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.
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import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
Matplotlib has different styles. Run the cell to check which styles are available.
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plt.style.available
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To set a style you want use stlye.use.
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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.
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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)
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)
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.
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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.
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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)
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.
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matplotlib.style.use('seaborn-white')
visu_imporovements makes a heatmap of the improvements data and writes improvements values in the center of cells.
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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)
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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.
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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/.
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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
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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)
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