本文主要是介绍Matplotlib for presenting results（论文画图matplotlib jupyter文档），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

参考jupyter的官方文档点击打开链接

第一部分讲了matplotlib 自带的各种风格，可以画出不同背景样式的图

第二部分讲的是绘制heatmap，利用heatmap可以将自己的结果和别的模型进对比（用不同深重颜色代表差异）

第三部分利用TSNE这个库可以把高维数据表示为二维图中（没看懂）

第四部分画堆叠bar图

Learning curves¶

Make matplotlib graphics to show up inline.

In [8]:

%matplotlib inline

Import matplotlib. If you want to generate images without having a window appear (if you run your scripts on servers), use a non-interactive backend such as Agg (for PNGs), PDF, SVG or PS. To do so, uncomment the second line in the following cell.

In [9]:

import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt

Matplotlib has different styles. Run the cell to check which styles are available.

In [10]:

plt.style.available

Out[10]:

[u'seaborn-darkgrid',u'Solarize_Light2',u'seaborn-notebook',u'classic',u'seaborn-ticks',u'grayscale',u'bmh',u'seaborn-talk',u'dark_background',u'ggplot',u'fivethirtyeight',u'_classic_test',u'seaborn-colorblind',u'seaborn-deep',u'seaborn-whitegrid',u'seaborn-bright',u'seaborn-poster',u'seaborn-muted',u'seaborn-paper',u'seaborn-white',u'fast',u'seaborn-pastel',u'seaborn-dark',u'seaborn',u'seaborn-dark-palette']

To set a style you want use stlye.use.

In [11]:

matplotlib.style.use('seaborn-darkgrid')

plot_learning_curves plots train, dev, test measure-time curves. Play around with different parameters to get a figure that suits you the best. flist is the list of size 3; the first element is the list of train scores, the second of dev scors and the third of test scores.

In [12]:

def plot_learning_curves(fig_path, n_epochs, flist, style=''):measure = 'f1'steps_measure = 'epochs'plt.figure(dpi=400)plt.rcParams['font.size'] = 10plt.rcParams['axes.labelsize'] = 12plt.rcParams['axes.labelweight'] = 'bold'plt.rcParams['axes.titlesize'] = 12plt.rcParams['xtick.labelsize'] = 10plt.rcParams['ytick.labelsize'] = 10plt.rcParams['legend.fontsize'] = 10plt.rcParams['figure.titlesize'] = 12steps = range(1, n_epochs+1)plt.title('learning curves' + style)plt.plot(steps, flist[0], linewidth=1, color='#6699ff', linestyle='-', marker='o',markeredgecolor='black',markeredgewidth=0.5, label='train')plt.plot(steps, flist[1], linewidth=3, color='#ff4d4d', linestyle='-', marker='D',markeredgecolor='black',markeredgewidth=0.5, label='test')plt.plot(steps, flist[2], linewidth=2, color='#ffcc66', linestyle='-', marker='s',markeredgecolor='black',markeredgewidth=0.5, label='dev')plt.xlabel(steps_measure)plt.xticks(steps)plt.ylabel(measure)plt.legend(loc='best', numpoints=1, fancybox=True)plt.show()plt.savefig(fig_path)

Let's generate a random examples to illustrate one figure with learning curves.

In [16]:

fig_path = 'figs/'
train_f1 = [0.2, 0.3, 0.4, 0.5, 0.6, 0.62, 0.65, 0.67, 0.68, 0.67, 0.69, 0.72, 0.721, 0.719, 0.72]
dev_f1 = [0.1, 0.2, 0.3, 0.4, 0.5, 0.55, 0.57, 0.59, 0.6, 0.62, 0.61, 0.615, 0.614, 0.6159, 0.62]
test_f1 = [0.05, 0.15, 0.2, 0.25, 0.3, 0.4, 0.45, 0.43, 0.5, 0.51, 0.55, 0.52, 0.53, 0.525, 0.531]
flist = [train_f1, dev_f1, test_f1]
n_epochs = 15
plot_learning_curves(fig_path + 'learning_curve.png', n_epochs , flist)

<matplotlib.figure.Figure at 0x1074e1e50>

And the same figure with all available styles.

