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