Description
StandardScaler transforms a dataset, normalizing each feature to have unit standard deviation and/or zero mean.
Parameters
Name |
Description |
Type |
Required? |
Default Value |
selectedCol |
Name of the selected column used for processing |
String |
✓ |
|
withMean |
Centers the data with mean before scaling. |
Boolean |
|
true |
withStd |
Scales the data to unit standard deviation. true by default |
Boolean |
|
true |
Script Example
Script
data = np.array([["a", "10.0, 100"],\
["b", "-2.5, 9"],\
["c", "100.2, 1"],\
["d", "-99.9, 100"],\
["a", "1.4, 1"],\
["b", "-2.2, 9"],\
["c", "100.9, 1"]])
df = pd.DataFrame({"col" : data[:,0], "vector" : data[:,1]})
data = dataframeToOperator(df, schemaStr="col string, vector string",op_type="batch")
dataStream = dataframeToOperator(df, schemaStr="col string, vector string",op_type="stream")
trainOp = VectorStandardScalerTrainBatchOp().setSelectedCol("vector")
model = trainOp.linkFrom(data)
VectorStandardScalerPredictStreamOp(model).linkFrom(dataStream).print()
StreamOperator.execute()
Result
col1 |
vec |
a |
-0.07835182408093559,1.4595814453461897 |
c |
1.2269606224811418,-0.6520885789229323 |
b |
-0.2549018445693762,-0.4814485769617911 |
a |
-0.20280511721213143,-0.6520885789229323 |
c |
1.237090541689495,-0.6520885789229323 |
b |
-0.25924323851581327,-0.4814485769617911 |
d |
-1.6687491397923802,1.4595814453461897 |