Sklearn

In [ ]:

    

from sklearn import datasets


    

In [ ]:

    

%pylab inline


    

In [ ]:

    

circles = datasets.make_circles()


    

In [ ]:

    

print "features: {}".format(circles[0][:10])
print "target: {}".format(circles[1][:10])


    

In [ ]:

    

from matplotlib.colors import ListedColormap


    

In [ ]:

    

colors = ListedColormap(['red', 'yellow'])

pyplot.figure(figsize(8, 8))
pyplot.scatter(map(lambda x: x[0], circles[0]), map(lambda x: x[1], circles[0]), c = circles[1], cmap = colors)


    

In [ ]:

    

def plot_2d_dataset(data, colors):
    pyplot.figure(figsize(8, 8))
    pyplot.scatter(map(lambda x: x[0], data[0]), map(lambda x: x[1], data[0]), c = data[1], cmap = colors)


    

In [ ]:

    

noisy_circles = datasets.make_circles(noise = 0.15)


    

In [ ]:

    

plot_2d_dataset(noisy_circles, colors)


    

In [ ]:

    

simple_classification_problem = datasets.make_classification(n_features = 2, n_informative = 1, 
                                                            n_redundant = 1, n_clusters_per_class = 1,
                                                            random_state = 1 )


    

In [ ]:

    

plot_2d_dataset(simple_classification_problem, colors)


    

In [ ]:

    

classification_problem = datasets.make_classification(n_features = 2, n_informative = 2, n_classes = 4, 
                                                      n_redundant = 0, n_clusters_per_class = 1, random_state = 1)

colors = ListedColormap(['red', 'blue', 'green', 'yellow'])


    

In [ ]:

    

plot_2d_dataset(classification_problem, colors)


    

In [ ]:

    

iris = datasets.load_iris()


    

In [ ]:

    

iris


    

In [ ]:

    

iris.keys()


    

In [ ]:

    

print iris.DESCR


    

In [ ]:

    

print "feature names: {}".format(iris.feature_names)
print "target names: {names}".format(names = iris.target_names)


    

In [ ]:

    

iris.data[:10]


    

In [ ]:

    

iris.target


    

In [ ]:

    

from pandas import DataFrame


    

In [ ]:

    

iris_frame = DataFrame(iris.data)
iris_frame.columns = iris.feature_names
iris_frame['target'] = iris.target


    

In [ ]:

    

iris_frame.head()


    

In [ ]:

    

iris_frame.target = iris_frame.target.apply(lambda x : iris.target_names[x])


    

In [ ]:

    

iris_frame.head()


    

In [ ]:

    

iris_frame[iris_frame.target == 'setosa'].hist('sepal length (cm)')


    

In [ ]:

    

pyplot.figure(figsize(20, 24))

plot_number = 0
for feature_name in iris['feature_names']:
    for target_name in iris['target_names']:
        plot_number += 1
        pyplot.subplot(4, 3, plot_number)
        pyplot.hist(iris_frame[iris_frame.target == target_name][feature_name])
        pyplot.title(target_name)
        pyplot.xlabel('cm')
        pyplot.ylabel(feature_name[:-4])


    

In [ ]:

    

import seaborn as sns


    

In [ ]:

    

sns.pairplot(iris_frame, hue = 'target')


    

In [ ]:

    

?sns.set()


    

In [ ]:

    

sns.set(font_scale = 1.3)
data = sns.load_dataset("iris")
sns.pairplot(data, hue = "species")