Plots

PREDICT.Plots.AUPRCPlot(log)

Plot the AUPRC of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.AUROCPlot(log)

Plot the AUROC of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.AccuracyPlot(log, recalthreshold=None)

Plot the accuracy of the model over time.

Parameters:

  • log (dict) – Log of model metrics over time and when the model was updated.

  • recalthreshold (float, int) – Threshold to trigger recalibration. Defaults to None.

PREDICT.Plots.BayesianCoefsPlot(log)

Plots the mean coefficients (with standard deviation as the error bar) of the Bayesian model over time. Note: this is only suitable for the BayesianModel and .addLogHook(TrackBayesianCoefs(model)) must be used.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.CITLPlot(log)

Plot the Calibration-in-the-Large of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.CalibrationSlopePlot(log)

Plot the calibration slope of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.CoxSnellPlot(log)

Plot the Cox-Snell R^2 of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.ErrorSPCPlot(log, model)

Plots the error over time as a statistical process control chart with upper control limits indicating warning and danger zones when model performance drops.

Parameters:

  • log (dict) – Log of model metrics over time and when the model was updated.

  • model (PREDICTModel) – The model to evaluate, must have a predict method.

PREDICT.Plots.F1ScorePlot(log)

Plot the F1 Score of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.MonitorChangeSPC(input_data, trackCol, timeframe, windowSize, largerSD=3, smallerSD=2)

Generate a statistical process control chart to observe data changes over time. Plot shows prevalence or mean of a dataframe column over time with control limits for ± x and y standard deviations from the mean (where x and y default to 2 and 3 respectively). This function is useful for tracking changes that might control to model error increasing.

Parameters:

  • input_data (pd.DataFrame) – The input data to monitor data changes.

  • trackCol (str) – Column of input data to monitor.

  • timeframe (str) – How often to plot the data points of the tracked variable. Can be ‘Day’, ‘Week’, ‘Month’ or ‘Year’.

  • windowSize (int) – How many timeframes to use as a the rolling control limit window size e.g. if timeframe is ‘week’ and the window_size = 4 then the window covers 4 weeks.

  • largerSD (float) – Red line upper and lower most control limits. Defaults to 3.

  • smallerSD (float) – Yellow line inner control limts. Defaults to 2.

Raises:

ValueError – If timeframe variable is not ‘Day’, ‘Week’, ‘Month’, or ‘Year’.

PREDICT.Plots.NormalisedSumOfDiffPlot(log)

Plot the error of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.OEPlot(log)

Plot the observation to expectation ratio of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.PrecisionPlot(log)

Plot the Precision of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.ProbOverTimePlot(log, x_axis_min=None, x_axis_max=None, predictor=None, outcome='outcome')

Plots the probability of an outcome given a specific predictor. Note: this is only suitable for the BayesianModel and .addLogHook(TrackBayesianCoefs(model)) must be used.

Parameters:

  • log (dict) – Log of model metrics over time and when the model was updated.

  • x_axis_min (float, optional) – Minimum value for the x axis representing the predictor. Defaults to None.

  • x_axis_max (float, optional) – Maximum value for the x axis representing the predictor. Defaults to None.

  • predictor (str, optional) – Name of the predictor to assess. Defaults to None.

  • outcome (str, optional) – Name of the outcome being predicted. Defaults to “outcome”.

Raises:

  • ValueError – Raises error if x_axis_min is not provided.

  • ValueError – Raises error if x_axis_max is not provided.

  • ValueError – Raises error if predictor is not provided.

PREDICT.Plots.SensitivityPlot(log)

Plot the Sensitivity of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.

PREDICT.Plots.SpecificityPlot(log)

Plot the Specificity of the model over time.

Parameters:

log (dict) – Log of model metrics over time and when the model was updated.