Graph the results of a Sklearn gridsearch

graph_girdsearch.py

#!/usr/bin/env python

import collections

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import sklearn as skl
import itertools


def get_data(gs):
    graph_data = {}
    print(gs.grid_scores_)
    for gscore in gs.grid_scores_:
        key = 'features: %s, split: %s, oob: %s' % (gscore[0]['max_features'], gscore[0]['min_samples_split'], gscore[0]['oob_score'])
        x = gscore[0]['n_estimators']
        y = gscore[1]
        if key in graph_data:
            graph_data[key]['x'].append(x)
            graph_data[key]['y'].append(y)
        else:
            graph_data[key] = {'x': [x], 'y': [y]}
    return collections.OrderedDict(sorted(graph_data.items()))


def main():
    # This loads a grid search which has been executed and saved with joblib.save().
    gs = skl.externals.joblib.load('gridsearch.pkl')
    graph_data = get_data(gs)
    num_plots = len(graph_data)

    plt.figure(1)
    colormap = plt.cm.gist_ncar
    plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])

    marker = itertools.cycle(('o', 'v', '^', '<', '>', 's', '8', 'p', '*', 'D', 'x', 'H'))
    labels = []
    for k, v in graph_data.iteritems():
        labels.append(k)
        print(v)
        plt.plot(v['x'], v['y'], marker=marker.next(), clip_on=False)
    plt.legend(labels, ncol=4, loc='upper center',
               bbox_to_anchor=[0.5, 1.1],
               columnspacing=1.0, labelspacing=0.0,
               handletextpad=0.0, handlelength=1.5,
               fancybox=True, shadow=True)
    plt.xlabel('# Estimators')
    plt.ylabel('Mean score')
    plt.grid(True)
    ax = plt.gca()
    ax.get_yaxis().get_major_formatter().set_useOffset(False)
    plt.show()


if __name__ == '__main__':
    main()