From 54c0e2bb12908a05332a46ebea9bc6ab0a71badd Mon Sep 17 00:00:00 2001 From: Sabrina Hoppe Date: Sat, 5 May 2018 22:24:41 +0200 Subject: [PATCH] Update README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 80edb65..5908ce1 100644 --- a/README.md +++ b/README.md @@ -31,21 +31,21 @@ reproducing the paper results step by step: `./01 train_classifiers.sh` to reproduce the evaluation setting described in the paper in which each classifier was trained 100 times. `./02_train_specialized_classifiers.sh` to train specialized classifiers on parts of the data (specifically on data from inside the shop or on the way). - If the scripts cannot be executed, you might not have the right access permissions to do so. On Linux, you can try `chmod +x 01_train_classifiers.sh`,`chmod +x 02_train_specialized_classifiers.sh` and `chmod +x 03_label_permutation_test.sh` (see below for when/how to use the last script). + If the scripts cannot be executed, you might not have the right access permissions to do so. On Linux, you can try `chmod +x 01_train_classifiers.sh`,`chmod +x 02_train_specialized_classifiers.sh` and `chmod +x 03_label_permutation_test.sh` (see below for when/how to use the last script). - In case you want to call the script differently, e.g. to speed-up the computation or try with different parameters, you can pass the following arguments to `classifiers.train_classifier`: + In case you want to call the script differently, e.g. to speed-up the computation or try with different parameters, you can pass the following arguments to `classifiers.train_classifier`: `-t` trait index between 0 and 6 `-l` lowest number of repetitions, e.g. 0 `-m` max number of repetitions, e.g. 100 `-a` using partial data only: 0 (all data), 1 (way data), 2(shop data) - In case of performance issues, it might be useful to check `_conf.py` and change `max_n_jobs` to restrict the number of jobs (i.e. threads) running in parallel. + In case of performance issues, it might be useful to check `_conf.py` and change `max_n_jobs` to restrict the number of jobs (i.e. threads) running in parallel. - The results will be saved in `results/A0` for all data, `results/A1` for way data only and `results/A2` for data inside a shop. Each file is named `TTT_XXX.npz`, where TTT is the abbreviation of the personality trait (`O`,`C`,`E`,`A`,`N` for the Big Five and `CEI` or `PCS` for the two curiosity measures). XXX enumerates the classifiers (remember that we always train 100 classifiers for evaluation because there is some randomness involved in the training process). + The results will be saved in `results/A0` for all data, `results/A1` for way data only and `results/A2` for data inside a shop. Each file is named `TTT_XXX.npz`, where TTT is the abbreviation of the personality trait (`O`,`C`,`E`,`A`,`N` for the Big Five and `CEI` or `PCS` for the two curiosity measures). XXX enumerates the classifiers (remember that we always train 100 classifiers for evaluation because there is some randomness involved in the training process). 3. __Train baselines__ - * To train a classifier that always predicts the most frequent personality score range from its current training set, please execute `python 03_train_baseline.py` - * To train classifiers on permuted labels, i.e. perform the so-called label permutation test, please execute `./04_label_permutation_test.sh` + * To train a classifier that always predicts the most frequent personality score range from its current training set, please execute `python 03_train_baseline.py` + * To train classifiers on permuted labels, i.e. perform the so-called label permutation test, please execute `./04_label_permutation_test.sh` 4. __Performance analysis__