我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用seaborn.diverging_palette()。
def correlation(self, vec_col, method="pearson"): """ Compute the correlation matrix for the input dataset of Vectors using the specified method. Method mapped from pyspark.ml.stat.Correlation. :param vec_col: The name of the column of vectors for which the correlation coefficient needs to be computed. This must be a column of the dataset, and it must contain Vector objects. :param method: String specifying the method to use for computing correlation. Supported: pearson (default), spearman. :return: Heatmap plot of the corr matrix using seaborn. """ assert isinstance(method, str), "Error, method argument provided must be a string." assert method == 'pearson' or ( method == 'spearman'), "Error, method only can be 'pearson' or 'sepearman'." cor = Correlation.corr(self._df, vec_col, method).head()[0].toArray() return sns.heatmap(cor, mask=np.zeros_like(cor, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True))
def plot_heatmaps(img_arr, img_names, titles, heatmaps, labels, out_dir): # construct cmap pal = sns.diverging_palette(240, 10, n=30, center="dark") my_cmap = ListedColormap(sns.color_palette(pal).as_hex()) min_val, max_val = np.min(heatmaps), np.max(heatmaps) for j, (img, img_name, h_map, title, y) in enumerate(zip(img_arr, img_names, heatmaps, titles, labels)): fig, ax = plt.subplots() img = np.transpose(img, (1, 2, 0)) plt.clf() plt.imshow(img, cmap='Greys', interpolation='bicubic') plt.imshow(h_map, cmap=my_cmap, alpha=0.7, interpolation='nearest') #, vmin=-.05, vmax=.05) plt.colorbar() plt.axis('off') plt.title(title) class_name = CLASSES[y] class_dir = make_sub_dir(out_dir, class_name) plt.savefig(join(class_dir, img_name), bbox_inches='tight', dpi=300)
def sns_triangle(matrix, plt_title, only_class=None): sns.set(style="white") # Generate a mask for the upper triangle mask = np.zeros_like(matrix, dtype=np.bool) mask[np.triu_indices_from(mask)] = True # Set up the matplotlib figure f, ax = subplots(figsize=(11, 9)) # Generate a custom diverging colormap cmap = sns.diverging_palette(220, 10, as_cmap=True) # Draw the heatmap with the mask and correct aspect ratio sns.heatmap(matrix.as_matrix(), mask=mask, cmap=cmap, vmax=.3, square=True, xticklabels=5, yticklabels=5, linewidths=.5, cbar_kws={"shrink": .5}, ax=ax) title(plt_title) xlabel('Preprocessed Features') ylabel('Preprocessed Features') if only_class is None: only_class = '' savefig('images/triangle'+only_class+'.png')
def plot_corr_heatmap(corr, labels, heading): sns.set(style="white") # Generate a mask for the upper triangle mask = np.zeros_like(corr, dtype=np.bool) mask[np.triu_indices_from(mask)] = True # Set up the matplotlib figure f, ax = plt.subplots(figsize=(8, 8)) # Generate a custom diverging colormap cmap = sns.diverging_palette(220, 10, as_cmap=True) # Draw the heatmap with the mask and correct aspect ratio sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, square=True, xticklabels=labels, yticklabels=labels, linewidths=.5, ax=ax, cbar_kws={"shrink": .5}, annot=True) ax.set_title(heading) plt.show()
def plot_difference_matrix(fname, confmat1, confmat2, target_names, title='', cmap='Blues', perc=True,figsize=[5,4],cbar=True, **kwargs): """Plot Confusion Matrix.""" figsize = deepcopy(figsize) if cbar == False: figsize[0] = figsize[0] - 0.6 cm1 = confmat1 cm2 = confmat2 cm1 = 100 * cm1.astype('float') / cm1.sum(axis=1)[:, np.newaxis] cm2 = 100 * cm2.astype('float') / cm2.sum(axis=1)[:, np.newaxis] cm = cm2 - cm1 cm_eye = np.zeros_like(cm) cm_eye[np.eye(len(cm_eye), dtype=bool)] = cm.diagonal() df = pd.DataFrame(data=cm_eye, columns=target_names, index=target_names) plt.figure(figsize=figsize) g = sns.heatmap(df, annot=cm, fmt=".1f" , linewidths=.5, vmin=-10, vmax=10, cmap='coolwarm_r',annot_kws={"size": 13},cbar=cbar,**kwargs)#sns.diverging_palette(20, 220, as_cmap=True)) g.set_title(title) g.set_ylabel('True sleep stage',fontdict={'fontsize' : 12, 'fontweight':'bold'}) g.set_xlabel('Predicted sleep stage',fontdict={'fontsize' : 12, 'fontweight':'bold'}) plt.tight_layout() g.figure.savefig(os.path.join('plots', fname))
