[GitHub] [systemml] j143 commented on a change in pull request #943: [DOC] Documentation for builtin OutlierIQR function

[GitBox <gi...@apache.org>]

j143 commented on a change in pull request #943:
URL: https://github.com/apache/systemml/pull/943#discussion_r435093560



##########
File path: dev/docs/builtins-reference.md
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@@ -291,34 +291,95 @@ y = X %*% rand(rows=ncol(X), 1)
 [C, S] = steplm(X = X, y = y, icpt = 1);
 ```
 
-## `slicefinder`-Function
+## `outlier-Function
 
-The `slicefinder`-function returns top-k worst performing subsets according to a model calculation.
+An outlier in a probability distribution function is a number that is more than 1.5 times the length of the data set away from either the lower or upper quartiles. 
+Specifically, if a number is less than Q1−1.5×IQR or greater than Q3+1.5×IQR, then it is an outlier.
 
 ### Usage
 ```r
-slicefinder(X,W, y, k, paq, S);
+outlier(X,opposite);
 ```
 
 ### Arguments
 | Name    | Type           | Default  | Description |
 | :------ | :------------- | -------- | :---------- |
 | X       | Matrix[Double] | required | Recoded dataset into Matrix |
-| W       | Matrix[Double] | required | Trained model |
-| y       | Matrix[Double] | required | 1-column matrix of response values. |
-| k       | Integer        | 1        | Number of subsets required |
-| paq     | Integer        | 1        | amount of values wanted for each col, if paq = 1 then its off |
-| S       | Integer        | 2        | amount of subsets to combine (for now supported only 1 and 2) |
+|opposite| Boolean | required | Used for xor gate evaluation |
 
 ### Returns
 | Type           | Description |
 | :------------- | :---------- |
-| Matrix[Double] | Matrix containing the information of top_K slices (relative error, standart error, value0, value1, col_number(sort), rows, cols,range_row,range_cols, value00, value01,col_number2(sort), rows2, cols2,range_row2,range_cols2) |
+| Matrix[Double] | 1-column matrix of weights. |
 
-### Usage
+### Example
 ```r
 X = rand (rows = 50, cols = 10)
-y = X %*% rand(rows=ncol(X), 1)
-w = lm(X = X, y = y)
-ress = slicefinder(X = X,W = w, Y = y,  k = 5, paq = 1, S = 2);
+opposite = 1
+outlier(X=X,opposite=opposite)
 ```
+## outlierByIQR - Function
+
+Builtin function for detecting and repairing outliers using Interquartile Range.
+A commonly used rule says that a data point is an outlier if it is more than 1.5 IQR
+above the third quartile or below the first quartile.
+outlierByIQR function computes the matrix and set's a lower-bound quartile range and upper-bound quartile range 
+and the number which is less then the lower-bound or higher then the upper-bound is treated as a outlier, hence
+removed from the matrix.
+
+
+### Usage
+```r
+outlierByIQR(X,k,repair_method,max_iterations,verbose)
+`
+### Arguments
+| Name    | Type           | Default  | Description |
+| :------ | :------------- | -------- | :---------- |
+| X       | Matrix[Double] | required | matrix with outliers |
+|k         |     Double 	   |  1.5         | a constant used to discern outliers k*IQR 
+ |isIterative|  Boolean | TRUE   |iterative repair or single repair 
+ |repairMethod|   Integer|  1           | values: 0 = delete rows having outliers, 
+                                                              1 = replace outliers with zeros 
+                                            		      2 = replace outliers as missing values 
+ |max_iterations|  Integer | 0      | values: 0 = arbitrary number of iteraition until all outliers are removed, 
+                                                            n = any constant defined by user
+### Returns
+| Type           | Description |
+| :------------- | :---------- |
+| Matrix[Double] | matrix without any outlier. |
+
+### Example
+```r
+X = rand (rows=10,cols=10)
+opposite = 1
+Y = outlier(X = X, opposite = opposite)
+Z = outlierByIQR(X=Y,k=1.5,repairMethod=0,max_iterations=3,verbose=1)
+print("\n"+toString(Z))
+`
+###outlierBySd - function
+Builtin function for detecting and repairing outliers using standard deviation
+

Review comment:
       This is PR related to `OutlierIQR`, but we are having `OutlierBySd` changes. 🍰 




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