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cs525-sp18-g07
spark
Commits
81a8bd46
Commit
81a8bd46
authored
11 years ago
by
Ameet Talwalkar
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docs/mllib-guide.md
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docs/mllib-guide.md
mllib/data/sample_svm_data.txt
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mllib/data/sample_svm_data.txt
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docs/mllib-guide.md
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View file @
81a8bd46
...
@@ -43,26 +43,20 @@ import org.apache.spark.mllib.classification.SVMWithSGD
...
@@ -43,26 +43,20 @@ import org.apache.spark.mllib.classification.SVMWithSGD
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.regression.LabeledPoint
// Load and parse the data file
// Load and parse the data file
val data = sc.textFile("
sample_wiki_ngrams
.txt")
val data = sc.textFile("
mllib/data/sample_svm_data
.txt")
val parsedData = data.map(line => {
val parsedData = data.map(line => {
val parts = line.split(' ')
val parts = line.split(' ')
LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray)
LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray)
})
})
// Run training algorithm
// Run training algorithm
val stepSizeVal = 1.0
val numIterations = 20
val regParamVal = 0.1
val numIterationsVal = 200
val miniBatchFractionVal = 1.0
val model = SVMWithSGD.train(
val model = SVMWithSGD.train(
parsedData,
parsedData,
numIterationsVal,
numIterations)
stepSizeVal,
regParamVal,
miniBatchFractionVal)
// Evaluate model on training examples and compute training error
// Evaluate model on training examples and compute training error
val labelAnPreds = parsedData.map(r => {
val labelAn
d
Preds = parsedData.map(r => {
val prediction = model.predict(r.features)
val prediction = model.predict(r.features)
(r.label, prediction)
(r.label, prediction)
})
})
...
@@ -70,30 +64,31 @@ val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedDa
...
@@ -70,30 +64,31 @@ val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedDa
println("trainError = " + trainErr)
println("trainError = " + trainErr)
{% endhighlight %}
{% endhighlight %}
The
`SVMWithSGD`
algorithm performs L2 regularization by default. If we want to
The
`SVMWithSGD.train()`
method by default performs L2 regularization with the
configure this algorithm to generate an L1 regularized variant of SVMs, we can
regularization parameter set to 1.0. If we want to configure this algorithm, we
use the builder design pattern as follows:
can customize
`SVMWithSGD`
further by creating a new object directly and
calling setter methods. All other MLlib algorithms support customization in
this way as well. For example, the following code produces an L1 regularized
variant of SVMs with regularization parameter set to 0.1, and runs the training
algorithm for 200 iterations.
{% highlight scala %}
{% highlight scala %}
import org.apache.spark.mllib.optimization.L1Updater
import org.apache.spark.mllib.optimization.L1Updater
val svmAlg = new SVMWithSGD()
val svmAlg = new SVMWithSGD()
svmAlg.optimizer.setNumIterations(200)
svmAlg.optimizer.setNumIterations(200)
.setStepSize(1.0)
.setRegParam(0.1)
.setRegParam(0.1)
.setMiniBatchFraction(1.0)
.setUpdater(new L1Updater)
svmAlg.optimizer.setUpdater(new L1Updater)
val modelL1 = svmAlg.run(parsedData)
val modelL1 = svmAlg.run(parsedData)
{% endhighlight %}
{% endhighlight %}
Both of the code snippets above can be executed in
`spark-shell`
to generate a
Both of the code snippets above can be executed in
`spark-shell`
to generate a
classifier for the provided dataset. Moreover, note that static methods and
classifier for the provided dataset.
builder patterns, similar to the ones displayed above, are available for all
algorithms in MLlib.
[
SVMWithSGD
](
`api/mllib/index.html#org.apache.spark.mllib.
classification
.SVMWithSGD`
)
Available algorithms for binary
classification
:
[
LogisticRegressionWithSGD
](
`api/mllib/index.html#org.apache.spark.mllib.classification.LogistictRegressionWithSGD`
)
*
[
SVMWithSGD
](
api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD
)
*
[
LogisticRegressionWithSGD
](
api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD
)
# Linear Regression
# Linear Regression
...
@@ -108,7 +103,11 @@ The regression algorithms in MLlib also leverage the underlying gradient
...
@@ -108,7 +103,11 @@ The regression algorithms in MLlib also leverage the underlying gradient
descent primitive (described
[
below
](
#gradient-descent-primitive
)
), and have
descent primitive (described
[
below
](
#gradient-descent-primitive
)
), and have
the same parameters as the binary classification algorithms described above.
the same parameters as the binary classification algorithms described above.
[
RidgeRegressionWithSGD
](
`api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD`
)
Available algorithms for linear regression:
*
[
LinearRegressionWithSGD
](
api/mllib/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD
)
*
[
RidgeRegressionWithSGD
](
api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD
)
*
[
LassoWithSGD
](
api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD
)
# Clustering
# Clustering
...
@@ -134,7 +133,9 @@ a given dataset, the algorithm returns the best clustering result).
...
@@ -134,7 +133,9 @@ a given dataset, the algorithm returns the best clustering result).
*
*initializiationSteps*
determines the number of steps in the k-means
\|\|
algorithm.
*
*initializiationSteps*
determines the number of steps in the k-means
\|\|
algorithm.
*
*epsilon*
determines the distance threshold within which we consider k-means to have converged.
*
*epsilon*
determines the distance threshold within which we consider k-means to have converged.
[
KMeans
](
`api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans`
)
Available algorithms for clustering:
*
[
KMeans
](
api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans
)
# Collaborative Filtering
# Collaborative Filtering
...
