yunfengzhou-hub commented on code in PR #116:
URL: https://github.com/apache/flink-ml/pull/116#discussion_r905794591
##########
docs/content/docs/operators/classification/knn.md:
##########
@@ -175,15 +176,10 @@ predict_data = t_env.from_data_stream(
knn = KNN()
model = knn.fit(train_data)
output = model.transform(predict_data)[0]
-output.execute().print()
+print([result for result in
t_env.to_data_stream(output).execute_and_collect()])
# output
-#
+----+--------------------------------+--------------------------------+--------------------------------+
-# | op | features | label |
prediction |
-#
+----+--------------------------------+--------------------------------+--------------------------------+
-# | +I | [4.0, 4.1] | 5.0 |
5.0 |
-# | +I | [300.0, 42.0] | 2.0 |
2.0 |
-#
+----+--------------------------------+--------------------------------+--------------------------------+
+# [<Row(DenseVector([4.0, 4.1]), 5.0, 5.0)>, <Row(DenseVector([300.0, 42.0]),
2.0, 2.0)>]
Review Comment:
Would it be better if we reformat the output to improve its readability? For
example, the KMeans Java example would print the results as follows.
```java
for (CloseableIterator<Row> it = output.execute().collect(); it.hasNext(); )
{
Row row = it.next();
DenseVector vector = (DenseVector) row.getField(featuresCol);
int clusterId = (Integer) row.getField(predictionCol);
System.out.println("Vector: " + vector + "\tCluster ID: " + clusterId);
}
##########
docs/content/docs/operators/classification/logisticregression.md:
##########
@@ -148,23 +149,8 @@ logistic_regression =
LogisticRegression().set_weight_col('weight')
model = logistic_regression.fit(binomial_data_table)
output = model.transform(binomial_data_table)[0]
-output.execute().print()
-
-# output
-#
+----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
-# | op | features | label |
weight | prediction |
rawPrediction |
-#
+----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
-# | +I | [1.0, 2.0, 3.0, 4.0] | 0.0 |
1.0 | 0.0 |
[0.9731815427669942, 0.0268... |
-# | +I | [5.0, 2.0, 3.0, 4.0] | 0.0 |
5.0 | 0.0 |
[0.8158018538556746, 0.1841... |
-# | +I | [14.0, 2.0, 3.0, 4.0] | 1.0 |
4.0 | 1.0 |
[0.03753179912156068, 0.962... |
-# | +I | [3.0, 2.0, 3.0, 4.0] | 0.0 |
3.0 | 0.0 |
[0.926886620226911, 0.07311... |
-# | +I | [12.0, 2.0, 3.0, 4.0] | 1.0 |
2.0 | 1.0 |
[0.10041228069167174, 0.899... |
-# | +I | [4.0, 2.0, 3.0, 4.0] | 0.0 |
4.0 | 0.0 |
[0.8822580948141717, 0.1177... |
-# | +I | [13.0, 2.0, 3.0, 4.0] | 1.0 |
3.0 | 1.0 |
[0.061891528893188164, 0.93... |
-# | +I | [2.0, 2.0, 3.0, 4.0] | 0.0 |
2.0 | 0.0 |
[0.9554533965544176, 0.0445... |
-# | +I | [11.0, 2.0, 3.0, 4.0] | 1.0 |
1.0 | 1.0 |
[0.15884837044317868, 0.841... |
-# | +I | [15.0, 2.0, 3.0, 4.0] | 1.0 |
5.0 | 1.0 |
[0.022529496926532833, 0.97... |
-#
+----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
+print([result for result in
t_env.to_data_stream(output).execute_and_collect()])
+
Review Comment:
nit: there might also be an `#output` section here.
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