pred_tagged_essays_train = dill.load(f)
test_fname = coref_output_folder + "test_crel_anatagged_essays_most_recent_code.dill"
with open(test_fname, "rb") as f:
pred_tagged_essays_test = dill.load(f)
# Get Test Data In Order to Get Test CRELS
# load the test essays to make sure we compute metrics over the test CR labels
config = get_config(coref_output_folder)
results_processor = ResultsProcessor(dbname="metrics_causal_model")
########################################################
logger.info("Started at: " + str(datetime.datetime.now()))
logger.info("Number of pred tagged essays %i" % len(pred_tagged_essays_train)) # should be 902
cr_tags = get_cr_tags(train_tagged_essays=pred_tagged_essays_train, tag_essays_test=pred_tagged_essays_test)
cv_folds = [(pred_tagged_essays_train, pred_tagged_essays_test)] # type: List[Tuple[Any,Any]]
def evaluate_model(
collection_prefix: str,
folds: List[Tuple[Any, Any]],
extractor_fn_names_lst: List[str],
cost_function_name: str,
beta: float,
ngrams: int,
stemmed: bool,
max_epochs: int,
down_sample_rate=1.0) -> float:
if down_sample_rate < 1.0:
new_folds = [] # type: List[Tuple[Any, Any]]
# Get Test Data In Order to Get Test CRELS
# load the test essays to make sure we compute metrics over the test CR labels
test_config = get_config(test_folder)
tagged_essays_test = load_process_essays(**test_config)
########################################################
fname = rnn_predictions_folder + "essays_train_bi_directional-True_hidden_size-256_merge_mode-sum_num_rnns-2_use_pretrained_embedding-True.dill"
with open(fname, "rb") as f:
pred_tagged_essays = dill.load(f)
logger.info("Started at: " + str(datetime.datetime.now()))
logger.info("Number of pred tagged essays %i" %
len(pred_tagged_essays)) # should be 902
cr_tags = get_cr_tags(train_tagged_essays=pred_tagged_essays,
tag_essays_test=tagged_essays_test)
cv_folds = cross_validation(pred_tagged_essays,
CV_FOLDS) # type: List[Tuple[Any,Any]]
def evaluate_model(collection_prefix: str,
folds: List[Tuple[Any, Any]],
extractor_fn_names_lst: List[str],
cost_function_name: str,
beta: float,
ngrams: int,
stemmed: bool,
max_epochs: int,
down_sample_rate=1.0) -> float:
if down_sample_rate < 1.0:
