def get_inputs(inp_dir, dataset, embed, embed_mode, mode, layer):
""" Read data from pkl file and prepare for training. """
file = open(
inp_dir + dataset + '-' + embed + '-' + embed_mode + '-' + mode +
'.pkl', 'rb')
data = pickle.load(file)
orders, data_x, data_y = list(zip(*data))
file.close()
# alphaW is responsible for which BERT layer embedding we will be using
if (layer == 'all'):
alphaW = np.full([n_hl], 1 / n_hl)
else:
alphaW = np.zeros([n_hl])
alphaW[int(layer) - 1] = 1
# just changing the way data is stored (tuples of minibatches) and getting the output for the required layer of BERT using alphaW
inputs = []
targets = []
author_ids = []
n_batches = len(data_y)
print(len(orders))
for ii in range(n_batches):
inputs.extend(np.einsum('k,kij->ij', alphaW, data_x[ii]))
targets.extend(data_y[ii])
author_ids.extend(orders[ii])
print('inputs shape: ', np.array(inputs).shape)
print('author_ids shape: ', np.array(author_ids).shape)
inputs = pd.DataFrame(np.array(inputs))
inputs['order'] = author_ids
inputs = inputs.set_index(['order'])
full_targets = pd.DataFrame(np.array(targets))
full_targets['order'] = author_ids
full_targets = full_targets.set_index(['order'])
if dataset == 'essays':
dump_data = dataset_processors.load_essays_df(
'../data/essays/essays.csv')
trait_labels = ['EXT', 'NEU', 'AGR', 'CON', 'OPN']
elif dataset == 'kaggle':
dump_data = dataset_processors.load_Kaggle_df(
'../data/kaggle/kaggle.csv')
trait_labels = ['E', 'N', 'F', 'J']
_, _, _, other_features_df = feature_utils.get_psycholinguist_data(
dump_data, dataset, feature_flags)
inputs, full_targets = merge_features(inputs, other_features_df,
trait_labels)
return inputs, full_targets, trait_labels
def get_inputs(inp_dir, dataset, embed, embed_mode, mode, layer):
"""Read data from pkl file and prepare for training."""
file = open(
inp_dir + dataset + "-" + embed + "-" + embed_mode + "-" + mode + ".pkl", "rb"
)
data = pickle.load(file)
orders, data_x, data_y = list(zip(*data))
file.close()
# alphaW is responsible for which BERT layer embedding we will be using
if layer == "all":
alphaW = np.full([n_hl], 1 / n_hl)
else:
alphaW = np.zeros([n_hl])
alphaW[int(layer) - 1] = 1
# just changing the way data is stored (tuples of minibatches) and getting the output for the required layer of BERT using alphaW
inputs = []
targets = []
author_ids = []
n_batches = len(data_y)
print(len(orders))
for ii in range(n_batches):
inputs.extend(np.einsum("k,kij->ij", alphaW, data_x[ii]))
targets.extend(data_y[ii])
author_ids.extend(orders[ii])
print("inputs shape: ", np.array(inputs).shape)
print("author_ids shape: ", np.array(author_ids).shape)
inputs = pd.DataFrame(np.array(inputs))
inputs["order"] = author_ids
inputs = inputs.set_index(["order"])
full_targets = pd.DataFrame(np.array(targets))
full_targets["order"] = author_ids
full_targets = full_targets.set_index(["order"])
if dataset == "essays":
dump_data = dataset_processors.load_essays_df("../data/essays/essays.csv")
trait_labels = ["EXT", "NEU", "AGR", "CON", "OPN"]
elif dataset == "kaggle":
dump_data = dataset_processors.load_Kaggle_df("../data/kaggle/kaggle.csv")
trait_labels = ["E", "N", "F", "J"]
_, _, _, other_features_df = feature_utils.get_psycholinguist_data(
dump_data, dataset, feature_flags
)
inputs, full_targets = merge_features(inputs, other_features_df, trait_labels)
return inputs, full_targets, trait_labels
def get_inputs(dataset):
"""Read data from metafeature files and prepare for training."""
if dataset == "essays":
dump_data = dataset_processors.load_essays_df(
"../data/essays/essays.csv")
trait_labels = ["EXT", "NEU", "AGR", "CON", "OPN"]
elif dataset == "kaggle":
dump_data = dataset_processors.load_Kaggle_df(
"../data/kaggle/kaggle.csv")
trait_labels = ["E", "N", "F", "J"]
print("dataset loaded! Getting psycholinguistic features...")
inputs, full_targets, _, _ = feature_utils.get_psycholinguist_data(
dump_data, dataset, feature_flags)
inputs = np.array(inputs)
full_targets = np.array(full_targets)
return inputs, full_targets, trait_labels
def get_inputs(dataset):
""" Read data from metafeature files and prepare for training. """
if dataset == 'essays':
dump_data = dataset_processors.load_essays_df(
'../data/essays/essays.csv')
trait_labels = ['EXT', 'NEU', 'AGR', 'CON', 'OPN']
elif dataset == 'kaggle':
dump_data = dataset_processors.load_Kaggle_df(
'../data/kaggle/kaggle.csv')
trait_labels = ['E', 'N', 'F', 'J']
print('dataset loaded! Getting psycholinguistic features...')
inputs, full_targets, _, _ = feature_utils.get_psycholinguist_data(
dump_data, dataset, feature_flags)
inputs = np.array(inputs)
full_targets = np.array(full_targets)
return inputs, full_targets, trait_labels
path = 'explogs/'
n_classes = 2
network = 'MLP'
print(network)
nrc, nrc_vad, readability, mairesse = [True, True, True, True]
feature_flags = [nrc, nrc_vad, readability, mairesse]
np.random.seed(jobid)
tf.random.set_seed(jobid)
start = time.time()
if __name__ == "__main__":
if dataset == 'essays':
dump_data = dataset_processors.load_essays_df('data/essays/essays.csv')
trait_labels = ['EXT', 'NEU', 'AGR', 'CON', 'OPN']
elif dataset == 'kaggle':
dump_data = dataset_processors.load_Kaggle_df('data/kaggle/kaggle.csv')
trait_labels = ['E', 'N', 'F', 'J']
print('dataset loaded! Getting psycholinguistic features...')
inputs, full_targets, feature_names, _ = feature_utils.get_psycholinguist_data(
dump_data, dataset, feature_flags)
inputs = np.array(inputs)
full_targets = np.array(full_targets)
print(inputs.shape)
print(full_targets.shape)
print(feature_names)
print('starting k-fold cross validation...')