arsenalgear.plotter Namespace Reference

Functions
def	plot_AMS (predictions, label_vectors, weights)
	plot_AMS More...
def	plotter_complex (real_part, imaginary_part, a, b, n, coefficient)
	plotter_complex More...
def	plot_learning_curve (estimator, title, X, y, axes=None, ylim=None, cv=None, n_jobs=None, scoring=None, train_sizes=np.linspace(0.1, 1.0, 5))
	plot_learning_curve More...

Function Documentation

plot_AMS()

def arsenalgear.plotter.plot_AMS	(		predictions,
			label_vectors,
			weights
	)

plot_AMS

Function used to plot the AMS function.

Args:
    predictions ( numpy array ): is a binary array, defined from the set of data that we're considering.
    label_vectors ( numpy array ): is a binary array constructed from the dataset, used for each model, that distinguishes an event between signal and background.
    weights ( numpy array ): it takes the weights associated to each data of my dataset.

plot_learning_curve()

def arsenalgear.plotter.plot_learning_curve	(		estimator,
			title,
			X,
			y,
			axes = None,
			ylim = None,
			cv = None,
			n_jobs = None,
			scoring = None,
			train_sizes = np.linspace( 0.1, 1.0, 5 )
	)

plot_learning_curve

Generate 3 plots: the test and training learning curve, the training samples vs fit times curve, the fit times vs score curve. Taken from here: https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html.
Args:
    estimator (sklearn): An estimator instance implementing `fit` and `predict` methods which
    will be cloned for each validation.
    title (str): Title for the chart.
    X (numpy.array): array-like of shape (n_samples, n_features). Training vector, where ``n_samples`` is the number of samples and
    ``n_features`` is the number of features.
    y (numpy.array): array-like of shape (n_samples) or (n_samples, n_features). Target relative to ``X`` for classification or regression;
    axes (numpy.array, optional): array-like of shape (3,). Axes to use for plotting the curves. Defaults to None.
    ylim (numpy.array, optional): tuple of shape (2,). Defines minimum and maximum y-values plotted, e.g. (ymin, ymax). Defaults to None.
    cv (int, optional): cross-validation generator or an iterable. Determines the cross-validation splitting strategy.
    Possible inputs for cv are:. Defaults to None.
    n_jobs (int, optional): nt or None. Number of jobs to run in parallel. Defaults to None.
    scoring (str, optional): a str (see model evaluation documentation) or a scorer callable object / function with signature
    ``scorer(estimator, X, y)``.. Defaults to None.
    train_sizes (numpy.array, optional): array-like of shape (n_ticks,). Relative or absolute numbers of training examples that will be used to generate the learning curve.. Defaults to np.linspace( 0.1, 1.0, 5 ).

plotter_complex()

def arsenalgear.plotter.plotter_complex	(		real_part,
			imaginary_part,
			a,
			b,
			n,
			coefficient
	)

plotter_complex

Function used to plot a given function for an index n.
Args:
    real_part (string): mathematical real expression part.
    imaginary_part (string): mathematical imaginary expression part.
    a (any): lower integration extreme.
    b (any): higher integration extreme.
    n (int): wave function index.
    coefficient (any): value of the normalization coefficient.
Returns:
    plot: the wave-function plot for the index n is returned.