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import numpy as np
def softmax(x: np.ndarray, axis=1) -> np.ndarray:
"""
Computes softmax array along the specified axis.
"""
e_x = np.exp(x)
return e_x / e_x.sum(axis=axis, keepdims=True)
def calibrate_sentiment_score(
sentiment: float,
thresh_neg: float,
thresh_pos: float,
zero: float = 0,
) -> float:
if thresh_neg != (zero - 1) / 2:
alpha_neg = -(3 * zero - 1 - 4 * thresh_neg) / (2 * zero - 2 - 4 * thresh_neg) / 2
if -1 < alpha_neg and alpha_neg < 0:
raise ValueError(f"Incorrect value: {thresh_neg=} is too far from -0.5!")
if thresh_pos != (zero + 1) / 2:
alpha_pos = -(4 * thresh_pos - 1 - 3 * zero) / (2 + 2 * zero - 4 * thresh_pos) / 2
if 0 < alpha_pos and alpha_pos < 1:
raise ValueError(f"Incorrect value: {thresh_pos=} is too far from 0.5!")
if sentiment < 0:
return (2 * zero - 2 - 4 * thresh_neg) * sentiment**2 + (3 * zero - 1 - 4 * thresh_neg) * sentiment + zero
elif sentiment > 0:
return (2 + 2 * zero - 4 * thresh_pos) * sentiment**2 + (4 * thresh_pos - 1 - 3 * zero) * sentiment + zero
return zero
def calibrate_sentiment(
sentiments: np.ndarray[float],
thresh_neg: float,
thresh_pos: float,
zero: float,
) -> np.ndarray[np.float64]:
result = np.array(
[
calibrate_sentiment_score(sentiment, thresh_neg=thresh_neg, thresh_pos=thresh_pos, zero=zero)
for sentiment in sentiments
]
)
return result.astype(np.float64)
def scale_value(value, in_min, in_max, out_min, out_max):
if in_min <= value <= in_max:
scaled_value = (value - in_min) / (in_max - in_min) * (out_max - out_min) + out_min
return scaled_value.round(3)
else:
raise ValueError(f"Input value must be in the range [{in_min}, {in_max}]")
def get_sentiment(
logits: np.ndarray,
thresh_neg: float,
thresh_pos: float,
zero: float,
):
probabilities = softmax(logits, axis=1)
sentiments = np.matmul(probabilities, np.arange(5)) / 2 - 1
score = calibrate_sentiment(
sentiments=sentiments,
thresh_neg=thresh_neg,
thresh_pos=thresh_pos,
zero=zero,
)[0]
if score < -0.33:
return scale_value(score, -1, -0.33, 0, 1), "NEGATIVE"
elif score < 0.33:
return scale_value(score, -0.33, 0.33, 0, 1), "NEUTRAL"
else:
return scale_value(score, 0.33, 1, 0, 1), "POSITIVE" |