In [17]:

for st in plt.style.available:matplotlib.style.use(st)plot_learning_curves(fig_path + 'learning_curve.png', n_epochs , flist, '--' + st)

<matplotlib.figure.Figure at 0x107517250>

<matplotlib.figure.Figure at 0x10a271f10>

<matplotlib.figure.Figure at 0x10a246790>

<matplotlib.figure.Figure at 0x10a878750>

<matplotlib.figure.Figure at 0x10af8a850>

<matplotlib.figure.Figure at 0x107254ad0>

<matplotlib.figure.Figure at 0x10af91ad0>

<matplotlib.figure.Figure at 0x10a8afc10>

<matplotlib.figure.Figure at 0x10bfd8d10>

<matplotlib.figure.Figure at 0x10a583210>

<matplotlib.figure.Figure at 0x10a9d4cd0>

<matplotlib.figure.Figure at 0x10a749d10>

<matplotlib.figure.Figure at 0x10a6573d0>

<matplotlib.figure.Figure at 0x10a5f3f10>

<matplotlib.figure.Figure at 0x10a1ec310>

<matplotlib.figure.Figure at 0x10a118d90>

<matplotlib.figure.Figure at 0x10af57110>

<matplotlib.figure.Figure at 0x10cf635d0>

<matplotlib.figure.Figure at 0x10a9c7fd0>

<matplotlib.figure.Figure at 0x10a1cc550>

<matplotlib.figure.Figure at 0x10a986590>

<matplotlib.figure.Figure at 0x10a

<matplotlib.figure.Figure at 0x10a300f10>

<matplotlib.figure.Figure at 0x10a21ee90>

<matplotlib.figure.Figure at 0x10bfd1e90>

In your scripts after every epoch or after every 1K iterations evaluate your model on train, dev and test data and append corresponding scripts.

Heatmaps¶

Visualize LSTM outputs¶

Now import prettyplotlib.

In [18]:

import prettyplotlib as ppl

visu_lstm_outputs illustrates a heatmap of LSTM outputs. More examples can be found here: https://github.com/olgabot/prettyplotlib/wiki/Examples-with-code.

In [19]:

def visu_lstm_outputs(fig_path, LSTM_outputs, sentence_tokenized):plt.rcParams['xtick.labelsize'] = 20plt.rcParams['ytick.labelsize'] = 20slen = len(sentence_tokenized)fig, ax = ppl.subplots(1)fig.set_figheight(15)fig.set_figwidth(20)ppl.pcolormesh(fig, ax, LSTM_outputs)ax.set_xticks(np.arange(0.5, slen + 0.5, 1))ax.set_xticklabels(sentence_tokenized)ax.set_title('lstm outputs', fontsize = 25)plt.show()fig.savefig(fig_path)

Let's see how it works for a randomly generated example.

In [20]:

import numpy as npsentence = 'today is a beautiful day'
sentence_tokenized = sentence.split(' ')
slen = len(sentence_tokenized)
LSTM_hidden_size = 15
LSTM_outputs = np.random.rand(LSTM_hidden_size, slen) fig_path = "figs/heatmap_lstm_hidden.png"
visu_lstm_outputs(fig_path, LSTM_outputs, sentence_tokenized)

/Users/ana/tf_cpu/lib/python2.7/site-packages/matplotlib/__init__.py:800: MatplotlibDeprecationWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.mplDeprecation)

For a tensorflow model you need to retrieve ourputs with outputs_op = graph.get_operation_by_name(op_name).outputs[0], run the operation outputs = session.run(outputs_op) and transpose them np.asarray(outputs).transpose().

