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Commit dab032d8 authored by Kaif Siddique's avatar Kaif Siddique
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-Cleanup: 1. Removed 'english' folder and remane the main folder name to... 

-Cleanup: 1. Removed 'english' folder and remane the main folder name to 'lang'. 2. Created new executable
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Source/Skript/english/run_SAG_mnli.py deleted 100644 → 0
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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Code is originally from https://github.com/nlpyang/pytorch-transformers/blob/master/examples/run_glue.py
# Adapted to the SAG task by Ulrike Pado, HFT Stuttgart: Run a fine-tuned model on given input data to predict short-answer grades.
from __future__ import absolute_import, division, print_function
import argparse
import os
import random
import sys
import numpy as np
import torch
from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
from sklearn.metrics import f1_score, accuracy_score
from sklearn.metrics import classification_report
from transformers import (
BertConfig,
BertForSequenceClassification,
BertTokenizer,
)
from transformers import glue_compute_metrics as compute_metrics
from transformers import (
glue_convert_examples_to_features as convert_examples_to_features,
)
from transformers.data.processors.utils import (
DataProcessor,
InputExample,
)
#logger = logging.getLogger(__name__)
MODEL_CLASSES = {
"bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
}
def set_seed():
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
def evaluate(args, model, tokenizer, prefix=""):
# Loop to handle MNLI double evaluation (matched, mis-matched)
# and SemEval evaluation (unseen questions, unseen answers, unseen domains)
eval_task_names = ("sag",
)
eval_outputs_dirs = (
(args.output_dir, )
)
results = {}
for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
eval_dataset = load_and_cache_examples(
args, eval_task, tokenizer
)
if not os.path.exists(eval_output_dir):
os.makedirs(eval_output_dir)
args.eval_batch_size = 8
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(
eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size
)
# Eval!
#logger.info("***** Running evaluation {} *****".format(prefix))
#logger.info(" Task name = {}".format(eval_task))
#logger.info(" Num examples = %d", len(eval_dataset))
#logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
for batch in eval_dataloader:
#logger.info(" Starting eval for batch")
model.eval()
batch = tuple(t.to(args.device) for t in batch)
#logger.info(" Batch converted to tuple")
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
"labels": batch[3],
}
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
#logger.info("Eval loss: %d", eval_loss)
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0
)
#logger.info("Prediction generation done")
# classification task; choose maximum label
preds = np.argmax(preds, axis=1)
# if evaluating SAG, return both accuracy and F1
task = "sag"
# logger.info("starting to compute metrics")
result = my_compute_metrics(task, preds, out_label_ids)
results.update(result)
# print predictions made by the current model
if args.do_print_predictions:
print_predictions(args, preds)
output_eval_file = os.path.join(
eval_output_dir, prefix + "-eval_results.txt")
#logger.info("sending output to "+str(output_eval_file));
with open(output_eval_file, "w") as writer:
#logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(result.keys()):
#logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
return results
def load_and_cache_examples(args, task, tokenizer):
examples = []
# choose the correct processor to read the data
processor = (
SemEvalProcessor()
)
output_mode = "classification"
#logger.info("Creating features from dataset file at %s", args.data_dir)
label_list = processor.get_labels()
examples = (
processor.get_test_examples(args.data_dir)
)
# We are continuing to train mnli models, so task = mnli to create
# the correct type of features
feature_task = "mnli" if task.startswith("sag") else task
features = convert_examples_to_features(
examples,
tokenizer,
label_list=label_list,
max_length=args.max_seq_length,
output_mode=output_mode,
task=feature_task
)
# Convert to Tensors and build dataset
all_input_ids = torch.tensor(
[f.input_ids for f in features], dtype=torch.long)
all_attention_mask = torch.tensor(
[f.attention_mask for f in features], dtype=torch.long
)
all_token_type_ids = torch.tensor(
[f.token_type_ids for f in features], dtype=torch.long
)
# do classification setup
all_labels = torch.tensor(
[f.label for f in features], dtype=torch.long)
dataset = TensorDataset(
all_input_ids, all_attention_mask, all_token_type_ids, all_labels
)
return dataset
def main():
