Python matplotlib.pyplot.figure() Examples
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Example #1
Source File: __init__.py From EDeN with MIT License | 11 votes |
def plot_confusion_matrix(y_true, y_pred, size=None, normalize=False): """plot_confusion_matrix.""" cm = confusion_matrix(y_true, y_pred) fmt = "%d" if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] fmt = "%.2f" xticklabels = list(sorted(set(y_pred))) yticklabels = list(sorted(set(y_true))) if size is not None: plt.figure(figsize=(size, size)) heatmap(cm, xlabel='Predicted label', ylabel='True label', xticklabels=xticklabels, yticklabels=yticklabels, cmap=plt.cm.Blues, fmt=fmt) if normalize: plt.title("Confusion matrix (norm.)") else: plt.title("Confusion matrix") plt.gca().invert_yaxis()
Example #2
Source File: dataset.py From neural-combinatorial-optimization-rl-tensorflow with MIT License | 8 votes |
def visualize_2D_trip(self, trip): plt.figure(figsize=(30,30)) rcParams.update({'font.size': 22}) # Plot cities plt.scatter(trip[:,0], trip[:,1], s=200) # Plot tour tour=np.array(list(range(len(trip))) + [0]) X = trip[tour, 0] Y = trip[tour, 1] plt.plot(X, Y,"--", markersize=100) # Annotate cities with order labels = range(len(trip)) for i, (x, y) in zip(labels,(zip(X,Y))): plt.annotate(i,xy=(x, y)) plt.xlim(0,100) plt.ylim(0,100) plt.show() # Heatmap of permutations (x=cities; y=steps)
Example #3
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 8 votes |
def plot_wh_methods(): # from utils.utils import *; plot_wh_methods() # Compares the two methods for width-height anchor multiplication # https://github.com/ultralytics/yolov3/issues/168 x = np.arange(-4.0, 4.0, .1) ya = np.exp(x) yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 fig = plt.figure(figsize=(6, 3), dpi=150) plt.plot(x, ya, '.-', label='yolo method') plt.plot(x, yb ** 2, '.-', label='^2 power method') plt.plot(x, yb ** 2.5, '.-', label='^2.5 power method') plt.xlim(left=-4, right=4) plt.ylim(bottom=0, top=6) plt.xlabel('input') plt.ylabel('output') plt.legend() fig.tight_layout() fig.savefig('comparison.png', dpi=200)
Example #4
Source File: util.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 8 votes |
def compute_roc(y_true, y_pred, plot=False): """ TODO :param y_true: ground truth :param y_pred: predictions :param plot: :return: """ fpr, tpr, _ = roc_curve(y_true, y_pred) auc_score = auc(fpr, tpr) if plot: plt.figure(figsize=(7, 6)) plt.plot(fpr, tpr, color='blue', label='ROC (AUC = %0.4f)' % auc_score) plt.legend(loc='lower right') plt.title("ROC Curve") plt.xlabel("FPR") plt.ylabel("TPR") plt.show() return fpr, tpr, auc_score
Example #5
Source File: dataset.py From neural-combinatorial-optimization-rl-tensorflow with MIT License | 7 votes |
def visualize_2D_trip(self,trip,tw_open,tw_close): plt.figure(figsize=(30,30)) rcParams.update({'font.size': 22}) # Plot cities colors = ['red'] # Depot is first city for i in range(len(tw_open)-1): colors.append('blue') plt.scatter(trip[:,0], trip[:,1], color=colors, s=200) # Plot tour tour=np.array(list(range(len(trip))) + [0]) X = trip[tour, 0] Y = trip[tour, 1] plt.plot(X, Y,"--", markersize=100) # Annotate cities with TW tw_open = np.rint(tw_open) tw_close = np.rint(tw_close) time_window = np.concatenate((tw_open,tw_close),axis=1) for tw, (x, y) in zip(time_window,(zip(X,Y))): plt.annotate(tw,xy=(x, y)) plt.xlim(0,60) plt.ylim(0,60) plt.show() # Heatmap of permutations (x=cities; y=steps)
Example #6
Source File: __init__.py From EDeN with MIT License | 7 votes |
