本文整理汇总了Python中pyspark.ml.Pipeline类的典型用法代码示例。如果您正苦于以下问题:Python Pipeline类的具体用法?Python Pipeline怎么用?Python Pipeline使用的例子?那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了Pipeline类的20个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: model
def model(classifiers, training, testing, week):
results = ""
timing = []
for classifier in classifiers:
timeStart = time.time()
clf = get_classifier(classifier)
labelIndexer = StringIndexer(inputCol="label", outputCol="indexed")
featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures")
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, clf])
model = pipeline.fit(training)
prediction = model.transform(testing)
metrics = BinaryClassificationMetrics(prediction.select("label","prediction").rdd)
results = results + "new," + classifier + "," + week + "," + str(metrics.areaUnderROC) + "," +str(metrics.areaUnderPR) + "\n"
timing.append(time.time()-timeStart)
return results, timing
开发者ID:dmwm,项目名称:DMWMAnalytics,代码行数:26,代码来源:pyspark_ml.py
示例2: main
def main(sc, spark):
# Load and vectorize the corpus
corpus = load_corpus(sc, spark)
vector = make_vectorizer().fit(corpus)
# Index the labels of the classification
labelIndex = StringIndexer(inputCol="label", outputCol="indexedLabel")
labelIndex = labelIndex.fit(corpus)
# Split the data into training and test sets
training, test = corpus.randomSplit([0.8, 0.2])
# Create the classifier
clf = LogisticRegression(
maxIter=10, regParam=0.3, elasticNetParam=0.8,
family="multinomial", labelCol="indexedLabel", featuresCol="tfidf")
# Create the model
model = Pipeline(stages=[
vector, labelIndex, clf
]).fit(training)
# Make predictions
predictions = model.transform(test)
predictions.select("prediction", "indexedLabel", "tfidf").show(5)
# Select (prediction, true label) and compute test error
evaluator = MulticlassClassificationEvaluator(
labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))
gbtModel = model.stages[2]
print(gbtModel) # summary only
开发者ID:yokeyong,项目名称:atap,代码行数:34,代码来源:sc_classification.py
示例3: testLogisticMLPipeline1
def testLogisticMLPipeline1(self):
training = sqlCtx.createDataFrame([
("a b c d e spark", 1.0),
("b d", 2.0),
("spark f g h", 1.0),
("hadoop mapreduce", 2.0),
("b spark who", 1.0),
("g d a y", 2.0),
("spark fly", 1.0),
("was mapreduce", 2.0),
("e spark program", 1.0),
("a e c l", 2.0),
("spark compile", 1.0),
("hadoop software", 2.0)
], ["text", "label"])
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol="words", outputCol="features", numFeatures=20)
lr = LogisticRegression(sqlCtx)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
model = pipeline.fit(training)
test = sqlCtx.createDataFrame([
("spark i j k", 1.0),
("l m n", 2.0),
("mapreduce spark", 1.0),
("apache hadoop", 2.0)], ["text", "label"])
result = model.transform(test)
predictionAndLabels = result.select("prediction", "label")
evaluator = MulticlassClassificationEvaluator()
score = evaluator.evaluate(predictionAndLabels)
self.failUnless(score == 1.0)
开发者ID:d-behi,项目名称:incubator-systemml,代码行数:30,代码来源:test_mllearn.py
示例4: main
def main(sc, spark):
# Load the Corpus
corpus = load_corpus(sc, spark)
# Create the vector/cluster pipeline
pipeline = Pipeline(stages=[
Tokenizer(inputCol="text", outputCol="tokens"),
Word2Vec(vectorSize=7, minCount=0, inputCol="tokens", outputCol="vecs"),
BisectingKMeans(k=10, featuresCol="vecs", maxIter=10),
])
# Fit the model
model = pipeline.fit(corpus)
corpus = model.transform(corpus)
# Evaluate clustering.
bkm = model.stages[-1]
cost = bkm.computeCost(corpus)
