(np.random.randint(5, 20))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 100))
msg = "Name is string with length > 0."
_instantiate_must_fail(name="",
num_nodes=(1 + negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
log_level=logging.DEBUG,
msg=msg)
msg = "num_nodes must > 0."
_instantiate_must_fail(name=name,
num_nodes=TYPE_INT(0),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
log_level=logging.DEBUG,
msg=msg)
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_embedding_instance_properties_access_to_fail(caplog):
"""
Objective:
Verify the layer class validates the parameters have been initialized before accessed.
Expected:
Initialization detects the access to the non-initialized parameters and fails.
"""
caplog.set_level(logging.DEBUG)
name = "test_020_embedding_instance_properties_access_to_fail"
dictionary: EventIndexing = _instantiate_event_indexing()
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
# First validate the correct configuration, then change parameter one by one.
target_size = TYPE_INT(np.random.randint(1, 10))
context_size = TYPE_INT(2 * np.random.randint(1, 10))
negative_sample_size = TYPE_INT(np.random.randint(5, 20))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 100))
W: TYPE_TENSOR = np.random.randn(dictionary.vocabulary_size, event_vector_size)
embedding, event_context = _must_succeed(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
msg="must succeed"
)
# --------------------------------------------------------------------------------
# To fail
# --------------------------------------------------------------------------------
msg = "Accessing uninitialized property of the layer must fail."
try:
print(embedding.X)
raise RuntimeError(msg)
except AssertionError:
pass
try:
print(embedding.N)
raise RuntimeError(msg)
except AssertionError:
pass
try:
print(embedding.dX)
raise RuntimeError(msg)
except AssertionError:
pass
try:
print(embedding.dW)
raise RuntimeError(msg)
except AssertionError:
pass
try:
print(embedding.Y)
raise RuntimeError(msg)
except AssertionError:
pass
try:
print(embedding.dY)
raise RuntimeError(msg)
except AssertionError:
pass
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_embedding_instantiation_to_fail():
"""
Objective:
Verify the layer class validates the initialization parameter constraints.
Expected:
Initialization detects parameter constraints not meet and fails.
"""
name = "test_020_embedding_instantiation_to_fail"
# First validate the correct configuration, then change parameter one by one.
dictionary: EventIndexing = _instantiate_event_indexing()
target_size = TYPE_INT(np.random.randint(1, 10))
context_size = TYPE_INT(2 * np.random.randint(1, 10))
negative_sample_size = TYPE_INT(np.random.randint(5, 20))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 100))
W: TYPE_TENSOR = np.random.randn(dictionary.vocabulary_size, event_vector_size)
_must_succeed(
name=name,
num_nodes=TYPE_INT(1),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
msg="must succeed"
)
_must_succeed(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
log_level=logging.DEBUG,
msg="must succeed without W"
)
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
target_size = TYPE_INT(np.random.randint(1, 10))
context_size = TYPE_INT(2 * np.random.randint(1, 10))
negative_sample_size = TYPE_INT(np.random.randint(5, 20))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 100))
msg = "Name is string with length > 0."
_must_fail(
name="",
num_nodes=(1 + negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
log_level=logging.DEBUG,
msg=msg
)
msg = "num_nodes must > 0."
_must_fail(
name=name,
num_nodes=TYPE_INT(0),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
log_level=logging.DEBUG,
msg=msg
)
# msg = "num_nodes is 1+negative_sample_size but does not has to be enforced"
# _must_fail(
# name=name,
# num_nodes=TYPE_INT(1+negative_sample_size),
# target_size=target_size,
# context_size=context_size,
# negative_sample_size=negative_sample_size,
# event_vector_size=event_vector_size,
# dictionary=dictionary,
# log_level=logging.DEBUG,
# msg=msg
# )
msg = "target size must be >0."
_must_fail(
name=name,
num_nodes=(1 + negative_sample_size),
target_size=TYPE_INT(0),
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
log_level=logging.DEBUG,
msg=msg
)
msg = "context_size must be >0."
