APIs Reference#
This is the class and function reference of scikit-plots. Please refer to the full user guide for further details, as the raw specifications of classes and functions may not be enough to give full guidelines on their uses. For reference on concepts repeated across the APIs, see scikit-plots Glossary.
Object |
Description |
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Context manager for global scikit-plots configuration. |
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Retrieve current values for configuration set by |
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Set global scikit-plots configuration. |
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Show libraries and system information on which SciPy was built |
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Print useful debugging information |
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Open the online documentation search page |
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Plots the 2-dimensional projection of PCA on a given dataset. |
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Plots PCA components’ explained variance ratios. (new in v0.2.2) |
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Generates a plot of a sklearn model’s feature importances. |
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Generates a plot of the train and test learning curves for a classifier. |
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Plot the elbow curve for different values of K in KMeans clustering. |
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Plot residuals and fit various distributions to assess their goodness of fit. |
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Generates various evaluation plots for a classifier, including confusion matrix, precision-recall curve, and ROC curve. |
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Generates a confusion matrix plot from predictions and true labels. |
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Generates the Precision-Recall AUC Curves from labels and predicted scores/probabilities. |
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Generates the ROC AUC curves from labels and predicted scores/probabilities. |
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Plot calibration curves for a set of classifier probability estimates. |
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Plots silhouette analysis of clusters provided. |
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Validates the labels passed into arguments such as
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Generate the data points necessary to plot the Cumulative Gain curve for binary classification tasks.
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Generate the data points necessary to plot the Kolmogorov-Smirnov (KS)
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Validate the provided axes and figure or create new ones if needed.
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Combine multiple figures into a single image,
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Save the current plot if the environment variable to save plots is enabled.
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Compute the expit (sigmoid) function of the input value
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Compute the logarithm of the expit (sigmoid) function for the input value
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Compute the logit function, which is the inverse of the sigmoid function, for the input value
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py_print(message: str = ‘Hello, from Pybind11 C++!’) -> None
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Compute the sigmoid function for the input array
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Compute the softmax function.
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Compute the log of the sum of exponentials of input elements.
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Compute the logarithm of the softmax function.
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A legend for the abbreviations of decile table column names. |
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Generates the Decile Table from labels and probabilities |
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Generates the Decile-wise Lift Plot from labels and probabilities |
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Generates the Decile based cumulative Lift Plot from labels and probabilities. |
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Generates the Decile-wise Lift Plot from labels and probabilities |
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Generates the KS Statistic Plot from labels and probabilities |
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Generates a decile table and four plots: |
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ModelPlotPy decile analysis. |
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Plotting response curve |
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Plotting cumulative response curve |
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Plotting cumulative lift curve |
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Plotting cumulative gains curve |
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Plotting cumulative gains curve |
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Plotting costs / revenue curve |
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Plotting profit curve |
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Plotting ROI curve |
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A probability scale for matplotlib Axes. |
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Probability, percentile, and quantile plots. |
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Compute the plotting positions for a dataset. Heavily borrows from |
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Fits a line to x-y data in various forms (linear, log, prob scales). |
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int([x]) -> integer |
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int([x]) -> integer |
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int([x]) -> integer |
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int([x]) -> integer |
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int([x]) -> integer |
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int([x]) -> integer |
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int([x]) -> integer |
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int([x]) -> integer |
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A custom logging handler inherited from |
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A custom logging formatter inherited from |
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Logs a message at the CRITICAL log level. |
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Logs a message at the DEBUG log level. |
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Logs a message at the ERROR log level. |
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Empty helper method. |
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Logs a message at the FATAL - CRITICAL log level. |
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Return how much logging output will be produced. |
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Return SP (scikitplot) logger instance. |
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Log ‘msg % args’ at level ‘level’ only if condition is fulfilled. |
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Sets the threshold for what messages will be logged. |
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Logs a message at the specified log level. |
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Logs a message at the WARN - WARNING log level. |
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Logs a message at the WARNING log level. |
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A singleton logger class that provides a shared logger instance with customizable |
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An instance of |
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Bayesian blocks fitness for binned or unbinned events. |
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Base class for bayesian blocks fitness functions. |
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Bayesian blocks fitness for point measures. |
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Bayesian blocks fitness for regular events. |
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Compute optimal segmentation of data with Scargle’s Bayesian Blocks. |
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Binomial proportion and confidence interval in bins of a continuous |
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Binomial proportion confidence interval given k successes, |
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Performs bootstrap resampling on numpy arrays. |
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Construct a callable piecewise-linear CDF from a pair of arrays. |
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Fold the weighted intervals to the interval (0,1). |
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Convert a string or number to a floating point number, if possible. |
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Convert a string or number to a floating point number, if possible. |
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Histogram of a piecewise-constant weight function. |
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Compute the length of overlap of two intervals. |
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Compute the Kuiper statistic. |
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Compute the false positive probability for the Kuiper statistic. |
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Compute the Kuiper statistic to compare two samples. |
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Calculate the median absolute deviation (MAD). |
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Poisson parameter confidence interval given observed counts. |
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Computes the signal to noise ratio for source being observed in the |
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Calculate histogram bin edges like |
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Return the optimal histogram bin width using the Freedman-Diaconis rule. |
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Enhanced histogram function, providing adaptive binnings. |
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Return the optimal histogram bin width using Knuth’s rule. |
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Return the optimal histogram bin width using Scott’s rule. |
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Computes the Akaike Information Criterion (AIC). |
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Computes the Akaike Information Criterion assuming that the observations |
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Computes the Bayesian Information Criterion (BIC) given the log of the |
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Computes the Bayesian Information Criterion (BIC) assuming that the |
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A Tweedie continuous random variable inherited |
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An instance of |
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Generates an architectural visualization for a given linear Keras |
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Generates an architectural visualization for a given linear Keras |
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A dummy layer to add spacing or other custom behavior. |
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scikit-plots Factory API |
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Generates the ROC curves from labels and predicted scores/probabilities |
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Generates the Precision Recall Curve from labels and probabilities |