plot_kernel_density, plot_box_plot) from diogenes.grid_search import Experiment from diogenes.grid_search import std_clfs as std_clfs from diogenes.utils import remove_cols data = open_csv_url( 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv', delimiter=';') y = data['quality'] M = remove_cols(data, 'quality') y = y < np.average(y) if False: for x in describe_cols(M): print x if False: plot_correlation_scatter_plot(M) plot_correlation_matrix(M) plot_kernel_density(M['f0']) #no designation of col name plot_box_plot(M['f0']) #no designation of col name exp = Experiment(M, y, clfs=std_clfs) exp.make_csv()
M = cast_np_nd_to_sa(M) #M is multi class, we want to remove those rows. keep_index = np.where(labels != 2) labels = labels[keep_index] M = M[keep_index] if False: for x in describe_cols(M): print x if False: plot_correlation_scatter_plot(M) plot_correlation_matrix(M) plot_kernel_density(M['f0']) #no designation of col name plot_box_plot(M['f0']) #no designation of col name if False: from diogenes.generate import val_between, choose_rows_where, append_cols #val_btwn, where #generate a composite rule M = choose_rows_where(M, [{ 'func': val_between, 'col_name': 'f0', 'vals': (3.5, 5.0) }, { 'func': val_between, 'col_name': 'f1', 'vals': (2.7, 3.1) }], 'a new col_name')
def test_plot_kernel_density(self): np.random.seed(0) data = np.random.normal(size=(1000,)) fig = dsp.plot_kernel_density(data, verbose=False) self.add_fig_to_report(fig, 'plot_kernel_density')