sizes = bkm.summary.clusterSizes
# TODO: compute cost of each cluster individually
# Get the text representation of each cluster.
wvec = model.stages[-2]
table = [["Cluster", "Size", "Terms"]]
for ci, c in enumerate(bkm.clusterCenters()):
ct = wvec.findSynonyms(c, 7)
size = sizes[ci]
terms = " ".join([row.word for row in ct.take(7)])
table.append([ci, size, terms])
# Print Results
print(tabulate(table))
print("Sum of square distance to center: {:0.3f}".format(cost))
开发者ID:yokeyong,项目名称:atap,代码行数:34,代码来源:sc_clustering.py
示例5: test_cv_lasso_with_mllib_featurization
def test_cv_lasso_with_mllib_featurization(self):
data = [('hi there', 0.0),
('what is up', 1.0),
('huh', 1.0),
('now is the time', 5.0),
('for what', 0.0),
('the spark was there', 5.0),
('and so', 3.0),
('were many socks', 0.0),
('really', 1.0),
('too cool', 2.0)]
data = self.sql.createDataFrame(data, ["review", "rating"])
# Feature extraction using MLlib
tokenizer = Tokenizer(inputCol="review", outputCol="words")
hashingTF = HashingTF(inputCol="words", outputCol="features", numFeatures=20000)
pipeline = Pipeline(stages=[tokenizer, hashingTF])
data = pipeline.fit(data).transform(data)
df = self.converter.toPandas(data.select(data.features.alias("review"), "rating"))
pipeline = SKL_Pipeline([
('lasso', SKL_Lasso(max_iter=1))
])
parameters = {
'lasso__alpha': (0.001, 0.005, 0.01)
}
grid_search = GridSearchCV(self.sc, pipeline, parameters)
skl_gs = grid_search.fit(df.review.values, df.rating.values)
assert len(skl_gs.cv_results_['params']) == len(parameters['lasso__alpha'])
开发者ID:smurching,项目名称:spark-sklearn,代码行数:31,代码来源:test_grid_search_2.py
示例6: test_nnclassifier_in_pipeline
def test_nnclassifier_in_pipeline(self):
if self.sc.version.startswith("1"):
from pyspark.mllib.linalg import Vectors
df = self.sqlContext.createDataFrame(
[(Vectors.dense([2.0, 1.0]), 1.0),
(Vectors.dense([1.0, 2.0]), 2.0),
(Vectors.dense([2.0, 1.0]), 1.0),
(Vectors.dense([1.0, 2.0]), 2.0),
], ["features", "label"])
scaler = MinMaxScaler().setInputCol("features").setOutputCol("scaled")
model = Sequential().add(Linear(2, 2))
criterion = ClassNLLCriterion()
classifier = NNClassifier(model, criterion, MLlibVectorToTensor([2]))\
.setBatchSize(4) \
.setLearningRate(0.01).setMaxEpoch(1).setFeaturesCol("scaled")
pipeline = Pipeline(stages=[scaler, classifier])
pipelineModel = pipeline.fit(df)
res = pipelineModel.transform(df)
assert type(res).__name__ == 'DataFrame'
开发者ID:ru003ar,项目名称:analytics-zoo,代码行数:25,代码来源:test_nn_classifier.py
示例7: fit_kmeans
def fit_kmeans(spark, products_df):
step = 0
step += 1
tokenizer = Tokenizer(inputCol="title", outputCol=str(step) + "_tokenizer")
step += 1
stopwords = StopWordsRemover(inputCol=tokenizer.getOutputCol(), outputCol=str(step) + "_stopwords")
step += 1
tf = HashingTF(inputCol=stopwords.getOutputCol(), outputCol=str(step) + "_tf", numFeatures=16)
step += 1
idf = IDF(inputCol=tf.getOutputCol(), outputCol=str(step) + "_idf")
step += 1
normalizer = Normalizer(inputCol=idf.getOutputCol(), outputCol=str(step) + "_normalizer")
step += 1
kmeans = KMeans(featuresCol=normalizer.getOutputCol(), predictionCol=str(step) + "_kmeans", k=2, seed=20)
kmeans_pipeline = Pipeline(stages=[tokenizer, stopwords, tf, idf, normalizer, kmeans])
model = kmeans_pipeline.fit(products_df)
words_prediction = model.transform(products_df)
model.save("./kmeans") # the whole machine learning instance is saved in a folder
return model, words_prediction
开发者ID:ohliumliu,项目名称:flash_deals_c9,代码行数:27,代码来源:kmean_model.py
示例8: RunRandomForest
def RunRandomForest(tf, ctx):
sqlContext = SQLContext(ctx)
rdd = tf.map(parseForRandomForest)
