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[SPARK-6083] [MLLib] [DOC] Make Python API example consistent in Naiv…
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…eBayes

Author: MechCoder <[email protected]>

Closes apache#4834 from MechCoder/spark-6083 and squashes the following commits:

1cdd7b5 [MechCoder] Add parse function
65bbbe9 [MechCoder] [SPARK-6083] Make Python API example consistent in NaiveBayes
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MechCoder authored and mengxr committed Mar 2, 2015
1 parent aedbbaa commit 3f00bb3
Showing 1 changed file with 16 additions and 10 deletions.
26 changes: 16 additions & 10 deletions docs/mllib-naive-bayes.md
Original file line number Diff line number Diff line change
Expand Up @@ -115,22 +115,28 @@ used for evaluation and prediction.

Note that the Python API does not yet support model save/load but will in the future.

<!-- TODO: Make Python's example consistent with Scala's and Java's. -->
{% highlight python %}
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import NaiveBayes
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint

def parseLine(line):
parts = line.split(',')
label = float(parts[0])
features = Vectors.dense([float(x) for x in parts[1].split(' ')])
return LabeledPoint(label, features)

data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)

# an RDD of LabeledPoint
data = sc.parallelize([
LabeledPoint(0.0, [0.0, 0.0])
... # more labeled points
])
# Split data aproximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4], seed = 0)

# Train a naive Bayes model.
model = NaiveBayes.train(data, 1.0)
model = NaiveBayes.train(training, 1.0)

# Make prediction.
prediction = model.predict([0.0, 0.0])
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p : (model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
{% endhighlight %}

</div>
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