33 lines
1.4 KiB
Bash
Executable File
33 lines
1.4 KiB
Bash
Executable File
#!/usr/bin/env bash
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set -e
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set -x
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SRCDIR=`dirname $0`
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# functional tests
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#mkdir ./embeddings
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#wget --no-verbose http://nlp.stanford.edu/data/glove.840B.300d.zip ; unzip glove.840B.300d.zip ; mv glove.840B.300d.zip embeddings/ ; rm glove.42B.300d.zip
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#wget --no-verbose http://www.logos.t.u-tokyo.ac.jp/~hassy/publications/arxiv2016jmt/jmt_pre-trained_embeddings.tar.gz ; tar -xzvf jmt_pre-trained_embeddings.tar.gz; mv jmt_pre-trained_embeddings embeddings/; rm jmt_pre-trained_embeddings.tar.gz
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TMPDIR=`pwd`
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workdir=`mktemp -d $TMPDIR/decaNLP-tests-XXXXXX`
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i=0
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for hparams in "" ; do
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# train
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pipenv run python3 $SRCDIR/../train.py --train_tasks almond --train_iterations 4 --preserve_case --save_every 2--log_every 2 --val_every 2 --save $workdir/model_$i --data dataset/ $hparams --exist_ok --skip_cache --no_glove_and_char --elmo 0
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# greedy decode
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pipenv run python3 $SRCDIR/../predict.py --tasks almond --evaluate test --path ~/$workdir/model_$i --overwrite --eval_dir $workdir/model_$i/eval_results/ --data dataset/ --no_glove_and_char --elmo 0
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# export prediction results
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pipenv run python3 $SRCDIR/../utils/post_process_decoded_results.py --original_data dataset/test.tsv --gold_program $workdir/model_$i/eval_results/almond.gold.txt --predicted_program $workdir/model_$i/eval_results/almond.txt --output_file $workdir/model_$i/results.tsv
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i=$((i+1))
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done
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trap { rm -rf $workdir } EXIT |