@@ -154,7 +155,9 @@ following parameters:
...
@@ -154,7 +155,9 @@ following parameters:
*
*iterations*
is the number of iterations to run.
*
*iterations*
is the number of iterations to run.
*
*lambda*
specifies the regularization parameter in ALS.
*
*lambda*
specifies the regularization parameter in ALS.
[
ALS
](
`api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS`
)
Available algorithms for collaborative filtering:
*
[
ALS
](
api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS
)
# Gradient Descent Primitive
# Gradient Descent Primitive
...
@@ -183,4 +186,6 @@ stepSize / sqrt(t).
...
@@ -183,4 +186,6 @@ stepSize / sqrt(t).
*
*miniBatchFraction*
is the fraction of the data used to compute the gradient
*
*miniBatchFraction*
is the fraction of the data used to compute the gradient
at each iteration.
at each iteration.
[
GradientDescent
](
`api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent`
)
Available algorithms for gradient descent:
*
[
GradientDescent
](
api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent
)
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mllib/data/sample_svm_data.txt
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1 0 2.52078447201548 0 0 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 0 2.061393766919624 0 0 2.004684436494304 0 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 0 0 2.061393766919624 2.619965104088255 0 2.004684436494304 2.000347299268466 0 0 0 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 2.061393766919624 2.619965104088255 0 2.004684436494304 0 0 0 0 0 2.055002875864414 0 0 0 0
0 2.857738033247042 0 2.061393766919624 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
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1 0 0 0 2.619965104088255 0 2.004684436494304 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
0 2.857738033247042 0 2.061393766919624 2.619965104088255 0 2.004684436494304 2.000347299268466 2.122974378789621 2.228387042742021 2.228387042742023 0 0 0 0 12.72816758217773 0
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0 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 0 0 0 0 2.004684436494304 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
0 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 2.122974378789621 2.228387042742021 2.228387042742023 0 0 0 0 0 0
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0 2.857738033247042 0 0 0 4.745052855503306 0 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
0 2.857738033247042 2.52078447201548 0 2.619965104088255 0 0 0 2.122974378789621 0 0 0 2.055002875864414 0 0 0 0
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0 2.857738033247042 0 2.061393766919624 2.619965104088255 4.745052855503306 2.004684436494304 2.000347299268466 0 0 0 0 2.055002875864414 0 0 0 0
0 2.857738033247042 0 2.061393766919624 2.619965104088255 0 0 0 2.122974378789621 0 0 0 2.055002875864414 0 0 0 0
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0 2.857738033247042 0 0 2.619965104088255 4.745052855503306 2.004684436494304 0 2.122974378789621 2.228387042742021 2.228387042742023 0 0 0 0 0 0
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0 2.857738033247042 0 0 2.619965104088255 0 0 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
1 0 2.52078447201548 2.061393766919624 2.619965104088255 0 2.004684436494304 0 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
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1 2.857738033247042 0 0 2.619965104088255 0 0 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 0 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 0 0 0 0 2.000347299268466 2.122974378789621 0 0 6.857275130999357 2.055002875864414 0 0 0 0
0 2.857738033247042 0 2.061393766919624 2.619965104088255 4.745052855503306 2.004684436494304 0 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 2.857738033247042 0 2.061393766919624 2.619965104088255 0 2.004684436494304 0 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 2.52078447201548 0 2.619965104088255 0 0 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 0 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 0 0 0 2.055002875864414 0 0 0 0
0 2.857738033247042 2.52078447201548 0 2.619965104088255 0 0 2.000347299268466 2.122974378789621 0 0 0 0 0 0 0 0
0 2.857738033247042 0 0 2.619965104088255 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
0 0 0 2.061393766919624 0 0 2.004684436494304 2.000347299268466 2.122974378789621 0 0 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 2.857738033247042 2.52078447201548 2.061393766919624 0 0 0 0 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 0 0 0 2.619965104088255 0 0 2.000347299268466 0 2.228387042742021 2.228387042742023 6.857275130999357 0 0 0 0 0
1 2.857738033247042 0 2.061393766919624 2.619965104088255 0 0 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
0 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 0 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 0 0 0 2.055002875864414 0 0 0 0
0 2.857738033247042 0 2.061393766919624 2.619965104088255 0 2.004684436494304 2.000347299268466 0 0 0 0 2.055002875864414 0 0 0 0
0 2.857738033247042 2.52078447201548 2.061393766919624 2.619965104088255 0 0 2.000347299268466 0 0 0 0 0 0 0 0 0
0 2.857738033247042 0 0 2.619965104088255 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
0 2.857738033247042 0 2.061393766919624 2.619965104088255 0 2.004684436494304 0 0 0 0 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 2.122974378789621 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 2.52078447201548 0 0 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 0 2.061393766919624 2.619965104088255 4.745052855503306 0 0 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 0 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 2.52078447201548 0 0 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 2.857738033247042 2.52078447201548 0 2.619965104088255 0 2.004684436494304 0 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 0 0 2.619965104088255 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 0 2.061393766919624 0 0 2.004684436494304 0 2.122974378789621 0 0 0 0 0 10.4087817597473 0 0
0 2.857738033247042 0 2.061393766919624 2.619965104088255 0 2.004684436494304 0 2.122974378789621 0 0 0 0 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
0 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 2.122974378789621 0 0 0 0 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 0 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0
1 0 2.52078447201548 0 2.619965104088255 0 0 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
1 2.857738033247042 0 0 2.619965104088255 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0
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