Python clustering.KMeans類代碼示例

def kmeans(df):
	kmeans = KMeans(k=2,seed=1)
	model = kmeans.fit(df)
	centers = model.clusterCenters()
	print len(centers)
	kmFeatures = model.transform(df).select("features", "prediction")
	dfwrite(kmFeatures,'kmFeatures')	

 def test_kmeans_cosine_distance(self):
     data = [(Vectors.dense([1.0, 1.0]),), (Vectors.dense([10.0, 10.0]),),
             (Vectors.dense([1.0, 0.5]),), (Vectors.dense([10.0, 4.4]),),
             (Vectors.dense([-1.0, 1.0]),), (Vectors.dense([-100.0, 90.0]),)]
     df = self.spark.createDataFrame(data, ["features"])
     kmeans = KMeans(k=3, seed=1, distanceMeasure="cosine")
     model = kmeans.fit(df)
     result = model.transform(df).collect()
     self.assertTrue(result[0].prediction == result[1].prediction)
     self.assertTrue(result[2].prediction == result[3].prediction)
     self.assertTrue(result[4].prediction == result[5].prediction)

def clustering(input_df, input_col_name, n):
    """ KMeans and PCA """
    input_df = input_df.select('state','categories','stars',input_col_name)
    norm = Normalizer(inputCol=input_col_name, outputCol="features", p=1.0)
    df = norm.transform(input_df)
    kmeans = KMeans(k=n, seed=2)
    KMmodel = kmeans.fit(df)
    predicted = KMmodel.transform(df).cache()
    pca = PCA(k=2, inputCol='features', outputCol="pc")
    df =  pca.fit(dfsample).transform(dfsample).cache()
    return df

 def test_kmeans_param(self):
     algo = KMeans()
     self.assertEqual(algo.getInitMode(), "k-means||")
     algo.setK(10)
     self.assertEqual(algo.getK(), 10)
     algo.setInitSteps(10)
     self.assertEqual(algo.getInitSteps(), 10)

 def test_kmeans_summary(self):
     data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
             (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
     df = self.spark.createDataFrame(data, ["features"])
     kmeans = KMeans(k=2, seed=1)
     model = kmeans.fit(df)
     self.assertTrue(model.hasSummary)
     s = model.summary
     self.assertTrue(isinstance(s.predictions, DataFrame))
     self.assertEqual(s.featuresCol, "features")
     self.assertEqual(s.predictionCol, "prediction")
     self.assertTrue(isinstance(s.cluster, DataFrame))
     self.assertEqual(len(s.clusterSizes), 2)
     self.assertEqual(s.k, 2)
     self.assertEqual(s.numIter, 1)

 def test_kmean_pmml_basic(self):
     # Most of the validation is done in the Scala side, here we just check
     # that we output text rather than parquet (e.g. that the format flag
     # was respected).
     data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
             (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
     df = self.spark.createDataFrame(data, ["features"])
     kmeans = KMeans(k=2, seed=1)
     model = kmeans.fit(df)
     path = tempfile.mkdtemp()
     km_path = path + "/km-pmml"
     model.write().format("pmml").save(km_path)
     pmml_text_list = self.sc.textFile(km_path).collect()
     pmml_text = "\n".join(pmml_text_list)
     self.assertIn("Apache Spark", pmml_text)
     self.assertIn("PMML", pmml_text)

def kmeans(inputdir,df,alg,k):
	from pyspark.ml.clustering import KMeans
        from numpy import array
        from math import sqrt	
	kmeans = KMeans(k=int(k), seed=1,initSteps=5, tol=1e-4, maxIter=20, initMode="k-means||", featuresCol="features")
        model = kmeans.fit(df)
        kmFeatures = model.transform(df).select("labels", "prediction")
        erFeatures = model.transform(df).select("features", "prediction")
	###Evaluation
        rows = erFeatures.collect()
        WSSSE = 0
        for i in rows:
		WSSSE += sqrt(sum([x**2 for x in (model.clusterCenters()[i[1]]-i[0])]))
        print("Within Set Sum of Squared Error = " + str(WSSSE))

	output_data = writeOutClu(inputdir,kmFeatures,alg,k,WSSSE)
	return output_data

 def test_kmeans(self):
     kmeans = KMeans(k=2, seed=1)
     path = tempfile.mkdtemp()
     km_path = path + "/km"
     kmeans.save(km_path)
     kmeans2 = KMeans.load(km_path)
     self.assertEqual(kmeans.uid, kmeans2.uid)
     self.assertEqual(type(kmeans.uid), type(kmeans2.uid))
     self.assertEqual(kmeans2.uid, kmeans2.k.parent,
                      "Loaded KMeans instance uid (%s) did not match Param's uid (%s)"
                      % (kmeans2.uid, kmeans2.k.parent))
     self.assertEqual(kmeans._defaultParamMap[kmeans.k], kmeans2._defaultParamMap[kmeans2.k],
                      "Loaded KMeans instance default params did not match " +
                      "original defaults")
     try:
         rmtree(path)
     except OSError:
         pass

def cluster():
    ld = load(open(DATAP+'\\temp\olangdict.json','r',encoding='UTF-8'))

