def run():
data_set = load_text_data_set("CDS")
# exclude the following words, all longer than three syllables, which is not supported by the system ued to
# construct phonological feature vectors
more_than_three_syllables = [
"actually",
"anybody",
"investigator",
"helicopter",
"interesting",
"cookie_monster",
"definitely",
"refrigerator",
"oh_my_goodness",
"humpty_dumpty",
"interested",
"everybody",
"father_christmas",
"helicopter",
"alligator",
"caterpillar",
"everybody's",
"hippopotamus",
]
# also exclude the empty string, which occasionally occurs in the corpus
empty_string = [""]
vocabulary = get_target_words(
2000, tags={"n", "v", "adj", "fn"}, exclude_words=more_than_three_syllables + empty_string
)
# auto encoder for reducing the dimensionality of the phonological feature vectors
phon_ae = PhonAutoEncoder(
vocabulary=vocabulary, epochs=200, learning_rate=0.1, n_hidden=30, corruption_level=0.1, batch_size=1
)
phon_ae.train()
for training_condition in ["context", "context_stress", "context_phonology"]:
# if 'phonology' is part of the model name, get dimensionality-reduced phonological feature vectors
if "phonology" in training_condition:
phon_vs = phon_ae.get_hidden_vectors()
phon_vs_size = 30
else:
phon_vs = None
phon_vs_size = 0
# if 'stress' is part of the model name, get dimensionality-reduced lexical stress feature vectors
if "stress" in training_condition:
stress_vs = get_primary_stress_vectors(vocabulary)
stress_vs_size = 3
else:
stress_vs = None
stress_vs_size = 0
# auto encoder for projecting left-context and phonological / lexical stress feature vector into a shared
# dimensionality-reduced space
bm_ae = BiModalAutoEncoder(
data_set=data_set,
context_feature_size=2000,
phon_vectors=phon_vs,
phon_feature_size=phon_vs_size,
stress_vectors=stress_vs,
stress_feature_size=stress_vs_size,
learning_rate=0.01,
n_hidden=500,
corruption_level=0.1,
batch_size=1,
)
# vectors of left-context frequencies, plus phonological and / or lexical stress features
input_embeddings = dict(zip(bm_ae.vocabulary, bm_ae.embeddings_matrix.get_value()))
# write predicted categories to file -- files can be used for additional processing, e.g. significance testing
# on predictions made at two different stages in the training process
write_preds_to_file(input_embeddings, "majority_vote", "%s_majority_vote" % training_condition)
write_preds_to_file(input_embeddings, "stratified", "%s_stratified_sampling" % training_condition)
write_preds_to_file(input_embeddings, "10-NN", "%s_10-NN_before_stage1" % training_condition)
# create a new file with results by training stage -- write majority vote baseline results to this file
micro_f1, macro_f1, classification_report = get_f1_and_classification_report(input_embeddings, "majority_vote")
results_to_disk(
micro_f1,
macro_f1,
classification_report,
epoch="model was not trained at this stage",
condition=training_condition,
training_stage="BASELINE 1: MAJORITY VOTE",
newfile=True,
)
# append stratified sampling baseline results to file
micro_f1, macro_f1, classification_report = get_f1_and_classification_report(input_embeddings, "stratified")
results_to_disk(
micro_f1,
macro_f1,
classification_report,
epoch="model was not trained at this stage",
condition=training_condition,
training_stage="BASELINE 2: STRATIFIED SAMPLING",
newfile=False,
)
# append 10-NN results obtained from input vectors to file
micro_f1, macro_f1, classification_report = get_f1_and_classification_report(input_embeddings, "10-NN")
results_to_disk(
micro_f1,
macro_f1,
classification_report,
epoch="model was not trained at this stage",
condition=training_condition,
training_stage="BEFORE STAGE 1",
newfile=False,
)
# train the bimodal auto encoder
bm_ae.train()
# get the dimensionality-reduced embeddings after training (vectors of hidden unit activation values)
embeddings = bm_ae.embeddings_over_epochs[-1]
# # append 10-NN results obtained from hidden embeddings to file
micro_f1, macro_f1, classification_report = get_f1_and_classification_report(embeddings, "10-NN")
results_to_disk(
micro_f1,
macro_f1,
classification_report,
epoch=bm_ae.epochs,
condition=training_condition,
training_stage="AFTER STAGE 1",
newfile=False,
)
# plot word embeddings, reduced to two dimensions via the T-SNE algorithm
plot_2D_embeddings(embeddings, training_condition, training_stage="after_stage_1")
# plot accuracy over all training epochs
plot_metric(
plot_name="%s_auto_encoder" % training_condition,
plot_type="micro_f1_over_epochs",
ys=bm_ae.f1_over_epochs,
label="micro_f1",
)
# plot training error over all training epochs
plot_metric(
plot_name="%s_auto_encoder" % training_condition,
plot_type="training_error_over_epochs",
ys=bm_ae.training_error_over_epochs,
label="training_error",
)
# create and save a new data set with the new embeddings -- for further training with the softmax model
save_bimodal_data_set(bm_ae, data_set_name=training_condition, embeddings_dict=embeddings)
from TrainingData.data_sets.extract_from_corpus import create_and_save_data_set
from TrainingData.load_corpus.load_corpus_data import get_target_words
# exclude the following words, all longer than three syllables, which is not supported by the system ued to
# construct phonological feature vectors
more_than_three_syllables = ['actually', 'anybody', 'investigator', 'helicopter', 'interesting', 'cookie_monster',
'definitely', 'refrigerator', 'oh_my_goodness', 'humpty_dumpty', 'interested',
'everybody', 'father_christmas', 'helicopter', 'alligator', 'caterpillar', "everybody's",
'hippopotamus']
# also exclude the empty string, which occasionally occurs in the corpus
empty_string = ['']
# get vocabulary -- this is a list of the 2000 most frequent word strings,
# with each word is given in the format 'word-pos_tag'.
# there are four POS tags: 'v' (verb), 'n' (noun), 'adj' (adjective) 'fn' (function word / closed class word)
vocabulary = get_target_words(2000, tags={'n', 'v', 'adj', 'fn'},
exclude_words=more_than_three_syllables + empty_string)
# the vocabulary composition by POS tag is as follows:
# nouns: 1010
# adjectiveS: 166
# closed class: 522
# verbs: 302
# go through the corpus and extract the text-based part of the data set, consisting of (1) vectors with frequency counts
# for each target word's left context and sampled (2) words from the right context; save extracted data set to disk
create_and_save_data_set('CDS', vocabulary=vocabulary, window_size=3)
