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License: GNU General Public License v3.0
Python implementation of the Zeta score for contrastive text analysis
License: GNU General Public License v3.0
I tried running run_pyzeta
with doyle and roman20 corpus and I get this error:
Traceback (most recent call last):
File "run_pyzeta.py", line 101, in <module>
calculate.main(datafolder, dtmfolder, metadatafile, separator, contrast, logaddition, resultsfolder, segmentlength, featuretype, absolutefreqs, relativefreqs, binaryfreqs)
File "/Users/reiterns/Documents/Projects/fanfiction/pyzeta/scripts/calculate.py", line 375, in main
idlists = make_idlists(metadatafile, separator, contrast)
File "/Users/reiterns/Documents/Projects/fanfiction/pyzeta/scripts/calculate.py", line 36, in make_idlists
metadata.set_index("idno", inplace=True)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py", line 4391, in set_index
raise KeyError("None of {} are in the columns".format(missing))
KeyError: "None of ['idno'] are in the columns"
Any ideas?
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
Move basic parameter settings to a separate, plain text settings file to be loaded by run_pyzeta.
Add howto separate from run_pytzeta.py.
This is an idea of AZ. Make sure that longer documents don't skew the results by sampling a fix number of segments from each document. In this way, each document will have an equal weight in the overall Zeta score of a group of texts. It does mean some loss of information, because more text is discarded.
Require utf-8 input (in docs and ind code)
Add sample input (texts and metadata)
Offer the option to run a comparison not on a meaningful partitioning of the data, but on a random one, for a better understanding of the level of differences to be expected if the partitioning is not meaningful.
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