def dendrogram(df, number_of_clusters=int(df.shape[1] / 1.2)): # Create Dendrogram agglomerated_features = FeatureAgglomeration(n_clusters=number_of_clusters) used_networks = np.arange(0, number_of_clusters, dtype=int) # Create a custom palette to identify the networks network_pal = sns.cubehelix_palette(len(used_networks), light=.9, dark=.1, reverse=True, start=1, rot=-2) network_lut = dict(zip(map(str, df.columns), network_pal)) # Convert the palette to vectors that will be drawn on the side of the matrix networks = df.columns.get_level_values(None) network_colors = pd.Series(networks, index=df.columns).map(network_lut) sns.set(font="monospace") # Create custom colormap cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True) cg = sns.clustermap(df.astype(float).corr(), cmap=cmap, linewidths=.5, row_colors=network_colors, col_colors=network_colors) plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0) plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(), rotation=90) plt.show()
def __init__(self, parent, data, labels, width=6, height=6, dpi=100): figure = Figure(figsize=(width, height), dpi=dpi, tight_layout=True) axes = figure.add_subplot(111) super(CorrelationPlot, self).__init__(figure) self.setParent(parent) sns.set(style="darkgrid") corr = data # cmap = sns.diverging_palette(220, 10, as_cmap=True) # corrplot(data, names=labels, annot=True, sig_stars=False, # diag_names=True, cmap=cmap, ax=axes, cbar=True) df = pd.DataFrame(data=data, columns=labels) corr = df.corr() # Generate a mask for the upper triangle mask = np.zeros_like(corr, dtype=np.bool) mask[np.triu_indices_from(mask)] = True # Draw the heatmap with the mask and correct aspect ratio vmax = np.abs(corr.values[~mask]).max() # vmax = np.abs(corr).max() sns.heatmap(corr, mask=mask, cmap=plt.cm.PuOr, vmin=-vmax, vmax=vmax, square=True, linecolor="lightgray", linewidths=1, ax=axes) for i in range(len(corr)): axes.text(i + 0.5, i + 0.5, corr.columns[i], ha="center", va="center", rotation=0) for j in range(i + 1, len(corr)): s = "{:.3f}".format(corr.values[i, j]) axes.text(j + 0.5, i + 0.5, s, ha="center", va="center") axes.axis("off") # If uncommented, fills widget self.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) self.updateGeometry() self.setMinimumSize(self.size())
def radiocolorf(freq): """Replace radio code with color.""" ffreq = (float(freq) - 1.0) / (45.0 - 1.0) pal = sns.diverging_palette(200, 60, l=80, as_cmap=True, center="dark") return rgb2hex(pal(ffreq))
def dendrogram(df, number_of_clusters, agglomerated_feature_labels): import seaborn as sns # Todo: Create Dendrogram # used networks are the labels occuring in agglomerated_features.labels_ # which corresponds to np.arange(0, number_of_clusters) # number_of_clusters = int(df.shape[1] / 1.2) # used_networks = np.arange(0, number_of_clusters, dtype=int) used_networks = np.unique(agglomerated_feature_labels) # used_networks = [1, 5, 6, 7, 8, 11, 12, 13, 16, 17] # In our case all columns are clustered, which means used_columns is true in every element # used_columns = (df.columns.get_level_values(None) # .astype(int) # .isin(used_networks)) # used_columns = (agglomerated_feature_labels.astype(int).isin(used_networks)) # df = df.loc[:, used_columns] # Create a custom palette to identify the networks network_pal = sns.cubehelix_palette(len(used_networks), light=.9, dark=.1, reverse=True, start=1, rot=-2) network_lut = dict(zip(map(str, df.columns), network_pal)) # Convert the palette to vectors that will be drawn on the side of the matrix networks = df.columns.get_level_values(None) # networks = agglomerated_feature_labels network_colors = pd.Series(networks, index=df.columns).map(network_lut) # plt.figure() # cg = sns.clustermap(df, metric="correlation") # plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0) sns.set(font="monospace") # Create custom colormap cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True) cg = sns.clustermap(df.astype(float).corr(), cmap=cmap, linewidths=.5, row_colors=network_colors, col_colors=network_colors) plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0) plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(), rotation=90) # plt.xticks(rotation=90) plt.show()
