# 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