def plot_roc_curve(y_true, y_score, size=None): """plot_roc_curve.""" false_positive_rate, true_positive_rate, thresholds = roc_curve( y_true, y_score) if size is not None: plt.figure(figsize=(size, size)) plt.axis('equal') plt.plot(false_positive_rate, true_positive_rate, lw=2, color='navy') plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--') plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.ylim([-0.05, 1.05]) plt.xlim([-0.05, 1.05]) plt.grid() plt.title('Receiver operating characteristic AUC={0:0.2f}'.format( roc_auc_score(y_true, y_score)))
Example #7
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 7 votes |
def plot_evolution_results(hyp): # from utils.utils import *; plot_evolution_results(hyp) # Plot hyperparameter evolution results in evolve.txt x = np.loadtxt('evolve.txt', ndmin=2) f = fitness(x) weights = (f - f.min()) ** 2 # for weighted results fig = plt.figure(figsize=(12, 10)) matplotlib.rc('font', **{'size': 8}) for i, (k, v) in enumerate(hyp.items()): y = x[:, i + 5] # mu = (y * weights).sum() / weights.sum() # best weighted result mu = y[f.argmax()] # best single result plt.subplot(4, 5, i + 1) plt.plot(mu, f.max(), 'o', markersize=10) plt.plot(y, f, '.') plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters print('%15s: %.3g' % (k, mu)) fig.tight_layout() plt.savefig('evolve.png', dpi=200)
Example #8
Source File: helper.py From Stock-Price-Prediction with MIT License | 7 votes |
def plot_mul(Y_hat, Y, pred_len): """ PLots the predicted data versus true data Input: Predicted data, True Data, Length of prediction Output: return plot Note: Run from timeSeriesPredict.py """ fig = plt.figure(facecolor='white') ax = fig.add_subplot(111) ax.plot(Y, label='Y') # Print the predictions in its respective series-length for i, j in enumerate(Y_hat): shift = [None for p in range(i * pred_len)] plt.plot(shift + j, label='Y_hat') plt.legend() plt.show()
Example #9
Source File: inference.py From mmdetection with Apache License 2.0 | 6 votes |
def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)): """Visualize the detection results on the image. Args: model (nn.Module): The loaded detector. img (str or np.ndarray): Image filename or loaded image. result (tuple[list] or list): The detection result, can be either (bbox, segm) or just bbox. score_thr (float): The threshold to visualize the bboxes and masks. fig_size (tuple): Figure size of the pyplot figure. """ if hasattr(model, 'module'): model = model.module img = model.show_result(img, result, score_thr=score_thr, show=False) plt.figure(figsize=fig_size) plt.imshow(mmcv.bgr2rgb(img)) plt.show()
Example #10
Source File: test_bayestar.py From dustmaps with GNU General Public License v2.0 | 6 votes |
def atest_plot_samples(self): dm = np.linspace(4., 19., 1001) samples = [] for dm_k in dm: d = 10.**(dm_k/5.-2.) samples.append(self._interp_ebv(self._test_data[0], d)) samples = np.array(samples).T # print samples import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(1,1,1) for s in samples: ax.plot(dm, s, lw=2., alpha=0.5) plt.show()
Example #11
Source File: massachusetts_road_segm.py From Recipes with MIT License | 6 votes |
def plot_some_results(pred_fn, test_generator, n_images=10): fig_ctr = 0 for data, seg in test_generator: res = pred_fn(data) for d, s, r in zip(data, seg, res): plt.figure(figsize=(12, 6)) plt.subplot(1, 3, 1) plt.imshow(d.transpose(1,2,0)) plt.title("input patch") plt.subplot(1, 3, 2) plt.imshow(s[0]) plt.title("ground truth") plt.subplot(1, 3, 3) plt.imshow(r) plt.title("segmentation") plt.savefig("road_segmentation_result_%03.0f.png"%fig_ctr) plt.close() fig_ctr += 1 if fig_ctr > n_images: break