# The schema is encoded in a string.
schema = ['genre', 'track_id', 'features']
# Apply the schema to the RDD.
songDF = sqlContext.createDataFrame(rdd, schema)
# Register the DataFrame as a table.
songDF.registerTempTable("genclass")
labelIndexer = StringIndexer().setInputCol("genre").setOutputCol("indexedLabel").fit(songDF)
trainingData, testData = songDF.randomSplit([0.8, 0.2])
labelConverter = IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels)
rfc = RandomForestClassifier().setMaxDepth(10).setNumTrees(2).setLabelCol("indexedLabel").setFeaturesCol("features")
#rfc = SVMModel([.5, 10, 20], 5)
#rfc = LogisticRegression(maxIter=10, regParam=0.01).setLabelCol("indexedLabel").setFeaturesCol("features")
pipeline = Pipeline(stages=[labelIndexer, rfc, labelConverter])
model = pipeline.fit(trainingData)
predictions = model.transform(testData)
predictions.show()
evaluator = MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction").setMetricName("precision")
accuracy = evaluator.evaluate(predictions)
print 'Accuracy of RandomForest = ', accuracy * 100
print "Test Error = ", (1.0 - accuracy) * 100
开发者ID:Sunhick,项目名称:music-cognita,代码行数:30,代码来源:genre_classification.py
示例9: run
def run(start1, end1, start2, end2, df, sc, sql_context, is_pred):
lp_data= get_labeled_points(start1, end2, df, sc, sql_context)
print lp_data.count()
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(lp_data)
td = labelIndexer.transform(lp_data)
label2index = {}
for each in sorted(set([(i[0], i[1]) for i in td.select(td.label, td.indexedLabel).distinct().collect()]),
key=lambda x: x[0]):
label2index[int(each[0])] = int(each[1])
print label2index
featureIndexer = \
VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(lp_data)
rf = get_model()
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf])
lp_train = lp_data.filter(lp_data.date3<end1).filter(lp_data.is_labeled == 1)
model = pipeline.fit(lp_train)
lp_check = lp_data.filter(lp_data.date2>start2)
predictions = model.transform(lp_check)
predictions = val(predictions, label2index, sql_context)
if is_pred:
predictions = predictions.filter(predictions.is_labeled ==0).filter(predictions.date2 == get_cur()).sort(predictions.prob.desc())
dfToTableWithPar(sql_context, predictions, "predictions", get_cur())
for each in predictions.take(10):
print each
开发者ID:hongbin0908,项目名称:bintrade,代码行数:30,代码来源:diff_feature_cls.py
示例10: main
def main(input_file):
# Load and parse the data file, converting it to a DataFrame.
data = MLUtils.loadLabeledPoints(sc, input_file)
