本文整理汇总了Python中pyspark.ml.Pipeline.fit方法的典型用法代码示例。如果您正苦于以下问题:Python Pipeline.fit方法的具体用法?Python Pipeline.fit怎么用?Python Pipeline.fit使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.ml.Pipeline
的用法示例。
在下文中一共展示了Pipeline.fit方法的15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,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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
示例2: train_lg
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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
示例3: test_nnclassifier_in_pipeline
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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'
示例4: run
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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
示例5: model
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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
示例6: test_nested_pipeline_persistence
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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
示例7: main
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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))
示例8: testLogisticMLPipeline1
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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)
示例9: test_cv_lasso_with_mllib_featurization
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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'])
示例10: fit_kmeans
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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
示例11: RunRandomForest
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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
示例12: textPredict
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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})
示例13: sparking_your_interest
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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())
示例14: model
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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")
示例15: event_pipeline
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import fit [as 别名]
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