_must_fail(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=TYPE_INT(0),
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
log_level=logging.DEBUG,
msg=msg
)
msg = "negative_sample_size must be >0."
_must_fail(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=TYPE_INT(0),
event_vector_size=event_vector_size,
dictionary=dictionary,
log_level=logging.DEBUG,
msg=msg
)
msg = "event_vector_size must be >0."
_must_fail(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=TYPE_INT(0),
dictionary=dictionary,
log_level=logging.DEBUG,
msg=msg
)
msg = "dictionary must be of type EventIndexing"
_must_fail(
name=name,
num_nodes=(1+negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=["hoge", None][np.random.randint(0, 2)],
log_level=logging.DEBUG,
msg=msg
)
msg = "vocabulary_size must >= " \
"(context_size + target_size) + " \
"negative_sample_size + " \
"len(EVENT_META_ENTITIES)"
length = (context_size + target_size) + negative_sample_size
corpus = " ".join(str(i) for i in range(length))
_indexing_dummy = EventIndexing(
name=__name__,
num_nodes=1,
corpus=corpus
)
assert _indexing_dummy.vocabulary_size == length + len(EVENT_META_ENTITIES)
_must_succeed(
name=name,
num_nodes=(1+negative_sample_size),
target_size=TYPE_INT(target_size),
context_size=TYPE_INT(context_size),
negative_sample_size=TYPE_INT(negative_sample_size),
event_vector_size=TYPE_INT(event_vector_size),
dictionary=_indexing_dummy,
log_level=logging.DEBUG,
msg=msg
)
_must_fail(
name=name,
num_nodes=(1+negative_sample_size),
target_size=TYPE_INT(target_size) + 1,
context_size=TYPE_INT(context_size),
negative_sample_size=TYPE_INT(negative_sample_size),
event_vector_size=TYPE_INT(event_vector_size),
dictionary=_indexing_dummy,
log_level=logging.DEBUG,
msg=msg
)
msg = "vocabulary_size must be > W.shape[0]"
w = np.random.randn(
dictionary.vocabulary_size - np.random.randint(1, dictionary.vocabulary_size),
event_vector_size
)
_must_fail(
name=name,
num_nodes=(1+negative_sample_size),
target_size=TYPE_INT(target_size),
context_size=TYPE_INT(context_size),
negative_sample_size=TYPE_INT(negative_sample_size),
event_vector_size=TYPE_INT(event_vector_size),
dictionary=dictionary,
W=w,
log_level=logging.DEBUG,
msg=msg
)
msg = "event_vector_size must be W.shape[1]"
offset = np.random.randint(1, event_vector_size) * (1 if random.random() < 0.5 else -1)
assert (event_vector_size + offset) > 0, \
"%s %s %s" % (event_vector_size, offset, (event_vector_size + offset))
w = np.random.randn(
dictionary.vocabulary_size,
(event_vector_size + offset)
)
_must_fail(
name=name,
num_nodes=(1+negative_sample_size),
target_size=TYPE_INT(target_size),
context_size=TYPE_INT(context_size),
negative_sample_size=TYPE_INT(negative_sample_size),
event_vector_size=TYPE_INT(event_vector_size),
dictionary=dictionary,
W=w,
log_level=logging.DEBUG,
msg=msg
)
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_adapt_embedding_loss_adapter_function_ye_to_succeed(caplog):
"""
Objective:
Verify the Adapter function handles Y in shape
- Y:(N, 1+SL)
- ys:(N,SL)
- ye:(N,1)
Expected:
Adapter.function(Y) returns
- For Y:(N, 1+SL), the return is in shape (N*(1+SL),1).
Log loss T is set to the same shape
- For Y:(N, SL), the return is in shape (N*SL,1).
Log loss T is set to the same shape
- For Y:(N,), the return is in shape (N,1).