Visualize improvements¶

If you compare your models with another model, you can visualize improvements and make your results table easier to interpret.

In [21]:

matplotlib.style.use('seaborn-white')

visu_imporovements makes a heatmap of the improvements data and writes improvements values in the center of cells.

In [22]:

def visu_improvements(improvements_data):y_labels = ['model1', 'model2', 'model3', 'model4']x_labels = ['measure1', 'measure2', 'measure3']fig, ax = plt.subplots(1)fig.set_figheight(15)fig.set_figwidth(30)#plt.pcolormesh(fig, ax, my_data)plt.pcolor(improvements_data, cmap=plt.cm.YlGn)ax.set_aspect('auto')for y in range(improvements_data.shape[0]):for x in range(improvements_data.shape[1]):plt.text(x + 0.5, y + 0.5, '%.2f' % improvements_data[y, x],horizontalalignment='center',verticalalignment='center',size=30,weight='bold')ax.set_yticks(np.arange(0.5, len(y_labels) + 0.5, 1))ax.set_yticklabels(y_labels, size=25, weight='bold')ax.set_xticks(np.arange(0.5, len(x_labels) + 0.5, 1))ax.set_xticklabels(x_labels, size=17, weight='bold')ax.tick_params(axis='both', labelsize=25)ax.set_title('improvements', fontsize = 40)cbar = plt.colorbar()cbar.ax.tick_params(labelsize=30)fig.savefig(fig_path)

Make a random example.

In [23]:

fig_path = "improvements.png"
n_models = 4, 
n_measures = 3
improvements_data = np.random.random_sample((4,3))
np.savetxt(fname='improvements.txt', X=improvements_data, fmt='%10.5f', delimiter='\t')
visu_improvements(improvements_data)

You will write differences of your models and another model in a text file (one row for one model) and read it as a numpy array.

In [24]:

improvements_data = np.loadtxt('improvements.txt', delimiter='\t')
visu_improvements(improvements_data)

tSNE¶

Using tSNE you can visualize your high-dimensional vectors in 2D space. Learn first how to use tSNE effectively https://distill.pub/2016/misread-tsne/.

In [25]:

def plot(candidates_tsne, n_classes, tags, ids, file_path):almost_black = '#262626'fig, ax = plt.subplots(1)# hard-coded for the example with 3 classescolors = ['red', 'blue']for k in range(n_classes):begin = sum(sizes[:k])end = sum(sizes[:k]) + sizes[k]x = [candidates_tsne[i, 0] for i in range(begin, end)]y = [candidates_tsne[i, 1] for i in range(begin, end)]# marks every vector with "id (tag)", where id and tag could be anything you like, e.g. word (POS)# every vector point is colored with the corresponding class colortext = [str(ids[i]) + " (" + str(tags[i]) + ")" for i in range(begin, end)]ax.scatter(x, y, label='class'+str(k+1), alpha=0.5, edgecolor=almost_black, facecolor=colors[k], linewidth=0.15)for i, txt in enumerate(text):ax.annotate(txt, (x[i], y[i]))# remove top and right axesspines_to_remove = ['top', 'right']for spine in spines_to_remove:ax.spines[spine].set_visible(False)ax.xaxis.set_ticks_position('none')ax.yaxis.set_ticks_position('none')spines_to_keep = ['bottom', 'left']for spine in spines_to_keep:ax.spines[spine].set_linewidth(0.5)ax.spines[spine].set_color(almost_black)# make axis almost blackax.xaxis.label.set_color(almost_black)ax.yaxis.label.set_color(almost_black)ax.set_xticks([])ax.set_yticks([])ax.title.set_color(almost_black)ax.set_title('tsne', fontsize = 20)# make the legend background light graylight_grey = np.array([float(248)/float(255)]*3)legend = ax.legend(frameon=True, scatterpoints=1)rect = legend.get_frame()rect.set_facecolor(light_grey)rect.set_linewidth(0.0)# change the legend label colors to almost blacktexts = legend.textsfor t in texts:t.set_color(almost_black)ax.grid(False)plt.show()fig.savefig(str(file_path), dpi=200)plt.close()

Make random classification dataset.

