Futrell2018 SPRT benchmark using GAMs + control predictors #107
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mschrimpf
reviewed
Dec 31, 2022
| data_mask = ~data.isna().any(axis=1) | ||
| data = data[data_mask] | ||
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| # TODO check that columns match formula variable names |
| data["prev_surp"] = data["surprisal"].shift(1) | ||
| data["len"] = self.data[data_mask].word_core.str.len() | ||
| data["prev_len"] = data["len"].shift(1) | ||
| data["freq"] = surprisals # HACK need to look this up. |
| r_mgcv = importr("mgcv") | ||
| model = r_mgcv.gam(formula, data=data) | ||
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| # TODO held out data |
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| surprisals = candidate.digest_text(stimuli)['behavior'] | ||
| attach_presentation_meta(surprisals, self.data['presentation']) | ||
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| # exclude first words | ||
| surprisals = surprisals[surprisals['word_within_sentence_id'] != 1] | ||
| data_mask = self.data['word_within_sentence_id'] != 1 | ||
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| # Fit and evaluate GAM model | ||
| model, predictions, targets = self.fit(surprisals, data_mask) |
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| surprisals = candidate.digest_text(stimuli)['behavior'] | |
| attach_presentation_meta(surprisals, self.data['presentation']) | |
| # exclude first words | |
| surprisals = surprisals[surprisals['word_within_sentence_id'] != 1] | |
| data_mask = self.data['word_within_sentence_id'] != 1 | |
| # Fit and evaluate GAM model | |
| model, predictions, targets = self.fit(surprisals, data_mask) | |
| model_reading_times = candidate.digest_text(stimuli)['behavior'] | |
| attach_presentation_meta(surprisals, self.data['presentation']) | |
| # exclude first words | |
| model_reading_times = model_reading_times[model_reading_times['word_within_sentence_id'] != 1] | |
| data_mask = self.data['word_within_sentence_id'] != 1 | |
| # Fit and evaluate GAM model | |
| model, predictions, targets = self.fit(model_reading_times, data_mask) |
| return score | ||
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| class SplitHalvesConsistency: |
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could from ../futrell2018.benchmark import SplitHalvesConsistency (
benchmarks/futrell2018 plugin? I'm fine with either, slightly leaning towards adding this to the futrell2018 plugin
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Hi @hans just checking in on this PR |
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I'm starting a benchmark implementation for reading time evaluation that uses control predictors (word length and frequency; spillover effects from previous word(s)) as well as a more advanced statistical model (GAMs).
FWIW this PR is also a fun test case of a benchmark with Conda dependencies (needs R and an R package, which obviously can't be installed via pip).
Still to-do (& happy to accept help if anyone is interested):
mgcv.