Python matplotlib.pyplot.text() Examples
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Example #1
Source File: feature_vis.py From transferlearning with MIT License | 8 votes |
def plot_tsne(self, save_eps=False): ''' Plot TSNE figure. Set save_eps=True if you want to save a .eps file. ''' tsne = TSNE(n_components=2, init='pca', random_state=0) features = tsne.fit_transform(self.features) x_min, x_max = np.min(features, 0), np.max(features, 0) data = (features - x_min) / (x_max - x_min) del features for i in range(data.shape[0]): plt.text(data[i, 0], data[i, 1], str(self.labels[i]), color=plt.cm.Set1(self.labels[i] / 10.), fontdict={'weight': 'bold', 'size': 9}) plt.xticks([]) plt.yticks([]) plt.title('T-SNE') if save_eps: plt.savefig('tsne.eps', dpi=600, format='eps') plt.show()
Example #2
Source File: draw_plot.py From TaobaoAnalysis with MIT License | 6 votes |
def draw_quality_histogram(items): """ 画质量直方图 """ from analyze.quality import get_item_quality qualities = [get_item_quality(item) for item in items if len(item.reviews) >= 20] plt.title('质量直方图') plt.xlabel('质量') plt.ylabel('分布密度') plt.hist(qualities, bins=100, range=(0, 1), density=True) # 拟合正态分布 mean = np.mean(qualities) std = np.std(qualities) x = np.arange(0, 1, 0.01) y = stats.norm.pdf(x, loc=mean, scale=std) plt.plot(x, y) plt.text(0, 5, r'$N={},\mu={:.3f},\sigma={:.3f}$' .format(len(qualities), mean, std))
Example #3
Source File: plot.py From pytorch-sgns with MIT License | 6 votes |
def plot(args): wc = pickle.load(open(os.path.join(args.data_dir, 'wc.dat'), 'rb')) words = sorted(wc, key=wc.get, reverse=True)[:args.top_k] if args.model == 'pca': model = PCA(n_components=2) elif args.model == 'tsne': model = TSNE(n_components=2, perplexity=30, init='pca', method='exact', n_iter=5000) word2idx = pickle.load(open('data/word2idx.dat', 'rb')) idx2vec = pickle.load(open('data/idx2vec.dat', 'rb')) X = [idx2vec[word2idx[word]] for word in words] X = model.fit_transform(X) plt.figure(figsize=(18, 18)) for i in range(len(X)): plt.text(X[i, 0], X[i, 1], words[i], bbox=dict(facecolor='blue', alpha=0.1)) plt.xlim((np.min(X[:, 0]), np.max(X[:, 0]))) plt.ylim((np.min(X[:, 1]), np.max(X[:, 1]))) if not os.path.isdir(args.result_dir): os.mkdir(args.result_dir) plt.savefig(os.path.join(args.result_dir, args.model) + '.png')
Example #4
Source File: plot.py From TaskBot with GNU General Public License v3.0 | 6 votes |
def plot_attention(sentences, attentions, labels, **kwargs): fig, ax = plt.subplots(**kwargs) im = ax.imshow(attentions, interpolation='nearest', vmin=attentions.min(), vmax=attentions.max()) plt.colorbar(im, shrink=0.5, ticks=[0, 1]) plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") ax.set_yticks(range(len(labels))) ax.set_yticklabels(labels, fontproperties=getChineseFont()) # Loop over data dimensions and create text annotations. for i in range(attentions.shape[0]): for j in range(attentions.shape[1]): text = ax.text(j, i, sentences[i][j], ha="center", va="center", color="b", size=10, fontproperties=getChineseFont()) ax.set_title("Attention Visual") fig.tight_layout() plt.show()
Example #5
Source File: utilities_general_v2.py From MachineLearningSamples-ImageClassificationUsingCntk with MIT License | 6 votes |
