Repository: spark Updated Branches: refs/heads/master 815c8245f -> 873ab0f96
[SPARK-9490] [DOCS] [MLLIB] MLlib evaluation metrics guide example python code uses deprecated print statement Use print(x) not print x for Python 3 in eval examples CC sethah mengxr -- just wanted to close this out before 1.5 Author: Sean Owen <[email protected]> Closes #7822 from srowen/SPARK-9490 and squashes the following commits: 01abeba [Sean Owen] Change "print x" to "print(x)" in the rest of the docs too bd7f7fb [Sean Owen] Use print(x) not print x for Python 3 in eval examples Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/873ab0f9 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/873ab0f9 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/873ab0f9 Branch: refs/heads/master Commit: 873ab0f9692d8ea6220abdb8d9200041068372a8 Parents: 815c824 Author: Sean Owen <[email protected]> Authored: Fri Jul 31 13:45:28 2015 -0700 Committer: Xiangrui Meng <[email protected]> Committed: Fri Jul 31 13:45:28 2015 -0700 ---------------------------------------------------------------------- docs/ml-guide.md | 2 +- docs/mllib-evaluation-metrics.md | 66 ++++++++++++++++---------------- docs/mllib-feature-extraction.md | 2 +- docs/mllib-statistics.md | 20 +++++----- docs/quick-start.md | 2 +- docs/sql-programming-guide.md | 6 +-- docs/streaming-programming-guide.md | 2 +- 7 files changed, 50 insertions(+), 50 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/873ab0f9/docs/ml-guide.md ---------------------------------------------------------------------- diff --git a/docs/ml-guide.md b/docs/ml-guide.md index 8c46adf..b6ca50e 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -561,7 +561,7 @@ test = sc.parallelize([(4L, "spark i j k"), prediction = model.transform(test) selected = prediction.select("id", "text", "prediction") for row in selected.collect(): - print row + print(row) sc.stop() {% endhighlight %} http://git-wip-us.apache.org/repos/asf/spark/blob/873ab0f9/docs/mllib-evaluation-metrics.md ---------------------------------------------------------------------- diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md index 4ca0bb0..7066d5c 100644 --- a/docs/mllib-evaluation-metrics.md +++ b/docs/mllib-evaluation-metrics.md @@ -302,10 +302,10 @@ predictionAndLabels = test.map(lambda lp: (float(model.predict(lp.features)), lp metrics = BinaryClassificationMetrics(predictionAndLabels) # Area under precision-recall curve -print "Area under PR = %s" % metrics.areaUnderPR +print("Area under PR = %s" % metrics.areaUnderPR) # Area under ROC curve -print "Area under ROC = %s" % metrics.areaUnderROC +print("Area under ROC = %s" % metrics.areaUnderROC) {% endhighlight %} @@ -606,24 +606,24 @@ metrics = MulticlassMetrics(predictionAndLabels) precision = metrics.precision() recall = metrics.recall() f1Score = metrics.fMeasure() -print "Summary Stats" -print "Precision = %s" % precision -print "Recall = %s" % recall -print "F1 Score = %s" % f1Score +print("Summary Stats") +print("Precision = %s" % precision) +print("Recall = %s" % recall) +print("F1 Score = %s" % f1Score) # Statistics by class labels = data.map(lambda lp: lp.label).distinct().collect() for label in sorted(labels): - print "Class %s precision = %s" % (label, metrics.precision(label)) - print "Class %s recall = %s" % (label, metrics.recall(label)) - print "Class %s F1 Measure = %s" % (label, metrics.fMeasure(label, beta=1.0)) + print("Class %s precision = %s" % (label, metrics.precision(label))) + print("Class %s recall = %s" % (label, metrics.recall(label))) + print("Class %s F1 Measure = %s" % (label, metrics.fMeasure(label, beta=1.0))) # Weighted stats -print "Weighted recall = %s" % metrics.weightedRecall -print "Weighted precision = %s" % metrics.weightedPrecision -print "Weighted F(1) Score = %s" % metrics.weightedFMeasure() -print "Weighted F(0.5) Score = %s" % metrics.weightedFMeasure(beta=0.5) -print "Weighted false positive rate = %s" % metrics.weightedFalsePositiveRate +print("Weighted recall = %s" % metrics.weightedRecall) +print("Weighted precision = %s" % metrics.weightedPrecision) +print("Weighted F(1) Score = %s" % metrics.weightedFMeasure()) +print("Weighted