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Comment pouvez-vous dire la ml.DecisionTreeClassifier de marquer sur les caractéristiques en continu au lieu de caractéristiques qualitatives sans avoir à utiliser la méthode Bucketizer ou QuantileDiscretizer? Ci-dessous est le code où je passe des fonctionnalités continues dans le DecisionTreeClassifier dans ML et sans Binning (Buckizer) les fonctionnalités, la majorité de l'ensemble de scores est ignorée au lieu de score (étincelle 2.1 ne supporte pas).ML DecisionTreeClassifier - Caractéristiques continu
from pyspark.mllib.linalg import Vectors
from pyspark.ml import Pipeline
from pyspark.sql import Row, SparkSession, SQLContext
from pyspark.sql.types import StringType, DoubleType
from pyspark.ml.feature import StringIndexer, VectorAssembler, OneHotEncoder
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark import SparkConf, SparkContext
from pyspark.sql.functions import udf
# Load the training set that is in parquet format into a data frame
train_df = sqlContext.read.parquet("/data/training_set")
# convert data types to double
train_df.withColumn("income", train_df["income"].cast(DoubleType())
train_df.withColumn("age", train_df["age"].cast(DoubleType())
# StringIndexer - Target
# First we will StringIndexer to get numeric categorical features
indexer1 = StringIndexer(inputCol="target", outputCol="target_numeric", handleInvalid="skip")
############
# StringIndexer/OneHotEncoder - age_in_two_year_increments_2nd_individual
# First we will StringIndexer to get numeric categorical features
indexer2 = StringIndexer(inputCol="income", outputCol="income_numeric", handleInvalid='skip')
# Next we change the categorical feature into binarizing via OneHotEncoder
encoder2 = OneHotEncoder(inputCol="income_numeric", outputCol="income_vector")
############
############
# StringIndexer/OneHotEncoder - age_in_two_year_increments_2nd_individual
# First we will StringIndexer to get numeric categorical features
indexer3 = StringIndexer(inputCol="age", outputCol="age_numeric", handleInvalid='skip')
# Next we change the categorical feature into binarizing via OneHotEncoder
encoder3 = OneHotEncoder(inputCol="age_numeric", outputCol="age_vector")
############
# Create distinct StringIndexer transformers with the outputCol
# parameter set to be the name of the input column appended
indexedcols = [
"income_vector",
"age_vector"
]
# FEATURES need to be in a Vector which is why this is converted using a VectorAssembler
# The VectorAssember is going to take as input our index columns and our output will be the features.
# Create a VectorAssembler transformer to combine all of the indexed
# categorical features into a vector. Provide the "indexedcols" list
# created above as the inputCols parameter, and name the outputCol "features".
va = VectorAssembler(inputCols = indexedcols, outputCol = 'features')
# Create a DecisionTreeClassifier, setting the label column to your
# indexed label column ("label_ix") and the features column to the
# newly created column from the VectorAssembler above ("features").
# Store the new StringIndexer transformers, the VectorAssembler,
# as well as the DecisionTreeClassifier in a list called "steps"
clf = DecisionTreeClassifier(labelCol="target_numeric", impurity="gini", maxBins=32, maxMemoryInMB=1024)
# Create steps for transform for the ml pipeline
steps = [indexer1,
indexer2, encoder2,
indexer3, encoder3,
va, clf]
# Create a ML pipeline named "pl" using the steps list to set the stages parameter
pl = Pipeline(stages=steps)
# Run the fit method of the pipeline on the DataFrame
# model in a new variable called "plmodel"
plmodel = pl.fit(train_df)
######################################################################################
# Scoring Set
######################################################################################
# Now get the data you want to run the model against
scoring_df = sqlContext.read.parquet("/data/scoring_set")
# convert data types to double
scoring_df.withColumn("income", scoring_df["income"].cast(DoubleType())
scoring_df.withColumn("age", scoring_df["age"].cast(DoubleType())
# Run the transform method of the pipeline model created above
# on the "test_df" DataFrame to create a new DataFrame called "predictions"
#
# skip past any labels that are not in the training set. If you don't skip then errors will be produced
#saying unseen label:40 which means the scoring set has a new element that did not exist in the training set for the feature.
predictions = plmodel.transform(scoring_DF)
vector_udf1 = udf(lambda vector: float(vector[1]))
vector_udf0 = udf(lambda vector: float(vector[0]))
# Save dataframe to hdfs
outputDF = predictions.select("age", \
"income", \
"prediction", \
vector_udf1("probability").alias("probability0")), \
vector_udf1("probability").alias("probability1")).write.format("parquet").mode("overwrite").save("/data/algo_scored")