def eval(model_path, our_gen_file, human_file):
gen = open(our_gen_file, 'r').readlines()
gen = [i.strip() for i in gen]
human = open(human_file, 'r').readlines()
human = [i.strip() for i in human]
assert len(human) - len(gen) == 0, "please balance the eval file"
test_df = pd.DataFrame(gen+human)
test_input = test_df.sample(frac=1, random_state=123)
train_args={
'learning_rate':3e-5,
'num_train_epochs': 5,
'reprocess_input_data': True,
'overwrite_output_dir': False,
'process_count': 10,
'train_batch_size': 4,
'eval_batch_size': 400,
'max_seq_length': 300,
"fp16":False
}
model = ClassificationModel('roberta', model_path, num_labels=4, use_cuda=True, cuda_device=0, args=train_args)
result, model_outputs, wrong_predictions = model.eval_model(test_input)
print(result)
def train_model(args, output_dir, cache_dir):
"""
Train a SimpleTransformers model based on the given arguments, save and return it.
:param args: Arguments as processed by parse_args() containing architecture and epochs.
:param output_dir: Path to the directory in which the model should be stored.
:param cache_dir: Path to the directory in which the cache should be stored.
:return: SimpleTransformers model trained based on the given arguments.
"""
print('=> Training model...')
# Set model arguments
model_args = {
'num_train_epochs': args.num_epochs,
'train_batch_size': 32,
'eval_batch_size': 32,
'output_dir': output_dir,
'cache_dir': cache_dir
}
# Train the model
pretrained = get_transformer_model(args.arch)
model = ClassificationModel(args.arch,
pretrained,
use_cuda=True,
args=model_args)
train = load_corpus('train')
model.train_model(train)
return model
def main():
f_path = 'Breast Cancer(Raw_data_2_Classes).csv'
data = loadDataAsDataFrame(f_path)
X = data
y = data['Class'].tolist()
training_set_size = int(0.8 * len(X))
training_rows, test_rows, training_classes, test_classes = train_test_split(
X, y, train_size=training_set_size, random_state=42069)
training_rows, test_rows, training_classes, test_classes = train_test_split(
X, y, train_size=training_set_size, random_state=42069)
model_args = {'overwrite_output_dir': True}
# Create a TransformerModel
model = ClassificationModel('roberta',
'roberta-base',
use_cuda=False,
args=model_args)
#model = ClassificationModel('roberta', 'roberta-base', use_cuda=True, args=model_args)
#change our data into a format that simpletransformers can process
training_rows['text'] = training_rows['Text']
training_rows['labels'] = training_rows['Class']
test_rows['text'] = test_rows['Text']
test_rows['labels'] = test_rows['Class']
# Train the model
model.train_model(training_rows)
# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(test_rows)
print("f1 score")
precision = result['tp'] / (result['tp'] + result['fp'])
recall = result['tp'] / (result['tp'] + result['fn'])
f1score = 2 * precision * recall / (precision + recall)
print(f1score)
def train():
wandb.init(WAND_PROJECT_NAME)
modelArgs = { "max_seq_length": self.maxSeqLength, "output_dir": self.modelOutputDir, "overwrite_output_dir": True, "best_model_dir": self.bestModelOutputDir,
"wandb_project": WAND_PROJECT_NAME, "num_training_epochs": wandb.config.epochs, "learning_rate": wandb.config.learning_rate,
"do_lower_case": True, "cache_dir": self.modelCacheDir, "encoding": "utf-8", "train_batch_size": 5, "eval_batch_size": 5,
"evaluate_during_training_steps": 50, "evaluate_during_training_verbose": True, "logging_steps": 5, "sliding_window": True,
"reprocess_input_data": True, "evaluate_during_training": True, "use_multiprocessing": True,
"labels_list": SECTOR_LABELS }
model = ClassificationModel(self.modelType, self.modelNameOrPath, args=modelArgs, sweep_config=wandb.config, use_cuda=torch.cuda.is_available(), num_labels=len(SECTOR_LABELS), )
# Training and evaluation
try:
log.info(f"Started training/finetuning BERT on multi-class classification task..")
