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import pickle
import matplotlib.pyplot as plt
import seaborn as sns
import torch
from datasets import DatasetDict, Dataset
from sklearn.metrics import confusion_matrix
def get_datasetdict_object(df_train, df_test):
mapper = {"#2_tweet": "tweet", "#3_country_label": "label"}
columns_to_keep = ["tweet", "label"]
df_train = df_train.rename(columns=mapper)[columns_to_keep]
df_test = 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 extract_hidden_state(input_text, tokenizer, language_model):
tokens = tokenizer(input_text, padding=True, return_tensors="pt")
with torch.no_grad():
outputs = language_model(**tokens)
return outputs.last_hidden_state[:,0].numpy()
def serialize_data(data, output_path:str):
with open(output_path, "wb") as f:
pickle.dump(data, f)
def load_data(input_path:str):
with open(input_path, "rb") as f:
return pickle.load(f)
def plot_confusion_matrix(y_true, y_preds):
labels = sorted(set(y_true.tolist() + y_preds.tolist()))
cm = confusion_matrix(y_true, y_preds)
plt.figure(figsize=(12, 10))
sns.heatmap(cm, annot=True, cmap="Blues",
xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('Confusion Matrix')
plt.show() |