def cmPlot(confMatrix, classes, normalize=False, title='Confusion matrix', cmap=[]): if normalize: confMatrix = confMatrix.astype('float') / confMatrix.sum(axis=1)[:, np.newaxis] confMatrix = np.round(confMatrix * 100,1) if cmap == []: cmap = plt.cm.Blues #Actual plotting of the values thresh = confMatrix.max() / 2. for i, j in itertools.product(range(confMatrix.shape[0]), range(confMatrix.shape[1])): plt.text(j, i, confMatrix[i, j], horizontalalignment="center", color="white" if confMatrix[i, j] > thresh else "black") avgAcc = np.mean([float(confMatrix[i, i]) / sum(confMatrix[:, i]) for i in range(confMatrix.shape[1])]) plt.imshow(confMatrix, interpolation='nearest', cmap=cmap) plt.title(title + " (avgAcc={:2.2f}%)".format(100*avgAcc)) plt.colorbar() plt.xticks(np.arange(len(classes)), classes, rotation=45) plt.yticks(np.arange(len(classes)), classes) plt.ylabel('True label') plt.xlabel('Predicted label')
Example #6
Source File: plot.py From ML_CIA with MIT License | 6 votes |
def plot(args): wc = pickle.load(open(os.path.join(args.data_dir, 'wc.dat'), 'rb')) words = sorted(wc, key=wc.get, reverse=True)[:args.top_k] if args.model == 'pca': model = PCA(n_components=2) elif args.model == 'tsne': model = TSNE(n_components=2, perplexity=30, init='pca', method='exact', n_iter=5000) word2idx = pickle.load(open('data/word2idx.dat', 'rb')) idx2vec = pickle.load(open('data/idx2vec.dat', 'rb')) X = [idx2vec[word2idx[word]] for word in words] X = model.fit_transform(X) plt.figure(figsize=(18, 18)) for i in range(len(X)): plt.text(X[i, 0], X[i, 1], words[i], bbox=dict(facecolor='blue', alpha=0.1)) plt.xlim((np.min(X[:, 0]), np.max(X[:, 0]))) plt.ylim((np.min(X[:, 1]), np.max(X[:, 1]))) if not os.path.isdir(args.result_dir): os.mkdir(args.result_dir) plt.savefig(os.path.join(args.result_dir, args.model) + '.png')
Example #7
Source File: parsecircuit.py From viznet with MIT License | 6 votes |
def dict2circuit(datamap, handler=None, blockdict=None, putstart=None): ''' parse a dict (probabily from a yaml file) to a circuit. Args: datamap (dict): the dictionary defining a circuit. handler (None|QuantumCircuit): the handler. blockdict (dict, default=datamap): the dictionary for block includes. putstart (bool, default=handler==None): put a start at the begining if True. ''' if putstart is None: putstart = handler is None if handler is None: handler = QuantumCircuit(num_bit=datamap['nline']) if blockdict is None: blockdict = dict(datamap) if putstart: # text |0>s for i in range(datamap['nline']): plt.text(-0.4, -i, r'$\vert0\rangle$', va='center', ha='center', fontsize=setting['fontsize']) handler.x += 0.8 if isinstance(datamap, str): vizcode(handler, datamap, blockdict=blockdict) else: for block in datamap['blocks']: dict2circuit(block, handler, blockdict, putstart=False)
Example #8
Source File: test_brush.py From viznet with MIT License | 6 votes |
def test_edge(): edge_list = ['-', '..-', '->--', '<=>', '===->', '->-....-<-'] clink_list = ['->', '<->', '>.>'] offsets = [(), (-0.2, 0.2), (0.15, 0.3, -0.3)] with DynamicShow(figsize=(8, 6)) as ds: for i, style in enumerate(edge_list): edge = EdgeBrush(style, ds.ax) p1 = Pin((0, -i * 0.1)) p2 = Pin((1, -i * 0.1)) ei = edge >> (p1, p2) p1.text('"%s"'%style, 'left') if i == 0: ei.text('EdgeBrush', 'top') for j, (style, offset) in enumerate(zip(clink_list, offsets)): p1 = Pin((0, -i * 0.1 - (j+1)*0.3)) p2 = Pin((1, -i * 0.1 - (j+1)*0.3)) clink = CLinkBrush(style, color='r', roundness=0.05, offsets=offset) ei = clink >> (p1, p2) if j == 0: ei.text('CLinkBrush', 'top') p1.text('"%s", %s'%(style, str(offset)), 'left')
Example #9
Source File: image.py From eht-imaging with GNU General Public License v3.0 | 6 votes |