model.train_model(train_df=self.trainDataset, eval_df=self.evalDataset, show_running_loss=True,
output_dir=self.modelOutputDir,
mcc=sklearn.metrics.matthews_corrcoef,
acc=sklearn.metrics.balanced_accuracy_score, )
log.info(f"Finished finetuning and evaluating our fine-tuned model on multi-class classification task. Check the folder '{self.modelOutputDir}' for finetuned weights.")
log.info(f"It took {round((time.time() - startTime) / 3600, 1)} hours to finetune and evaluate our fine-tuned model on multi-class classification task.")
except:
exc_type, exc_value, exc_traceback = sys.exc_info()
err = f"Error occurred while training and evaluating the finetuned model on multi-class classification task. Error is: {exc_type}; {exc_value}."
log.error(err)
wandb.join()
def train_model(train_df, num_labels):
model_type, model_name = MODELNAME.split(";")
model_output = 'models/{}-{}-{}'.format(TAG, model_type, model_name.replace("/", "-"))
if OVERWRITE is False and os.path.exists(model_output):
logging.info("Skipping training of {}".format(model_name))
sys.exit(0)
logging.info("Starting training of {}".format(model_name))
run = wandb.init(project=model_output.split("/")[-1], reinit=True)
model = ClassificationModel(
model_type, model_name, num_labels=num_labels, args={
'output_dir': model_output,
'overwrite_output_dir': OVERWRITE,
'best_model_dir': '{}/best'.format(model_output),
'evaluate_during_training': False,
'manual_seed': 42,
'num_train_epochs': 4,
# 'learning_rate': 2e-5, # For BERT, 5e-5, 3e-5, 2e-5
# For BERT 16, 32. It could be 128, but with gradient_acc_steps set to 2 is equivalent
'train_batch_size': 8 if "large" in model_name else 32,
'eval_batch_size': 8 if "large" in model_name else 32,
# Doubles train_batch_size, but gradients and weights are calculated once every 2 steps
'gradient_accumulation_steps': 2 if "large" in model_name else 1,
'max_seq_length': 256,
'sliding_window': False,
'wandb_project': model_output.split("/")[-1],
# "adam_epsilon": 3e-5, # 1e-8
"silent": False,
"fp16": False, # By default it uses 32 bit floating point
"n_gpu": 1,
})
# train the model
model.train_model(train_df)
return model, run
def load_models(self):
args = {'eval_batch_size': 32, 'silent': True}
self.model = ClassificationModel(self.model_emb,
self.tr_path,
num_labels=self.num_classes,
args=args,
use_cuda=False)
def train(human_file, gen_file, our_gen_file, output_dir):
data = []
data += [(i.strip(), 1) for i in open(human_file,'r').readlines()]
data += [(i.strip(), 0) for i in open(gen_file,'r').readlines()]
data += [(i.strip(), 0) for i in open(our_gen_file,'r').readlines()]
all_df = pd.DataFrame(data)
train_args = {
'overwrite_output_dir':True,
'num_train_epochs': 10,
'process_count': 10,
'train_batch_size': 10,
'eval_batch_size': 20,
'max_seq_length': 300,
'reprocess_input_data':True,
'learning_rate':1e-5,
"evaluate_during_training": True,
"use_early_stopping":True,
'early_stopping_patience':3,
"early_stopping_metric": "eval_loss",
"early_stopping_metric_minimize": True,
"no_cache":True,
'output_dir':output_dir
}
model = ClassificationModel('roberta', "roberta-base", args=train_args) # You can set class weights by using the optional weight argument
# Train the model
model.train_model(all_df)
print("finish the training")
def train_stance_clf(data_dir, output_dir, **kwargs):
headlines, bodies, labels = fnc(
os.path.join(data_dir, 'combined_stances_train.csv'),
os.path.join(data_dir, 'combined_bodies_train.csv'))
list_of_tuples = list(zip(headlines, bodies, labels))
df = pd.DataFrame(list_of_tuples, columns=['text_a', 'text_b', 'label'])
train_df, val_df = train_test_split(df, random_state=123)
