# In[60]:
utils.caption(4, "Writing sense feature to TF")
TF = Fabric(locations=thisTempTf, silent=True)
TF.save(nodeFeatures=nodeFeatures, edgeFeatures={}, metaData=metaData)
# # Diffs
#
# Check differences with previous versions.
# In[30]:
# In[30]:
utils.checkDiffs(thisTempTf, thisTf, only=set(nodeFeatures))
# # Deliver
#
# Copy the new TF feature from the temporary location where it has been created to its final destination.
# In[31]:
utils.deliverFeatures(thisTempTf, thisTf, nodeFeatures)
# # Compile TF
# In[ ]:
utils.caption(4, "Load and compile the new TF features")
# Check differences with previous versions. # # The new dataset has been created in a temporary directory, # and has not yet been copied to its destination. # # Here is your opportunity to compare the newly created features with the older features. # You expect some differences in some features. # # We check the differences between the previous version of the features and what has been generated. # We list features that will be added and deleted and changed. # For each changed feature we show the first line where the new feature differs from the old one. # We ignore changes in the metadata, because the timestamp in the metadata will always change. # In[9]: utils.checkDiffs(thisTempTf, thisTf) # # Deliver # # Copy the new TF dataset from the temporary location where it has been created to its final destination. # In[10]: utils.deliverDataset(thisTempTf, thisTf) # # Compile TF # # Just to see whether everything loads and the precomputing of extra information works out. # Moreover, if you want to work with these features, then the precomputing has already been done, and everything is quicker in subsequent runs. # # We issue load statement to trigger the precomputing of extra data.
# Check differences with previous versions.
#
# The new dataset has been created in a temporary directory,
# and has not yet been copied to its destination.
#
# Here is your opportunity to compare the newly created features with the older features.
# You expect some differences in some features.
#
# We check the differences between the previous version of the features and what has been generated.
# We list features that will be added and deleted and changed.
# For each changed feature we show the first line where the new feature differs from the old one.
# We ignore changes in the metadata, because the timestamp in the metadata will always change.
# In[20]:
utils.checkDiffs(thisTempTf, thisTf, only=changedFeatures)
# # Deliver
#
# Copy the new TF dataset from the temporary location where it has been created to its final destination.
# In[21]:
utils.deliverFeatures(thisTempTf,
thisTf,
changedFeatures,
deleteFeatures=deleteFeatures)
# # Compile TF
#
# We load the new features, use the new format, check some values
