skcriteria.core.plot
module¶
Plot helper for the DecisionMatrix object.
- class skcriteria.core.plot.DecisionMatrixPlotter(dm)[source]¶
Bases:
object
Make plots of DecisionMatrix.
- area(**kwargs)[source]¶
Draw an criteria stacked area plot.
An area plot displays quantitative data visually. This function wraps the matplotlib area function.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
DataFrame.plot.area()
.- Returns
Area plot, or array of area plots if subplots is True.
- Return type
matplotlib.axes.Axes or numpy.ndarray
- bar(**kwargs)[source]¶
Criteria vertical bar plot.
A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
DataFrame.plot.bar
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- barh(**kwargs)[source]¶
Criteria horizontal bar plot.
A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
DataFrame.plot.barh
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- box(**kwargs)[source]¶
Make a box plot of the criteria.
A box plot is a method for graphically depicting groups of numerical data through their quartiles.
For further details see Wikipedia’s entry for boxplot.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.boxplot
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- heatmap(**kwargs)[source]¶
Plot the alternative matrix as a color-encoded matrix.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.heatmap
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- hist(**kwargs)[source]¶
Draw one histogram of the criteria.
A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one
matplotlib.axes.Axes
.- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.histplot
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- kde(**kwargs)[source]¶
Criteria kernel density plot using Gaussian kernels.
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function (PDF) of a random variable. This function uses Gaussian kernels and includes automatic bandwidth determination.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.kdeplot
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- ogive(**kwargs)[source]¶
Criteria empirical cumulative distribution plot.
In statistics, an empirical distribution function (eCDF) is the distribution function associated with the empirical measure of a sample. This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified value of the measured variable is the fraction of observations of the measured variable that are less than or equal to the specified value.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.ecdfplot
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- wbar(**kwargs)[source]¶
Weights vertical bar plot.
A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
DataFrame.plot.bar
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- wbarh(**kwargs)[source]¶
Weights horizontal bar plot.
A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
DataFrame.plot.barh
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- wbox(**kwargs)[source]¶
Make a box plot of the weights.
A box plot is a method for graphically depicting groups of numerical data through their quartiles.
For further details see Wikipedia’s entry for boxplot.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.boxplot
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- wheatmap(**kwargs)[source]¶
Plot weights as a color-encoded matrix.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.heatmap
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- whist(**kwargs)[source]¶
Draw one histogram of the weights.
A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one
matplotlib.axes.Axes
.- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.histplot
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- wkde(**kwargs)[source]¶
Weights kernel density plot using Gaussian kernels.
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function (PDF) of a random variable. This function uses Gaussian kernels and includes automatic bandwidth determination.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.kdeplot
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them
- wogive(**kwargs)[source]¶
Weights empirical cumulative distribution plot.
In statistics, an empirical distribution function (eCDF) is the distribution function associated with the empirical measure of a sample. This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified value of the measured variable is the fraction of observations of the measured variable that are less than or equal to the specified value.
- Parameters
**kwargs – Additional keyword arguments are passed and are documented in
seaborn.ecdfplot
.- Returns
- Return type
matplotlib.axes.Axes or numpy.ndarray of them