Example #12
Source File: zipf_law.py From pyhanlp with Apache License 2.0 | 6 votes |
def plot(token_counts, title='MSR语料库词频统计', ylabel='词频'): from matplotlib import pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 fig = plt.figure( # figsize=(8, 6) ) ax = fig.add_subplot(111) token_counts = list(zip(*token_counts)) num_elements = np.arange(len(token_counts[0])) top_offset = max(token_counts[1]) + len(str(max(token_counts[1]))) ax.set_title(title) ax.set_xlabel('词语') ax.set_ylabel(ylabel) ax.xaxis.set_label_coords(1.05, 0.015) ax.set_xticks(num_elements) ax.set_xticklabels(token_counts[0], rotation=55, verticalalignment='top') ax.set_ylim([0, top_offset]) ax.set_xlim([-1, len(token_counts[0])]) rects = ax.plot(num_elements, token_counts[1], linewidth=1.5) plt.show()
Example #13
Source File: plotting.py From medicaldetectiontoolkit with Apache License 2.0 | 6 votes |
def __init__(self, cf): self.file_name = cf.plot_dir + '/monitor_{}'.format(cf.fold) self.exp_name = cf.fold_dir self.do_validation = cf.do_validation self.separate_values_dict = cf.assign_values_to_extra_figure self.figure_list = [] for n in range(cf.n_monitoring_figures): self.figure_list.append(plt.figure(figsize=(10, 6))) self.figure_list[-1].ax1 = plt.subplot(111) self.figure_list[-1].ax1.set_xlabel('epochs') self.figure_list[-1].ax1.set_ylabel('loss / metrics') self.figure_list[-1].ax1.set_xlim(0, cf.num_epochs) self.figure_list[-1].ax1.grid() self.figure_list[0].ax1.set_ylim(0, 1.5) self.color_palette = ['b', 'c', 'r', 'purple', 'm', 'y', 'k', 'tab:gray']
Example #14
Source File: test.py From MomentumContrast.pytorch with MIT License | 6 votes |
def show(mnist, targets, ret): target_ids = range(len(set(targets))) colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k', 'violet', 'orange', 'purple'] plt.figure(figsize=(12, 10)) ax = plt.subplot(aspect='equal') for label in set(targets): idx = np.where(np.array(targets) == label)[0] plt.scatter(ret[idx, 0], ret[idx, 1], c=colors[label], label=label) for i in range(0, len(targets), 250): img = (mnist[i][0] * 0.3081 + 0.1307).numpy()[0] img = OffsetImage(img, cmap=plt.cm.gray_r, zoom=0.5) ax.add_artist(AnnotationBbox(img, ret[i])) plt.legend() plt.show()
Example #15
Source File: plot_part1.py From cs294-112_hws with MIT License | 6 votes |
def plot_13(data): r1, r2, r3, r4 = data plt.figure() add_plot(r3, 'MeanReward100Episodes'); add_plot(r3, 'BestMeanReward', 'gamma = 0.9'); add_plot(r2, 'MeanReward100Episodes'); add_plot(r2, 'BestMeanReward', 'gamma = 0.99'); add_plot(r4, 'MeanReward100Episodes'); add_plot(r4, 'BestMeanReward', 'gamma = 0.999'); plt.legend(); plt.xlabel('Time step'); plt.ylabel('Reward'); plt.savefig( os.path.join('results', 'p13.png'), bbox_inches='tight', transparent=True, pad_inches=0.1 )
Example #16
Source File: recall.py From mmdetection with Apache License 2.0 | 6 votes |
def plot_iou_recall(recalls, iou_thrs): """Plot IoU-Recalls curve. Args: recalls(ndarray or list): shape (k,) iou_thrs(ndarray or list): same shape as `recalls` """ if isinstance(iou_thrs, np.ndarray): _iou_thrs = iou_thrs.tolist() else: _iou_thrs = iou_thrs if isinstance(recalls, np.ndarray): _recalls = recalls.tolist() else: _recalls = recalls import matplotlib.pyplot as plt f = plt.figure() plt.plot(_iou_thrs + [1.0], _recalls + [0.]) plt.xlabel('IoU') plt.ylabel('Recall') plt.axis([iou_thrs.min(), 1, 0, 1]) f.show()