# Where are we?
location=".";
if getattr(sys, 'frozen', False):
# running in a bundle
location = sys._MEIPASS
# open a log file next to the executable with line buffering
#out = open("log.txt", "a", buffering=1);
#print("Started English processing in", location, file=out);
parser = argparse.ArgumentParser()
# Required parameters - adapt to current directory
parser.add_argument(
"--data_dir",
# default=None,
default=location+"\\Skript\\outputs\\",
type=str,
# required=True,
required=False,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
)
parser.add_argument(
"--model_type",
# default=None,
default="bert",
type=str,
# required=True,
required=False,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES),
)
parser.add_argument(
"--model_name_or_path",
# default=None,
#default= "textattack/bert-base-uncased-MNLI",
default=location+"\\Skript\\english\\seb-bert-mnli",
type=str,
# required=True,
required=False,
help="Path to pre-trained model",
)
parser.add_argument(
"--tokenizer_name",
default="textattack/bert-base-uncased-MNLI",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--output_dir",
# default=None,
default=location+"\\Skript\\english\\seb-bert-mnli",
type=str,
# required=True,
required=False,
help="The output directory where checkpoints will be written.",
)
parser.add_argument(
"--config_name",
default=location+"\\Skript\\english\\seb-bert-mnli\\config.json",
type=str,
help="Pretrained config name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from s3",
)
parser.add_argument(
"--max_seq_length",
# default=128,
default=256,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
# "--do_test", action="store_true", help="Whether to run eval on the test set."
"--do_test", action="store_false", help="Whether to run eval on the test set."
),
parser.add_argument(
#"--do_print_predictions",action="store_true",help="Whether to print the model predictions for manual inspection.",
"--do_print_predictions",
action="store_false",
help="Whether to print the model predictions for manual inspection.",
),
parser.add_argument(
"--do_lower_case",
# action="store_true",
action="store_false",
help="Set this flag if you are using an uncased model.",
)
parser.add_argument(
"--overwrite_output_dir",
# action="store_true",
action="store_false",
help="Overwrite the content of the output directory",
)
args = parser.parse_args()
if (
os.path.exists(args.output_dir)
and os.listdir(args.output_dir)
and not args.overwrite_output_dir
):
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
args.output_dir
)
)
# Setup CPU processing
device = torch.device("cpu")
args.device = device
# Setup logging
#logging.basicConfig(
# format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
# datefmt="%m/%d/%Y %H:%M:%S",
# filename='log.txt',
# filemode='a',
# level=logging.INFO,
#)
#logger.warning(
# "Device: %s",
# device
#)
# Set seed to 42
set_seed()
processor = (
SemEvalProcessor()
)
args.output_mode = (
"classification"
)
label_list = processor.get_labels()
num_labels = len(label_list)
args.model_type = args.model_type.lower()
#logger.info("Model %s", args.model_type)
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name if args.tokenizer_name else
args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model.to(args.device)
#logger.info("Training/evaluation parameters %s", args)
# Evaluation
results = {}
if args.do_test:
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name if args.tokenizer_name else
args.model_name_or_path,
do_lower_case=args.do_lower_case,
)
checkpoints = [args.output_dir]
#logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
"-")[-1] if len(checkpoints) > 1 else ""
prefix = str(global_step)
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
result = dict((k + "_{}".format(global_step), v)
for k, v in result.items())
results.update(result)
else: # use currently active model
result = evaluate(args, model, tokenizer, prefix="test")
#results.update(result)
return results
# define a new data processor for the SemEval data/SAG task
class SemEvalProcessor(DataProcessor):
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
)
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
)
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test"
)
def get_labels(self):
"""See base class."""
return ["correct", "incorrect", "NONE"]
def _create_examples(self, lines, set_type):
"""Creates examples for the test set."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = line[0]
text_a = line[1]
text_b = line[2]
label = line[-1]
examples.append(
InputExample(guid=guid, text_a=text_a,
text_b=text_b, label=label)
)
return examples
# custom metrics for SAG: F1 and Accuracy
def my_compute_metrics(eval_task, preds, labels):
result = {}
if eval_task == "sag":
acc = accuracy_score(y_pred=preds, y_true=labels)
f1_weighted = f1_score(y_pred=preds, y_true=labels, average="weighted")
f1_macro = f1_score(y_pred=preds, y_true=labels, average="macro")
result = {"f1-weighted": f1_weighted,
"f1-macro": f1_macro, "accuracy": acc}
else:
result = compute_metrics(eval_task, preds, labels)
return result
def print_predictions(args, preds):
# generate data set part of output path
dir_name = (""
)
# get examples
processor = (
SemEvalProcessor()
)
examples = (
processor.get_test_examples(args.data_dir)
)
# observed grade list created
obs_grade = [ex.label for ex in examples]
# suggested grade list created
sugg_grade = ['correct' if pred == 0 else 'incorrect' for pred in preds]