def load_txt(fname, polrep='stokes', pol_prim=None, pulse=ehc.PULSE_DEFAULT, zero_pol=True): """Read in an image from a text file. Args: fname (str): path to input text file pulse (function): The function convolved with the pixel values for continuous image. polrep (str): polarization representation, either 'stokes' or 'circ' pol_prim (str): The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for Circular zero_pol (bool): If True, loads any missing polarizations as zeros Returns: (Image): loaded image object """ return ehtim.io.load.load_im_txt(fname, pulse=pulse, polrep=polrep, pol_prim=pol_prim, zero_pol=True)
Example #10
Source File: yolo_image.py From ai-platform with MIT License | 6 votes |
def draw_boxes(filename, v_boxes, v_labels, v_scores, output_photo_name): # load the image data = pyplot.imread(filename) # plot the image pyplot.imshow(data) # get the context for drawing boxes ax = pyplot.gca() # plot each box for i in range(len(v_boxes)): box = v_boxes[i] # get coordinates y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax # calculate width and height of the box width, height = x2 - x1, y2 - y1 # create the shape rect = Rectangle((x1, y1), width, height, fill=False, color='white') # draw the box ax.add_patch(rect) # draw text and score in top left corner label = "%s (%.3f)" % (v_labels[i], v_scores[i]) pyplot.text(x1, y1, label, color='white') # show the plot #pyplot.show() pyplot.savefig(output_photo_name)
Example #11
Source File: main.py From WxConn with MIT License | 6 votes |
def draw(self): self.title_canvas = tk.Canvas(self, bg=self.bgcolor, width=width, height=90, bd=0, highlightthickness=0, relief='ridge') self.title_pic = self._resize_ads_qrcode(RES_APP_TITLE, size=(260, 90)) self.title_canvas.create_image(0, 0, anchor='nw', image=self.title_pic) self.title_canvas.pack(padx=35, pady=15) self.qrcode = tk.Canvas(self, bg=self.bgcolor, width=200, height=200) #self.qrcode_pic = self._resize_ads_qrcode('qrcode.png', size=(200, 200)) #self.qrcode.create_image(0, 0, anchor='nw', image=self.qrcode_pic) self.qrcode.pack(pady=30) # 提示 self.lable_tip = tk.Label(self, text='请稍等', # 标签的文字 bg=self.bgcolor, # 背景颜色 font=('楷体',12), # 字体和字体大小 width=15, height=2 # 标签长宽 ) self.lable_tip.pack(pady=2,fill=tk.BOTH) # 固定窗口位置
Example #12
Source File: feature_show.py From person-reid-lib with MIT License | 6 votes |
def plot_embedding(data, label, title): ids = np.unique(label) label_color = label.copy() for i, label_id in enumerate(ids): label_color[label_color==label_id] = i x_min, x_max = np.min(data, 0), np.max(data, 0) data = (data - x_min) / (x_max - x_min) fig = plt.figure() ax = plt.subplot(111) for i in range(data.shape[0]): plt.text(data[i, 0], data[i, 1], str(label[i]), color=plt.cm.Set1(label_color[i] / 10.), fontdict={'weight': 'bold', 'size': 9}) plt.xticks([]) plt.yticks([]) plt.title(title) plt.show() return fig
Example #13
Source File: visual_callbacks.py From squeezenet-keras with MIT License | 6 votes |
def update(self, conf_mat, classes, normalize=False): """This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ plt.imshow(conf_mat, interpolation='nearest', cmap=self.cmap) plt.title(self.title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) if normalize: conf_mat = conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis] thresh = conf_mat.max() / 2. for i, j in itertools.product(range(conf_mat.shape[0]), range(conf_mat.shape[1])): plt.text(j, i, conf_mat[i, j], horizontalalignment="center", color="white" if conf_mat[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') plt.draw()
Example #14
Source File: main.py From WxConn with MIT License | 5 votes |
def update_to_pre_login_success(self): self.lable_tip.config(text='登录成功,请稍等') self.lable_tip.update()