F(0.5) Score = %s" % metrics.weightedFMeasure(beta=0.5)) +print("Weighted false positive rate = %s" % metrics.weightedFalsePositiveRate) {% endhighlight %} </div> @@ -881,28 +881,28 @@ scoreAndLabels = sc.parallelize([ metrics = MultilabelMetrics(scoreAndLabels) # Summary stats -print "Recall = %s" % metrics.recall() -print "Precision = %s" % metrics.precision() -print "F1 measure = %s" % metrics.f1Measure() -print "Accuracy = %s" % metrics.accuracy +print("Recall = %s" % metrics.recall()) +print("Precision = %s" % metrics.precision()) +print("F1 measure = %s" % metrics.f1Measure()) +print("Accuracy = %s" % metrics.accuracy) # Individual label stats labels = scoreAndLabels.flatMap(lambda x: x[1]).distinct().collect() for label in labels: - print "Class %s precision = %s" % (label, metrics.precision(label)) - print "Class %s recall = %s" % (label, metrics.recall(label)) - print "Class %s F1 Measure = %s" % (label, metrics.f1Measure(label)) + print("Class %s precision = %s" % (label, metrics.precision(label))) + print("Class %s recall = %s" % (label, metrics.recall(label))) + print("Class %s F1 Measure = %s" % (label, metrics.f1Measure(label))) # Micro stats -print "Micro precision = %s" % metrics.microPrecision -print "Micro recall = %s" % metrics.microRecall -print "Micro F1 measure = %s" % metrics.microF1Measure +print("Micro precision = %s" % metrics.microPrecision) +print("Micro recall = %s" % metrics.microRecall) +print("Micro F1 measure = %s" % metrics.microF1Measure) # Hamming loss -print "Hamming loss = %s" % metrics.hammingLoss +print("Hamming loss = %s" % metrics.hammingLoss) # Subset accuracy -print "Subset accuracy = %s" % metrics.subsetAccuracy +print("Subset accuracy = %s" % metrics.subsetAccuracy) {% endhighlight %} @@ -1283,10 +1283,10 @@ scoreAndLabels = predictions.join(ratingsTuple).map(lambda tup: tup[1]) metrics = RegressionMetrics(scoreAndLabels) # Root mean sqaured error -print "RMSE = %s" % metrics.rootMeanSquaredError +print("RMSE = %s" % metrics.rootMeanSquaredError) # R-squared -print "R-squared = %s" % metrics.r2 +print("R-squared = %s" % metrics.r2) {% endhighlight %} @@ -1479,17 +1479,17 @@ valuesAndPreds = parsedData.map(lambda p: (float(model.predict(p.features)), p.l metrics = RegressionMetrics(valuesAndPreds) # Squared Error -print "MSE = %s" % metrics.meanSquaredError -print "RMSE = %s" % metrics.rootMeanSquaredError +print("MSE = %s" % metrics.meanSquaredError) +print("RMSE = %s" % metrics.rootMeanSquaredError) # R-squared -print "R-squared = %s" % metrics.r2 +print("R-squared = %s" % metrics.r2) # Mean absolute error -print "MAE = %s" % metrics.meanAbsoluteError +print("MAE = %s" % metrics.meanAbsoluteError) # Explained variance -print "Explained variance = %s" % metrics.explainedVariance +print("Explained variance = %s" % metrics.explainedVariance) {% endhighlight %} http://git-wip-us.apache.org/repos/asf/spark/blob/873ab0f9/docs/mllib-feature-extraction.md ---------------------------------------------------------------------- diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index a69e41e..de86aba 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -221,7 +221,7 @@ model = word2vec.fit(inp) synonyms = model.findSynonyms('china', 40) for word, cosine_distance in synonyms: - print "{}: {}".format(word, cosine_distance) + print("{}: {}".format(word, cosine_distance)) {% endhighlight %} </div> </div> http://git-wip-us.apache.org/repos/asf/spark/blob/873ab0f9/docs/mllib-statistics.md ---------------------------------------------------------------------- diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md index de5d648..be04d0b 100644 --- a/docs/mllib-statistics.md +++ b/docs/mllib-statistics.md @@ -95,9 +95,9 @@ mat = ... # an RDD of Vectors # Compute column summary statistics. summary = Statistics.colStats(mat) -print summary.mean() -print summary.variance() -print summary.numNonzeros() +print(summary.mean()) +print(summary.variance()) +print(summary.numNonzeros()) {% endhighlight %} </div> @@ -183,12 +183,12 @@ seriesY = ... # must have the same number of partitions and cardinality as serie # Compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. If a # method is