Example #17
Source File: recall.py From mmdetection with Apache License 2.0 | 6 votes |
def plot_num_recall(recalls, proposal_nums): """Plot Proposal_num-Recalls curve. Args: recalls(ndarray or list): shape (k,) proposal_nums(ndarray or list): same shape as `recalls` """ if isinstance(proposal_nums, np.ndarray): _proposal_nums = proposal_nums.tolist() else: _proposal_nums = proposal_nums if isinstance(recalls, np.ndarray): _recalls = recalls.tolist() else: _recalls = recalls import matplotlib.pyplot as plt f = plt.figure() plt.plot([0] + _proposal_nums, [0] + _recalls) plt.xlabel('Proposal num') plt.ylabel('Recall') plt.axis([0, proposal_nums.max(), 0, 1]) f.show()
Example #18
Source File: plot_part1.py From cs294-112_hws with MIT License | 6 votes |
def plot_12(data): r1, r2, r3, r4 = data plt.figure() add_plot(r1, 'MeanReward100Episodes'); add_plot(r1, 'BestMeanReward', 'vanilla DQN'); add_plot(r2, 'MeanReward100Episodes'); add_plot(r2, 'BestMeanReward', 'double DQN'); plt.xlabel('Time step'); plt.ylabel('Reward'); plt.legend(); plt.savefig( os.path.join('results', 'p12.png'), bbox_inches='tight', transparent=True, pad_inches=0.1 )
Example #19
Source File: util.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
def compute_roc_rfeinman(probs_neg, probs_pos, plot=False): """ TODO :param probs_neg: :param probs_pos: :param plot: :return: """ probs = np.concatenate((probs_neg, probs_pos)) labels = np.concatenate((np.zeros_like(probs_neg), np.ones_like(probs_pos))) fpr, tpr, _ = roc_curve(labels, probs) auc_score = auc(fpr, tpr) if plot: plt.figure(figsize=(7, 6)) plt.plot(fpr, tpr, color='blue', label='ROC (AUC = %0.4f)' % auc_score) plt.legend(loc='lower right') plt.title("ROC Curve") plt.xlabel("FPR") plt.ylabel("TPR") plt.show() return fpr, tpr, auc_score
Example #20
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 6 votes |
def plot_images(imgs, targets, paths=None, fname='images.jpg'): # Plots training images overlaid with targets imgs = imgs.cpu().numpy() targets = targets.cpu().numpy() # targets = targets[targets[:, 1] == 21] # plot only one class fig = plt.figure(figsize=(10, 10)) bs, _, h, w = imgs.shape # batch size, _, height, width bs = min(bs, 16) # limit plot to 16 images ns = np.ceil(bs ** 0.5) # number of subplots for i in range(bs): boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T boxes[[0, 2]] *= w boxes[[1, 3]] *= h plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0)) plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-') plt.axis('off') if paths is not None: s = Path(paths[i]).name plt.title(s[:min(len(s), 40)], fontdict={'size': 8}) # limit to 40 characters fig.tight_layout() fig.savefig(fname, dpi=200) plt.close()
Example #21
Source File: simulate_sin.py From deep-learning-note with MIT License | 6 votes |
def run_eval(sess, test_X, test_y): ds = tf.data.Dataset.from_tensor_slices((test_X, test_y)) ds = ds.batch(1) X, y = ds.make_one_shot_iterator().get_next() with tf.variable_scope("model", reuse=True): prediction, _, _ = lstm_model(X, [0.0], False) predictions = [] labels = [] for i in range(TESTING_EXAMPLES): p, l = sess.run([prediction, y]) predictions.append(p) labels.append(l) predictions = np.array(predictions).squeeze() labels = np.array(labels).squeeze() rmse = np.sqrt(((predictions-labels) ** 2).mean(axis=0)) print("Mean Square Error is: %f" % rmse) plt.figure() plt.plot(predictions, label='predictions') plt.plot(labels, label='real_sin') plt.legend() plt.show()
