Re: Python API Documentation Mismatch

[Roberto Pagliari]

Hi Yanbo,
You mean pyspark.mllib.recommendation right? That is the one used in the 
official tutorial.

Thank you,

From: Yanbo Liang <yblia...@gmail.com<mailto:yblia...@gmail.com>>
Date: Friday, 4 December 2015 03:17
To: Felix Cheung <felixcheun...@hotmail.com<mailto:felixcheun...@hotmail.com>>
Cc: Roberto Pagliari 
<roberto.pagli...@asos.com<mailto:roberto.pagli...@asos.com>>, 
"user@spark.apache.org<mailto:user@spark.apache.org>" 
<user@spark.apache.org<mailto:user@spark.apache.org>>
Subject: Re: Python API Documentation Mismatch

Hi Roberto,

There are two ALS available: 
ml.recommendation.ALS<http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.recommendation>
 and 
mllib.recommendation.ALS<http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#module-pyspark.mllib.recommendation>
 .
They have different usage and methods. I know it's confusion that Spark provide 
two version of the same algorithm. I strongly recommend to use the ALS 
algorithm at ML package.

Yanbo

2015-12-04 1:31 GMT+08:00 Felix Cheung 
<felixcheun...@hotmail.com<mailto:felixcheun...@hotmail.com>>:
Please open an issue in JIRA, thanks!





On Thu, Dec 3, 2015 at 3:03 AM -0800, "Roberto Pagliari" 
<roberto.pagli...@asos.com<mailto:roberto.pagli...@asos.com>> wrote:

Hello,
I believe there is a mismatch between the API documentation (1.5.2) and the 
software currently available.

Not all functions mentioned here
http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.recommendation

are, in fact available. For example, the code below from the tutorial works

# Build the recommendation model using Alternating Least Squaresrank = 
10numIterations = 10model = ALS.train(ratings, rank, numIterations)

While the alternative shown in the API documentation will not (it will complain 
that ALS takes no arguments. Also, but inspecting the module with Python 
utilities I could not find several methods mentioned in the API docs)

>>> df = sqlContext.createDataFrame(... [(0, 0, 4.0), (0, 1, 2.0), (1, 1, 
>>> 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)],... ["user", "item", 
>>> "rating"])>>> als = ALS(rank=10, maxIter=5)>>> model = als.fit(df)


Thank you,

Re: Python API Documentation Mismatch

[Yanbo Liang]

Hi Roberto,

There are two ALS available: ml.recommendation.ALS

and mllib.recommendation.ALS

 .
They have different usage and methods. I know it's confusion that Spark
provide two version of the same algorithm. I strongly recommend to use the
ALS algorithm at ML package.

Yanbo

2015-12-04 1:31 GMT+08:00 Felix Cheung :

> Please open an issue in JIRA, thanks!
>
>
>
>
>
> On Thu, Dec 3, 2015 at 3:03 AM -0800, "Roberto Pagliari" <
> roberto.pagli...@asos.com> wrote:
>
> Hello,
> I believe there is a mismatch between the API documentation (1.5.2) and
> the software currently available.
>
> Not all functions mentioned here
>
> http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.recommendation
>
> are, in fact available. For example, the code below from the tutorial works
>
> # Build the recommendation model using Alternating Least Squaresrank = 
> 10numIterations = 10model = ALS.train(ratings, rank, numIterations)
>
>
> While the alternative shown in the API documentation will not (it will
> complain that ALS takes no arguments. Also, but inspecting the module with
> Python utilities I could not find several methods mentioned in the API docs)
>
> >>> df = sqlContext.createDataFrame(... [(0, 0, 4.0), (0, 1, 2.0), (1, 1, 
> >>> 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)],... ["user", "item", 
> >>> "rating"])>>> als = ALS(rank=10, maxIter=5)>>> model = als.fit(df)
>
>
>
> Thank you,
>
>

Re: Python API Documentation Mismatch

[Felix Cheung]

Please open an issue in JIRA, thanks!






On Thu, Dec 3, 2015 at 3:03 AM -0800, "Roberto Pagliari" 
 wrote:





Hello,
I believe there is a mismatch between the API documentation (1.5.2) and the 
software currently available.

Not all functions mentioned here
http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.recommendation

are, in fact available. For example, the code below from the tutorial works

# Build the recommendation model using Alternating Least Squares
rank = 10
numIterations = 10
model = ALS.train(ratings, rank, numIterations)

While the alternative shown in the API documentation will not (it will complain 
that ALS takes no arguments. Also, but inspecting the module with Python 
utilities I could not find several methods mentioned in the API docs)

>>> df = sqlContext.createDataFrame(
... [(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 
2, 5.0)],
... ["user", "item", "rating"])
>>> als = ALS(rank=10, maxIter=5)
>>> model = als.fit(df)


Thank you,