Log loss T is set to the same shape
"""
caplog.set_level(logging.DEBUG)
name = "test_020_adapt_embedding_logistic_loss_function_multi_lines"
sentences = """
Verify the EventIndexing function can handle multi line sentences
the asbestos fiber <unk> is unusually <unk> once it enters the <unk>
with even brief exposures to it causing symptoms that show up decades later researchers said
"""
dictionary: EventIndexing = _instantiate_event_indexing()
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
# First validate the correct configuration, then change parameter one by one.
E = target_size = TYPE_INT(np.random.randint(1, 3))
C = context_size = TYPE_INT(2 * np.random.randint(1, 5))
SL = negative_sample_size = TYPE_INT(np.random.randint(1, 5))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 20))
W: TYPE_TENSOR = np.random.randn(dictionary.vocabulary_size,
event_vector_size)
loss, adapter, embedding, event_context = _instantiate(
name=name,
num_nodes=TYPE_INT(1),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
)
sequences = dictionary.function(sentences)
target_context_pairs = event_context.function(sequences)
Y = embedding.function(target_context_pairs)
N, _ = embedding.tensor_shape(Y)
# ********************************************************************************
# Constraint:
# - Adapter function returns (N,1) with the same values of ye
# - Adapter function has set T:(N,1) with value 1 in the loss layer
# ********************************************************************************
msg = "ye must succeed"
ye = Y[::, 0]
EZ = expected_Z = embedding.reshape(ye, shape=(N, 1))
Z = _function_must_succeed(adapter=adapter, Y=ye, msg=msg)
assert embedding.all_close(
Z, EZ,
"Z must close to EZ. Z:\n%s\nEZ\n%s\nDiff\n%s\n" % (Z, EZ,
(EZ - Z)))
T = np.ones(shape=(N, 1), dtype=TYPE_LABEL)
assert embedding.all_equal(T, loss.T), \
"Expected T must equals loss.T. Expected\n%s\nLoss.T\n%s\n" % (T, loss.T)
profiler.disable()
profiler.print_stats(sort="cumtime")
def test_020_cross_entropy_log_loss_1d(caplog):
"""
Objective:
Test the categorical log loss values for P in 1 dimension.
Constraints:
1. The numerical gradient gn = (-t * logarithm(p+h) + t * logarithm(p-h)) / 2h.
2. The numerical gradient gn is within +/- u within the analytical g = -T/P.
P: Probabilities from softmax of shape (M,)
M: Number of nodes in the cross_entropy_log_loss layer.
T: Labels
Note:
log(P=1) -> 0
dlog(x)/dx = 1/x
"""
def f(P: np.ndarray, T: np.ndarray):
return np.sum(cross_entropy_log_loss(P, T))