# Automatically identify categorical features, and index them.
# Set maxCategories so features with > 4 distinct values are treated as continuous.
featureIndexer =\
VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=10).fit(data)
# Split the data into training and test sets (30% held out for testing)
(trainingData, testData) = data.randomSplit([0.7, 0.3])
# Train a RandomForest model.
rf = RandomForestRegressor(featuresCol="indexedFeatures")
# Chain indexer and forest in a Pipeline
pipeline = Pipeline(stages=[featureIndexer, rf])
# Train model. This also runs the indexer.
model = pipeline.fit(trainingData)
# Make predictions.
predictions = model.transform(testData)
# Select example rows to display.
predictions.select("prediction", "label", "features").show(5)
# Select (prediction, true label) and compute test error
evaluator = RegressionEvaluator(
labelCol="label", predictionCol="prediction", metricName="rmse")
rmse = evaluator.evaluate(predictions)
print("Root Mean Squared Error (RMSE) on test data = {}".format(rmse))
rfModel = model.stages[1]
print(rfModel) # summary only
开发者ID:garethdavidjones,项目名称:Election-Contrib,代码行数:35,代码来源:random_forest.py
示例11: test_nested_pipeline_persistence
def test_nested_pipeline_persistence(self):
"""
Pipeline[HashingTF, Pipeline[PCA]]
"""
sqlContext = SQLContext(self.sc)
temp_path = tempfile.mkdtemp()
try:
df = sqlContext.createDataFrame([(["a", "b", "c"],), (["c", "d", "e"],)], ["words"])
tf = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
pca = PCA(k=2, inputCol="features", outputCol="pca_features")
p0 = Pipeline(stages=[pca])
pl = Pipeline(stages=[tf, p0])
model = pl.fit(df)
pipeline_path = temp_path + "/pipeline"
pl.save(pipeline_path)
loaded_pipeline = Pipeline.load(pipeline_path)
self._compare_pipelines(pl, loaded_pipeline)
model_path = temp_path + "/pipeline-model"
model.save(model_path)
loaded_model = PipelineModel.load(model_path)
self._compare_pipelines(model, loaded_model)
finally:
try:
rmtree(temp_path)
except OSError:
pass
开发者ID:Bella-Lin,项目名称:spark,代码行数:29,代码来源:tests.py
示例12: train_lg
def train_lg(training_data, collection):
# Configure an ML pipeline, which consists of the following stages: hashingTF, idf, and lr.
hashingTF = HashingTF(inputCol="filtered", outputCol="TF_features")
idf = IDF(inputCol=hashingTF.getOutputCol(), outputCol="features")
pipeline1 = Pipeline(stages=[hashingTF, idf])
# Fit the pipeline1 to training documents.
model1 = pipeline1.fit(training_data)
lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
pipeline2 = Pipeline(stages=[model1, lr])
paramGrid = ParamGridBuilder() \
.addGrid(hashingTF.numFeatures, [10, 100, 1000, 10000]) \
.addGrid(lr.regParam, [0.1, 0.01]) \
.build()
crossval = CrossValidator(estimator=pipeline2,
estimatorParamMaps=paramGrid,
evaluator=BinaryClassificationEvaluator(),
numFolds=5)
# Run cross-validation, and choose the best set of parameters.
cvModel = crossval.fit(training_data)
# model_path = os.path.join(models_dir , time.strftime("%Y%m%d-%H%M%S") + '_'
# + collection["Id"] + '_'
# + collection["name"])
# cvModel.save(sc, model_path)
return cvModel
开发者ID:hosamshahin,项目名称:Spring2016_IR_Project,代码行数:30,代码来源:text_classification_02.py
示例13: textPredict
def textPredict(request):
"""6.文本聚类,热度预测"""
label = request.POST['label']
title = request.POST['title']
conf = SparkConf().setAppName('textPredict').setMaster('spark://HP-Pavilion:7077')
sc = SparkContext(conf=conf)
sqlContext = SQLContext(sc)
"""处理数据集,生成特征向量"""
dfTitles = sqlContext.read.parquet('data/roll_news_sina_com_cn.parquet')
print(dfTitles.dtypes)
