binding_sites = bs other = o binding_sites_labels = np.ones(binding_sites.shape[0], dtype=np.uint8) other_labels = np.zeros(other.shape[0], dtype=np.uint8) X = np.concatenate((binding_sites, other)) y = np.concatenate((binding_sites_labels, other_labels)) # %% visualizer = ClassBalance(labels=class_names) visualizer.fit(y) visualizer.poof() # %% visualizer = ParallelCoordinates() visualizer.fit_transform(X, y) visualizer.poof() # %% visualizer = Rank1D() visualizer.fit(X, y) visualizer.transform(X) visualizer.poof() # %% visualizer = Rank2D() visualizer.fit_transform(X) visualizer.poof() # %% visualizer = FeatureCorrelation()
visualizer.show() #%% from yellowbrick.target import FeatureCorrelation visualizer = FeatureCorrelation(labels=features) visualizer.fit(X[features], y) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure #%% from yellowbrick.features import JointPlotVisualizer visualizer = JointPlotVisualizer() visualizer.fit_transform(X["grade"], y) # Fit and transform the data visualizer.show() # Finalize and render the figure #%% from yellowbrick.features import Manifold viz = Manifold(manifold="tsne", classes=class_labels) viz.fit_transform(X[features], y) # Fit the data to the visualizer viz.show() # Finalize and render the figure #%% from yellowbrick.features import Rank2D visualizer = Rank2D(algorithm='pearson')
