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import numpy as np |
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import pandas as pd |
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import os |
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import matplotlib.pyplot as plt |
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from pathlib import Path |
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from typing import Union |
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import os |
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def delete_file(file_pt: Path) -> None: |
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try: |
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file_pt.unlink() |
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except FileNotFoundError: |
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pass |
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def full_path(inp_dir_or_path: str) -> Path: |
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"""Returns full path""" |
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return Path(inp_dir_or_path).expanduser().resolve() |
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def mkdir_p(inp_dir_or_path: Union[str, Path]) -> Path: |
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"""Give a file/dir path, makes sure that all the directories exists""" |
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inp_dir_or_path = full_path(inp_dir_or_path) |
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if inp_dir_or_path.suffix: |
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inp_dir_or_path.parent.mkdir(parents=True, exist_ok=True) |
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else: |
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inp_dir_or_path.mkdir(parents=True, exist_ok=True) |
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return inp_dir_or_path |
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def similarity_between_sent(sent1_encoded, sent2_encoded): |
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"""report the avg. cosine similarity score b.w two pairs of sentences""" |
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similarity_scores = [] |
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for i in range(len(sent1_encoded)): |
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similarity_scores.append(cosine_similarity( |
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sent1_encoded[i], sent2_encoded[i])) |
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return np.mean(similarity_scores),similarity_scores |
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def cosine_similarity(a, b): |
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""" |
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Takes 2 vectors a, b and returns the cosine similarity according |
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to the definition of the dot product |
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""" |
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dot_product = np.dot(a, b) |
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norm_a = np.linalg.norm(a) |
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norm_b = np.linalg.norm(b) |
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return dot_product / (norm_a * norm_b) |
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def load_data(path): |
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if path.endswith(".csv"): |
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data=pd.read_csv(path) |
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else: |
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data=pd.read_csv(path,sep="\t") |
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if not isinstance(data,pd.DataFrame): |
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raise ValueError("Data should be in pandas DataFrame format") |
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return data |
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def read_data(dataset): |
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if dataset == "mrpc": |
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data = load_data("/home/yash/EMNLP-2024/data/mrpc.csv") |
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data = data.copy() |
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elif dataset == "qqp": |
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data = load_data("/home/yash/EMNLP-2024/data/qoura.csv") |
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data = data.copy().dropna() |
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data.columns = data.columns.str.strip() |
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data = data.rename(columns={"is_duplicate":"label",'question1':"sentence1","question2":"sentence2"}) |
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elif dataset in ["paws","paw","wiki"]: |
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path = "/home/yash/EMNLP-2024/data/paw_wiki.tsv" |
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data = load_data(path) |
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data = data.copy() |
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else: |
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ValueError("No dataset found.") |
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return data |
