Getting started

The core functionality of PRESC is to investigate the performance of a machine learning model using different evaluation methods. This is similar to the use of standard performance metrics such as accuracy, but the PRESC evaluations allow for more granular feedback. Rather than a single scalar value, the outputs of the evaluations to be functions or distributions. Evaluations can be run individually, eg. in a Jupyter notebook, or as a part of a collective report.

A full example including the code snippets below is available in the project repository.


PRESC is designed to be applied to a dataset and pretrained machine learning classifier.


A user’s dataset should be wrapped in a presc.dataset.Dataset prior to being used in evaluations. This is mainly used to provide uniform access to feature and label columns.

Currently, an input dataset must be a Pandas DataFrame containing named columns for the model features and the sample labels. The project repository includes a few well-known sample datasets. Here is an example using the winequality.csv dataset from that directory:

import pandas as pd
from presc.dataset import Dataset

df = pd.read_csv("winequality.csv")
# Drop the `quality` column as it has been binarized as `recommend`
# in this version of the dataset.
df = df.drop(columns=["quality"])

dataset = Dataset(df, label_col="recommend")


The evaluations generally operate by querying the classifer for predictions on a test set, and in some cases by retraining it under different conditions. The input classifier should be wrapped in a presc.model.ClassificationModel.

Currently, PRESC is designed to work with the scikit-learn machine learning framework. The input classifier should be a scikit-learn Classifier instance, such as sklearn.linear_model.LogisticRegression. It should be pretrained on a training dataset prior to running the evaluations. If necessary, this can be done from the ClassificationModel instance using a Dataset instance as input.

from sklearn.linear_model import LogisticRegression
from presc.model import ClassificationModel

clf = LogisticRegression()
cm = ClassificationModel(clf)

# Split the wine dataset into train and test portions and train the model.
splitter = ShuffleSplit(test_size=0.3).split(dataset.features)
train_ind, test_ind = next(splitter)
# dataset.subset() returns another Dataset instance
train_dataset = dataset.subset(train_ind, by_position=True)
test_dataset = dataset.subset(test_ind, by_position=True)


Note that some preprocessing of the data, such as scaling, is often needed to achieve meaningful model fits. It is generally recommended to bundle these together with the classifier using a sklearn.pipeline.Pipeline, so that the same preprocessing will get applied any time the classifer is retrained. In this case, the ClassificationModel wrapper should be applied to the Pipeline instance.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

pl = Pipeline([("scaler", StandardScaler()), ("clf", clf)])
cm = ClassificationModel(pl)


With the inputs prepared, you can now run the PRESC report. By default, it runs all available evaluations, but its behaviour can be configured by setting option values or by specifying a configuration file. The report is built using Jupyter Book to execute evaluations from Jupyter notebooks and render them as HTML pages.

The report is managed by a instance pointing to the desired output directory. The report will be written to <output_dir>/presc_report/. By default, it is executed out of a temporary directory, but this can be changed in order to access the execution artifacts for debugging purposes. Additionally, default configuration settings can be overridden by passing a path to a YAML file in the appropriate format.

The report is run by calling the run() method on a ClassificationModel and Dataset. Evaluations generally require a test dataset to run on. Some may use a training dataset as well, although this is not required to run the report (although, if not specified, evaluations that require this will fail). Settings can also be overridden by passing a dict of option values to run().

from import ReportRunner

report = ReportRunner(output_path="./output", config_filepath="my_config.yml")
# This may take a few minutes, test_dataset=test_dataset, train_dataset=train_dataset)

Once completed, the path to the report main page is accessible using the report_html attribute, and the open() method will attempt to open it in the default browser.

# Show the path to the report main page.
# Open in browser.

The path to the the Jupyter Book build log is accessible from the jb_build_log attribute. This can be useful in diagnosing problems.