Does anybody have any advice on how to setup a tabular databunch and learner for a regression and classification problem?
The TabularPandas class y_block argument won’t take a list of RegressionBlock() and MultiCategoryBlock. It’s looking for a single TransformBlock and is giving an error that the list doesn’t have a ‘type_tfms’ attribute. This is what I’m trying right now
to = TabularPandas(df, procs = [Categorify, Normalize, FillMissing],
y_names = targets,
y_block = (RegressionBlock(n_out = 15), MultiCategoryBlock()),
cat_names = cat_names,
cont_names = cont_names,
splits = ColSplitter('is_valid')(df),
)
which gives the following error
AttributeError Traceback (most recent call last)
/tmp/ipykernel_24346/3903240160.py in <module>
----> 1 to = TabularPandas(df, procs = [Categorify, Normalize, FillMissing],
2 y_names = targets,
3 y_block = [RegressionBlock(n_out = 15), MultiCategoryBlock()],
4 cat_names = cat_names,
5 cont_names = cont_names,
~/miniconda3/envs/emp_path/lib/python3.9/site-packages/fastai/tabular/core.py in __init__(self, df, procs, cat_names, cont_names, y_names, y_block, splits, do_setup, device, inplace, reduce_memory)
161 if y_block is not None and do_setup:
162 if callable(y_block): y_block = y_block()
--> 163 procs = L(procs) + y_block.type_tfms
164 self.cat_names,self.cont_names,self.procs = L(cat_names),L(cont_names),Pipeline(procs)
165 self.split = len(df) if splits is None else len(splits[0])
AttributeError: 'list' object has no attribute 'type_tfms'
To give some more details on my dataset I’ve got tabular dataset representing a spectroscopy measurement which I’d like to predict the location of peaks (regression targets) and the classification of that peak for each example. For example (not my actual problem, but same idea) I’d like to train the model to take in the spectra and identify the location and classification of the peaks as shown in the figure here.
