def paper_runs(embeddings, avg_embeddings):
import weights as wgts
for header in HEADERS:
train, test, dev = load_hannah_split(config.CONNO_DIR,
header,
binarize=True,
remove_neutral=False)
print("###################################", header,
"###################################")
do_weighted_run(train, test, dev, embeddings, avg_embeddings,
wgts.metoo_header_to_weights[header],
wgts.metoo_header_to_avg_token_weights[header])
print(
"################################### Power ###################################"
)
train, test, dev = load_power_split(config.POWER_AGENCY)
do_weighted_run(train, test, dev, embeddings, avg_embeddings,
wgts.power_logistic_regression,
wgts.power_token_regression)
majority_class(test, embeddings)
print(
"################################### Agency ###################################"
)
train, test, dev = load_agency_split(config.POWER_AGENCY)
do_weighted_run(train, test, dev, embeddings, avg_embeddings,
wgts.agency_logistic_regression,
wgts.agency_token_regression)
majority_class(test, embeddings)
def run_connotation_frames(embeddings, avg_embeddings):
for header in HEADERS:
print(header)
train, test, dev = load_hannah_split(config.CONNO_DIR,
header,
binarize=True,
remove_neutral=False)
format_runs(embeddings, avg_embeddings, train, test, dev)
print("Power")
train, test, dev = load_power_split(config.POWER_AGENCY)
format_runs(embeddings, avg_embeddings, train, test, dev)
majority_class(test, embeddings)
print("Agency")
train, test, dev = load_agency_split(config.POWER_AGENCY)
format_runs(embeddings, avg_embeddings, train, test, dev)
majority_class(test, embeddings)
def loadAgency():
return load_agency_split(POWER_AGENCY_DIR)