Spaces:
Sleeping
Sleeping
File size: 4,810 Bytes
3698678 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
from datasets import DatasetDict, Dataset
import numpy as np
import pandas as pd
import torch
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score
from transformers import AutoModel, AutoTokenizer
from .utils import serialize_data, load_data
class PreProcessor:
def __init__(self, model_name, train_path:str, test_path:str, output_path:str):
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
self.model = AutoModel.from_pretrained(model_name).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.df_train = pd.read_csv(train_path, sep="\t")
self.df_test = pd.read_csv(test_path, sep="\t")
self.output_path = output_path
def _get_datasetdict_object(self):
mapper = {"#2_tweet": "tweet", "#3_country_label": "label"}
columns_to_keep = ["tweet", "label"]
df_train = self.df_train.rename(columns=mapper)[columns_to_keep]
df_test = self.df_test.rename(columns=mapper)[columns_to_keep]
train_dataset = Dataset.from_pandas(df_train)
test_dataset = Dataset.from_pandas(df_test)
data = DatasetDict({'train': train_dataset, 'test': test_dataset})
return data
def _tokenize(self, batch):
return self.tokenizer(batch["tweet"], padding=True)
def _encode_data(self, data):
data_encoded = data.map(self._tokenize, batched=True, batch_size=None)
return data_encoded
def _extract_hidden_states(self, batch):
inputs = {k:v.to(self.device) for k,v in batch.items()
if k in self.tokenizer.model_input_names}
with torch.no_grad():
last_hidden_state = self.model(**inputs).last_hidden_state
return {"hidden_state": last_hidden_state[:,0].cpu().numpy()}
def _get_features(self, data_encoded):
data_encoded.set_format("torch", columns=["input_ids", "attention_mask", "label"])
data_hidden = data_encoded.map(self._extract_hidden_states, batched=True, batch_size=50)
return data_hidden
def preprocess_data(self):
data = self._get_datasetdict_object()
data_encoded = self._encode_data(data)
data_hidden = self._get_features(data_encoded)
serialize_data(data_hidden, output_path=self.output_path)
class Model():
def __init__(self, data_input_path:str, model_name:str):
self.model_name = model_name
self.model = None
self.data = load_data(input_path=data_input_path)
self.X_train = np.array(self.data["train"]["hidden_state"])
self.X_test = np.array(self.data["test"]["hidden_state"])
self.y_train = np.array(self.data["train"]["label"])
self.y_test = np.array(self.data["test"]["label"])
def _train_logistic_regression(X_train, y_train):
lr_model = LogisticRegression(multi_class='multinomial',
class_weight="balanced",
max_iter=1000,
random_state=2024)
lr_model.fit(X_train, y_train)
return lr_model
def train_model(self, output_path):
if self.model_name != "lr":
raise ValueError(f"Model name {self.model_name} does not exist. Please try 'lr'!")
lr_model = self._train_logistic_regression(self.X_train, self.y_train)
self.model = lr_model
serialize_data(lr_model, output_path)
def _get_metrics(self, y_true, y_preds):
accuracy = accuracy_score(y_true, y_preds)
f1_macro = f1_score(y_true, y_preds, average="macro")
f1_weighted = f1_score(y_true, y_preds, average="weighted")
print(f"Accuracy: {accuracy}")
print(f"F1 macro average: {f1_macro}")
print(f"F1 weighted average: {f1_weighted}")
def evaluate_predictions(self):
train_preds = self.model.predict(self.X_train)
test_preds = self.model.predict(self.X_test)
print(self.model_name)
print("\nTrain set:")
self._get_metrics(self.y_train, train_preds)
print("-"*50)
print("Test set:")
self._get_metrics(self.y_test, test_preds)
def main():
file_path = "../data/data_hidden.pkl"
preprocessor = PreProcessor(model_name="moussaKam/AraBART",
train_path="../data/DA_train_labeled.tsv",
test_path="../data/DA_dev_labeled.tsv",
output_path=file_path)
preprocessor.preprocess_data()
model = Model(data_input_path=file_path, model_name="lr")
model.train_model("../models/logistic_regression.pkl")
model.evaluate_predictions()
if __name__ == "__main__":
main()
|