docker_README.md

pxtextmining: Text Classification of Patient Experience feedback

This Docker container contains the pxtextmining machine learning models trained as part of the Patient Experience Qualitative Data Categorisation project.

To use this Docker container to predict your unlabelled text:

1. Set up your folders. You will need to set up a folder containing two other folders, data_in and data_out, as below.

docker_data/
├─ data_in/
├─ data_out/

2. Prepare your data. Save the data you wish to pass through the machine learning models as json, in the data_in folder. The data should be in the following format:

In Python, a list containing as many dicts as there are comments to be predicted. Each dict has three compulsory keys:

• comment_id: Unique ID associated with the comment, in str format. Each Comment ID per API call must be unique.
• comment_text: Text to be classified, in str format.
• question_type: The type of question asked to elicit the comment text. Questions are different from trust to trust, but they all fall into one of three categories:
  • what_good: Any variation on the question "What was good about the service?", or "What did we do well?"
  • could_improve: Any variation on the question "Please tell us about anything that we could have done better", or "How could we improve?"
  • nonspecific: Any other type of nonspecific question, e.g. "Please can you tell us why you gave your answer?", or "What were you satisfied and/or dissatisfied with?".

# In Python

text_data = [
              { 'comment_id': '1', # The comment_id values in each dict must be unique.
                'comment_text': 'This is the first comment. Nurse was great.',
                'question_type': 'what_good' },
              { 'comment_id': '2',
                'comment_text': 'This is the second comment. The ward was freezing.',
                'question_type': 'could_improve' },
              { 'comment_id': '3',
                'comment_text': '',  # This comment is an empty string.
                'question_type': 'nonspecific' }
            ]

# In R

library(jsonlite)

comment_id <- c("1", "2", "3")
comment_text <- c(
  "This is the first comment. Nurse was great.",
  "This is the second comment. The ward was freezing.",
  ""
)
question_type <- c("what_good", "could_improve", "nonspecific")
df <- data.frame(comment_id, comment_text, question_type)
text_data <- toJSON(df)

3. Save the JSON data in the data_in folder, as follows:

# In Python

json_data = json.dumps(text_data)
with open("data_in/file_01.json", "w") as outfile:
    outfile.write(json_data)

# In R

json_data <- toJSON(text_data, pretty = TRUE)
write(json_data, file = "data_in/file_01.json")

4. Your file structure should now look like this:

docker_data/
├─ data_in/
│  ├─ file_01.json
├─ data_out/

5. Mount the docker_data folder as the data volume for the Docker container and run the container. Pass the filename for the input JSON as the first argument. The following arguments are also available:
   • --local-storage or -l flag for local storage (does not delete the files in data_in after completing predictions)
   • --target or -t to select the machine learning models used. Options are m for multilabel, s for sentiment, or ms for both. Defaults to ms if nothing is selected.

A sample command would be: docker run --rm -it -v /docker_data:/data ghcr.io/the-strategy-unit/pxtextmining:latest file_01.json -l

6. The predictions will be outputted as a json file in the data_out folder, with the same filename. After running successfully, the final folder structure should be:

docker_data/
├─ data_in/
│  ├─ file_01.json
├─ data_out/
   ├─ file_01.json