I'm detecting anomalies in a time series data using pycaret. I'm taking in the data at every call, detecting and returning it. Everything is fine, but when coming to improving the performance, I'm planning to load the saved model, re-train it with less data(say daily instead of getting some 1000 days of data at once) and save the model again. Here its performance increases a lot, as it is training on only less data.
The problem is to update/re-train the model. I couldn't find any method to update the model.
Base Initially:
setup(dataframe)
model=createmodel(modelName)
results=assign_model(model)
What I'm trying to do.
try loading the model if already present.
setup(data_frame_new)
if model.exists:
retrain_model
else:
model=createmodel(modelName)
save_model(model)
results=assign_model(model)
So, now I have trained model and new data, how can I integrate both.
Is there any way to retrain the model? I couldn't see any documentation on that so far.
Or I might have overlooked. Please put forth your valuable comments to let me know how to achieve this.
Related
I am currently working on a Record Linkage (identifying data sets, which describe the same entity in the real world) Program. Herefore, I am using the Python Record Linkage Toolkit (https://recordlinkage.readthedocs.io/en/latest/ref-classifiers.html#classifiers) and the provided ECMClassifier. I get correct results, but right now I need to train the Classifier again every time I run my script. The relevant codelines are:
ecm = recordlinkage.ECMClassifier(binarize=0.4)
matches = ecm.fit_predict(comparison_vectors)
Now, my question is, can I just save the Classifier after the training and reload it when I run the script the next time ? This way I could save training time and maybe train the Classifier more and more each time.
Saving ML-models is not new and there are easy python packages to do this like pickle5 (as described here: https://www.projectpro.io/recipes/save-trained-model-in-python ).
What I'm concerned about is:
Does the classifier itself change/learn with each use or is everything happening only in the _fit_predict function and therefore the progress can not be saved ?
Is it a problem, that training and prediction happen in one method ? Is it not useful, to save the classifier after both steps have already been undertaken ?
Are there other things to consider when saving/reloading a classifer that are not in line with the default way pickle saves and loads objects?
I am using Python version: 3.8.13.
Thanks in advance!
My spacy version is 2.3.7. I have an existing trained custom NER model with NER and Entity Ruler pipes.
I want to update and retrain this existing pipeline.
The code to create the entity ruler pipe was as follows-
ruler = EntityRuler(nlp)
for i in patt_dict:
ruler.add_patterns(i)
nlp.add_pipe(ruler, name = "entity_ruler")
Where patt_dict is the original patterns dictionary I had made.
Now, after finishing the training, now I have more input data and want to train the model more with the new input data.
How can I modify the above code to add more of patterns dictionary to the entity ruler when I load the spacy model later and want to retrain it with more input data?
It is generally better to retrain from scratch. If you train only on new data you are likely to run into "catastrophic forgetting", where the model forgets anything not in the new data.
This is covered in detail in this spaCy blog post. As of v3 the approach outlined there is available in spaCy, but it's still experimental and needs some work. In any case, it's still kind of a workaround, and the best thing is to train from scratch with all data.
I'd also recommend polm23's suggestion to retrain fully in this situation.
Here is why: we are asking the model to produce inferences based on weights derived from matching input data to labels/classes/whatever over and over. These weights are toggled via backprop to reduce the error gradient vis a vis the labels/classes/whatever. When the weights, given whatever data, produce errors as close to 0 as possible eventually the loss reaches an equilibrium or you just call it via hyper parameters (epochs).
However, by only using the new data, you will only optimize for that specific data. The model will generalize poorly, but really only because it is learning exactly what you asked it to learn and nothing else. When you add in that retraining fully is usually not the end of the world, it just kinda makes sense as a best practice.
(This is my imperfect understanding of the catastrophic forgetting issue, happy to learn more if other's have deeper knowledge).
I am trying to produce a simple CNN with data I have generated and have been struggling for a few days now. I simply cannot get it to fit the data at all. After reading online I am assuming there is a data issue somewhere but I cannot find it. I have tried multiple combinations of data manipulation and model changes (more or fewer parameters) with no effect. I am looking at the data going in and it seems fine to me, I've look over it multiple times with nothing unusual coming up.
My outputs for the model are essentially nothing. no increase at all in validation accuracy.
SOLVED
See Below
SOLVED
For those struggling like I was:
DATA
DATA
DATA
DATA
Better Data, More Data, smaller models, and more training.
I have 1000 real data examples but I generated 30k fake examples. After training on the fake example transfer learning gave me an immediate 87% accuracy.
Overall I highly recommend this method if you have little data and custom problems that you cannot find premade models for.
Check out my generated and real data below.
I've been using Rasa NLU for a project which involves making sense of structured text. My use case requires me to keep updating my training set by adding new examples of text corpus entities. However, this means that I have to keep retraining my model every few days, thereby taking more time for the same owing to increased training set size.
Is there a way in Rasa NLU to update an already trained model by only training it with the new training set data instead of retraining the entire model again using the entire previous training data set and the new training data set?
I'm trying to look for an approach where I can simply update my existing trained model by training it with incremental additional training data set every few days.
To date, the most recent Github issue on the topic states there is no way to retrain a model adding just the new utterances.
Same for previous issues cited therein.
You're right: having to retrain periodically with increasingly long files gets more and more time-consuming. Although, retraining in place is not a good idea in production.
Excellent example in a user comment:
Retraining on the same model can be a problem for production systems. I used to overwrite my models and then at some point, one of the training didn't work perfectly and I started to see a critical drop in my responses confidence. I had to find where the problem was coming from and retrain the model.
Training new model all the time (with a timestamp) is good because it makes rollbacks easier (and they will happen in production systems). I then fetch the up-to-date model names from DB.
I have a real time data feed of health patient data that I connect to with python. I want to run some sklearn algorithms over this data feed so that I can predict in real time if someone is going to get sick. Is there a standard way in which one connects real time data to sklearn? I have traditionally had static datasets and never an incoming stream so this is quite new to me. If anyone has sort of some general rules/processes/tools used that would be great.
With most algorithms training is slow and predicting is fast. Therefore it is better to train offline using training data; and then use the trained model to predict each new case in real time.
Obviously you might decide to train again later if you acquire more/better data. However there is little benefit in retraining after every case.
It is feasible to train the model from a static dataset and predict classifications for incoming data with the model. Retraining the model with each new set of patient data not so much. Also breaks the train/test mode of testing a ML model.
Trained models can be saved to file and imported in the code used for real time prediction.
In python scikit learn, this is via the pickle package.
R programming saves to an rda object. saveRDS
yay... my first answering a ML question!