Repository: piskvorky/gensim

Issue:
LdaMulticore parameter alpha: 'auto' not supported but documentation says it is
When using the value 'auto' for parameter "alpha" for LdaMulticore, the following exception is raised:
```
NotImplementedError: auto-tuning alpha not implemented in multicore LDA; use plain LdaModel.
```
(ldamulticore.py, line 146)

However the documentation string of the `__init__` states that "auto" is supported.

This may be a copy/paste error, but is there any chance to see auto get supported for the Multicore version as well? 

#### Versions
```
Linux-4.15.0-35-generic-x86_64-with-debian-buster-sid
Python 3.6.3 |Anaconda custom (64-bit)| (default, Oct 13 2017, 12:02:49) 
[GCC 7.2.0]
NumPy 1.14.2
SciPy 1.0.0
gensim 3.4.0
FAST_VERSION 1
```

However, the exception code is still present in the latest master as well, I think. 
LdaMulticore parameter alpha: 'auto' not supported but documentation says it is
When using the value 'auto' for parameter "alpha" for LdaMulticore, the following exception is raised:
```
NotImplementedError: auto-tuning alpha not implemented in multicore LDA; use plain LdaModel.
```
(ldamulticore.py, line 146)

However the documentation string of the `__init__` states that "auto" is supported.

This may be a copy/paste error, but is there any chance to see auto get supported for the Multicore version as well? 

#### Versions
```
Linux-4.15.0-35-generic-x86_64-with-debian-buster-sid
Python 3.6.3 |Anaconda custom (64-bit)| (default, Oct 13 2017, 12:02:49) 
[GCC 7.2.0]
NumPy 1.14.2
SciPy 1.0.0
gensim 3.4.0
FAST_VERSION 1
```

However, the exception code is still present in the latest master as well, I think. 


Discussion:
Hello @johann-petrak, thanks for the report: that's really copy-paste issue.

> is there any chance to see auto get supported for the Multicore version as well

Most likely no, sorry
> Hello @johann-petrak, thanks for the report: that's really copy-paste issue.
> 
> > is there any chance to see auto get supported for the Multicore version as well
> 
> Most likely no, sorry

Could you explain why? The Mallet implementation does this while being mult-threaded (I understand how multi-processing is different from multi-threaded). I would just like to understand what the limitation is. 
Estimating alpha from the data is something one often wants to do, but having to resort to a single thread is a high cost for that. 
Hello @johann-petrak, thanks for the report: that's really copy-paste issue.

> is there any chance to see auto get supported for the Multicore version as well

Most likely no, sorry
> Hello @johann-petrak, thanks for the report: that's really copy-paste issue.
> 
> > is there any chance to see auto get supported for the Multicore version as well
> 
> Most likely no, sorry

Could you explain why? The Mallet implementation does this while being mult-threaded (I understand how multi-processing is different from multi-threaded). I would just like to understand what the limitation is. 
Estimating alpha from the data is something one often wants to do, but having to resort to a single thread is a high cost for that. 

Resolving patch:
diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py
--- a/gensim/models/ldamodel.py
+++ b/gensim/models/ldamodel.py
@@ -385,7 +385,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None,
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
                 * 'asymmetric': Uses a fixed normalized asymmetric prior of `1.0 / topicno`.
-                * 'auto': Learns an asymmetric prior from the corpus.
+                * 'auto': Learns an asymmetric prior from the corpus (not available if `distributed==True`).
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be:
 
diff --git a/gensim/models/ldamulticore.py b/gensim/models/ldamulticore.py
--- a/gensim/models/ldamulticore.py
+++ b/gensim/models/ldamulticore.py
@@ -134,7 +134,6 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, workers=None,
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
                 * 'asymmetric': Uses a fixed normalized asymmetric prior of `1.0 / topicno`.
-                * 'auto': Learns an asymmetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be:
 


Test patch: