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Produces quality reports for Machine Learning (ML) models

Project description

Model Quality Report

This packages enables a quick creation of a model quality report, which is returned as a dict.

Main ingredients are a data splitter creating test and training data according various rules and the quality report itself. The quality report takes care of the splitting, fitting, predicting and finally deriving quality metrics.

Installing the package

Latest available code:

pip install git+

With pipenv:

pipenv install git+

Specific version:

pip install git+


  • The RandomDataSplitter splits data randomly using sklearn.model_selection.train_test_split:
X = pd.DataFrame({'a': [1, 2, 3, 4, 5], 'b': ['a', 'b', 'c', 'd', 'e']})
y = pd.Series(data=range(5))

splitter = RandomDataSplitter(test_size=0.33, random_state=2)
X_train, X_test, y_train, y_test = splitter.split(X, y)
  • The TimeDeltaDataSplitter divides such that data from last period of length time_delta is used as test data. Here a pd.Timedelta and the date column name is provided:
splitter = TimeDeltaDataSplitter(date_column_name='shipping_date', time_delta=pd.Timedelta(3, unit='h')) 
X_train, X_test, y_train, y_test = splitter.split(X, y)
  • The SplitDateDataSplitter splits such that data after a provided date are used as test data. Additionally the name of the date column has to be provided:
splitter = SplitDateDataSplitter(date_column_name='shipping_date', split_date=pd.Timstamp('2016-01-01'))
X_train, X_test, y_train, y_test = splitter.split(X, y)
  • The SortedDataSplitter requires a column with sortable values. Data are divided such that the test data set encompasses last fraction test_size. Sorting can be in ascending and descending order.
splitter = SortedDataSplitter(sortable_column_name='shipping_date', test_size=0.2, ascending=True)
X_train, X_test, y_train, y_test = splitter.split(X, y)
  • Using RegressionQualityReport class a quality report for a regression model can be created as following:
splitter = SplitDateDataSplitter(date_column_name='shipping_date', split_date=pd.Timstamp('2016-01-01'))
model = sklearn.linear_model.LinearRegression()
quality_reporter = RegressionQualityReport(model, splitter)
report = quality_reporter.create_quality_report_and_return_dict(X, y)

An exemplary report looks as follows:

    {'explained_variance_score': -6.018595041322246, 
     'mape': 0.3863636363636345, 
     'mean_absolute_error': 4.242424242424224, 
     'mean_squared_error': 29.426997245178825, 
     'median_absolute_error': 2.272727272727268, 
     'r2_score': -10.03512396694206}, 
    {'true': {3: 10, 4: 12, 2: 8}, 
     'predicted': {3: 12.272727272727268, 4: 20.999999999999964, 2: 6.545454545454561}}}  

Note that the model must have a and a model.predict function.

Available Features

Data Splitter

RandomDataSplitter: splits randomly

TimeDeltaDataSplitter: uses data in last period of length as test data

SplitDateDataSplitter: uses data with timestamp newer than split date as test data

SortedDataSplitter: sorts data along given column and takes last fraction of size x_test as test data

TimeSeriesCrossValidationDataSplitter: produces a list of splits of temporal data such that each consecutive train set has one more observation and test set one less

Quality Report

RegressionQualityReport: creates a quality report for a regression model

Quality Metrics

RegressionQualityMetrics: holds following functions:

  • explained_variance_score
  • mean_absolute_error
  • mean_squared_error
  • median_absolute_error
  • r2_score
  • mape

Project details

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