train_args = {
'learning_rate': 3e-5,
'num_train_epochs': 5,
'reprocess_input_data': True,
'overwrite_output_dir': False,
'process_count': 10,
'train_batch_size': 4,
'eval_batch_size': 20,
'max_seq_length': 300,
"fp16": False,
'output_dir': output_dir
}
model = ClassificationModel('roberta',
"roberta-base",
num_labels=4,
use_cuda=True,
cuda_device=0,
args=train_args)
model.train_model(train_df)
def fake_classify(train_set, eval_set, test_set, seed):
# Create a TransformerModel
model = ClassificationModel('bert',
'bert-base-multilingual-uncased',
args={
'max_seq_length': 512,
'num_train_epochs': 3,
'overwrite_output_dir': True,
'manual_seed': seed
},
use_cuda=True)
print(model.args)
# Train the model
model.train_model(train_set)
# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(
test_set,
f1=sklearn.metrics.f1_score,
acc=sklearn.metrics.accuracy_score)
#print('Evaluation results = ', results(results))
return result, model_outputs, wrong_predictions
def __init__(self, model_type, model_name, model_args):
self.train_df = pd.read_pickle("D:/Language Models/train_df_500000")
self.eval_df = pd.read_pickle("D:/Language Models/test_df_500000")
self.model = ClassificationModel(model_type,
model_name,
use_cuda=False,
args=model_args)
def train(self, split=0.7, num_epochs=10):
self.le = preprocessing.LabelEncoder()
print(list(self.dat.keys()))
self.le.fit(list(self.dat.keys()))
train_data = []
eval_data = []
for k, v in self.dat.items():
len_train = int(round(len(v) * split))
train_data.extend([[i, self.le.transform([k])[0]]
for i in v[:len_train]])
eval_data.extend([[i, self.le.transform([k])[0]]
for i in v[len_train:]])
print(train_data, eval_data)
train_df = pd.DataFrame(train_data)
eval_df = pd.DataFrame(eval_data)
train_args = {
'overwrite_output_dir': True,
'num_train_epochs': num_epochs,
}
self.model = ClassificationModel(self.model_type,
self.model_name,
num_labels=len(list(self.dat.keys())),
use_cuda=self.use_cuda,
cuda_device=0,
args=train_args)
# Train the model
self.model.train_model(train_df, eval_df=eval_df)
# Evaluate the model
result, model_outputs, wrong_predictions = self.model.eval_model(
eval_df, acc=sklearn.metrics.accuracy_score)
def generate_prob_matrix(arguments):
my_args = {
"max_seq_length": 256,
"train_batch_size": 16,
"eval_batch_size": 16,
"do_lower_case": True,
"manual_seed": 17
}
model = ClassificationModel('bert', "relation_processing/model/bert", use_cuda=False, args=my_args)
num_arguments = len(arguments)
prob_matrix = np.zeros((num_arguments, num_arguments))
for rel_from in range(1, num_arguments):
for rel_to in arguments[rel_from].compare_list:
if rel_from == rel_to:
continue
logging.info("calculating: " + str(rel_from) + "-->" + str(rel_to))
timer = datetime.now()
predictions, raw_outputs = model.predict([[arguments[rel_to].sentence, arguments[rel_from].sentence]])
rel = softmax(raw_outputs, axis=1)
Stats.h_bert_time += datetime.now() - timer
Stats.h_bert += 1
logging.debug(rel)
prob_matrix[rel_to][rel_from] = rel[0][1]
return prob_matrix
def train(self, train_data: object, eval_data: object) -> object:
"""
Create and train the chosen model based on the args
Parameters
----------
train_data : object
train split of the train_data.
eval_data : object
validation split of the train_data.
Returns
-------
object
model.
"""
# Create a ClassificationModel
model = ClassificationModel(
self.model_name,
self.model_type,
args=self.model_args,
use_cuda=self.cuda,
num_labels=len(self.labels) - 1,
)
# Train the model
model.train_model(train_df=train_data,
eval_df=eval_data,
accuracy=accuracy_score)
return model
def test_binary_classification(model_type, model_name):
