华为云AI开发平台ModelArts最小二乘法_云淘科技
概述
ALS（交替最小二乘）是一种求解矩阵分解问题的最优化方法。
“交替最小二乘”节点用于推荐，它通过矩阵分解手段快速实现用户对物品评分的预测。
输入
|
参数 |
子参数 |
参数说明 |
|---|---|---|
|
inputs |
dataframe |
inputs为字典类型，dataframe为pyspark中的DataFrame类型对象 |
输出
spark pipeline类型的模型
参数说明
|
参数 |
子参数 |
参数说明 |
|---|---|---|
|
user_col |
– |
用户id所在的列名 |
|
item_col |
– |
项目id所在的列名 |
|
rating_col |
– |
评分所在的列名 |
|
recommend_nums |
– |
推荐物品的个数，默认为10 |
|
prediction_col |
– |
预测列列名，默认为”prediction” |
|
cold_start_strategy |
– |
冷启动策略，默认为”nan” |
|
alpha |
– |
矩阵分解的正则化系数，默认为1.0 |
|
implicit_prefs |
– |
是否使用隐含偏好，默认为Flase |
|
max_iter |
– |
最大迭代次数，默认为50 |
|
non_negative |
– |
是否使用非负限制，默认为False |
|
rank |
– |
因子分解的秩，默认为10 |
|
reg_param |
– |
正则化系数，默认为0.0 |
样例
inputs = {
"dataframe": None # @input {"label":"dataframe","type":"DataFrame"}
}
params = {
"inputs": inputs,
"user_col": "", # @param {"label":"user_col","type":"string","required":"true","helpTip":""}
"item_col": "", # @param {"label":"item_col","type":"string","required":"true","helpTip":""}
"rating_col": "", # @param {"label":"rating_col","type":"string","required":"true","helpTip":""}
"recommend_nums": 10, # @param {"label":"recommend_nums","type":"integer","required":"false","range":"(0,2147483647)","helpTip":""}
"prediction_col": "prediction", # @param {"label":"prediction_col","type":"string","required":"false","helpTip":""}
"cold_start_strategy": "nan", # @param {"label":"cold_start_strategy","type":"string","required":"false","helpTip":""}
"alpha": 1, # @param {"label":"alpha","type":"number","required":"false","range":"(none,none)","helpTip":""}
"implicit_prefs": False, # @param {"label":"implicit_prefs","type":"boolean","required":"false","helpTip":""}
"max_iter": 10, # @param {"label":"max_iter","type":"integer","required":"false","range":"(0,2147483647)","helpTip":""}
"non_negative": False, # @param {"label":"non_negative","type":"boolean","required":"false","helpTip":""}
"rank": 10, # @param {"label":"rank","type":"integer","required":"false","range":"(0,2147483647)","helpTip":""}
"reg_param": 0.1 # @param {"label":"reg_param","type":"number","required":"false","range":"(none,none)","helpTip":""}
}
als____id___ = MLSALS(**params)
als____id___.run()
# @output {"label":"pipeline_model","name":"als____id___.get_outputs()['output_port_1']","type":"PipelineModel"}
# @output {"label":"dataframe","name":"als____id___.get_outputs()['output_port_2']","type":"DataFrame"}
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