TSNE Class Reference

Inheritance diagram for TSNE:

Collaboration diagram for TSNE:

Public Member Functions
def	add (self, data, columns, *masks)
def	finish (self)

Detailed Description

Plots multivariate distribution using TSNE algorithm

Definition at line 1144 of file plotting.py.

Member Function Documentation

add()

def add	(		self,
			data,
			columns,
		*	masks
	)

Add a new correlation plot.
@param data pandas.DataFrame containing all data
@param columns which are used to calculate the correlations
@param masks different classes to show in TSNE

Reimplemented from Plotter.

Definition at line 1149 of file plotting.py.

    def add(self, data, columns, *masks):
        """
        Add a new correlation plot.
        @param data pandas.DataFrame containing all data
        @param columns which are used to calculate the correlations
        @param masks different classes to show in TSNE
        """
        try:
            import sklearn
            import sklearn.manifold
            model = sklearn.manifold.TSNE(n_components=2, random_state=0)
            data = numpy.array([data[column] for column in columns]).T
            model.fit(data)
            for mask in masks:
                data = numpy.array([data[column][mask] for column in columns]).T
                data = model.transform(data)
                self.axis.scatter(data[:, 0], data[:, 1], rasterized=True)
        except ImportError:
            print("Cannot create TSNE plot. Install sklearn if you want it")
        return self
 

finish()

def finish

(

self

)

Sets limits, title, axis-labels and legend of the plot

Reimplemented from Plotter.

Definition at line 1170 of file plotting.py.

    def finish(self):
        """
        Sets limits, title, axis-labels and legend of the plot
        """
        return self
 
 

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The documentation for this class was generated from the following file:

• mva/scripts/basf2_mva_evaluation/plotting.py