Machine Learning with QuantGov

QuantGov provides a framework for training machine learning estimators using a corpus, packaging those estimators, and using those models to make predictions about other corpora.

QuantGov builds on the scikit-learn library, and users training new models should familiarize themselves with the models they employ.

Training a QuantGov Estimator

Initializing the Estimator

To start a new quantgov estimator, use the following command quantgov start estimator myname where myname is the name you want to give to the estimator. This will copy the estimator skeleton to the folder with that name.

Customize the Vectorizer and Vectorize Trainers

In order to use mathematical statistical techniques on text documents, those documents need to be converted to set of numbers, or vector. This process is called vectorization, and the Python objects used to create carry this out are called vectorizers.

QuantGov estimators currently expect a joblib-pickled scikit-learn vectorizer, such as a CountVectorizer. Most users will simply want to customize the CountVectorizer defined in the scripts/ script in the estimator skeleton.

Advanced users may implement their own vectorizers subclassing the sklearn.base.BaseEstimator and the sklearn.base.TransformerMixin classes; all vectorizers should take an iterable of text objects to their fit and fit_transform methods.

In evaluating models, the vectorization step takes place before segmentation for cross-valdiation; this means that users should take care not to let information correlated with the variable of interest be used here. Instead, include a transformer step in the candidate model pipeline, which is used after the test-train split.

For efficiency, the trainer corpus is expected to be vectorized before model evaluation. This can be accomplished with the command quantgov ml vectorize which takes two positional arguments:

  • vectorizer: the path to the saved vectorizer object
  • corpus: the path to the target corpus

The following argument must also be specified:

  • –outfile or -o: the path to which to save the vectorized Trainers.

Generating the Labels

If you are training a supervised model—that is, a classification model or regression model—you need to generate the training values for the documents in the trainer corpus. An example generating (meaningless) labels is in the skeleton estimator in the script scripts/ Labels should be stored in a object, which takes three arguments:

  • index: a sequence holding the index values for each document being labeled.
  • label_names: a sequence holding one name for each label. NB: even when there is only one label, this parameter should be a sequence, such as ['label'] or ('label',).
  • labels: an array-like of label values with shape [n_samples x n_labels].

The Labels object has a save method which can be used to save the object to a file.

For classification problems, the nature of the Labels object determines the type of classification model to be trained. If the values are True and False, the problem is assumed to be binary classification. If other values are given, the problem is assumed to be multiclass. If a numpy array or pandas DataFrame of zeroes and ones are given, the problem is assumed to be multilabel classification, where each row is assumed to represent a document and each column is assumed to represent a label.

Specifying candidate models

QuantGov is able to test multiple candidate models, each with a variety of hyperparameters. Candidate Models must be specified in a Python module as a module-level variable named models which is a sequence of objects. CandidateModels are initialized with three arguments:

  • name: the user-facing name of the model
  • model: a scikit-learn estimator or pipeline, or a class that implements the scikit-learn interface
  • parameters: a dictionary of hyperparameters to tune, where the keys are the parameter names and the values are a sequence of possible values. See the documentation for grid_param in the scikit-learn documentation in the narrative documentaion and in the api documentaion.

Starter sets of candidate models can be seen in the module. These can be imported directly (as in the estimator skeleton) or copied and customized as needed.

Evaluating Candidate Models

At this point, things get easier. The candidate models can be trained using the quantgov ml evaluate command, which takes the following positional arguments:

  • modeldefs: the path to the module defining the candidate models
  • trainers: the path to the saved Trainers object
  • labels: the path to the saved Labels object
  • output_results: the path to a csv file which will list the results of every model evaluated, with every combination of hyperparameters.
  • output_suggestion: the path to a file which will hold the configuration of the best performing models.

The following arguments are optional:

  • –folds (defaults to 5): the number of folds to use in cross-validation.
  • –score (defaults to 'f1’): the scoring metric for comparing models. See the scikit-learn documentation for a list of valid options.

The evaluation command will automatically select the highest performing score and output its model and parameters to the file specified in the output_suggestion parameter. Users should inspect the full scoring results, however, and select a model that balances simplicity and performance. One common choice, for example, is to employ the simplest set of values which performs within one standard deviation of the best model. The desired configuration can be set by editing the suggestion file, which follows the ConfigParser format.

Training the selected model.

Once a model has been selected and the configuration file edited, the final model can be trained on the full trainer set with the quantgov ml train command, which takes the following positional arguments:

  • modeldefs: the path to the module defining the candidate models
  • configfile: the path to the configuration file defining the model
  • trainers: the path to the saved Trainers object
  • labels: the path to the saved Labels object

The following argument must also be specified:

  • –outfile or -o: the path to which to save the estimator. By convention, QuantGov estimators have the extension .qge.

The resulting Estimator is a self-contained file that can be distributed; however, users may need to ensure that they are using compatible versions of the underlying libraries, such as scikit-learn and pandas; these should be documented by the author of the estimator.

Using a QuantGov Estimator to Make Predictions

With a trained QuantGov estimator, predictions about a new corpus can be made with the quantgov ml estimate command, which takes the following positional arguments:

  • estimator: the path to the estimator file
  • corpus: the path to the corpus to be analyzed

The following optional arguments are also available:

  • –probability: for a classification problem, produce probability estimates instead of class predictions
  • –precision (defaults to 4): when probabilities are produced, the desired number of decimal points of precision
  • –outfile or -o (defaults to standard out): the path to a file where results should be saved.