def kernel_to_heatmap(K, title, max_scale, save=True): # Generate a mask for the upper triangle #mask = np.triu(np.ones_like(K, dtype=np.bool)) # Generate a custom diverging colormap cmap = "YlGn" rotation = 0 x = [0.5, 1.5, 2.5] name_dict = get_name_dict() handles = name_dict.values() # Draw the heatmap with the mask and correct aspect ratio sns.heatmap( K, #mask=mask, cmap=cmap, vmax=max_scale, #center=0, square=True, linewidths=.75, cbar_kws={ 'shrink': .75, 'label': 'Kernel values' }, xticklabels=True, yticklabels=True, annot=True, ) sns.set(font_scale=2) sns.set(style="white") plt.xticks(x, handles, rotation=rotation) plt.yticks(x, handles, rotation=0) plt.title(title) if save: filename = clean_filename(title, 'png', plot_directory) print(f'saving plot to {filename}') plt.savefig(filename) plt.show()
def array_to_matrix(arr): mat = np.zeros(shape=(3, 3)) k = 0 for i in range(3): for j in range(i, 3): if i == j: mat[i][j] = 1.0 else: mat[i][j] = arr[k] mat[j][i] = mat[i][j] k += 1 return mat if __name__ == '__main__': t0 = time.time() name_dict = get_name_dict() stemmer_flag = False max_ngram = 2 # Load tweets from csv files names = list() handles = get_handles() for handle in handles: names.append(name_dict[handle]) raw_corpus, raw_labels = load_all_tweets() corpus, labels = iterate_preprocess(raw_corpus, raw_labels, handles, stemmer_flag) for ngram in range(1, max_ngram + 1): # loop through ngrams