0.5.dev0+git.20260216.503625b - February 16, 2026 17:01 UTC
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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str(object=’’) -> str |
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str(object=’’) -> str |
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Open the online documentation search page. |
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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 or return debugging information about the system, Python, dependencies, and hardware. |
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Return SP (scikitplot) logger instance. |
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Annoy Approximate Nearest Neighbors Index.
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Compiled with GCC/Clang. Using 512-bit AVX instructions. |
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Compiled with GCC/Clang. Using 512-bit AVX instructions. |
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High-level ANNoy index composed from mixins. |
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Compression used for |
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Mixin adding explicit Annoy-native persistence helpers. |
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Mixin that exports and restores index metadata. |
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NumPy / SciPy / pandas interoperability for Annoy-like indexes. |
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Mixin adding pickle support. |
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Persistence strategy used by |
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Mixin that adds convenient plotting methods to high-level Annoy wrappers. |
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User-facing neighbor queries for Annoy-like backends. |
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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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Generate 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, |
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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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Compute the sigmoid (expit) of a scalar input. |
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Compute the natural logarithm of the sigmoid (expit) of a scalar input. |
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Compute the logit (inverse sigmoid) of a scalar input. |
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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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High-level Python interface for the C++ ANNoy backend. |
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Compiled with GCC/Clang. Using 512-bit AVX instructions. |
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Compiled with GCC/Clang. Using 512-bit AVX instructions. |
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Compiled with GCC/Clang. Using 512-bit AVX instructions. |
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Astropy is a package intended to contain core functionality and some |
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This subpackage contains statistical tools provided for or used by Astropy. |
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Fortran to Python Interface Generator. |
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Return the directory that contains the |
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str(object=’’) -> str |
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str(object=’’) -> str |
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Compile and import a Cython extension module and return the loaded module. |
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Compile and import a Cython extension module from source text. |
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Compile/import a Cython module from a |
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Compile/import a Cython module from a |
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Compile and import all |
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Build and import a multi-module extension package and return loaded modules. |
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Build and import a multi-module extension package from code strings. |
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Build and import a multi-module extension package and return loaded modules. |
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Build and import a multi-module extension package from |
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Import a cached module entry and return the loaded module. |
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Import a cached module entry by cache key. |
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Import the newest cached module entry matching |
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Import a cached package and return the loaded modules. |
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Import a cached package entry by cache key. |
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Register an existing compiled extension artifact on disk, then import it. |
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Register a compiled extension artifact from bytes and import it. |
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Import a compiled extension artifact from a path. |
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Import a compiled extension artifact from raw bytes. |
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Export a cache entry directory to a destination folder. |
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Resolve (and create) the cache root directory. |
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List cached module entries. |
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List cached package entries. |
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Return cache statistics for the given cache root. |
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Garbage-collect cached builds deterministically. |
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Delete the entire cache directory. |
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Pin a cache key under a human-friendly alias. |
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Remove a pinned alias. |
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List alias→key mappings in the pin registry. |
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Import a pinned alias and return the loaded module(s). |
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Import a pinned alias. |
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Check whether build prerequisites are importable. |
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Result of compiling/importing a single Cython extension module. |
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Result of compiling/importing a package of extension modules. |
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Cache statistics for the compiled-artifact cache root. |
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Result of a cache garbage-collection operation. |
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A compiled module cache entry. |
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A compiled package cache entry (multi-module build). |
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Structured metadata for a template. |
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Return the on-disk template root directory. |
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List available templates. |
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Resolve a template ID to an on-disk path. |
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Read template source text. |
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Read metadata for a template and return a |
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Load template metadata from an adjacent |
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Compile and import a Cython template and return the loaded module. |
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Compile and import a Cython template and return a structured result. |
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List available multi-module package examples. |
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Resolve a package example name to its on-disk folder path. |
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Load package example metadata from |
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Build and import a multi-module package example and return a structured result. |
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Build and import a multi-module package example and return loaded modules. |
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List available workflow template folders. |
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Resolve a workflow name to its on-disk folder path. |
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Return the workflow CLI template path. |
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Copy a workflow template folder to a destination directory. |
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Generate Sphinx |
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Return a path to the cache directory for example datasets. |
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Report available example datasets, useful for reporting issues. |
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Load an example dataset from the online repository (requires internet). |
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Display a legend for the abbreviations of decile table column names. |
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Generate the Decile Table from labels and probabilities. |
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Generate the Decile-wise Lift Plot from labels and probabilities. |
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Generate the Decile based cumulative Lift Plot from labels and probabilities. |
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Generate the Decile-wise Lift Plot from labels and probabilities. |
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Generate the KS Statistic Plot from labels and probabilities. |
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Generate a decile table and four plots. |
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ModelPlotPy decile analysis. |
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Decile/ntile analysis for sklearn classifiers. |
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Plot response curve. |
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Plot cumulative response curve. |
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Plot cumulative lift curve. |
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Plot cumulative gains curve. |
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Plot response, cumulative response, cumulative lift, and cumulative gains as a 2x2 panel. |
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Plot costs and revenues curves. |