# Train and Evaluation data needs to be in a Pandas Dataframe of two columns.
# The first column is the text with type str, and the second column is the
# label with type int.
train_data = [
["Example sentence belonging to class 1", 1],
["Example sentence belonging to class 0", 0],
]
train_df = pd.DataFrame(train_data)
eval_data = [
["Example eval sentence belonging to class 1", 1],
["Example eval sentence belonging to class 0", 0],
]
eval_df = pd.DataFrame(eval_data)
# Create a ClassificationModel
model = ClassificationModel(
model_type,
model_name,
use_cuda=False,
args={
"reprocess_input_data": True,
"overwrite_output_dir": True
},
)
# Train the model
model.train_model(train_df)
# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
def run_trainers(bucket_dir, train_args=None):
os.makedirs('irl_models', exist_ok=True)
if os.path.isfile('completed_irl.txt'):
with open("completed_irl.txt", 'r') as f:
done = [d.replace('\n', '') for d in f.readlines()]
else:
open('completed_irl.txt', 'a').close()
with open("completed_irl.txt", 'r') as f:
done = [d.replace('\n', '') for d in f.readlines()]
for train_file in os.listdir(bucket_dir):
print(train_file[5:])
print(done)
if train_file[5:] not in done:
train_df = pd.read_csv(bucket_dir + '/' + train_file +
'/data_all.tsv',
sep='\t')
train_args['output_dir'] = f'irl_models/{train_file[5:]}/'
train_args['cache_dir'] = f'cache_{train_file[5:]}/'
train_args.update({'wandb_kwargs': {'name': train_file[5:]}})
model = ClassificationModel('roberta',
'roberta-base',
args=train_args)
print(train_df.head())
model.train_model(train_df)
with open("completed_irl.txt", 'a') as f:
f.write(f"{train_file[5:]}\n")
exit()
with open("done.runs", 'w') as f:
f.write(f"Done at {datetime.datetime.now()}")
def eval_stance_clf(model_path, src_path, gen_path, **kwargs):
src = open(src_path, 'r').readlines()
gen = open(gen_path, 'r').readlines()
gen = [i.strip() for i in gen]
src = [i.strip() for i in src]
train_args = {
'learning_rate': 3e-5,
'num_train_epochs': 5,
'reprocess_input_data': True,
'overwrite_output_dir': False,
'process_count': 10,
'train_batch_size': 4,
'eval_batch_size': 400,
'max_seq_length': 300,
"fp16": False
}
model = ClassificationModel('roberta',
model_path,
num_labels=4,
use_cuda=True,
cuda_device=0,
args=train_args)
input = [[i, j] for i, j in zip(src, gen)]
predictions, raw_outputs = model.predict(input)
th = Counter(predictions)
th = sorted(th.items(), key=lambda x: x[0])
print(th)
def transformer(train_df, eval_df, datafile):
#tokenizer = BertTokenizer.from_pretrained("bert-base-dutch-cased")
model = ClassificationModel(
"bert", "bert-base-dutch-cased", use_cuda=False, num_labels=2
) # You can set class weights by using the optional weight argument
# Train the model
model.train_model(train_df)
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
print(model_outputs)
predlist = []
model1_outputs = model_outputs.tolist()
for output in model1_outputs:
if output[0] > output[1]:
prediction = 0
else:
prediction = 1
predlist.append(prediction)
labels = eval_df["labels"].tolist()
print(labels)
print(predlist)
print(classification_report(labels, predlist))
print(confusion_matrix(labels, predlist))
print(accuracy_score(labels, predlist))
def predict_export(data):
X = data[args.predict_partition]['text']
predictions = {}
for class_name in ['arousal', 'valence', 'topic']: #
if class_name in ['arousal', 'valence']:
class_no = 3
else:
class_no = 10
trained_model_path = os.path.join('experiments/best_model/',
class_name + str(False))
model = ClassificationModel(args.model_type,
trained_model_path,
num_labels=class_no)
predictions['prediction_' + class_name], _ = model.predict(X)
predictions['id'] = data[args.predict_partition]['id']
predictions['segment_id'] = data[args.predict_partition]['segment_id']
df = pd.DataFrame.from_dict(predictions) # , orient='index' .T
header_names = [
'id', 'segment_id', 'prediction_arousal', 'prediction_valence',
'prediction_topic'
]
df[header_names].to_csv(output_path + args.predict_partition + '.csv',
header=header_names,
index=False)
def __init__(self,
use_cuda=torch.cuda.is_available(),
cuda_device=0,
batch_size=16):
self.model_type = "empathy"
train_args["eval_batch_size"] = batch_size
model_path = os.path.join(os.path.dirname(__file__), "models/empathy/")
model_file = os.path.join(os.path.dirname(__file__),
"models/empathy.tar.gz")
if not os.path.isdir(model_path):
model = f'{self.model_type}_model'
if not os.path.isfile(model_file):
logger.info(
f'Model {self.model_type} does not exist at {model_path}. Attempting to download it.'
