Create postprocess.py

postprocess.py

@@ -0,0 +1,76 @@
+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"