I'm using the pystruct Python module for a structured learning problem in classifying posts in discussion threads, and I've run into an issue when tying to train the OneSlackSSVM for use with the LinearChainCRF. I'm following the OCR example from the docs, but can't seem to call the .fit() method on the SSVM. Here is the error I'm getting:
Traceback (most recent call last):
File "<ipython-input-47-da804d135818>", line 1, in <module>
ssvm.fit(X_train, y_train)
File "/Users/kylefth/anaconda/lib/python2.7/site-
packages/pystruct/learners/one_slack_ssvm.py", line 429, in fit
joint_feature_gt = self.model.batch_joint_feature(X, Y)
File "/Users/kylefth/anaconda/lib/python2.7/site-
packages/pystruct/models/base.py", line 40, in batch_joint_feature
joint_feature_ += self.joint_feature(x, y)
File "/Users/kylefth/anaconda/lib/python2.7/site-
packages/pystruct/models/graph_crf.py", line 197, in joint_feature
unary_marginals[gx, y] = 1
IndexError: index 7 is out of bounds for axis 1 with size 7
Below is the code I've written. I've tired to structure the data as in the docs example where the overall data structure is a dict with keys for data, labels, and folds.
from pystruct.models import LinearChainCRF
from pystruct.learners import OneSlackSSVM
# Printing out keys of overall data structure
print threads.keys()
>>> ['folds', 'labels', 'data']
# Creating instances of models
crf = LinearChainCRF()
ssvm = OneSlackSSVM(model=crf)
# Splitting up data into training and test sets as in example
X, y, folds = threads['data'], threads['labels'], threads['folds']
X_train, X_test = X[folds == 1], X[folds != 1]
y_train, y_test = y[folds == 1], y[folds != 1]
# Print out dimensions of first element in data and labels
print X[0].shape, y[0].shape
>>> (8, 211), (8,)
# Fitting the ssvm model
ssvm.fit(X_train, y_train)
>>> see error above
Directly after trying to fit the model, I get the above error. All instances of X_train, X_test, y_train, and y_test have 211 columns and all the label dimensions appear to match up with their corresponding training and testing data. Any help would be greatly appreciated.