# caplog.set_level(logging.DEBUG, logger=Logger.name)
h: TYPE_FLOAT = OFFSET_DELTA
u: TYPE_FLOAT = GRADIENT_DIFF_ACCEPTANCE_VALUE
# --------------------------------------------------------------------------------
# For (P, T): P[index] = True/1, OHE label T[index] = 1 where
# P=[0,0,0,...,1,...0], T = [0,0,0,...1,...0]. T[i] == 1
#
# Do not forget the Jacobian shape is (N,) and calculate each element.
# 1. For T=1, loss L = -log(Pi) = 0 and dL/dP=(1/Pi)= -1 is expected.
# 2. For T=0, Loss L = (-log(0+offset+h)-log(0+offset-h)) / 2h = 0 is expected.
# --------------------------------------------------------------------------------
M: TYPE_INT = np.random.randint(2, NUM_MAX_NODES)
index: TYPE_INT = TYPE_INT(np.random.randint(
0, M)) # Position of the true label in P
P1 = np.zeros(M, dtype=TYPE_FLOAT)
P1[index] = TYPE_FLOAT(1.0)
T1 = np.zeros(M, dtype=TYPE_LABEL)
T1[index] = TYPE_LABEL(1)
# Analytica correct gradient for P=1, T=1
AG = np.zeros_like(P1, dtype=TYPE_FLOAT)
AG[index] = TYPE_FLOAT(-1) # dL/dP = -1
EGN1 = np.zeros_like(P1, dtype=TYPE_FLOAT) # Expected numerical gradient
EGN1[index] = (-1 * logarithm(TYPE_FLOAT(1.0 + h)) + TYPE_FLOAT(1) *
logarithm(TYPE_FLOAT(1.0 - h))) / TYPE_FLOAT(2 * h)
assert np.all(np.abs(EGN1-AG) < u), \
"Expected EGN-1<%s but %s\nEGN=\n%s" % (u, (EGN1-AG), EGN1)
GN1 = numerical_jacobian(partial(f, T=T1), P1)
assert np.all(np.abs(GN1-AG) < u), \
"Expected GN-1<%s but %s\nGN=\n%s" % (u, (GN1-AG), GN1)
# The numerical gradient gn = (-t * logarithm(p+h) + t * logarithm(p-h)) / 2h
assert GN1.shape == EGN1.shape
assert np.all(np.abs(EGN1-GN1) < u), \
"Expected GN1==EGN1 but GN1-EGN1=\n%sP=\n%s\nT=%s\nEGN=\n%s\nGN=\n%s\n" \
% (np.abs(GN1-EGN1), P1, T1, EGN1, GN1)
# The numerical gradient gn is within +/- u within the analytical g = -T/P
G1 = np.zeros_like(P1, dtype=TYPE_FLOAT)
G1[T1 == 1] = -1 * (T1[index] / P1[index])
# G1[T1 != 0] = 0
check.equal(np.all(np.abs(G1 - GN1) < u), True,
"G1-GN1 %s\n" % np.abs(G1 - GN1))
# --------------------------------------------------------------------------------
# For (P, T): P[index] = np uniform(), index label T=index
# --------------------------------------------------------------------------------
for _ in range(NUM_MAX_TEST_TIMES):
M = np.random.randint(2, NUM_MAX_NODES) # M > 1
T2 = TYPE_LABEL(np.random.randint(0, M)) # location of the truth
P2 = np.zeros(M, dtype=TYPE_FLOAT)
while not (x := TYPE_FLOAT(
np.random.uniform(low=-BOUNDARY_SIGMOID,
high=BOUNDARY_SIGMOID))):
pass
p = softmax(x)
P2[T2] = p
# --------------------------------------------------------------------------------
# The Jacobian G shape is the same with P.shape.
# G:[0, 0, ...,g, 0, ...] where Gi is numerical gradient close to -1/(1+k).
# --------------------------------------------------------------------------------
N2 = np.zeros_like(P2, dtype=TYPE_FLOAT)
N2[T2] = TYPE_FLOAT(-1) * (logarithm(p + h) -
logarithm(p - h)) / TYPE_FLOAT(2 * h)
N2 = numerical_jacobian(partial(f, T=T2), P2)
# The numerical gradient gn = (-t * logarithm(p+h) + t * logarithm(p-h)) / 2h
assert N2.shape == N2.shape
assert np.all(np.abs(N2-N2) < u), \
f"Delta expected to be < {u} but \n{np.abs(N2-N2)}"
G2 = np.zeros_like(P2, dtype=TYPE_FLOAT)
G2[T2] = -1 / p
# The numerical gradient gn is within +/- u within the analytical g = -T/P
check.equal(np.all(np.abs(G2 - N2) < u), True,
"G2-N2 %s\n" % np.abs(G2 - N2))
def test_020_embedding_gradient_descent(caplog):
"""
Objective:
Verify the gradient descent, especially np.ufunc.at, is working as expected.
W:(V, D=3) where all elements are initialized to 1.0.
dL/dY:(1,E+SL) = I and E=1,SL=1
X=(target,context)=[3,4,5,6,5] where target_index=3.
Expected:
The context index 5 occurs twice so that W[5] should be updated twice
at the gradient descent as W[5] = W[5] - [lr * (1 + l2)] * 2.
For other context indices at 4, 6:
W[3] = W[3] - [lr * (1 + l2) * dWe].