tokenizer = Tokenizer(inputCol="title", outputCol="words")
wordsData = tokenizer.transform(dfTitles)
hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20)
featurizedData = hashingTF.transform(wordsData)
idf = IDF(inputCol="rawFeatures", outputCol="features")
idfModel = idf.fit(featurizedData)
rescaledData = idfModel.transform(featurizedData)
rescaledData.show()
for features_label in rescaledData.select("features", "rawFeatures").take(3):
print(features_label)
"""决策树模型培训"""
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(rescaledData)
featureIndexer =\
VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(rescaledData)
(trainingData, testData) = rescaledData.randomSplit([0.7, 0.3])
dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])
model = pipeline.fit(trainingData)
"""模型测试"""
predictions = model.transform(testData)
predictions.show()
predictions.select("prediction", "indexedLabel", "features").show(5)
"""用户数据测试,单个新闻测试"""
sentenceData = sqlContext.createDataFrame([
(label,title),
],['label',"title"])
tokenizer = Tokenizer(inputCol="title", outputCol="words")
wordsData = tokenizer.transform(sentenceData)
hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20)
featurizedData = hashingTF.transform(wordsData)
rescaledData = idfModel.transform(featurizedData)
myprediction = model.transform(rescaledData)
print("==================================================")
myprediction.show()
resultList = convertDfToList(myprediction)
"""模型评估"""
evaluator = MulticlassClassificationEvaluator(
labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g " % (1.0 - accuracy))
treeModel = model.stages[2]
print(treeModel)
sc.stop()
return render(request,{'resultList':resultList})
开发者ID:JallyHe,项目名称:networkPublicOpinionAnalysisSystem,代码行数:58,代码来源:views.py
示例14: sparking_your_interest
def sparking_your_interest():
df = SQLContext.read.json('speeches_dataset.json')
df_fillna=df.fillna("")
print(df_fillna.count())
print(df_fillna.printSchema())
df_utf=call_utf_encoder(df)
df_cleaned=call_para_cleanup(df_utf)
print(df_cleaned)
df_with_bigrams = call_ngrams(df_cleaned, 2)
df_with_trigrams = call_ngrams(df_with_bigrams, 3)
df_with_4grams = call_ngrams(df_with_trigrams, 4)
df_with_5grams = call_ngrams(df_with_4grams, 4)
df_with_6grams = call_ngrams(df_with_5grams, 4)
df_with_vocab_score = call_speech_vocab(df_with_6grams)
df_with_2grams_idf_vectors = tf_feature_vectorizer(df_with_vocab_score,100,'2grams')
df_with_3grams_idf_vectors = tf_feature_vectorizer(df_with_2grams_idf_vectors,100,'3grams')
df_with_4grams_idf_vectors = tf_feature_vectorizer(df_with_3grams_idf_vectors,100,'4grams')
assembler = VectorAssembler(
inputCols=["2gramsfeatures", "2gramsfeatures", "2gramsfeatures", "vocab_score"],
outputCol="features")
assembler_output = assembler.transform(df_with_4grams_idf_vectors)
output = assembler_output.selectExpr('speaker','speech_id','para_cleaned_text','features')
print(output.show())
print(output.count())
output_tordd = output.rdd
train_rdd,test_rdd = output_tordd.randomSplit([0.8, 0.2], 123)
train_df = train_rdd.toDF()
test_df = test_rdd.toDF()
print(train_df)
print(test_df)
print('Train DF - Count: ')
print(train_df.count())
print('Test DF - Count: ')
print(test_df.count())
print("Initializing RF Model")
labelIndexer = StringIndexer(inputCol="speaker", outputCol="indexedLabel").fit(train_df)
rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="features",numTrees=1000, featureSubsetStrategy="auto", impurity='gini', maxDepth=4, maxBins=32)
pipeline = Pipeline(stages=[labelIndexer,rf])
model = pipeline.fit(output)