Example #22
Source File: dataset.py From neural-combinatorial-optimization-rl-tensorflow with MIT License | 6 votes |
def visualize_sampling(self, permutations): max_length = len(permutations[0]) grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0 transposed_permutations = np.transpose(permutations) for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t city_indices, counts = np.unique(cities_t,return_counts=True,axis=0) for u,v in zip(city_indices, counts): grid[t][u]+=v # update grid with counts from the batch of permutations # plot heatmap fig = plt.figure() rcParams.update({'font.size': 22}) ax = fig.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(grid, interpolation='nearest', cmap='gray') plt.colorbar() plt.title('Sampled permutations') plt.ylabel('Time t') plt.xlabel('City i') plt.show()
Example #23
Source File: plot_lfads.py From DOTA_models with Apache License 2.0 | 5 votes |
def plot_priors(): g0s_prior_mean_bxn = train_modelvals['prior_g0_mean'] g0s_prior_var_bxn = train_modelvals['prior_g0_var'] g0s_post_mean_bxn = train_modelvals['posterior_g0_mean'] g0s_post_var_bxn = train_modelvals['posterior_g0_var'] plt.figure(figsize=(10,4), tight_layout=True); plt.subplot(1,2,1) plt.hist(g0s_post_mean_bxn.flatten(), bins=20, color='b'); plt.hist(g0s_prior_mean_bxn.flatten(), bins=20, color='g'); plt.title('Histogram of Prior/Posterior Mean Values') plt.subplot(1,2,2) plt.hist((g0s_post_var_bxn.flatten()), bins=20, color='b'); plt.hist((g0s_prior_var_bxn.flatten()), bins=20, color='g'); plt.title('Histogram of Prior/Posterior Log Variance Values') plt.figure(figsize=(10,10), tight_layout=True) plt.subplot(2,2,1) plt.imshow(g0s_prior_mean_bxn.T, interpolation='nearest', cmap='jet') plt.colorbar(fraction=0.025, pad=0.04) plt.title('Prior g0 means') plt.subplot(2,2,2) plt.imshow(g0s_post_mean_bxn.T, interpolation='nearest', cmap='jet') plt.colorbar(fraction=0.025, pad=0.04) plt.title('Posterior g0 means'); plt.subplot(2,2,3) plt.imshow(g0s_prior_var_bxn.T, interpolation='nearest', cmap='jet') plt.colorbar(fraction=0.025, pad=0.04) plt.title('Prior g0 variance Values') plt.subplot(2,2,4) plt.imshow(g0s_post_var_bxn.T, interpolation='nearest', cmap='jet') plt.colorbar(fraction=0.025, pad=0.04) plt.title('Posterior g0 variance Values') plt.figure(figsize=(10,5)) plt.stem(np.sort(np.log(g0s_post_mean_bxn.std(axis=0)))); plt.title('Log standard deviation of h0 means');
Example #24
Source File: plot_lfads.py From DOTA_models with Apache License 2.0 | 5 votes |
def plot_lfads(train_bxtxd, train_model_vals, train_ext_input_bxtxi=None, train_truth_bxtxd=None, valid_bxtxd=None, valid_model_vals=None, valid_ext_input_bxtxi=None, valid_truth_bxtxd=None, bidx=None, cf=1.0, output_dist='poisson'): # Plotting f = plt.figure(figsize=(18,20), tight_layout=True) plot_lfads_timeseries(train_bxtxd, train_model_vals, train_ext_input_bxtxi, truth_bxtxn=train_truth_bxtxd, conversion_factor=cf, bidx=bidx, output_dist=output_dist, col_title='Train') plot_lfads_timeseries(valid_bxtxd, valid_model_vals, valid_ext_input_bxtxi, truth_bxtxn=valid_truth_bxtxd, conversion_factor=cf, bidx=bidx, output_dist=output_dist, subplot_cidx=1, col_title='Valid') # Convert from figure to an numpy array width x height x 3 (last for RGB) f.canvas.draw() data = np.fromstring(f.canvas.tostring_rgb(), dtype=np.uint8, sep='') data_wxhx3 = data.reshape(f.canvas.get_width_height()[::-1] + (3,)) plt.close() return data_wxhx3
Example #25