Example #15
Source File: test_brush.py From viznet with MIT License | 5 votes |
def test_ghz(): num_bit = 4 handler = QuantumCircuit(num_bit=4) basic = NodeBrush('qc.basic') C = NodeBrush('qc.C') NOT = NodeBrush('qc.NOT', size='small') END = NodeBrush('qc.end') M = NodeBrush('qc.measure') BOX = NodeBrush('qc.box') with DynamicShow() as ds: handler.x += 0.5 handler.gate(basic, 0, 'X') for i in range(1, num_bit): handler.gate(basic, i, 'H') handler.x += 1 handler.gate((C, NOT), (1, 0)) handler.gate((C, NOT), (3, 2)) handler.x += 0.7 handler.gate((C, NOT), (2, 0)) handler.x += 0.7 handler.gate((C, NOT), (3, 2)) handler.x += 1 for i in range(num_bit): handler.gate(basic, i, 'H') handler.x += 1 handler.gate(BOX, (0, 1, 2, 3), '$e^{-iHt}$') handler.x += 1 for i in range(num_bit): handler.gate(M, i) handler.edge.ls = '=' handler.x += 0.8 for i in range(num_bit): handler.gate(END, i) # text |0>s for i in range(num_bit): plt.text(-0.4, -i, r'$\vert0\rangle_{Q_%d}$' % i, va='center', ha='center', fontsize=18)
Example #16
Source File: main.py From WxConn with MIT License | 5 votes |
def update_progress(self, value): text = '正在统计连接数...{value}%' self.lable_progress.config(text=text.format(value=value)) self.lable_progress.update() self.progress.config(value=value) self.progress.update()
Example #17
Source File: main.py From WxConn with MIT License | 5 votes |
def update_generating(self): text = '生成结果中,请稍后' self.lable_progress.config(text=text) self.lable_progress.update()
Example #18
Source File: phaseest.py From viznet with MIT License | 5 votes |
def qft(): num_bit = 5 num_bit2 = 3 with DynamicShow((7, 5), '_phaseest.png') as ds: handler = QuantumCircuit(num_bit=num_bit+num_bit2, y0=2., locs=-np.append(0.8*np.arange(num_bit), 0.9*num_bit+0.3*np.arange(num_bit2))) handler.x += 0.7 with handler.block(slice(0, num_bit-1), pad_x=0.2, pad_y=0.2) as boxes: [handler.gate(_.GATE, i, 'H') for i in range(num_bit)] boxes[0].text("A", 'top') handler.x +=1.2 _.BOX.size = (0.4, 0.3) with handler.block(slice(0, num_bit+num_bit2-1),pad_x=0.2, pad_y=0.2) as boxes: for i in range(num_bit): handler.gate((_.C, _.BOX), (i, slice(num_bit,num_bit+num_bit2-1)), ['', r'$U^{%d}$'%(2**i)]) if i!=num_bit-1: handler.x +=1.0 boxes[0].text("B", 'top') handler.x +=0.7 for i in range(num_bit2): handler.gate(viznet.NodeBrush('pin'), num_bit+i) handler.x +=0.7 _.BOX.size = (0.6, 0.3) handler.gate(_.BOX, slice(0, num_bit-1), r'QFT${}^\dagger$') handler.x +=1.0 for i in range(num_bit): handler.gate(_.END, i) # text |0>s for i in range(num_bit+num_bit2): plt.text(*handler.get_position(i, x=-0.5), r'$\left\vert %s\right\rangle_{Q_%d}$' % (0 if i<num_bit else '?', i+1), va='center', ha='center', fontsize=16) plt.text(3.5, -6, "Quantum Circuit for Phase Estimation", ha='center')
Example #19
Source File: ghz.py From viznet with MIT License | 5 votes |
def ghz4(): '''4 bit GHZ circuit, applicable on ibmqx4 circuit.''' num_bit = 4 with DynamicShow((5, 3), '_exact_ghz4_circuit.png') as ds: handler = QuantumCircuit(num_bit=4, y0=2.) handler.x += 0.5 handler.gate(_.GATE, 0, 'X') for i in range(1, num_bit): handler.gate(_.GATE, i, 'H') handler.x += 1 handler.gate((_.C, _.NOT), (1, 0)) handler.gate((_.C, _.NOT), (3, 2)) handler.x += 0.7 handler.gate((_.C, _.NOT), (2, 0)) handler.x += 0.7 handler.gate((_.C, _.NOT), (3, 2)) handler.x += 1 for i in range(num_bit): handler.gate(_.GATE, i, 'H') handler.x += 1 for i in range(num_bit): handler.gate(_.MEASURE, i) handler.edge.style = '=' handler.x += 0.8 for i in range(num_bit): handler.gate(_.END, i) # text |0>s for i in range(num_bit): plt.text(*handler.get_position(i, x=-0.5), r'$\left\vert0\right\rangle_{Q_%d}$' % i, va='center', ha='center', fontsize=18)