W[4] = W[4] - [lr * (1 + l2) * dWc].
W[6] = W[6] - [lr * (1 + l2) * dWc].
"""
caplog.set_level(logging.DEBUG)
name = "test_020_embedding_gradient_multi_lines"
dictionary: EventIndexing = _instantiate_event_indexing()
def L(x):
loss = Function.sum(
x, axis=None, keepdims=False
)
return loss
target_size = negative_sample_size = TYPE_INT(1)
context_size = TYPE_INT(4)
event_vector_size = TYPE_INT(3)
W: TYPE_TENSOR = np.ones(
shape=(dictionary.vocabulary_size, event_vector_size),
dtype=TYPE_FLOAT
)
embedding, event_context = _must_succeed(
name=name,
num_nodes=(1 + negative_sample_size),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
msg="must succeed"
)
del W # embedding deepcopy W to avoid unexpected changes
embedding.objective = L
target_context_pairs = np.array([[3, 4, 5, 6, 5]], dtype=TYPE_INT)
# --------------------------------------------------------------------------------
# Forward path
# --------------------------------------------------------------------------------
Y = embedding.function(target_context_pairs)
EDWe, EDWs, EDWc = embedding.gradient_numerical()
print(f"Loss {L(Y)}\n")
# --------------------------------------------------------------------------------
# Backward path
# --------------------------------------------------------------------------------
dY = Function.ones(shape=Function.tensor_shape(Y))
embedding.gradient(dY)
# --------------------------------------------------------------------------------
# Expected We, Wc (we do not know Ws as negative sample is stochastic)
# This is for SGD as the optimizer.
# --------------------------------------------------------------------------------
lr = embedding.lr
l2 = embedding.l2
expected_dWe = lr * (1+l2) * embedding.dWe
diff_We = embedding.optimizer.differential(dW=embedding.dWe)
msg_We = "dWe: expected\n%s\n but actual diff=:\n%s\n" % \
(expected_dWe, (expected_dWe-diff_We))
embedding.all_close(
expected_dWe, diff_We, msg=msg_We
)
EWe = embedding.W[3] - expected_dWe
expected_dWc = lr * (1+l2) * embedding.dWc
diff_Wc = embedding.optimizer.differential(dW=embedding.dWc)
msg_Wc = "dWc: expected\n%s\n but actual diff=:\n%s\n" % \
(expected_dWc, (expected_dWc-diff_Wc))
embedding.all_close(
expected_dWc, diff_Wc, msg=msg_Wc
)
EWc4 = np.subtract(embedding.W[4], expected_dWc)
EWc5 = np.subtract(embedding.W[5], expected_dWc * 2)
EWc6 = np.subtract(embedding.W[6], expected_dWc)
# --------------------------------------------------------------------------------
# Backward path: Gradient descent
# --------------------------------------------------------------------------------
assert np.array_equal(embedding.target_indices, np.array([3], dtype=TYPE_INT))
assert np.array_equal(embedding.context_indices, np.array([4, 5, 6, 5], dtype=TYPE_INT))
dWe, dWs, dWc = embedding.update()
# ********************************************************************************
# Constraint:
# - dW is close to EDW
# - dL/dWe = dL/dWs = Bc when dL/dY = I
# ********************************************************************************
assert Function.all_close(
EDWe, dWe, msg="Expected (EDWe==dWe)\n%s\ndifference\n%s\n" % (EDWe, EDWe - dWe)
)
assert Function.all_close(
EDWs, dWs, msg="Expected (EDWs==dWs)\n%s\ndifference\n%s\n" % (EDWs, EDWs - dWs)
)
assert Function.all_close(
EDWc, dWc, msg="Expected (EWc5==W[5])\n%s\ndifference\n%s\n" % (EDWc, EDWc - dWc)
)
assert Function.all_close(
dWe, dWs, msg="Expected (dWe==dWs) but dWe:\n%s\ndifference\n%s\n" % (dWe, dWe - dWs)
)
# ********************************************************************************
# Constraint:
# ********************************************************************************
assert np.array_equal(expected_dWe, lr * (1+l2) * dWe)
assert np.array_equal(expected_dWc, lr * (1+l2) * dWc)