# flag: do observed grades exist?
count=0
# Check if obs_grade contains "NONE" values or is empty
if not obs_grade or all(grade == 'NONE' for grade in obs_grade):
count += 1
else:
# classification report
classification_rep = classification_report(obs_grade, sugg_grade)
report_string = classification_rep
report_lines = report_string.split('\n')
# print(report_lines)
# accuracy line
formatted_accuracy_line = "\t".join(report_lines[5].split())
formatted_acc_line_with_tabs = (formatted_accuracy_line[:formatted_accuracy_line.index('\t',
formatted_accuracy_line.index(
'\t'))] + '\t\t' +
formatted_accuracy_line[
formatted_accuracy_line.index('\t', formatted_accuracy_line.index('\t')):])
# #weighted avg printing
#
wt_avg_line = "\t".join(report_lines[7].split())
new_wt_avg_line = wt_avg_line.replace("\t", " ", 1)
# Join the entire newly formatted list into a single string
formatted_output = "\n".join([
"\t precision \t recall \t f1-score \t support",
"\t".join(report_lines[2].split()),
"\t".join(report_lines[3].split()),
formatted_acc_line_with_tabs,
new_wt_avg_line
])
with open(args.data_dir + "/" + dir_name + "/predictions.txt", "w", encoding="utf8") as writer:
writer.write(
"question\treferenceAnswer\tstudentAnswer\tsuggested grade\tobserved grade\n")
for i in range(len(examples)):
# iterate over data
# print prediction as a text-based label
hrpred = "incorrect"
if preds[i] == 0:
hrpred = "correct"
# get guid, text, from inputExample
writer.write(
str(examples[i].guid)
+ "\t"
+ examples[i].text_a
+ "\t"
+ examples[i].text_b
+ "\t"
+ hrpred
+ "\t"
+ examples[i].label
+ "\n"
)
if count == 1:
writer.write("\nClassification Report cannot be printed as observed grade column is empty or filled "
"with 'NONE' or 'none' values\n")
else:
# Write the classification report to the file
writer.write(
"\nClassification Report - high Precision for classes correct or incorrect indicates that the class prediction is reliable:\n")
writer.write(formatted_output)
if __name__ == "__main__":
main()
Source/Skript/english/seb-bert-mnli/config.json deleted 100644 → 0
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- 35
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{
"_name_or_path": "textattack/bert-base-uncased-MNLI",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"finetuning_task": "sag-seb",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.2.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
Source/Skript/english/seb-bert-mnli/pytorch_model.bin deleted 100644 → 0
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- 0
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Source/Skript/german/models/clf_BERT.pickle deleted 100644 → 0
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File deleted
Source/Skript/german/LICENSE → Source/Skript/lang/LICENSE
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File moved
Source/Skript/german/run_LR_SBERT.py → Source/Skript/lang/run_LR_SBERT.py
+ 1
- 1
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......@@ -52,7 +52,7 @@ def main():
parser.add_argument(
"--model_dir",
# default=None,
default=location + "\\Skript\\german\\models",
default=location + "\\Skript\\lang\\models",
type=str,
# required=True,
required=False,
......
Source/main.py
+ 1
- 1
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......@@ -14,7 +14,7 @@ import tkinter
import pandas as pd
import sys
import os
from Skript.german.run_LR_SBERT import main as backend
from Skript.lang.run_LR_SBERT import main as backend
class App(ttk.Frame):
......
Source/main.spec
+ 2
- 2
View file @ dab032d8
......@@ -37,8 +37,8 @@ datas +=[('azure.tcl', '.'),
('.\\Skript','./Skript')]
a = Analysis(
# ['.\\main.py','.\\Skript\\german\\run_LR_SBERT.py','.\\Skript\\english\\run_SAG_mnli.py', ],
['.\\main.py','.\\Skript\\german\\run_LR_SBERT.py', ],
# ['.\\main.py','.\\Skript\\lang\\run_LR_SBERT.py','.\\Skript\\english\\run_SAG_mnli.py', ],
['.\\main.py','.\\Skript\\lang\\run_LR_SBERT.py', ],
# Hier den Pfad zu den site-packages (entweder des environments oder der zentralen Installation) angeben
pathex=[
'..\\venv\\Lib\\site-packages\\',
......
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