Example #20
Source File: differentiable.py From viznet with MIT License | 5 votes |
def diffcircuit(): '''illustration of block-focus scheme.''' num_bit = 4 depth = 3 with DynamicShow((10, 4), '_differentiable.png') as ds: plt.text(5, -4, "Differentiable Circuit of Size 4, depth 2") handler = QuantumCircuit(num_bit=num_bit) handler.x += 1.0 with handler.block(slice(0, num_bit-1), pad_x=0.2, pad_y=0.1) as b: rot_gate(handler, 1, mask=[0,1,1]) b[0].text('Rotation', 'top') handler.x += 1.0 # entangler block with handler.block(slice(0, num_bit-1), pad_x=0, pad_y = 0.1) as b: entangle_gate(handler) b[0].text('Entangler', 'top') for i in range(depth-2): handler.x += 1.0 rot_gate(handler, 2, mask=[1,1,1]) handler.x += 1.0 entangle_gate(handler) handler.x += 1.0 rot_gate(handler, 3, mask=[1,1,0]) handler.x += 1.0 for i in range(num_bit): handler.gate(_.MEASURE, i) handler.edge.style = '=' handler.x += 0.8 for i in range(num_bit): handler.gate(_.END, i) # text |0>s for i in range(num_bit): plt.text(-0.5, -i, r'$\left\vert0\right\rangle$' , va='center', ha='center', fontsize=16)
Example #21
Source File: qft.py From viznet with MIT License | 5 votes |
def qft(): num_bit = 5 with DynamicShow((10, 3.5), '_qft.png') as ds: handler = QuantumCircuit(num_bit=num_bit, y0=2.) handler.x += 0.7 for i in range(num_bit): if i ==1: context = handler.block(slice(i, num_bit-1), pad_y=0.1, pad_x=0.2) boxes = context.__enter__() for k in range(i, num_bit): if i==k: handler.gate(_.GATE, i, 'H') else: if i ==0 and k==1+2: context = handler.block(slice(i, k), pad_y=0.1, pad_x = 0.15) boxes = context.__enter__() handler.gate((_.C, _.WIDE), (k, i), ['',r'R${}_%d$'%(k-i+1)]) if i ==0 and k==1+2: context.__exit__(None, None, None) boxes[0].text("A", "top") if i==1 and k==num_bit-1: context.__exit__(None, None, None) boxes[0].text("B", "top") handler.x+=1.0 handler.x+=0.2 for i in range(num_bit): handler.gate(_.END, i) # text |0>s for i in range(num_bit): plt.text(*handler.get_position(i, x=-0.5), r'$\left\vert ?\right\rangle_{Q_%d}$' % (i+1), va='center', ha='center', fontsize=16) plt.text(8.5, -3, "Quantum Fourier Transformation circuit of size 5", ha='center')
Example #22
Source File: main.py From WxConn with MIT License | 5 votes |
def update_qrcode(self): self.lable_tip.config(text='微信扫一扫开始统计') self.lable_tip.update() self.qrcode_pic = self._resize_ads_qrcode(ALS.user_qrcode_path, size=(200, 200)) self.qrcode.create_image(0, 0, anchor='nw', image=self.qrcode_pic)
Example #23
Source File: main.py From WxConn with MIT License | 5 votes |
def _anim_button_emoji(self): """ 动态改变button中emoji """ if self.anim_button_running: button_text = HAPPY_EMOJI[random.randint(0, len(HAPPY_EMOJI) - 1)] self.btn_enter.config(text=button_text) self.btn_enter.update() self.master.after(1000, self._anim_button_emoji)
Example #24
Source File: main.py From WxConn with MIT License | 5 votes |
def generate_city_pic(self, city_data): """ 生成城市数据图片 因为plt在子线程中执行会出现自动弹出弹框并阻塞主线程的行为,plt行为均放在主线程中 :param data: :return: """ font = {'family': ['xkcd', 'Humor Sans', 'Comic Sans MS'], 'weight': 'bold', 'size': 12} matplotlib.rc('font', **font) cities = city_data['cities'] city_people = city_data['city_people'] # 绘制「性别分布」柱状图 plt.barh(range(len(cities)), width=city_people, align='center', color=self.bar_color, alpha=0.8) # 添加轴标签 plt.xlabel(u'Number of People') # 添加标题 plt.title(u'Top %d Cities of your friends distributed' % len(cities), fontsize=self.title_font_size) # 添加刻度标签 plt.yticks(range(len(cities)), cities) # 设置X轴的刻度范围 plt.xlim([0, city_people[0] * 1.1]) # 为每个条形图添加数值标签 for x, y in enumerate(city_people): plt.text(y + len(str(y)), x, y, ha='center') # 显示图形 plt.savefig(ALS.result_path + '/4.png') # todo 如果调用此处的关闭,就会导致应用本身也被关闭 # plt.close() # plt.show()