# - W[3] = W[3] - [lr * (1 + l2) * dWe].
assert Function.all_close(
# EWe, embedding.W[3], msg="Expected (EDWe==W[3])\n%s\ndifference\n%s\n" % (EWe, EWe - embedding.W[3])
EWe, embedding.WO[3], msg="Expected (EDWe==W[3])\n%s\ndifference\n%s\n" % (EWe, EWe - embedding.W[3])
)
# W[4] = W[4] - [lr * (1 + l2) * dWc]
assert Function.all_close(
EWc4, embedding.W[4], msg="Expected (EWc4==W[4])\n%s\ndifference\n%s\n" % (EWc4, EWc4 - embedding.W[4])
)
# W[5] = W[5] - [lr * (1 + l2) * 2 * dWc]
assert Function.all_close(
EWc5, embedding.W[5], msg="Expected (EWc5==W[5])\n%s\ndifference\n%s\n" % (EWc5, EWc5 - embedding.W[5])
)
# W[6] = W[6] - [lr * (1 + l2) * dWc]
assert Function.all_close(
EWc6, embedding.W[6], msg="Expected (EWc6==W[6])\n%s\ndifference\n%s\n" % (EWc6, EWc6 - embedding.W[6])
)
def sentence_to_sequence(
sentences: str,
event_to_index: Dict[str, TYPE_INT],
minimum_length: TYPE_INT = TYPE_INT(0)
) -> List[List[TYPE_INT]]:
"""Generate sequence of event indices from a text sentences
1. Skip an empty line or a line only contain non-word e.g punctuation.
2. Return [[]] if there is no sequence generated.
3. Pad each sequence to the length of max(sequence_len, or min_length).
Sentence length varies and a vectorization framework e.g. numpy may
require same length rows, e.g. numpy array cannot handle ragged numeric
rows. Hence pad a sequence to align, when minimum_length > 0.
A sentence can be short e.g. "I am". To create (event, context) pair,
minimum (event_length+context_length) is required. If a generated
sequence length < minimum_length, then pad it to meet the min length.
Args:
sentences:
A string including one ore more sentences to process.
A sentence is delimited by EOL('\n').
event_to_index: event to integer index mapping dictionary
minimum_length: minimum length of a generated sequence.
Returns: List of integer sequence per sentence
"""
assert isinstance(sentences, str)
sequences = []
max_sequence_length = 0
for line in sentences.split(EOL):
if len(line.strip()) > 0: # Skip empty line
sequence = [
event_to_index.get(
w, EVENT_META_ENTITY_TO_INDEX[EVENT_UNK.lower()])
for w in Function.standardize(line).split()
]
# A line may have punctuations only which results in null sequence
if len(sequence) > 0:
max_sequence_length = max(max_sequence_length,
len(sequence))
sequences.append(sequence)
else:
Logger.warning("Sentence is empty. Skipping...")
if len(sequences) > 0:
if minimum_length > 0: # padding required
max_sequence_length = max(max_sequence_length, minimum_length)
padded: List[List[TYPE_INT]] = [
np.pad(array=seq,
pad_width=(0, max_sequence_length - len(seq)),
constant_values=EVENT_META_ENTITY_TO_INDEX[
EVENT_NIL.lower()]).astype(TYPE_INT).tolist()
for seq in sequences
]
del sequences
else:
padded = sequences
else:
Logger.warning(
"Return [[]] as no valid sentences in the input \n[%s]\n",
sentences)
padded = [[]]
Logger.debug("Sequences generated for \n%s\n%s", sentences, padded)
return padded
def test_020_adapt_embedding_loss_adapter_gradient_to_succeed(caplog):
"""
Objective:
Verify the Adapter gradient method handles dY in shape (N, 1+SL)
Adapter.function(Y) returns
- For Y:(N, 1+SL), the return is in shape (N*(1+SL),1).