)
fetch_pretrained_model(model, model_file)
unzip_simple_transformer_model(model, model_path, model_file)
# Create a ClassificationModel
self.model = ClassificationModel('roberta',
model_path,
num_labels=1,
use_cuda=use_cuda,
cuda_device=cuda_device,
args=train_args)
def train(self, args={}, cleanFN=CleanText().cleanText):
self.logger.debug("Train Simpletransformer")
isCudaAvailable = torch.cuda.is_available()
if not isCudaAvailable:
self.logger.warning("Training on CPU!")
_modelArgs = self.modelArgs(args)
self.logger.debug("ModelArgs: ")
self.logger.debug("\n" + pformat(_modelArgs))
self.loadData(cleanFN, _modelArgs)
self.model = ClassificationModel(model_type=self.model_type,
model_name=self.model_name,
args=_modelArgs,
use_cuda=isCudaAvailable,
num_labels=2)
if _modelArgs["lazy_loading"]:
if not (isinstance(self.trainData, str)
and isinstance(self.testData, str)):
self.logger.error("Lazy loading requires a string to a path.")
self.logger.error(f"Train-Data-Type: {type(self.trainData)}")
self.logger.error(f"Test-Data-Type: {type(self.testData)}")
return None
return self.model.train_model(train_df=self.trainData,
eval_df=self.testData)
class TransformerModel:
"""
This class provides the Machine Learning model and classifies tenders based on previous training data.
"""
def load_model(self):
if not self.model:
from simpletransformers.classification import ClassificationModel
try:
self.model = ClassificationModel('bert',
'./outputs/',
use_cuda=False,
args=args)
except Exception as ex:
logger.error(
f"could not load model from /outputs due to {str(ex)}, creating new model"
)
self.create_new_model()
def __init__(self):
self.model = None
def __convert_to_input(self, tenders):
titles = list(map(lambda x: x.get_title("DE"), tenders))
return titles
def classify(self, tenders):
self.load_model()
titles = self.__convert_to_input(tenders)
predictions, raw_output = self.model.predict(titles)
tuples = zip(tenders, predictions)
selected_tenders = [t for t, p in tuples if p == 1]
return selected_tenders
def train(self, labelled_tenders):
self.load_model()
tenders = [i for i, j in labelled_tenders]
tenders = self.__convert_to_input(tenders)
labels = [j for i, j in labelled_tenders]
tenders_train, tenders_test, labels_train, labels_test = train_test_split(
tenders, labels, test_size=0.1, random_state=42)
data_input = pd.DataFrame(zip(tenders_train, labels_train))
self.model.train_model(data_input)
labels_pred, raw_output = self.model.predict(tenders_test)
tn, fp, fn, tp = confusion_matrix(labels_test, labels_pred).ravel()
logger.info(f"tn: {tn} fp: {fp}")
logger.info(f"fn: {fn} tp:{tp}")
def create_new_model(self):
from simpletransformers.classification import ClassificationModel
self.model = ClassificationModel('bert',
'bert-base-german-cased',
use_cuda=False,
args=args)
def objective(args):
pbar.update(1)
try:
# cast np values to python and convert list to dict
args = list(map(int, args[:3])) + list(map(float, args[3:]))
args = dict(
zip([
'train_batch_size', 'gradient_accumulation_steps',
'weight_decay', 'learning_rate', 'learning_rate',
'adam_epsilon', 'warmup_ratio', 'max_grad_norm'
], args))
args['overwrite_output_dir'] = True
args['eval_batch_size'] = args['train_batch_size']
model = ClassificationModel('albert', 'albert-base-v1', num_labels=5)
# train model, find reverse f1, force garbage collection
model.train_model(train, args=args)
result, *_ = model.eval_model(test,
f1=f1_multiclass,
acc=accuracy_score)
del model
return 1. - result['f1']
except:
print('skip')
return 1.
def __init__(self,
dir_path,
model_path,
resources_path,
use_cuda,
debugging=False):
self.dir_path = dir_path
self.model_path = model_path
self.resources_path = resources_path
self.debugging = debugging
self.mapper = FineGrainedClassifier(self.resources_path)
if os.path.exists(model_path) is False:
print('Model Path not found!')