Source File: vis_utils.py From ACAN with MIT License | 5 votes |
def imshow_rgb(images, nrow, ncol): """ Parameters ---------- images : numpy.ndarray shape [h, w, c] """ h, w, c = images.shape #fig = plt.figure(figsize=(w // 80 * ncol, h // 50 * nrow)) fig = plt.figure() plt.imshow(images) return fig
Example #26
Source File: plot_lfads.py From DOTA_models with Apache License 2.0 | 5 votes |
def _plot_item(W, name, full_name, nspaces): plt.figure() if W.shape == (): print(name, ": ", W) elif W.shape[0] == 1: plt.stem(W.T) plt.title(full_name) elif W.shape[1] == 1: plt.stem(W) plt.title(full_name) else: plt.imshow(np.abs(W), interpolation='nearest', cmap='jet'); plt.colorbar() plt.title(full_name)
Example #27
Source File: plotting.py From medicaldetectiontoolkit with Apache License 2.0 | 5 votes |
def plot_prediction_hist(label_list, pred_list, type_list, outfile): """ plot histogram of predictions for a specific class. :param label_list: list of 1s and 0s specifying whether prediction is a true positive match (1) or a false positive (0). False negatives (missed ground truth objects) are artificially added predictions with score 0 and label 1. :param pred_list: list of prediction-scores. :param type_list: list of prediction-types for stastic-info in title. """ preds = np.array(pred_list) labels = np.array(label_list) title = outfile.split('/')[-1] + ' count:{}'.format(len(label_list)) plt.figure() plt.yscale('log') if 0 in labels: plt.hist(preds[labels == 0], alpha=0.3, color='g', range=(0, 1), bins=50, label='false pos.') if 1 in labels: plt.hist(preds[labels == 1], alpha=0.3, color='b', range=(0, 1), bins=50, label='true pos. (false neg. @ score=0)') if type_list is not None: fp_count = type_list.count('det_fp') fn_count = type_list.count('det_fn') tp_count = type_list.count('det_tp') pos_count = fn_count + tp_count title += ' tp:{} fp:{} fn:{} pos:{}'. format(tp_count, fp_count, fn_count, pos_count) plt.legend() plt.title(title) plt.xlabel('confidence score') plt.ylabel('log n') plt.savefig(outfile) plt.close()
Example #28
Source File: plotting.py From medicaldetectiontoolkit with Apache License 2.0 | 5 votes |
def plot_stat_curves(stats, outfile): for c in ['roc', 'prc']: plt.figure() for s in stats: if s[c] is not None: plt.plot(s[c][0], s[c][1], label=s['name'] + '_' + c) plt.title(outfile.split('/')[-1] + '_' + c) plt.legend(loc=3 if c == 'prc' else 4) plt.xlabel('precision' if c == 'prc' else '1-spec.') plt.ylabel('recall') plt.savefig(outfile + '_' + c) plt.close()
Example #29
Source File: cli.py From tmhmm.py with MIT License | 5 votes |
def plot(posterior_file, outputfile): inside, membrane, outside = load_posterior_file(posterior_file) plt.figure(figsize=(16, 8)) plt.title('Posterior probabilities') plt.suptitle('tmhmm.py') plt.plot(inside, label='inside', color='blue') plt.plot(membrane, label='transmembrane', color='red') plt.fill_between(range(len(inside)), membrane, color='red') plt.plot(outside, label='outside', color='black') plt.legend(frameon=False, bbox_to_anchor=[0.5, 0], loc='upper center', ncol=3, borderaxespad=1.5) plt.tight_layout(pad=3) plt.savefig(outputfile)
Example #30
Source File: plot_utils.py From keras-anomaly-detection with MIT License | 5 votes |
def plot_confusion_matrix(y_true, y_pred): conf_matrix = confusion_matrix(y_true, y_pred) plt.figure(figsize=(12, 12)) sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt="d") plt.title("Confusion matrix") plt.ylabel('True class') plt.xlabel('Predicted class') plt.show()