zhengruifeng commented on a change in pull request #32822:
URL: https://github.com/apache/spark/pull/32822#discussion_r649624193
##########
File path: mllib-local/src/main/scala/org/apache/spark/ml/impl/Utils.scala
##########
@@ -94,4 +95,43 @@ private[spark] object Utils {
math.log1p(math.exp(x))
}
}
+
+ /**
+ * Perform in-place softmax conversion.
+ */
+ def softmax(values: Array[Double]): Unit = {
+ var maxValue = Double.MinValue
+ var i = 0
+ while (i < values.length) {
+ val value = values(i)
+ if (value.isPosInfinity) {
+ java.util.Arrays.fill(values, 0)
+ values(i) = 1.0
+ return
+ } else if (value > maxValue) {
+ maxValue = value
+ }
+ i += 1
+ }
+
+ var sum = 0.0
+ i = 0
+ if (maxValue > 0) {
Review comment:
sounds reasonable
##########
File path: mllib-local/src/main/scala/org/apache/spark/ml/impl/Utils.scala
##########
@@ -94,4 +95,43 @@ private[spark] object Utils {
math.log1p(math.exp(x))
}
}
+
+ /**
+ * Perform in-place softmax conversion.
+ */
+ def softmax(values: Array[Double]): Unit = {
+ var maxValue = Double.MinValue
+ var i = 0
+ while (i < values.length) {
+ val value = values(i)
+ if (value.isPosInfinity) {
+ java.util.Arrays.fill(values, 0)
+ values(i) = 1.0
+ return
+ } else if (value > maxValue) {
+ maxValue = value
+ }
+ i += 1
+ }
+
+ var sum = 0.0
+ i = 0
+ if (maxValue > 0) {
Review comment:
```py
def softmax(X, copy=True):
"""
Calculate the softmax function.
The softmax function is calculated by
np.exp(X) / np.sum(np.exp(X), axis=1)
This will cause overflow when large values are exponentiated.
Hence the largest value in each row is subtracted from each data
point to prevent this.
Parameters
----------
X : array-like of float of shape (M, N)
Argument to the logistic function.
copy : bool, default=True
Copy X or not.
Returns
-------
out : ndarray of shape (M, N)
Softmax function evaluated at every point in x.
"""
if copy:
X = np.copy(X)
max_prob = np.max(X, axis=1).reshape((-1, 1))
X -= max_prob
np.exp(X, X)
sum_prob = np.sum(X, axis=1).reshape((-1, 1))
X /= sum_prob
return X
```
softmax in scikit-learn does not check whether the maxvalue is positive or
not
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