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from __future__ import annotations
from ast import literal_eval
import numpy as np
import pandas as pd
from sklearn.metrics import precision_score, recall_score
def prec_recall_metric(y_pred, y_true):
return {
"precision": precision_score(y_true, y_pred, zero_division="warn", average="weighted"),
"recall": recall_score(y_true, y_pred, zero_division="warn", average="weighted"),
}
def softmax(x):
return np.exp(x - np.max(x)) / np.exp(x - np.max(x)).sum()
def eval_strings_if_necessary(series: pd.Series) -> pd.Series:
return series.map(literal_eval) if isinstance(series.iloc[0], str) else series
def filter_short_sentences(sentences, actual_reference_keywords):
return [x for x in sentences if len(x) > min(len(x) for x in actual_reference_keywords) + 20]
def prepare_reference_annotations_df(df: pd.DataFrame):
if df.loc[(df.label != "SELF") & (df.label.notnull())].empty:
raise ValueError("No expert annotations found in the dataset of references.")
df = df.loc[lambda df_: (df_.label != "SELF") & (df_.label.notnull())].assign(
segments=lambda df_: eval_strings_if_necessary(df_.segments)
)
df.segments = df.apply(
lambda row: filter_short_sentences(row["segments"], row["actual_reference_keywords"]), axis=1
)
df = df.loc[lambda df_: df_.segments.map(len) > 0]
return df
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