Extracts the coefficients from a regression model and turns them into a lexicon.
Argument and Default Value¶
Name of the lexicon to be created.
Use this switch to create a lexicon from a regression model. Either create the lexicon from a previously created model (using --load_model) or create a model using --train_regression. The name of the lexicon will be dd_ARGUMENT ; dd indicates the lexicon was data driven.
Only use regression algorithms that have linear coefficients (i.e by choosing the right --model), because the lexicon extraction equation won't make sense otherwise. This functionality hasn't totally been validated with advanced feature selection, so beware. Also note that the coefficients won't be efficient to distinguish what features best characterize the outcomes looked at, use --correlate or other univariate techniques to get at that type of insight.
- When creating the model, use --no_standardize or the model will not make any sense.
- Multiple feature tables are allowed.
- Lexica can be created with any word level feature (such as ngrams, dictionaries and topics) whose group norm is a relative frequency. Dictionaries and topics will be unrolled to the word level.
- You cannot combined features with different group norm encodings (for example, binary 1grams and tf-idf 1grams).
This command will train a Ridge regression model to predict age for users from 1grams (without standardizing) and create a lexicon called dd_ageLex1grams.
dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1gram$msgs$user_id$16to16$0_01' --outcome_table blog_outcomes --outcomes age --train_regression --no_standardize --regression_to_lexicon ageLex1grams --model ridge100
This command will train a Ridge regression model to predict age for users from 1grams and topics and create a lexicon called dd_ageLex1gramsTopics.
dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1gram$msgs$user_id$16to16$0_1' 'feat$cat_met_a30_2000_cp_w$msgs$user_id$1gra' --outcome_table blog_outcomes --outcomes age --train_regression --no_standardize --regression_to_lexicon ageLex1gramsTopics --model ridge100
- Sap, M., Park, G., Eichstaedt, J., Kern, M., Stillwell, D., Kosinski, M., ... & Schwartz, H. A. (2014, October). Developing age and gender predictive lexica over social media. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1146-1151).