本文整理汇总了Python中pyspark.ml.classification.LogisticRegression.explainParams方法的典型用法代码示例。如果您正苦于以下问题：Python LogisticRegression.explainParams方法的具体用法？Python LogisticRegression.explainParams怎么用？Python LogisticRegression.explainParams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.classification.LogisticRegression的用法示例。
在下文中一共展示了LogisticRegression.explainParams方法的2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: explainParams
# 需要导入模块: from pyspark.ml.classification import LogisticRegression [as 别名]
# 或者: from pyspark.ml.classification.LogisticRegression import explainParams [as 别名]
# COMMAND ----------
# MAGIC %md The evaluator currently accepts 2 kinds of metrics - areaUnderROC and areaUnderPR.
# MAGIC We can set it to areaUnderPR by using evaluator.setMetricName("areaUnderPR").
# COMMAND ----------
# MAGIC %md
# MAGIC Now we will try tuning the model with the ParamGridBuilder and the CrossValidator.
# MAGIC
# MAGIC If you are unsure what params are available for tuning, you can use explainParams() to print a list of all params.
# COMMAND ----------
print lr.explainParams()
# COMMAND ----------
# MAGIC %md As we indicate 5 values for regParam, 4 values for maxIter, and 5 values for elasticNetParam, this grid will have 5 x 4 x 5 = 100 parameter settings for CrossValidator to choose from. We will create a 5-fold cross validator.
# COMMAND ----------
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
# Create ParamGrid for Cross Validation
paramGrid = (ParamGridBuilder()
.addGrid(lr.regParam, [0.01, 0.1, 0.5, 1.0, 2.0])
.addGrid(lr.elasticNetParam, [0.0, 0.1, 0.5, 0.8, 1.0])
.addGrid(lr.maxIter, [1, 5, 10, 20])
.build())示例2: SparkContext
# 需要导入模块: from pyspark.ml.classification import LogisticRegression [as 别名]
# 或者: from pyspark.ml.classification.LogisticRegression import explainParams [as 别名]
sc = SparkContext(appName="ML Example")
sc.setLogLevel("FATAL")
sqlContext = SQLContext(sc)
# Prepare training data from a list of (label, features) tuples.
training = sqlContext.createDataFrame([
(1.0, Vectors.dense([0.0, 1.1, 0.1])),
(0.0, Vectors.dense([2.0, 1.0, -1.0])),
(0.0, Vectors.dense([2.0, 1.3, 1.0])),
(1.0, Vectors.dense([0.0, 1.2, -0.5]))], ["label", "features"])
# Create a LogisticRegression instance. This instance is an Estimator.
lr = LogisticRegression(maxIter=10, regParam=0.01)
# Print out the parameters, documentation, and any default values.
print("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
# Learn a LogisticRegression model. This uses the parameters stored in lr.
model1 = lr.fit(training)
# Since model1 is a Model (i.e., a transformer produced by an Estimator),
# we can view the parameters it used during fit().
# This prints the parameter (name: value) pairs, where names are unique IDs for this
# LogisticRegression instance.
print("Model 1 was fit using parameters: ")
print(model1.extractParamMap())
# We may alternatively specify parameters using a Python dictionary as a paramMap
paramMap = {lr.maxIter: 20}
paramMap[lr.maxIter] = 30 # Specify 1 Param, overwriting the original maxIter.
paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55}) # Specify multiple Params.