not specified, Pearson's method will be used by default. -print Statistics.corr(seriesX, seriesY, method="pearson") +print(Statistics.corr(seriesX, seriesY, method="pearson")) data = ... # an RDD of Vectors # calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method. # If a method is not specified, Pearson's method will be used by default. -print Statistics.corr(data, method="pearson") +print(Statistics.corr(data, method="pearson")) {% endhighlight %} </div> @@ -398,14 +398,14 @@ vec = Vectors.dense(...) # a vector composed of the frequencies of events # compute the goodness of fit. If a second vector to test against is not supplied as a parameter, # the test runs against a uniform distribution. goodnessOfFitTestResult = Statistics.chiSqTest(vec) -print goodnessOfFitTestResult # summary of the test including the p-value, degrees of freedom, - # test statistic, the method used, and the null hypothesis. +print(goodnessOfFitTestResult) # summary of the test including the p-value, degrees of freedom, + # test statistic, the method used, and the null hypothesis. mat = Matrices.dense(...) # a contingency matrix # conduct Pearson's independence test on the input contingency matrix independenceTestResult = Statistics.chiSqTest(mat) -print independenceTestResult # summary of the test including the p-value, degrees of freedom... +print(independenceTestResult) # summary of the test including the p-value, degrees of freedom... obs = sc.parallelize(...) # LabeledPoint(feature, label) . @@ -415,8 +415,8 @@ obs = sc.parallelize(...) # LabeledPoint(feature, label) . featureTestResults = Statistics.chiSqTest(obs) for i, result in enumerate(featureTestResults): - print "Column $d:" % (i + 1) - print result + print("Column $d:" % (i + 1)) + print(result) {% endhighlight %} </div> http://git-wip-us.apache.org/repos/asf/spark/blob/873ab0f9/docs/quick-start.md ---------------------------------------------------------------------- diff --git a/docs/quick-start.md b/docs/quick-start.md index bb39e41..ce2cc9d 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -406,7 +406,7 @@ logData = sc.textFile(logFile).cache() numAs = logData.filter(lambda s: 'a' in s).count() numBs = logData.filter(lambda s: 'b' in s).count() -print "Lines with a: %i, lines with b: %i" % (numAs, numBs) +print("Lines with a: %i, lines with b: %i" % (numAs, numBs)) {% endhighlight %} http://git-wip-us.apache.org/repos/asf/spark/blob/873ab0f9/docs/sql-programming-guide.md ---------------------------------------------------------------------- diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 95945eb..d31baa0 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -570,7 +570,7 @@ teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 1 # The results of SQL queries are RDDs and support all the normal RDD operations. teenNames = teenagers.map(lambda p: "Name: " + p.name) for teenName in teenNames.collect(): - print teenName + print(teenName) {% endhighlight %} </div> @@ -752,7 +752,7 @@ results = sqlContext.sql("SELECT name FROM people") # The results of SQL queries are RDDs and support all the normal RDD operations. names = results.map(lambda p: "Name: " + p.name) for name in names.collect(): - print name + print(name) {% endhighlight %} </div> @@ -1006,7 +1006,7 @@ parquetFile.registerTempTable("parquetFile"); teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19") teenNames = teenagers.map(lambda p: "Name: " + p.name) for teenName in teenNames.collect(): - print teenName + print(teenName) {% endhighlight %} </div> http://git-wip-us.apache.org/repos/asf/spark/blob/873ab0f9/docs/streaming-programming-guide.md ---------------------------------------------------------------------- diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 2f3013b..4663b3f 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -1525,7 +1525,7 @@ def getSqlContextInstance(sparkContext): words = ... # DStream of strings def process(time, rdd): - print "========= %s =========" % str(time) + print("========= %s =========" % str(time)) try: # Get the singleton instance of SQLContext sqlContext = getSqlContextInstance(rdd.context) --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