def plot_corrmat(in_csv, out_file=None): import seaborn as sn sn.set(style="whitegrid") dataframe = pd.read_csv(in_csv, index_col=False, na_values='n/a', na_filter=False) colnames = dataframe.columns.ravel().tolist() for col in ['subject_id', 'site', 'modality']: try: colnames.remove(col) except ValueError: pass # Correlation matrix corr = dataframe[colnames].corr() corr = corr.dropna((0,1), 'all') # Generate a mask for the upper triangle mask = np.zeros_like(corr, dtype=np.bool) mask[np.triu_indices_from(mask)] = True # Generate a custom diverging colormap cmap = sn.diverging_palette(220, 10, as_cmap=True) # Draw the heatmap with the mask and correct aspect ratio corrplot = sn.clustermap(corr, cmap=cmap, center=0., method='average', square=True, linewidths=.5) plt.setp(corrplot.ax_heatmap.yaxis.get_ticklabels(), rotation='horizontal') # , mask=mask, square=True, linewidths=.5, cbar_kws={"shrink": .5}) if out_file is None: out_file = 'corr_matrix.svg' fname, ext = op.splitext(out_file) if ext[1:] not in ['pdf', 'svg', 'png']: ext = '.svg' out_file = fname + '.svg' corrplot.savefig(out_file, format=ext[1:], bbox_inches='tight', pad_inches=0, dpi=100) return corrplot
def radiocolorf(freq): ffreq = (float(freq) - 1.0)/(45.0 - 1.0) pal = sns.diverging_palette(200, 60, l=80, as_cmap=True, center="dark") return rgb2hex(pal(ffreq))
def __init__(self, echonet): assert isinstance(echonet, EchoNet) self.echonet = echonet self.loss = [] self.train_score = [] self.validation_score = [] self.test_score = [] self.start_time = None # self.f = plt.figure(figsize=(12, 12)) self.cmap = sb.diverging_palette(220, 10, as_cmap=True)
def vis_activation(self, tweet, grads=False, activations=True, over_words=True, over_units=False): pred = self.predict(tweet) act_grad_matrix, layer_labels, text_labels = self._get_activations_gradients(tweet, grads, activations, over_words, over_units) plt.figure(figsize=(14,4)) cmap = sns.diverging_palette(220, 20, n=7) ax = sns.heatmap(act_grad_matrix, xticklabels=text_labels, yticklabels=layer_labels, cmap=cmap) ax.xaxis.tick_top() plt.yticks(rotation=0) plt.xticks(rotation=90) plt.title('Score:%s'%pred['score'].values[0]) plt.show()
def plot_2_corr_heatmaps(corr1, corr2, labels, title1, title2): fig=plt.figure(figsize=(9, 8)) gs = gridspec.GridSpec(1, 2) ax1 = fig.add_subplot(gs[0, 0]) ax2 = fig.add_subplot(gs[0, 1]) sns.set(style="white") # Generate a mask for the upper triangle mask = np.zeros_like(corr1, dtype=np.bool) mask[np.triu_indices_from(mask)] = True # Generate a custom diverging colormap cmap = sns.diverging_palette(220, 10, as_cmap=True) # Draw the heatmap with the mask and correct aspect ratio sns.heatmap(corr1, mask=mask, cmap=cmap, vmax=.3, square=True, xticklabels=labels, yticklabels=labels, linewidths=.5, ax=ax1, cbar_kws={"shrink": .3}, annot=True) ax1.set_title(title1) sns.heatmap(corr2, mask=mask, cmap=cmap, vmax=.3, square=True, xticklabels=labels, yticklabels=labels, linewidths=.5, ax=ax2, cbar_kws={"shrink": .3}, annot=True) ax2.set_title(title2) fig.tight_layout() plt.show() ############################################################################### # Attribution ###############################################################################
def heatmap(sources, refs, trans, actions, idx, atten=None, savefig=True, name='test', info=None, show=False): source = [s.strip() for s in sources[idx].decode('utf8').replace('@@', '--').split()] + ['||'] target = ['*'] + [s.strip() for s in trans[idx].decode('utf8').replace('@@', '--').split()] + ['||'] action = actions[idx] if atten: attention = numpy.array(atten[idx]) def track(acts, data, annote): x, y = 0, 0 for a in acts: x += a y += 1 - a # print a, x, y, target[x].encode('utf8') data[y, x] = 1 annote[y, x] = 'W' if a == 0 else 'C' return data, annote # print target data = numpy.zeros((len(source), len(target))) annote = numpy.chararray(data.shape, itemsize=8) annote[:] = '' data, annote = track(action, data, annote) data[0, 0] = 1 annote[0, 0] = 'S' if atten: data[:-1, 1:] += attention.T d = pd.DataFrame(data=data, columns=target, index=source) # p = sns.diverging_palette(220, 10, as_cmap=True) f, ax = plot.subplots(figsize=(11, 11)) f.set_canvas(plot.gcf().canvas) g = sns.heatmap(d, ax=ax, annot=annote, fmt='s') g.xaxis.tick_top() plot.xticks(rotation=90) plot.yticks(rotation=0) # plot.show() if savefig: if not os.path.exists('.images/C_{}'.format(name)): os.mkdir('.images/C_{}'.format(name)) filename = 'Idx={}||'.format(info['index']) for w in info: if w is not 'index': filename += '.{}={:.2f}'.format(w, float(info[w])) print 'saving...' f.savefig('.images/C_{}'.format(name) + '/{}'.format(filename) + '.pdf', dpi=100) if show: plot.show() print 'plotting done.' plot.close()