Log loss T is set to the same shape
Expected:
"""
caplog.set_level(logging.DEBUG)
name = "test_020_adapt_embedding_logistic_loss_function_multi_lines"
sentences = """
Verify the EventIndexing function can handle multi line sentences
the asbestos fiber <unk> is unusually <unk> once it enters the <unk>
with even brief exposures to it causing symptoms that show up decades later researchers said
"""
dictionary: EventIndexing = _instantiate_event_indexing()
profiler = cProfile.Profile()
profiler.enable()
for _ in range(NUM_MAX_TEST_TIMES):
# First validate the correct configuration, then change parameter one by one.
E = target_size = TYPE_INT(np.random.randint(1, 3))
C = context_size = TYPE_INT(2 * np.random.randint(1, 5))
SL = negative_sample_size = TYPE_INT(np.random.randint(1, 5))
event_vector_size: TYPE_INT = TYPE_INT(np.random.randint(5, 20))
W: TYPE_TENSOR = np.random.rand(dictionary.vocabulary_size,
event_vector_size)
loss, adapter, embedding, event_context = _instantiate(
name=name,
num_nodes=TYPE_INT(1),
target_size=target_size,
context_size=context_size,
negative_sample_size=negative_sample_size,
event_vector_size=event_vector_size,
dictionary=dictionary,
W=W,
log_level=logging.DEBUG,
)
# ================================================================================
# Forward path
# ================================================================================
# --------------------------------------------------------------------------------
# Event indexing
# --------------------------------------------------------------------------------
sequences = dictionary.function(sentences)
# --------------------------------------------------------------------------------
# Event context pairs
# --------------------------------------------------------------------------------
target_context_pairs = event_context.function(sequences)
# --------------------------------------------------------------------------------
# Embedding
# --------------------------------------------------------------------------------
Y = embedding.function(target_context_pairs)
N, _ = embedding.tensor_shape(Y)
batch_size = TYPE_FLOAT(N * (1 + SL))
# --------------------------------------------------------------------------------
# Adapter
# --------------------------------------------------------------------------------
Z = adapter.function(Y)
# --------------------------------------------------------------------------------
# Loss
# --------------------------------------------------------------------------------
L = loss.function(Z)
# ********************************************************************************
# Constraint:
# loss.T is set to the T by adapter.function()
# ********************************************************************************
T = np.zeros(shape=(N, (1 + SL)), dtype=TYPE_LABEL)
T[::, 0] = TYPE_LABEL(1)
assert embedding.all_equal(T.reshape(-1, 1), loss.T), \
"Expected T must equals loss.T. Expected\n%s\nLoss.T\n%s\n" % (T, loss.T)
# ********************************************************************************
# Constraint:
# Expected loss is sum(sigmoid_cross_entropy_log_loss(Y, T)) / (N*(1+SL))
# The batch size for the Log Loss is (N*(1+SL))
# ********************************************************************************
EJ, EP = sigmoid_cross_entropy_log_loss(X=Z, T=T.reshape(-1, 1))
EL = np.sum(EJ, dtype=TYPE_FLOAT) / batch_size
assert embedding.all_close(EL, L), \
"Expected EL=L but EL=\n%s\nL=\n%s\nDiff=\n%s\n" % (EL, L, (EL-L))
# ================================================================================
# Backward path
# ================================================================================
# ********************************************************************************
# Constraint:
# Expected dL/dY from the Log Loss is (P-T)/N
# ********************************************************************************
EDY = (sigmoid(Y) - T.astype(TYPE_FLOAT)) / batch_size
assert EDY.shape == Y.shape
dY = adapter.gradient(loss.gradient(TYPE_FLOAT(1)))
assert dY.shape == Y.shape
assert embedding.all_close(EDY, dY), \
"Expected EDY==dY. EDY=\n%s\nDiff\n%s\n" % (EDY, (EDY-dY))
profiler.disable()
profiler.print_stats(sort="cumtime")
