Add a tutorial for text data #1357
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Thanks! I'll check back when the docs are done building, should be visible here. The RTD build is queued at the moment, lots building. |
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| "automl.describe_pipeline(automl.rankings.iloc[0][\"id\"])" |
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I wouldn't expect the best pipeline to have a one hot encoder since in this case there is nothing to encode because the TextFeaturizer converts the Message column (the only input feature) into 5 numeric columns.
I don't think the OneHotEncoder is negatively impacting performance (maybe impacting the fit time a little bit?) but I think we should remove it from the pipeline in the future.
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@freddyaboulton that's a really interesting point! The OneHotEncoder does nothing on this dataset, but it's included in the dynamically generated pipelines nonetheless because _get_preprocessing_components considers the text column a categorical column. Once Woodwork is integrated into this part of AutoML, this should no longer be the case.
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Ah very good point! I'm working on a PR now which will patch this.
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| "Without the `Text Featurization Component`, the `'Message'` column was treated as a categorical column, and therefore the conversion of this text to numerical features happened in the `One Hot Encoder`. The best pipeline encoded the top 10 most frequent \"categories\" of these texts, meaning 10 text messages were one-hot encoded and all the others were dropped. Clearly, this removed almost all of the information from the dataset, as we can see the `best_pipeline_no_text` did not beat the random guess of predicting \"ham\" in every case." |
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Might be interesting to compare against a bag-of-words encoding in the future to get a sense of the improvement in performance of using the TextFeaturizer over a naive approach.
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I love this, great description!
Agree @freddyaboulton that would be a cool extension
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I see the docs generated! Reviewing now |
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@eccabay this is really great!! I love it. I especially like that you're showing why its important to use woodwork to select which columns are text.
I left a handful of little suggestions, but otherwise this is definitely ready to 🚢 !
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Without the `Text Featurization Component`, the `'Message'` column was treated as a categorical column, and therefore the conversion of this text to numerical features happened in the `One Hot Encoder`. The best pipeline encoded the top 10 most frequent \"categories\" of these texts, meaning 10 text messages were one-hot encoded and all the others were dropped. Clearly, this removed almost all of the information from the dataset, as we can see the `best_pipeline_no_text` did not beat the random guess of predicting \"ham\" in every case." |
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I love this, great description!
Agree @freddyaboulton that would be a cool extension
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I see the windows checkin tests failed. There's been a new flake lately with the conda installation on windows. If you rerun, should be all set. |
Adds a tutorial highlighting the
Text Featurizerand the significant performance improvements it allows!