In [26]:

import sklearn
from sklearn import datasetsn_classes = 2
X, y = datasets.make_classification(n_samples=10, n_features=20, class_sep=10)
types = [[] for _ in range(n_classes)]
sizes = [0]*n_classes
for i, l in enumerate(y):types[l].append(X[i])sizes[l] += 1for i in range(n_classes):types[i] = np.asarray(types[i]).reshape((sizes[i], 20))

And plot.

In [27]:

from sklearn.manifold import TSNEjoint = np.concatenate(types, 0)
tsne = TSNE(init='pca', n_iter=5000)
candidates_tsne = tsne.fit_transform(joint)
file_path = "figs/tsne.png"
tags = [1]*sizes[0] + [2]*sizes[1]
ids = range(sum(sizes))
plot(candidates_tsne, n_classes, tags, ids, file_path)

When running on serves comment plt.show for all examples.

Stacked bars plot¶

In [30]:

#from matplotlib.font_manager import FontProperties
from operator import add

In [91]:

def tags_stacked_bars(distr1, distr2):distr1.sort(key=lambda item: item[1], reverse=True)distr1_dict = dict(distr1)distr2.sort(key=lambda item: item[1], reverse=True)distr2_dict = dict(distr2)# plot tags which has positive frequency in the first distribution and higher than one in the secondplot_tags = [tag for tag, freq in distr1 if freq > 0.0]other = []for tag, freq in distr2:if freq > 1.0 and tag not in plot_tags:plot_tags.append(tag)else:other.append(freq)distr1_pruned_dict = {}for key in plot_tags:if key in distr1_dict:distr1_pruned_dict[key] = distr1_dict[key]else:distr1_pruned_dict[key] = 0distr2_pruned_dict = {}for key in plot_tags:if key in distr2_dict:distr2_pruned_dict[key] = distr2_dict[key]else:distr2_pruned_dict[key] = 0distr1_freq = []distr2_freq = []for tag in plot_tags:distr1_freq.append(distr1_pruned_dict[tag])distr2_freq.append(distr2_pruned_dict[tag])plot_tags.append("other")distr1_freq.append(0.0)distr2_freq.append(np.mean(np.asarray(other)))# plot stacked barwidth = 0.4height_cumulative = [0.0, 0.0]plots = []ind = [0, 0.5]fig = plt.figure(figsize=(11, 11))ax = fig.add_subplot(1, 1, 1)colors = ['#0066ff', '#ffcc99', '#adebad', '#ff5c33', '#ac3973', '#ffbf00', '#7979d2', '#00cc99']for k in range(len(plot_tags)):if k < 8:color = colors[k]else:color = np.random.rand(3)if k == 0:plots.append(ax.bar(ind, [distr1_freq[k], distr2_freq[k]], width, color=color, edgecolor='black'))else:plots.append(ax.bar(ind, [distr1_freq[k], distr2_freq[k]], width,bottom=height_cumulative, color=color, edgecolor='black'))height_cumulative = map(add, [distr1_freq[k], distr2_freq[k]], height_cumulative)plt.ylabel('median occurrence of a tag', fontsize=20)title = 'distribution of tags'plt.title(title, fontsize=25)plt.xticks([ind[0]+width/2.0, ind[1]+width/2], ["distr1", "distr2"], fontsize=20)plt.yticks(np.arange(0, sum(distr2_freq), 3))handles = [p[0] for p in plots]plt.legend(handles[::-1], plot_tags[::-1], prop={'size': 10}, loc='center left', bbox_to_anchor=(1, 0.5))fig_path = "figs/stacked_bars.png"plt.show()fig.savefig(fig_path)

