Code :
from gensim.models.word2vec import Word2Vec
w2v = Word2Vec()
training_data = w2v.generate_training_data(settings, corpus)
Error :
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-45-bae554564046> in <module>
1 w2v = Word2Vec()
2 # Numpy ndarray with one-hot representation for [target_word, context_words]
----> 3 training_data = w2v.generate_training_data(settings, corpus)
AttributeError: 'Word2Vec' object has no attribute 'generate_training_data'
I even tried importing gensim.models.word2vec and tried every possibility but couldn't get it done.
Can someone help me with it?
Thanks in advance !
Yes, the gensim Word2Vec class doesn't have that method – and as far as I know, it never has.
And from your example usage, I'm not even sure what it might purport to do: a Word2Vec model needs to be provided data in the right format – it doesn't "generate" it (even as a translation from some other corpus).
I suspect you are looking at docs or a code example from some other unrelated library.
For using gensim's Word2Vec, you should rely on the gensim documentation & examples. The class docs include some basic details of proper usage, and there's a Jupyter notebook word2vec.ipynb included with the library, in its docs/notebooks directory (and also viewable online).
Related
I am working on topic inference that will require to load a previously saved model.
However, I got a pickle error that says
Traceback (most recent call last):
File "topic_inference.py", line 35, in <module>
model_for_inference = gensim.models.LdaModel.load(model_name, mmap = 'r')
File "topic_modeling/env/lib/python3.8/site-packages/gensim/models/ldamodel.py", line 1663, in load
result = super(LdaModel, cls).load(fname, *args, **kwargs)
File "topic_modeling/env/lib/python3.8/site-packages/gensim/utils.py", line 486, in load
obj = unpickle(fname)
File "topic_modeling/env/lib/python3.8/site-packages/gensim/utils.py", line 1461, in unpickle
return _pickle.load(f, encoding='latin1') # needed because loading from S3 doesn't support readline()
TypeError: __randomstate_ctor() takes from 0 to 1 positional arguments but 2 were given
The code I use to load the model is simply
gensim.models.LdaModel.load(model_name, mmap = 'r')
Here is the code that I use to create and save the model
model = gensim.models.ldamulticore.LdaMulticore(
corpus=comment_corpus,
id2word=key_word_dict, ## This is now a gensim.corpora.Dictionary Object, previously it was the .id2token attribute
chunksize=chunksize,
alpha='symmetric',
eta='auto',
iterations=iterations,
num_topics=num_topics,
passes=epochs,
eval_every=eval_every,
workers = 15,
minimum_probability= 0.0)
model.save(output_model)
where output_model doesn't have an extension like .model or .pkl
In the past, I tried the similar approach with the exception that I passed in a .id2token attribute under the gensim.corpora.Dictionary object instead of the full gensim.corpora.Dictionary to the id2word parameter when I created the model, and the method loads the model fine back then. I wonder if passing in a corpora.Dictionary will make a difference in the loading output...? Back that time, I was using regular python, but now I am using anaconda. However, all the versions of the packages are the same.
Another report of an error about __randomstate_ctor (at https://github.com/numpy/numpy/issues/14210) suggests the problem may be related to numpy object pickling.
Is there a chance that the configuration where your load is failing is using a later version of numpy than when the save occurred? Could you try, at least temporarily, rolling back to some older numpy (that's still sufficient for whatever Gensim you're using) to see if it helps?
If you find any load that works, even in a suboptimal config, you might be able to null-out whatever random-related objects are causing the problem and re-save, then having a saved version that loads better in your truly-desired configuration. Then, if the random-related objects truly needed after reload, it may be possible to manually re-constitute them. (I haven't looked into this yet, but if you find any workaround allowing a load, but then aren't sure what to manually null/rebuild, I could take a closer look.)
I am trying to use the Palantir Foundry helper function shapefile_to_dataframe() in order to ingest shapefiles for later usage in geolocation features.
I have manually imported the shapefiles (.shp, .shx & .dbf) in a single dataset (no access issues through the filesystem API).
As per documentation, I have imported geospatial-tools and the GEOSPARK profiles + included dependencies in the transforms-python build.gradle.