print("Completed RF Model")
predictions = model.transform(test_df)
evaluator = MulticlassClassificationEvaluator(labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))
rfModel = model.stages[1]
print(rfModel) # summary only
print("Predictions: ")
print(predictions.show())
开发者ID:vikaasa,项目名称:Spark_Workshop,代码行数:54,代码来源:sparking_your_interest.py
示例15: model
def model(classifier, ftrain, fvalid, fprediction):
startTime = time.time()
ctx = SparkContext(appName="model_on_Spark")
sqlContext = SQLContext(ctx)
logger = SparkLogger(ctx)
logger.set_level('ERROR')
# load and prepare training and validation data
rawTrain, train = prepData(sqlContext, ctx, ftrain)
rawValid, valid = prepData(sqlContext, ctx, fvalid)
# is needed to join columns
valid = indexData(valid)
rawValid = indexData(rawValid)
classifiers = {
"RandomForestClassifier" : RFC
}
clf = classifiers[classifier]()
labelIndexer = StringIndexer(inputCol="label", outputCol="indexed")
featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures")
# train and predict
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, clf])
model = pipeline.fit(train)
predictions = model.transform(valid)
# write to file:
subsetPrediction = predictions.select("prediction", "index")
subsetValidData = rawValid.select("dataset", "index")
output = (subsetValidData
.join(subsetPrediction, subsetPrediction.index == subsetValidData.index)
.drop("index")
.drop("index"))
lines = output.map(toCSVLine)
lines.saveAsTextFile('output')
evaluator = MulticlassClassificationEvaluator(
labelCol="label", predictionCol="prediction", metricName="precision")
accuracy = evaluator.evaluate(predictions)
print "Test Error = %g" % (1.0 - accuracy)
executionTime = time.time() - startTime
row=classifier+','+str(executionTime)
ctx.parallelize([row]).saveAsTextFile("timing")
开发者ID:KiprasKancys,项目名称:DMWMAnalytics,代码行数:53,代码来源:pyspark_ml.py
示例16: group
def group(self):
reTokenizer = RegexTokenizer(inputCol=self.query_colname, outputCol="words", minTokenLength=2) #, pattern='\W'
hashingTF = HashingTF(numFeatures=self.num_features, inputCol="words", outputCol="tf")
if self.idf == True:
idf = IDF(minDocFreq=self.min_doc_freq, inputCol="tf", outputCol="idf")
kmeans = KMeans(featuresCol="idf", predictionCol="cluster_id", k=self.n)
pipeline = Pipeline(stages=[reTokenizer, hashingTF, idf, kmeans])
else:
kmeans = KMeans(featuresCol="tf", predictionCol="cluster_id", k=self.n)
pipeline = Pipeline(stages=[reTokenizer, hashingTF, kmeans])
model = pipeline.fit(self.df)
prediction = model.transform(self.df)
return prediction
开发者ID:siruix,项目名称:GroupSQLQuery,代码行数:17,代码来源:group_sql_query_spark.py
示例17: getPipeline
def getPipeline(self, df):
# notify pipeline
self.success('Initializing ML Pipeline ...')
# initialize our tokenizer, we're going to tokenize features
tokenizer = Tokenizer(inputCol='tag_features', outputCol='words')
# convert the tokenize data to vectorize data
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol='features')
# initialize logistic regression algorithm
lr = LogisticRegression(maxIter=10, regParam=0.01)
# create / initialize the ml pipeline
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
# fit the pipeline on our training dataframe
model = pipeline.fit(df)
return model
开发者ID:cjzamora,项目名称:machine-learning,代码行数:17,代码来源:MLPipeline.py
示例18: event_pipeline
def event_pipeline(dataset):
"""
"""
EventCodeI = StringIndexer(inputCol="EventCode", outputCol="EventCodeI")
EventBaseCodeI = StringIndexer(inputCol="EventBaseCode", outputCol="EventBaseCodeI")
EventRootCodeI = StringIndexer(inputCol="EventRootCode", outputCol="EventRootCodeI")
assembler = VectorAssembler(inputCols=["IsRootEvent", "EventCodeI", "EventBaseCodeI","EventRootCodeI", "QuadClass","GoldsteinScale","NumMentions","NumSources","NumArticles","AvgTone"], outputCol="features")
featureIndexer =\
VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=310)
pipeline = Pipeline(stages=[EventCodeI, EventBaseCodeI, EventRootCodeI,assembler,featureIndexer])
model = pipeline.fit(dataset)
output = model.transform(dataset)
data = output.map(lambda row: LabeledPoint(row[0], row[-1])).cache()
print "Data:"
print data.take(1)
return data
开发者ID:liber-pater,项目名称:ProjectThales,代码行数:17,代码来源:GdeltDecisionTree-mllib.py
示例19: event_pipeline
def event_pipeline(dataset):
EventCodeI = StringIndexer(inputCol="EventCode", outputCol="EventCodeI")
EventCodeV = OneHotEncoder(dropLast=True, inputCol="EventCodeI", outputCol="EventCodeV")
EventRootCodeI = StringIndexer(inputCol="EventRootCode", outputCol="EventRootCodeI")
EventRootCodeV = OneHotEncoder(dropLast=True, inputCol="EventRootCodeI", outputCol="EventRootCodeV")
EventBaseCodeI = StringIndexer(inputCol="EventBaseCode", outputCol="EventBaseCodeI")
EventBaseCodeV = OneHotEncoder(dropLast=True, inputCol="EventBaseCodeI", outputCol="EventBaseCodeV")
assembler = VectorAssembler(inputCols=["IsRootEvent", "EventCodeV", "EventBaseCodeV","EventRootCodeV", "QuadClass","GoldsteinScale","NumMentions","NumSources","NumArticles","AvgTone"], outputCol="features")
pipeline = Pipeline(stages=[EventCodeI, EventCodeV, EventRootCodeI, EventRootCodeV,EventBaseCodeI,EventBaseCodeV,assembler])
model = pipeline.fit(dataset)
output = model.transform(dataset)
data = output.map(lambda row: LabeledPoint(row[0], row[-1])).toDF().cache()
return data
开发者ID:liber-pater,项目名称:ProjectThales,代码行数:18,代码来源:GdeltDecisionTree.py
示例20: test_pipeline_persistence
def test_pipeline_persistence(self):
sqlContext = SQLContext(self.sc)
temp_path = tempfile.mkdtemp()
try:
df = sqlContext.createDataFrame([(["a", "b", "c"],), (["c", "d", "e"],)], ["words"])
tf = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
pca = PCA(k=2, inputCol="features", outputCol="pca_features")
pl = Pipeline(stages=[tf, pca])
model = pl.fit(df)
pipeline_path = temp_path + "/pipeline"
pl.save(pipeline_path)
loaded_pipeline = Pipeline.load(pipeline_path)
self.assertEqual(loaded_pipeline.uid, pl.uid)
self.assertEqual(len(loaded_pipeline.getStages()), 2)
[loaded_tf, loaded_pca] = loaded_pipeline.getStages()
self.assertIsInstance(loaded_tf, HashingTF)
self.assertEqual(loaded_tf.uid, tf.uid)
param = loaded_tf.getParam("numFeatures")
self.assertEqual(loaded_tf.getOrDefault(param), tf.getOrDefault(param))
self.assertIsInstance(loaded_pca, PCA)
self.assertEqual(loaded_pca.uid, pca.uid)
self.assertEqual(loaded_pca.getK(), pca.getK())
model_path = temp_path + "/pipeline-model"
model.save(model_path)
loaded_model = PipelineModel.load(model_path)
[model_tf, model_pca] = model.stages
[loaded_model_tf, loaded_model_pca] = loaded_model.stages
self.assertEqual(model_tf.uid, loaded_model_tf.uid)
self.assertEqual(model_tf.getOrDefault(param), loaded_model_tf.getOrDefault(param))
self.assertEqual(model_pca.uid, loaded_model_pca.uid)
self.assertEqual(model_pca.pc, loaded_model_pca.pc)
self.assertEqual(model_pca.explainedVariance, loaded_model_pca.explainedVariance)
finally:
try:
rmtree(temp_path)
except OSError:
pass
开发者ID:nampham2,项目名称:spark,代码行数:42,代码来源:tests.py
注:本文中的pyspark.ml.Pipeline类示例由纯净天空整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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