In [92]:

distr1 = [["NN", 5.0], ["NP", 3.0], ["PP", 2.0], ["ADV", 2.0], ["S", 2.0], ["ART", 2.0], ["$.", 1.0], ["KON", 1.0], ["ADJA", 1.0], ["APPR", 1.0], ["VVINF", 1.0], ["VVFIN", 1.0], ["$,", 1.0], ["KOUS", 1.0], ["VP", 1.0], ["VAFIN", 1.0], ["PPER", 1.0], ["CVZ", 0.0], ["AVP", 0.0], ["CAP", 0.0], ["CVP", 0.0], ["NE", 0.0], ["CPP", 0.0], ["VMFIN", 0.0], ["PTKNEG", 0.0], ["VAPP", 0.0], ["APPO", 0.0], ["PRF", 0.0], ["VVIZU", 0.0], ["NM", 0.0], ["PDAT", 0.0], ["PIAT", 0.0], ["ADJD", 0.0], ["$[", 0.0], ["PTKVZ", 0.0], ["PRELS", 0.0], ["PIS", 0.0], ["ROOT", 0.0], ["PROAV", 0.0], ["APZR", 0.0], ["PPOSAT", 0.0], ["CO", 0.0], ["CNP", 0.0], ["PDS", 0.0], ["VVPP", 0.0], ["AP", 0.0], ["XY", 0.0], ["PWAV", 0.0], ["CS", 0.0], ["PTKANT", 0.0], ["VZ", 0.0], ["PTKZU", 0.0], ["CARD", 0.0], ["PWS", 0.0], ["VMINF", 0.0], ["MPN", 0.0], ["VAINF", 0.0], ["APPRART", 0.0], ["KOKOM", 0.0], ["PTKA", 0.0], ["PIDAT", 0.0], ["TRUNC", 0.0], ["KOUI", 0.0], ["CAVP", 0.0]]
distr2 = [["NN", 10.0], ["NP", 7.0], ["S", 7.0], ["PP", 5.0], ["ADV", 4.0], ["ADJA", 3.0], ["APPR", 3.0], ["VP", 3.0], ["ART", 3.0], ["VVINF", 2.0], ["VVFIN", 2.0], ["VAFIN", 2.0], ["PPER", 2.0], ["KON", 1.0], ["VMFIN", 1.0], ["ADJD", 1.0], ["PTKNEG", 1.0], ["$.", 1.0], ["$,", 1.0], ["CNP", 1.0], ["KOUS", 1.0], ["PDS", 1.0], ["VVPP", 1.0], ["AP", 1.0], ["CS", 1.0], ["APPRART", 1.0], ["CVZ", 0.0], ["AVP", 0.0], ["CAP", 0.0], ["PWAT", 0.0], ["CVP", 0.0], ["NE", 0.0], ["CPP", 0.0], ["VAPP", 0.0], ["APPO", 0.0], ["PRF", 0.0], ["VVIZU", 0.0], ["NM", 0.0], ["PDAT", 0.0], ["PIAT", 0.0], ["FM", 0.0], ["$[", 0.0], ["PTKVZ", 0.0], ["PRELS", 0.0], ["PIS", 0.0], ["ROOT", 0.0], ["PROAV", 0.0], ["TRUNC", 0.0], ["PPOSAT", 0.0], ["CO", 0.0], ["XY", 0.0], ["PWAV", 0.0], ["PTKANT", 0.0], ["VZ", 0.0], ["PTKZU", 0.0], ["CARD", 0.0], ["PWS", 0.0], ["PRELAT", 0.0], ["VVIMP", 0.0], ["VMINF", 0.0], ["MPN", 0.0], ["VAINF", 0.0], ["KOKOM", 0.0], ["PTKA", 0.0], ["PIDAT", 0.0], ["APZR", 0.0], ["KOUI", 0.0], ["CAVP", 0.0]]tags_stacked_bars(distr1, distr2)