EMTK_docker

Dockerfile for building the EMTk image

Latest tag version

0.2.2 (2019-05-21)

Instructions

First, execute the Docker container in interactive mode. By default, the instruction below will execute the latest version.

# docker run --rm -it collabuniba/emtk

Polarity module

The -it option starts the container in the interactive mode, so the run command logs you in the container’s shell environment (>). From there, to execute the polarity module, run:

> emtk polarity -F A -i input.csv -oc output.csv -vd 600 [-W dsm.bin] [-L] [-ul unigramList -bl bigramList]

where:

• -F {A, S, L, K}, feature to evaluate A for All, S for Semantic, L for Lexicon, K for Keyword.
• -i <input.csv>: the input data to classify.
• -oc <output.csv>: the resulting predictions.
• -vd <N>: the vector size.
• -W <dsm.bin>: [optional] the wordspace to use; the default wordspace is /polarity/ClassificationTask/dsm.bin.
• -L: [optional] if present, the input corpus comes with a gold label in the label column.
• -ul <filename>: [optional] the unigram's list.
• -bl <filename>: [optional] the bigram's list.

Users can test-drive the polarity module by using the file /polarity_sample.csv, containing only a handful of documents.

Emotion module

Regarding the emotion classifier module, in the following, we show first how to train a new model and, then, how to test it on unseen data. To train a new model on a training set, run:

> emtk emotions train -i file.csv -d delimiter [-g] -e emotion

where:

• -i <file.csv>: the corpus to be classified, encoded in UTF-8 without BOM and with the following format:

• id;label;text
  …
  22;NO;"""Excellent! This is exactly what I needed. Thanks!"""
  23;YES;"""FEAR!!!!!!!!!!!"""
  …
  

• -d {c, sc}: the delimiter used in the csv file, where c stands for comma and sc for semicolon.

• -e {joy, anger, sadness, love, surprise, fear}: the emotion to be detected.

As a result, the script will generate an output folder in the present working directory named training_<file.csv>_<emotion>/, containing:

• n-grams/: a subfolder containing the extracted n-grams.
• idfs/: a subfolder containing the IDFs computed for n-grams and WordNet Affect emotion words.
• feature-<emotion>.csv: a file with the features extracted from the input corpus and used for training the model.
• liblinear/DownSampling/ and liblinear/NoDownSampling/, two folders each containing:
  • trainingSet.csv and testSet.csv.
  • eight models trained with liblinear model_<emotion>_<ID>.Rda, where ID refers to the liblinear model (with values in {0, ..., 7}).
  • performance_<emotion>_<IDMODEL>.txt, a file containing the results of the parameter tuning for the model (cost), the confusion matrix, and the Precision, Recall, and F-measure for the best cost for the specific <emotion>.
  • predictions_<emotion>_<IDMODEL>.csv, containing the test instances with the predicted labels for the specific <emotion>.

Finally, to execute the classification task, run:

> emtk emotions classify -i file.csv -d delimiter -e emotion [-m model] [-f /path/to/.../idfs] [-o /path/to/.../ngrams] [-l]

where:

• -i <file.csv>: same as above.
• -p: enables the extraction of features regarding politeness, mood and modality.
• -d {c, sc}: same as above.
• -e {joy, anger, sadness, love, surprise, fear}: same as above.
• -m model: [optional] the model file learned during the training step; if not specified, as default the model learned on the Stack Overflow gold standard will be used.
• -f /path/to/.../idfs: [optional] with custom models, also the path to the folder containing the dictionaries with IDFs computed during the training step is required; the folder must include IDFs for n-grams (uni- and bi-grams) and the WordNet Affect lists of emotion words.
• -o /path/to/.../ngrams: [optional] with custom models, also the path to the folder containing the dictionaries extracted during the training step; the folder must include n-grams (i.e., UnigramsList.txt and BigramsList.txt).
• -l: [optional] if present, the input corpus comes with a gold label in the column label.

As a result, the script will create an output folder in the present working directory named classification_<file.csv>_<emotion>, containing:

• predictions_<emotion>.csv: a csv file, containing a binary prediction (yes/no) for each line of the input corpus:

• id;predicted
  …
  22;NO
  23;YES
  …
  

• performance_<emotion>.txt: a file containing several performance metrics (Precision, Recall, F1, confusion matrix), created only if the input corpus <file.csv> contains the column label.

Users can test-drive the emotion classification module by using the file /emotions_sample.csv, which contains only a handful of documents. Other more complex sample datasets are available at /emotions/java/DatasetSO/StackOverflowCSV.

The /shared/ folder

To use the EMTk modules with custom datasets, users must access the /shared/ folder, which is mounted specifying the –v option in the docker run command shown above. The -v option defines the paths for the folder to be shared in both the host and the hosted machines:

docker run -v <pathInTheHostMachine>:<pathInTheContainer> [...].

For instance, on a Linux machine, -v ~/shared:/shared creates a folder named shared in the host system's home (if it doesn't already exist) and a folder named shared in the container's root. Whatever is put into the shared folder can be found on both the systems, allowing input and output file exchange. This is accomplished leveraging Docker's bind mounts.