    spark = SparkSession.builder\
                        .master("local")\
                        .appName("Word Count")\
                        .config("spark.some.config.option", "some-value")\
                        .getOrCreate()

    df = spark.createDataFrame([["0"],
                                ["1"],
                                ["2"],
                                ["3"],
                                ["4"]],
                               ["id"])
    df.show()

    vecAssembler = VectorAssembler(inputCols=["feat1", "feat2"], outputCol="features")
    new_df = vecAssembler.transform(df)

    kmeans = KMeans(k=2, seed=1)  # 2 clusters here
    model = kmeans.fit(new_df.select('features'))
    transformed = model.transform(new_df)
    print(transformed.show())

from pyspark.mllib.linalg import Vectors
from pyspark.ml.clustering import KMeans
from pyspark import SparkContext
from pyspark.sql import SQLContext

# sc = SparkContext(appName="test")
# sqlContext = SQLContext(sc)

data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),(Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
df = sqlContext.createDataFrame(data, ["features"])
kmeans = KMeans(k=2, seed=1)
model = kmeans.fit(df)

centers = model.clusterCenters()
model.transform(df).select("features", "prediction").collect()

sales = va.transform(spark.read.format("csv")
  .option("header", "true")
  .option("inferSchema", "true")
  .load("/data/retail-data/by-day/*.csv")
  .limit(50)
  .coalesce(1)
  .where("Description IS NOT NULL"))

sales.cache()


# COMMAND ----------

from pyspark.ml.clustering import KMeans
km = KMeans().setK(5)
print km.explainParams()
kmModel = km.fit(sales)


# COMMAND ----------

summary = kmModel.summary
print summary.clusterSizes # number of points
kmModel.computeCost(sales)
centers = kmModel.clusterCenters()
print("Cluster Centers: ")
for center in centers:
    print(center)

def assign_cluster(data):
    """Train kmeans on rescaled data and then label the rescaled data."""
    kmeans = KMeans(k=2, seed=1, featuresCol="features_scaled", predictionCol="label")
    model = kmeans.fit(data)
    label_df = model.transform(data)
    return label_df

# $example off$

from pyspark.sql import SparkSession

if __name__ == "__main__":
    spark = SparkSession\
        .builder\
        .appName("KMeansExample")\
        .getOrCreate()

    # $example on$
    # Loads data.
    dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")

    # Trains a k-means model.
    kmeans = KMeans().setK(2).setSeed(1)
    model = kmeans.fit(dataset)

    # Make predictions
    predictions = model.transform(dataset)

    # Evaluate clustering by computing Silhouette score
    evaluator = ClusteringEvaluator()

    silhouette = evaluator.evaluate(predictions)
    print("Silhouette with squared euclidean distance = " + str(silhouette))

    # Shows the result.
    centers = model.clusterCenters()
    print("Cluster Centers: ")
    for center in centers:

print(colStdDev)

#Place the means and std.dev values in a broadcast variable
bcMeans = sc.broadcast(colMeans)
bcStdDev = sc.broadcast(colStdDev)
csAuto = autoVector.map(centerAndScale)
#csAuto.collect()
#csAuto.foreach(println)
print(csAuto)

#Create Spark Data Frame
autoRows = csAuto.map(lambda f:Row(features=f))
autoDf = SQLContext.createDataFrame(autoRows)
autoDf.select("features").show(10)

kmeans = KMeans(k=3, seed=1)
model = kmeans.fit(autoDf)
predictions = model.transform(autoDf)
predictions.collect()
predictions.foreach(println)

#Plot the results in a scatter plot
unstripped = predictions.map(unstripData)
predList=unstripped.collect()
predPd = pd.DataFrame(predList)

# preparing to save the clustered data
list_current_gni_final_maped = current_gni_final_maped.collect()
list_current_gni_rdd = current_gni_rdd.collect()
list_predictions_pandas=predictions.toPandas()
list_predictions_temp=list_predictions_pandas.as_matrix()

trainingData = VectorAssembler(inputCols=["duration", "tempo", "loudness"], outputCol="features").transform(
    table("songsTable")
)

# COMMAND ----------

# MAGIC %md We can now pass this new DataFrame to the `KMeans` model and ask it to categorize different rows in our data to two different classes (`setK(2)`). We place the model in a variable named `model`.
# MAGIC
# MAGIC **Note:** This command multiple spark jobs (one job per iteration in the KMeans algorithm). You will see the progress bar starting over and over again.

# COMMAND ----------

from pyspark.ml.clustering import KMeans

model = KMeans().setK(2).fit(trainingData)

# COMMAND ----------

# MAGIC %md To see the result of our clustering, we produce a scatter plot matrix that shows interaction between input variables and learned clusters. To get that we apply the model on the original data and pick four columns: `prediction` and the original features (`duration`, `tempo`, and `loudness`).

# COMMAND ----------

transformed = model.transform(trainingData).select("duration", "tempo", "loudness", "prediction")

# COMMAND ----------

# MAGIC %md To comfortably visualize the data we produce a random sample.
# MAGIC Remember the `display()` function? We can use it to produce a nicely rendered table of transformed DataFrame.

# COMMAND ----------