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run_classifier.py
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"""
python supervised_classification.py
To run prediction:
python supervised_classification.py --predict
To run cross-validation:
python supervised_classification.py --cv
Specify column:
python supervised_classification.py --cv --col COLNAME
"""
from simpletransformers.classification import ClassificationModel, ClassificationArgs
from argparse import ArgumentParser
from sklearn.model_selection import KFold, train_test_split
from scipy.stats import pearsonr, spearmanr
import warnings
import pandas as pd
from sys import exit
import logging
import torch
warnings.filterwarnings("ignore")
def pearson_corr(preds, labels):
return pearsonr(preds, labels)[0]
def spearman_corr(preds, labels):
return spearmanr(preds, labels)[0]
def train(colname, train_df, eval_df, text_cols,
output_dir, model="roberta", num_labels=2,
num_train_epochs=10,
train_batch_size=4, gradient_accumulation_steps=4,
max_seq_length=512,
cross_validate=False):
print("Train size: %d" % len(train_df))
print("Eval size: %d" % len(eval_df))
print(train_df.head())
print(eval_df.head())
print(torch.cuda.is_available())
model_args = ClassificationArgs()
model_args.reprocess_input_data = True
model_args.overwrite_output_dir = True
model_args.evaluate_during_training = True # change if needed
model_args.max_seq_length = int(max_seq_length / len(text_cols))
model_args.num_train_epochs = num_train_epochs
model_args.evaluate_during_training_steps = 500
model_args.save_eval_checkpoints = False
model_args.save_model_every_epoch = False
model_args.wandb_project = colname
model_args.train_batch_size = train_batch_size
model_args.output_dir = output_dir + "/" + colname
model_args.best_model_dir = output_dir + "/" + colname + "/best_model"
model_args.cache_dir = output_dir + "/" + colname + "/cache"
model_args.tensorboard_dir = output_dir + "/" + colname + "/tensorboard"
model_args.regression = num_labels == 1
model_args.gradient_accumulation_steps = gradient_accumulation_steps
model_args.wandb_kwargs = {"reinit": True}
model_args.fp16 = False
model_args.fp16_opt_level = "O0"
model_args.no_cache = False
model_args.no_save = cross_validate
model_args.save_optimizer_and_scheduler = True
model = ClassificationModel(model.split("-")[0], model,
use_cuda=torch.cuda.is_available(),
num_labels=num_labels,
args=model_args)
model.train_model(train_df, eval_df=eval_df, pearson_corr=pearson_corr, spearman_corr=spearman_corr)
return model
def predict(fname, model_path, model=None,
model_type="roberta-base", predict_list=None,
index_list=None, index_colname="index"):
print(model_path)
if model is None:
model = ClassificationModel(
model_type.split("-")[0], model_path
)
preds, outputs = model.predict(predict_list)
with open(model_path + '/' + fname + '_preds.txt', 'w') as f:
f.write(f"{index_colname}\tpred\n")
for index, pred in zip(index_list, preds):
f.write(f"{index}\t{pred}\n")
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--train", action='store_true',
help="If true, train model.")
parser.add_argument("--train_data", type=str,
default="data/paired_annotations.csv",
help="Input csv file.")
parser.add_argument("--dev_split_size", type=float, default=0,
help="Percentage of data to hold out for validation.")
parser.add_argument("--num_train_epochs", type=int, default=5,
help="Number of training epochs")
parser.add_argument("--text_cols", type=str, help="Text columns, comma separated.")
parser.add_argument("--label_col", type=str, help="Column to evaluate.")
parser.add_argument("--cv", action='store_true',
help="If true, run cross validation.")
parser.add_argument("--cv_folds", type=int, default=5,
help="Number of folds for cross validation.")
parser.add_argument("--predict", action='store_true',
help="If true, predict.")
parser.add_argument("--predict_data", type=str,
default="data/paired_utterances.csv",
help="Input csv file.")
parser.add_argument("--predict_index_col", type=str,
help="Index column for mapping predictions to input.")
parser.add_argument("--model_type", type=str, default="roberta-base",
help="Model type.")
parser.add_argument("--output_dir", type=str, default="outputs/roberta",
help="Output directory.")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)
print("Loading data from %s" % args.train_data)
train_data = pd.read_csv(args.train_data).sample(frac=1)
train_data = train_data[~train_data[args.label_col].isnull()]
print("Loaded %d training examples." % len(train_data))
model_type = args.model_type
text_cols = args.text_cols.split(",")
print(text_cols)
output_dir = args.output_dir
model = None
if args.train:
print("Using %s as label" % args.label_col)
if len(text_cols) == 1:
train_data = train_data.rename(columns={text_cols[0]:
'text',
args.label_col: 'labels'})[["text", "labels"]].dropna()
elif len(text_cols) == 2:
train_data = train_data.rename(columns={text_cols[0]: 'text_a',
text_cols[1]: 'text_b',
args.label_col: 'labels'})[["text_a", "text_b",
"labels"]].dropna()
else:
print("You can have up to 2 texts to classify!")
exit()
if args.dev_split_size > 0:
train_df, eval_df = train_test_split(train_data, test_size=0.2)
else:
train_df = train_data
eval_df = train_data
model = train(args.label_col,
train_df,
eval_df,
text_cols,
output_dir,
model_type,
num_train_epochs=args.num_train_epochs)
if args.predict:
print("Loading data for prediction from %s" % args.input)
predict_data = pd.read_csv(args.predict_data)
if len(text_cols) == 1:
predict_df = predict_data.rename(columns={text_cols[0]: 'text'})[[args.predict_index_col,
"text"]].dropna()
predict_list = predict_df["text"].tolist()
elif len(text_cols) == 2:
predict_df = predict_data.rename(columns={text_cols[0]: 'text_a',
text_cols[1]: 'text_b',})[[args.predict_index_col,
"text_a", "text_b"]].dropna()
predict_list = predict_df[["text_a", "text_b"]].tolist()
else:
print("You can have up to 2 texts to classify!")
exit()
index_list = predict_df[args.predict_index_col].tolist()
fname = args.label_col + "_" + args.input.split("/")[-1].split(".")[0]
predict(fname, output_dir, model, model_type, predict_list=predict_list,
index_list=index_list, index_colname=args.predict_index_col)