return
#initializing models
bert_model_path = os.path.join(model_path, "bert_model")
self.bert_model = ClassificationModel('bert',
bert_model_path,
use_cuda=use_cuda,
args={'from_tf': False})
print("Initialized BERT model")
self.svm_est_model = pickle.load(
open(os.path.join(model_path, 'svm_estimator.sav'), 'rb'))
print("Initialized SVM model.")
self.lr_model = pickle.load(
open(os.path.join(model_path, 'lr.sav'), 'rb'))
print("Initialized LR model.")
def get_evaluation_parameter(model):
eval_df = pd.read_csv("data/reviews/new_test.csv", header=None)
eval_df.columns = ["text", "labels"]
model_type = f'outputs/{model}/best_model'
model = ClassificationModel(model, model_type)
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
print('Results:', result)
print('Outputs:', model_outputs)
plots = []
differences = []
max_difference = 0
min_difference = 5
for i in range(len(model_outputs)):
value = round(abs(model_outputs[i] - eval_df['labels'][i]), 2)
actual = round(eval_df['labels'][i], 2)
plots.append([actual, model_outputs[i], value])
if value > max_difference:
max_difference = value
if value < min_difference:
min_difference = value
differences.append(value)
print('Max Difference:', max_difference) # 3.8447265625
print('Min Difference:', min_difference) # 0.0
parameter = sum(differences) / len(differences)
print('Parameter:', parameter) # 0.40202807008058644
pd.DataFrame(differences).to_csv("test.csv", index=None)
pd.DataFrame(plots).to_csv("plots.csv", index=None)
def train(train_df, max_sub_len, output_dir):
model_type = 'distilbert'
lr = 2e-5
sent_length = max_sub_len
OUTPUT_DIR = output_dir \
+ str(datetime.datetime.now())[:19] + '_' + model_type + '_' + str(sent_length) + '_' + str(lr)
print("model is saved at: {}".format(OUTPUT_DIR))
training_config = {
'output_dir': OUTPUT_DIR,
'reprocess_input_data': True,
'overwrite_output_dir': True,
'num_train_epochs': 2,
'train_batch_size': 32,
'eval_batch_size': 32,
'learning_rate': lr,
'max_seq_length': sent_length
}
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)
model = ClassificationModel(model_type,
'distilbert-base-cased',
num_labels=4,
args=training_config)
torch.cuda.empty_cache()
model.train_model(train_df)
return model
def bert_predictions(tweet: pd.DataFrame, model: ClassificationModel):
"""
Bert Inference for prediction.
:param tweet: dataframe with tweets
:param model: Bert Model
:return: list of pr
"""
tweet = tweet.values.tolist()
try:
predictions, raw_outputs = model.predict(tweet)
except:
for element in tweet.iteritems():
model.predict([element])
print("STOPP")
auswertung = collections.Counter(predictions)
gc.collect()
# df = pd.DataFrame(raw_outputs)
# df['predictions'] = pd.DataFrame(predictions)
# df['tweets'] = pd.DataFrame(tweet)
# df = df.replace(r'\n', ' ', regex=True)
# df_softmax = pd.DataFrame(softmax(raw_outputs, axis=1))
# df['softmax0'] = df_softmax[0]
# df['softmax1'] = df_softmax[1]
# db_functions.df_to_sql(df, 'temp_table', 'replace')
return auswertung
def fake_classify(train_set, eval_set, test_set, seed):
# Create a TransformerModel
model = ClassificationModel('bert',
'bert-base-multilingual-uncased',
args={
'num_train_epochs': 3,
'overwrite_output_dir': True,
'manual_seed': seed
},
use_cuda=True)
print(model.args)
# Train the model
model.train_model(train_set)
# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(
test_set,
f1=sklearn.metrics.f1_score,
acc=sklearn.metrics.accuracy_score)
#print('Evaluation results = ', results(results))
#save the model
#import torch
#torch.save(model, path) --> no need to do this, model gets saved in output dir
return result, model_outputs, wrong_predictions
def __init__(self,
model_type: str,
model_name_or_path: Union[str, Path],
output_dir: Path,
class_weights: Optional[List[float]] = None
):
