def test_open_csv_url(self): url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv' try: urllib2.urlopen(url) except (urllib2.HTTPError, urllib2.URLError): utils_for_tests.print_in_box('skipping test_open_csv_url', 'remote resource not found') self.skipTest('couldn\'t get remote resource') sa = read.open_csv_url(url, delimiter=';') ctrl_dtype = [('fixed acidity', '<f8'), ('volatile acidity', '<f8'), ('citric acid', '<f8'), ('residual sugar', '<f8'), ('chlorides', '<f8'), ('free sulfur dioxide', '<f8'), ('total sulfur dioxide', '<f8'), ('density', '<f8'), ('pH', '<f8'), ('sulphates', '<f8'), ('alcohol', '<f8'), ('quality', '<i8')] self.assertEqual(sa.dtype, ctrl_dtype)
def test_open_csv_url(self): url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv' sa = read.open_csv_url(url, delimiter=';') ctrl_dtype = [('fixed acidity', '<f8'), ('volatile acidity', '<f8'), ('citric acid', '<f8'), ('residual sugar', '<f8'), ('chlorides', '<f8'), ('free sulfur dioxide', '<f8'), ('total sulfur dioxide', '<f8'), ('density', '<f8'), ('pH', '<f8'), ('sulphates', '<f8'), ('alcohol', '<f8'), ('quality', '<i8')] self.assertEqual(sa.dtype, ctrl_dtype)
import numpy as np import sklearn.datasets from diogenes.read import open_csv_url from diogenes.display import (plot_correlation_scatter_plot, plot_correlation_matrix, 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
import numpy as np import sklearn.datasets from diogenes.read import open_csv_url from diogenes.display import (plot_correlation_scatter_plot, plot_correlation_matrix, 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)