Here is my transform code, which is mostly extracted from the documentation:
from transforms.api import transform, Input, Output, configure
from geospatial_tools import geospatial
from geospatial_tools.parsers import shapefile_to_dataframe
#geospatial()
#transform(
raw = Input("ri.foundry.main.dataset.0d984138-23da-4bcf-ad86-39686a14ef21"),
output = Output("/Indhu/InDhu/Vincent/geo_energy/datasets/extract_coord/raw_df")
)
def compute(raw, output):
return output.write_dataframe(shapefile_to_dataframe(raw))
Code assist then become extremely slow to load, and then I am finally getting following error:
AttributeError: partially initialized module 'fiona' has no attribute '_loading' (most likely due to a circular import)
Traceback (most recent call last):
File "/myproject/datasets/shp_to_df.py", line 3, in <module>
from geospatial_tools.parsers import shapefile_to_dataframe
File "/scratch/standalone/3a553998-623b-48f5-9c3f-03de7e64f328/code-assist/contents/transforms-python/build/conda/env/lib/python3.8/site-packages/geospatial_tools/parsers.py", line 11, in <module>
from fiona.drvsupport import supported_drivers
File "/scratch/standalone/3a553998-623b-48f5-9c3f-03de7e64f328/code-assist/contents/transforms-python/build/conda/env/lib/python3.8/site-packages/fiona/__init__.py", line 85, in <module>
with fiona._loading.add_gdal_dll_directories():
AttributeError: partially initialized module 'fiona' has no attribute '_loading' (most likely due to a circular import)
Thanks a lot for your help,
Vincent
I was able to reproduce this error and it seems like it happens only in previews - running the full build seems to be working fine. The simplest way to get around it is to move the import inside the function:
from transforms.api import transform, Input, Output, configure
from geospatial_tools import geospatial
#geospatial()
#transform(
raw = Input("ri.foundry.main.dataset.0d984138-23da-4bcf-ad86-39686a14ef21"),
output = Output("/Indhu/InDhu/Vincent/geo_energy/datasets/extract_coord/raw_df")
)
def compute(raw, output):
from geospatial_tools.parsers import shapefile_to_dataframe
return output.write_dataframe(shapefile_to_dataframe(raw))
However, at the moment, the function shapefile_to_dataframe isn't going to work in the Preview anyway because the full transforms.api.FileSystem API isn't implemented - specifically, the functions ls doesn't implement the parameter glob which the full transforms API does.
I can't seem to figure out a way to pickle this, can anyone help?
It's because of the way reduce function is written for re.match.
Code:
import re
x = re.match('abcd', 'abcd')
print(type(x))
print(x.__reduce_ex__(3))
Output:
<class 're.Match'>
Traceback (most recent call last):
File "an.py", line 4, in <module>
print(x.__reduce_ex__(3))
TypeError: can't pickle re.Match objects
My exact issue is that I am trying to pickle an object of a lex / yacc parser implementation class after submitting a string to it to parse.
If I try to pickle the class object without parsing any string via it, it is able to pickle. Problem arises only after I parse a string using it and then try to pickle the class object.
Match objects does not have a __getstate__ and __setstate__ thus cannot be pickled, the entire iterator could not be pickled.
More about this subject can be found here:
https://docs.python.org/3/library/pickle.html#pickle-picklable
here is a further explanation on the desired objects:
https://docs.python.org/3/library/re.html#match-objects
An alternative solution is to implement __getstate__ and __setstate__ to help the pickling process, this will require you to create a custom class and implement this function, which seem to overcomplicated for this situation
Hope that helped
I'm having issues loading a pretrained xgboost model using the following code:
xgb_model = pickle.load(open('churnfinalunscaled.pickle.dat', 'rb'))
And when I do that, I get the following error:
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-29-31e7f426e19e> in <module>()
----> 1 xgb_model = pickle.load(open('churnfinalunscaled.pickle.dat', 'rb'))
ModuleNotFoundError: No module named 'sklearn.preprocessing._label'
I haven't seen anything online so any help would be much appreciated.
I was able to solve my issue. Simply update scikit-learn from 0.21.3 to 0.22.0 seems to solve the issue. Along the way I have to update my pandas version to 0.25.2 as well.
The cue is provided in this link: https://www.gitmemory.com/vruusmann, where it states:
During Scikit-Learn version upgrade from 0.21.X to 0.22.X many modules were renamed (typically, by prepending an underscore character to the module name). For example, sklearn.preprocessing.label.LabelEncoder became sklearn.preprocessing._label.LabelEncoder.
I want to persist a trained model in CNTK and found the 'persist' functionality after some amount of searching. However, there seems to be some error in importing it.
from cntk import persist
This is throwing ImportError.
Am I doing something the wrong way? Or is this no longer supported? Is there an alternate way to persist a model?
persist is from an earlier beta. save_model is now a method of every CNTK function. So instead of doing save_model(z, filename) you do z.save_model(filename). Load_model works the same as before but you import it from cntk.ops.functions. For an example, see: https://github.com/Microsoft/CNTK/blob/v2.0.beta7.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb or https://github.com/Microsoft/CNTK/blob/v2.0.beta7.0/bindings/python/cntk/tests/persist_test.py
The functionality has moved to cntk functions. The new way is mynetwork.save_model(...) where mynetwork represents the root of your computation (typically the prediction). For loading the model you can just say mynetwork = C.load_model(...)