print('class weights: {}'.format(class_weights))
self.output_dir = output_dir
self.cache_dir = output_dir / 'cache/'
self.tensorboard_dir = output_dir / 'runs/'
self.best_model_dir = output_dir / 'output/best_model/'
self.model_type = model_type
self.model_name_or_path = model_name_or_path
self.model = ClassificationModel(self.model_type,
str(self.model_name_or_path),
cache_dir='/media/sarthak/HDD/data_science/fnp_resources/pretrained_models/',
args={'fp_16': True,
'output_dir': str(self.output_dir),
'cache_dir': str(self.cache_dir),
'tensorboard_dir': str(self.tensorboard_dir),
'best_model_dir': str(self.best_model_dir)},
weight=class_weights
)
self.class_weights = class_weights
def main(source=source,
data_dir='data',
checkpoint_dir="outputs/eval2/roberta_finetune_nogptneo",
best_model_dir='outputs/eval2/best_model_roberta_finetune_nogptneo',
n_train=240000,
n_valid=4000,
n_test=4000,
n_epochs=10,
learning_rate=4e-05,
train_batch_size=64,
eval_batch_size=64,
evaluate_during_training=True,
evaluate_during_training_steps=2000,
reprocess_input=True,
overwrite_output_dir=True,
n_gpu=2):
# import pdb; pdb.set_trace()
train_df = data_loading.load_split(data_dir, source, 'train', n=n_train)
valid_df = data_loading.load_split(data_dir,
source_test,
'valid',
n=n_valid)
test_df = data_loading.load_split(data_dir, source_test, 'test', n=n_test)
# Optional model configuration
model_args = ClassificationArgs(
num_train_epochs=n_epochs,
evaluate_during_training=evaluate_during_training,
evaluate_during_training_steps=evaluate_during_training_steps,
best_model_dir=best_model_dir,
manual_seed=0,
train_batch_size=train_batch_size,
eval_batch_size=eval_batch_size,
overwrite_output_dir=overwrite_output_dir,
n_gpu=n_gpu,
output_dir=checkpoint_dir,
reprocess_input_data=reprocess_input,
learning_rate=learning_rate)
# Create a ClassificationModel
model = ClassificationModel("roberta",
model_name="roberta-large",
args=model_args,
use_cuda=True)
# Train the model
model.train_model(train_df,
eval_df=valid_df,
f1=sklearn.metrics.f1_score,
acc=sklearn.metrics.accuracy_score,
eer=eer)
# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(
test_df,
f1=sklearn.metrics.f1_score,
acc=sklearn.metrics.accuracy_score,
eer=eer)
class TransformerModel(TenderClassClassifier):
"""
This class provides the Machine Learning model and classifies tenders based on previous training data.
"""
def __init__(self):
self.model = None
def load(self, name):
self.model = ClassificationModel('bert',
'./outputs/',
use_cuda=cuda_available,
args=args)
def save(self, name):
pass
def __convert_to_input(self, tenders):
titles = list(map(lambda x: x.get_title("DE"), tenders))
return titles
def classify(self, tenders):
titles = self.__convert_to_input(tenders)
predictions, raw_output = self.model.predict(titles)
tuples = zip(tenders, predictions)
selected_tenders = [t for t, p in tuples if p == 1]
return selected_tenders
def train(self, labelled_tenders):
tenders = [i for i, j in labelled_tenders]
tenders = self.__convert_to_input(tenders)
labels = [j for i, j in labelled_tenders]
tenders_train, tenders_test, labels_train, labels_test = train_test_split(
tenders, labels, test_size=0.1, random_state=42)
data_input = pd.DataFrame(zip(tenders_train, labels_train))
start = time.time()
self.model.train_model(data_input)
end = time.time()
print(end - start)
labels_pred, raw_output = self.model.predict(tenders_test)
tn, fp, fn, tp = confusion_matrix(labels_test, labels_pred).ravel()
logger.info(f"tn: {tn} fp: {fp}")
logger.info(f"fn: {fn} tp:{tp}")
logger.info(
f"Accuracy Score: {accuracy_score(labels_test, labels_pred)}")
def create_new_model(self):
from simpletransformers.classification import ClassificationModel
self.model = ClassificationModel('bert',
'bert-base-german-cased',
use_cuda=cuda_available,
args=args)