Example #25
Source File: main.py From WxConn with MIT License | 5 votes |
def generate_sex_pic(self, sex_data): """ 生成性别数据图片 因为plt在子线程中执行会出现自动弹出弹框并阻塞主线程的行为,plt行为均放在主线程中 :param sex_data: :return: """ # 绘制「性别分布」柱状图 # 'steelblue' bar_figure = plt.bar(range(3), sex_data, align='center', color=self.bar_color, alpha=0.8) # 添加轴标签 plt.ylabel(u'Number') # 添加标题 plt.title(u'Male/Female in your Wechat', fontsize=self.title_font_size) # 添加刻度标签 plt.xticks(range(3), [u'Male', u'Female', u'UnKnown']) # 设置Y轴的刻度范围 # 0, male; 1, female; 2, unknown max_num = max(sex_data[0], max(sex_data[1], sex_data[2])) plt.ylim([0, max_num * 1.1]) # 为每个条形图添加数值标签 for x, y in enumerate(sex_data): plt.text(x, y + len(str(y)), y, ha='center') # 保存图片 plt.savefig(ALS.result_path + '/2.png') # todo 如果不调用此处的关闭,就会导致生成最后一个图像出现折叠、缩小的混乱 #bar_figure.remove() plt.clf() plt.delaxes() #plt.close() # 显示图形 # plt.show()
Example #26
Source File: plot_cm.py From Deep-Learning-with-TensorFlow-Second-Edition with MIT License | 5 votes |
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') print(cm) thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') # Compute confusion matrix
Example #27
Source File: classification_demo.py From Deep-Learning-with-TensorFlow-Second-Edition with MIT License | 5 votes |
def print_and_plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') return cm # Plot non-normalized confusion matrix
Example #28
Source File: utils.py From MCF-3D-CNN with MIT License | 5 votes |
def plot_confusion_matrix(cm, classes, save_tag = '', normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') print(cm) plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=0) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") # plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') plt.savefig('experiments/img/'+ save_tag + '_cfm.png') plt.close('all') # 关闭图
Example #29
Source File: link_prediction_utils.py From EDeN with MIT License | 5 votes |
def show_graph(g, vertex_color='typeof', size=15, vertex_label=None): """show_graph.""" degrees = [len(g.neighbors(u)) for u in g.nodes()] print(('num nodes=%d' % len(g))) print(('num edges=%d' % len(g.edges()))) print(('num non edges=%d' % len(list(nx.non_edges(g))))) print(('max degree=%d' % max(degrees))) print(('median degree=%d' % np.percentile(degrees, 50))) draw_graph(g, size=size, vertex_color=vertex_color, vertex_label=vertex_label, vertex_size=200, edge_label=None) # display degree distribution size = int((max(degrees) - min(degrees)) / 1.5) plt.figure(figsize=(size, 3)) plt.title('Degree distribution') _bins = np.arange(min(degrees), max(degrees) + 2) - .5 n, bins, patches = plt.hist(degrees, _bins, alpha=0.3, facecolor='navy', histtype='bar', rwidth=0.8, edgecolor='k') labels = np.array([str(int(i)) for i in n]) for xi, yi, label in zip(bins, n, labels): plt.text(xi + 0.5, yi, label, ha='center', va='bottom') plt.xticks(bins + 0.5) plt.xlim((min(degrees) - 1, max(degrees) + 1)) plt.ylim((0, max(n) * 1.1)) plt.xlabel('Node degree') plt.ylabel('Counts') plt.grid(linestyle=":") plt.show()
Example #30
Source File: main.py From WxConn with MIT License | 5 votes |
def done(self): text = '统计成功\n请在微信"文件传输助手"查看' self.lable_progress.config(text=text) self.lable_progress.update()