本文整理汇总了Python中pyspark.mllib.tree.DecisionTreeModel.predict方法的典型用法代码示例。如果您正苦于以下问题：Python DecisionTreeModel.predict方法的具体用法？Python DecisionTreeModel.predict怎么用？Python DecisionTreeModel.predict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.mllib.tree.DecisionTreeModel的用法示例。
在下文中一共展示了DecisionTreeModel.predict方法的3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: get_probs_classify
# 需要导入模块: from pyspark.mllib.tree import DecisionTreeModel [as 别名]
# 或者: from pyspark.mllib.tree.DecisionTreeModel import predict [as 别名]
def get_probs_classify (model, data):
# Collect the individual decision trees as JavaArray objects
trees = model._java_model.trees()
ntrees = model.numTrees()
scores = DecisionTreeModel(trees[0]).predict(data)
# For each tree, apply its prediction to the entire dataset and zip together the results
for i in range(1,ntrees):
dtm = DecisionTreeModel(trees[i])
scores = scores.zip(dtm.predict(data))
scores = scores.map(lambda x: x[0] + x[1])
# Divide the accumulated scores over the number of trees
return scores.map(lambda x: x/ntrees)示例2: predict_proba
# 需要导入模块: from pyspark.mllib.tree import DecisionTreeModel [as 别名]
# 或者: from pyspark.mllib.tree.DecisionTreeModel import predict [as 别名]
def predict_proba(rf_model, testRDD):
trees = rf_model._java_model.trees()
ntrees = rf_model.numTrees()
scores_dict = {i: 0 for i in range(0,10)}
scoresRDD = testRDD.map(lambda x: scores_dict.copy())
for tree in trees:
dtm = DecisionTreeModel(tree)
currentScoreRDD = dtm.predict(testRDD)
scoresRDD = scoresRDD.zip(currentScoreRDD)
def reduceTuple(x):
x[0][int(x[1])] += 1
return x[0]
scoresRDD = scoresRDD.map(reduceTuple)
return scoresRDD示例3: predict_proba
# 需要导入模块: from pyspark.mllib.tree import DecisionTreeModel [as 别名]
# 或者: from pyspark.mllib.tree.DecisionTreeModel import predict [as 别名]
def predict_proba(rf_model, data):
'''
This wrapper overcomes the "binary" nature of predictions in the native
RandomForestModel.
''' # Collect the individual decision tree models by calling the underlying
# Java model. These are returned as JavaArray defined by py4j.
trees = rf_model._java_model.trees()
ntrees = rf_model.numTrees()
scores = DecisionTreeModel(trees[0]).predict(data.map(
lambda row: [float(row.SearchID), float(row.AdID), float(row.Position), float(row.ObjectType),
float(row.HistCTR)]))
# For each decision tree, apply its prediction to the entire dataset and
# accumulate the results using 'zip'.
for i in range(1, ntrees):
dtm = DecisionTreeModel(trees[i])
scores = scores.zip(dtm.predict(data.map(lambda row : [float(row.SearchID),float(row.AdID),float(row.Position),float(row.ObjectType),float(row.HistCTR)])))
scores = scores.map(lambda x: x[0] + x[1])
# Divide the accumulated scores over the number of trees
return scores.map(lambda x: x / ntrees)