How to apply fastai tabular model to new data?

user_5452008 0
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I trained a model with fastai.tabular. Now, I have a fitted learner. Ultimately, models are there to be applied to new data and not just to be fitted on training set and evaluated on test set etc. I tried different things all resulting in errors or some weirdness. Is there a way to apply a model trained with fastai to previously unavailable data? Or do I have to train the model again and again and feed new test data in? That does not seem likely.

df_test = pd.read_parquet('generated_test.parquet').head(100)
test_data = TabularList.from_df(df_test, cat_names=cat_names, cont_names=cont_names)
prediction = learn.predict(test_data)

KeyError: 'atomic_distance'

atomic_distance is the name of a column present in both the training and test data and also contained in cont_names.

prediction = learn.get_preds(kaggle_test_data)

This does something, but it returns something weird:

[tensor([[136.0840],
         [ -2.0286],
         [ -2.0944],
         ...,
         [135.6165],
         [  2.7626],
         [  8.0316]]),
 tensor([ 84.8076, -11.2570, -11.2548,  ...,  81.0491,   0.8874,   4.1235])]

The documentation says:

Docstring: Return predictions and targets on ds_type dataset.

This is new, unlabeled data. I don't know why the returning object should have labels. Where are they coming from? Also the size does not make sense. I am expecting something with 100 values.

I found a way by passing in the dataframe row by row:

prediction = [float(learn.predict(df_test.loc[i])[0].data) for i in df_test.index]

There is also the method predict_batch available, but it does seem to accept datafames. Are there better ways to do this?

fast-ai
1 Answers

I use:

data_test = (TabularList.from_df(DF_TEST, path=path, cat_names=cat_names,cont_names=cont_vars, procs=procs)
                           .split_none()
                           .label_from_df(cols=dep_var))
data_test.valid = data_test.train
data_test=data_test.databunch()

learn.data.valid_dl = data_test.valid_dl
pred = learn.get_preds(ds_type=DatasetType.Valid)[0]

Where DF_TEST is the test dataframe, dep_var is the depended variable, and learn is your model. To be honest, it works most of the times, other times it give weird error and then I have to iterate each row to get prediction.

user_93018050
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