def pysparkLR():
"""
TrainValidationSplit Test
:return:
"""
spark = createLocalSparkSession()
df = getDatasetMinist(spark)
train, test = df.randomSplit([0.9, 0.1], seed=12345)
lr = RandomForestClassifier()
paramGrid = ParamGridBuilder() \
.addGrid(lr.maxDepth, [4, 5]) \
.addGrid(lr.numTrees, [10, 20]) \
.build()
tvs = TrainValidationSplit(estimator=lr,
estimatorParamMaps=paramGrid,
evaluator=MulticlassClassificationEvaluator(),
# 80% of the data will be used for training, 20% for validation.
trainRatio=0.8)
model = tvs.fit(train)
# Make predictions on test data. model is the model with combination of parameters
# that performed best.
model.transform(test) \
.select("features", "label", "prediction") \
.show(500)
def gbtr():
spark = createLocalSparkSession()
df = getDatasetMinist(spark)
# DecisionTreeClassifier.le = LabelEncoder()
clf = DecisionTreeClassifier()
model = clf.fit(df)
model.write().overwrite().save('file:///data/mapleleaf/work/algorithm/model/DecisionTreeClassifier_node_DecisionTreeClassifier_76017b.None/model')
def udfTest():
# conf = SparkConf().setAppName("test").setMaster("local[2]") \
# .set("spark.shuffle.service.enabled", "false").set("spark.dynamicAllocation.enabled", "false")
#
# sc = SparkContext(conf=conf)
spark = createLocalSparkSession()
dataX, dataY = getMnist()
def sklmodelPredict(model):
def f(vec):
p = model.predict(np.array(vec.values.data).reshape(1, -1))
return int(p)
return f
df = spark.createDataFrame([(user, Vectors.dense([i, i**2, i**3]),
0.0 + user + i + 2 * i**2 + 3 * i**3)
for user in range(3) for i in range(5)])
df = df.toDF("key", "features", "y")
pd = df.select('features', 'y').toPandas()
dataX = np.vstack(pd['features'].apply(lambda v: v.toArray()))
dataY = pd['y'].values.reshape(-1, 1)
model = linear_model.LinearRegression()
model.fit(dataX, dataY)
ufun = udf(sklmodelPredict(model))
df.withColumn("pred", ufun("features")).show()
def testLr():
spark = createLocalSparkSession()
df = getDatasetMinist(spark)
train, test = df.randomSplit([0.9, 0.1], seed=12345)
lr = TFNeuralNetwork()
model = lr.fit(train, {0.01: 0.01, 10: 10})
pred = model.transform(test)
pred.show()
def dfTry():
spark = createLocalSparkSession()
df = getDatasetMinist(spark)
rdd1 = spark.sparkContext.parallelize(np.arange(5000).tolist())
rdd2 = df.rdd.zip(rdd1).map(lambda d_r: d_r[0]+Row(pred=d_r[1]))
df2 = df.sql_ctx.createDataFrame(rdd2, df.schema.add("pred", LongType()))
df2.show()
def testCvWithLr():
spark = createLocalSparkSession()
df = getDatasetMinist(spark)
train, test = df.randomSplit([0.9, 0.1], seed=12345)
lr = TFNeuralNetwork()
paramGrid = ParamGridBuilder() \
.addGrid(lr.lr, [0.1, 0.01]) \
.addGrid(lr.maxIter, [10]) \
.build()
tvs = TrainValidationSplit(
estimator=lr,
estimatorParamMaps=paramGrid,
evaluator=MulticlassClassificationEvaluator(),
# 80% of the data will be used for training, 20% for validation.
trainRatio=0.8)
model = tvs.fit(train)
pred = model.transform(test)
pred.show()
"""
调参
Run with:
bin/spark-submit --py-files='/Users/t/python/spark-learning/src/utils.zip' \
/Users/t/python/spark-learning/src/ml/model_selection/grid_search_cv.py
"""
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
# from sklearn.model_selection import GridSearchCV
from spark_sklearn.util import createLocalSparkSession
from spark_sklearn.grid_search import GridSearchCV
digits = datasets.load_digits()
X, y = digits.data, digits.target
sc = createLocalSparkSession().sparkContext
param_grid = {
"max_depth": [3, None],
"max_features": [1, 3, 10],
"min_samples_split": [0.1, 0.2, 0.3],
"min_samples_leaf": [1, 3, 10],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"],
"n_estimators": [10, 20, 40, 80]
}
gs = GridSearchCV(sc, RandomForestClassifier(), param_grid=param_grid)
gs.fit(X, y)
# 获取最佳参数
best_params_ = None
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri May 11 01:43:41 2018 @author: Mujirin email: [email protected] """ from pyspark.ml.linalg import Vectors from spark_sklearn.util import createLocalSparkSession from pyspark.ml.clustering import GaussianMixture from pyspark.ml.clustering import GaussianMixtureModel spark = createLocalSparkSession() def hiperAdapter(hiperparameter): ''' Fungsi ini untuk menyesuaikan config yang tidak lengkap ke defaultnya. ''' hiperparameter_default = { "featuresCol": "features", "predictionCol": "prediction", "k": 2, "probabilityCol": "probability", "tol": 0.01, "maxIter": 100, "seed": None } hiperparameter_keys = list(hiperparameter.keys())
def load():
spark = createLocalSparkSession()
obj = DecisionTreeClassificationModel.load('tmp')
def setUpClass(cls):
cls.spark = createLocalSparkSession("Unit Tests")
if setup:
setup()
