fix: the type hint for function mcar() in comments

pypots/data/utils.py

@@ -76,19 +76,38 @@ def mcar(
  Parameters
  ----------
  X :
- The data to add missing values.
+ Data vector. If X has any missing values, they should be numpy.nan.
 
  rate :
- The missing rate.
+ Artificially missing rate, rate of the observed values which will be artificially masked as missing.
+
+ Note that,
+ `rate` = (number of artificially missing values) / np.sum(~np.isnan(self.data)),
+ not (number of artificially missing values) / np.product(self.data.shape),
+ considering that the given data may already contain missing values,
+ the latter way may be confusing because if the original missing rate >= `rate`,
+ the function will do nothing, i.e. it won't play the role it has to be.
 
  nan :
- The value to fill the missing values.
+ Value used to fill NaN values.
 
  Returns
  -------
- X :
- The data with added missing values.
+ X_intact : array,
+ Original data with missing values (nan) filled with given parameter `nan`, with observed values intact.
+ X_intact is for loss calculation in the masked imputation task.
+
+ X : array,
+ Original X with artificial missing values. X is for model input.
+ Both originally-missing and artificially-missing values are filled with given parameter `nan`.
+
+ missing_mask : array,
+ The mask indicates all missing values in X.
+ In it, 1 indicates observed values, and 0 indicates missing values.
 
+ indicating_mask : array,
+ The mask indicates the artificially-missing values in X, namely missing parts different from X_intact.
+ In it, 1 indicates artificially missing values, and other values are indicated as 0.
  """
  X = corruptor.mcar(X, rate, nan)
  return X