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Plot profit curve. |
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Plot ROI (return on investment) curve. |
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Module deprecation warning. |
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Generic exception thrown to surface failure information about external-facing operations. |
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Visible deprecation warning. |
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Enables ANNImputer |
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pipeline.py. |
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Mapping of envelope types to amplitude-modulation functions. |
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Generate a concatenated waveform from a musical composition input. |
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Play audio from a NumPy array using either IPython (for Jupyter) or sounddevice. |
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Plot the waveform of mono or multi-channel audio data. |
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Save waveform to an audio file using specified or auto-selected backend. |
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Save waveform as an MP3 file using pydub and ffmpeg, with support for mono or stereo. |
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Convert input sheet (str/list/dict) to a list of (note, octave, duration). |
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Display parsed notes or note frequencies from a musical sheet. |
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Serialize sheet notes to JSON or YAML string. |
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Enable serialization of compositions or note sheets. |
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Get response from LLM provider. |
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Load MLflow Gateway model configuration from a YAML file. |
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NumPy Array API compatibility library |
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Extra array functions built on top of the array API standard. |
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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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sphinxext. |
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Add a |
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A role and directive to display mathtext in Sphinx. |
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A directive for including a Matplotlib plot in a Sphinx document. |
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Custom roles for the Matplotlib documentation. |
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Sphinx compatibility shim for docutils |
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Tweedie Distribution Module. |
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An instance of |
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A Tweedie continuous random variable inherited |
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Approximate K-nearest-neighbours (KNN) imputer with pluggable ANN backends. |
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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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Return the effective level for the scikit-plots logger. |
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Return SP (scikitplot) logger instance. |
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Return the current verbosity level. |
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Set the logger’s level. |
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Set the verbosity level. |
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Log a message at the specified log level. |
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Log once per n calls from the same call site. |
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Log only for the first n calls from the same call site. |
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Log only if a condition is True. |
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Log a message at the specified log level. |
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Log a message at the DEBUG log level. |
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Log a message at the INFO log level. |
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Log a message at the WARNING log level. |
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Log a message at the WARN -> WARNING log level. |
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Log a message at the ERROR log level. |
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Log an error-like message at a specified level. |
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Log a message with severity ‘ERROR’ on the root logger. |
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Compatibility wrapper for legacy call sites. |
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Log a message at the CRITICAL log level. |
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Log a message at the FATAL -> CRITICAL log level. |
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Memory-mapped region with automatic resource management. |
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mmap_region(int size: int, int prot: int = PROT_READ | PROT_WRITE, int flags: int = MAP_PRIVATE | MAP_ANONYMOUS, int fd: int = -1, int offset: int = 0) -> MemoryMap |
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A handle that proxies the upstream |
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Create a strict, context-managed MLflow session. |
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Create an MLflow session using a shared project config file (TOML or YAML). |
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Create an MLflow session using a shared project TOML config. |
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Built-in immutable sequence. |
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Find a project root directory deterministically. |
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Load project MLflow config from TOML or YAML based on file extension. |
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Load project MLflow config from a TOML file. |
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Write a ProjectConfig to a YAML file. |
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Configuration that maps directly to |
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Session-level configuration for |
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Project-level configuration for MLflow usage across multiple scripts. |
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Base exception for scikitplot.mlflow errors. |
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Raised when MLflow is required but not installed. |
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Raised when a requested |
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Raised when the managed MLflow server fails to start or exits prematurely. |
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Run the built-in end-to-end MLflow workflow demo. |
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Return the path to the built-in demo config shipped with the package. |
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Export the built-in demo config into the current project. |
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Compute standard config file paths for a project. |
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Return the absolute path to the NumCpp C++ headers include directory. |
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dot(a: numpy.ndarray, b: numpy.ndarray) -> numpy.ndarray |
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Encode categorical features into dummy/indicator 0/1 variables. |
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Multi-column multi-label string column one-hot encoder [Rbcee2ffc57f9-1]. |
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32-bit KISS RNG with complete serialization support. |
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Low-level 64-bit KISS RNG with context manager support. |
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Factory function for auto-detecting 32-bit vs 64-bit RNG. |
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Seed sequence compatible with numpy.random.SeedSequence. |
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NumPy-compatible BitGenerator using KISS algorithm with complete serialization. |
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High-level random number generator using KISS algorithm. |
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NumPy RandomState-compatible interface with complete serialization. |
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Create default KISS random number generator. |
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Context manager for temporary RNG. |
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Random sample from array. |
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Random integers in [low, high) or [low, high]. |
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Normal distribution (Box-Muller transform). |
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Randomly permute sequence or return permuted range. |
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Random floats in [0, 1). |
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Shuffle array in-place (Fisher-Yates algorithm). |
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Uniform distribution in [low, high). |
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Plot PR or ROC curves with a seaborn-like API. |
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Visualization of the Confusion Matrix [Ra7e29df24177-1] alongside a text report showing key classification metrics. |
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Given binary labels y_true (0/1) and probabilities y_score 1d array, compute/plot a decile [Rd1ed195c7ca1-2] table. |
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Pretty-print the legend of decile table column names. |
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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 a robust standard deviation using the median absolute deviation (MAD). |
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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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Lightweight ⏱ timing context manager with |
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Generate portable, collision-resistant filenames and paths. |
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Make Convenience wrapper to build a unique path (callable with zero args). |
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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 factory class for dynamically generating a dummy Keras layer with custom spacing. |
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Scikit-plots Factory API module. |
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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 |