def ex1():
"""
Minimal example.
"""
test_string_1 = "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua."
test_string_2 = "At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
# tokenize words (approximately at least):
sets = []
for string in [test_string_1, test_string_2]:
# get a word list
words = string.split(' ')
# remove non alphanumeric characters
words = [''.join(ch for ch in word if ch.isalnum()) for word in words]
# convert to all lower case
words = [word.lower() for word in words]
sets.append(set(words))
# create visualisation
venn2_wordcloud(sets)
return
def ex3():
"""
Answer to
http://stackoverflow.com/questions/42812083/auto-venn-diagram-text-rendering/42839350#42839350
"""
just_dem = [
"sincerely", "women", "service", "newsletter", "program", "families",
"community", "funding", "important", "million", "department"
]
dem_and_rep = [
"country", "make", "support", "state", "people", "jobs", "American",
"care", "health", "president", "work", "veterans", "tax", "survey",
"years", "need", "economy"
]
just_rep = [
"security", "nation", "Obama", "energy", "law", "spending", "budget",
"states", "committee", "passed", "job", "business"
]
dem = just_dem + dem_and_rep
rep = just_rep + dem_and_rep
def color_func(word, *args, **kwargs):
if word in just_dem:
# return "#000080" # navy blue
return "#0000ff" # blue1
elif word in just_rep:
# return "#8b0000" # red4
return "#ff0000" # red1
else:
return "#0f0f0f" # gray6 (aka off-black)
words = just_dem + dem_and_rep + just_rep
# for testing, assign random word frequencies;
# frequencies = np.random.rand(len(words))
# frequencies /= np.sum(frequencies)
# word frequencies follow Zipf's law;
# words will probably be within the 10k most frequent words;
# however, we will probably exclude the 1000 most common words from analysis;
frequencies = 1. / np.arange(1000, 10000) # Zipf's law with alpha = 1
frequencies = np.random.choice(frequencies, size=len(words))
word_to_frequency = dict(zip(words, frequencies))
fig, ax = plt.subplots(1, 1)
ax.set_title("Congress says what?", fontsize=36)
venn2_wordcloud([set(dem), set(rep)],
set_labels=["Democrats", "Republicans"],
set_edgecolors=['b', 'r'],
word_to_frequency=word_to_frequency,
wordcloud_kwargs=dict(color_func=color_func,
relative_scaling=.5),
ax=ax)
return
def get_classification_results(attrs):
timestamp = time.time()
df = get_data_from_files()
df['processed'] = PreprocessTransformer(attrs).transform(df['text'])
pipeline = make_pipeline(VectorTransformer(attrs),
DecompositionTransformer(attrs))
features = pipeline.transform(df['processed'])
X_train, X_test, y_train, y_test = train_test_split(features,
df['label'],
test_size=0.3,
random_state=42,
stratify=df['label'])
results = []
for algorithm in attrs['algorithms']:
model, y_pred, y_prob = ClassifierSelector(attrs, algorithm,
X_test).transform(
X_train, y_train)
result = {
'Name': classifier_names[algorithm],
'Acc. (Train)': '{:.4f}'.format(model.score(X_train, y_train)),
'Acc. (Test)': '{:.4f}'.format(model.score(X_test, y_test)),
}
# update for multiclass/binary specific
result = update_binary(result, y_test, y_pred, model.classes_[1]) \
if attrs['labelsType'] == 'binary' \
else update_multiclass(result, y_test, y_pred)
results.append(result)
make_plots(y_test, y_pred, y_prob, algorithm, timestamp)
# venn word diagram
sets = []
for label in df['label'].unique():
docs = df[df['label'] == label]['processed'].values
text = ' '.join(docs)
counter = Counter(text.split())
sets.append(set([item[0] for item in counter.most_common(80)]))
venn2_wordcloud(sets)
plt.savefig('static/img/venn/venn_words-{}.png'.format(timestamp), dpi=200)
plt.close('all')
return results, timestamp
def draw_word_venn(datasets, dataset_name1, dataset_name2):
fig, ax = plt.subplots(1, 1, figsize=(30, 30))
set1 = set(get_names_from_files(datasets[dataset_name1]))
set2 = set(get_names_from_files(datasets[dataset_name2]))
v = venn2_wordcloud([set1, set2], (dataset_name1, dataset_name2), ax=ax, set_colors=['blue', 'yellow'])
for text in v.set_labels:
text.set_fontsize(30)
for text in v.subset_labels:
text.set_fontsize(18)
plt.show()
def ex6():
"""
Issue #5:
https://github.com/paulbrodersen/matplotlib_venn_wordcloud/issues/5
Allow user to specify max_font_size/min_font_size.
"""
test_string_1 = "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua."
test_string_2 = "At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
# tokenize words (approximately at least):
sets = []
for string in [test_string_1, test_string_2]:
# get a word list
words = string.split(' ')
# remove non alphanumeric characters
words = [''.join(ch for ch in word if ch.isalnum()) for word in words]
# convert to all lower case
words = [word.lower() for word in words]
sets.append(set(words))
# create visualisation
fig, axes = plt.subplots(2, 2, figsize=(20, 20))
ax2, ax3, ax4, ax5 = axes.ravel()
# These paramater values hould have no effect as the given min and
# max font size should be much larger than WC would want them to
# be anyway (negative control).
venn2_wordcloud(sets,
wordcloud_kwargs=dict(max_font_size=1000, min_font_size=0),
ax=ax2)
ax2.set_title('max_font_size and min_font_size outside range')
# Positive controls
venn2_wordcloud(sets, wordcloud_kwargs=dict(max_font_size=50), ax=ax3)
ax3.set_title('max_font_size=50')
venn2_wordcloud(sets, wordcloud_kwargs=dict(min_font_size=30), ax=ax4)
ax4.set_title('min_font_size=30')
venn2_wordcloud(sets,
wordcloud_kwargs=dict(max_font_size=50, min_font_size=30),
ax=ax5)
ax5.set_title('max_font_size=50, min_font_size=30')
def ex4():
"""
Issue #2:
https://github.com/paulbrodersen/matplotlib_venn_wordcloud/issues/2
"""
from matplotlib import pyplot as plt
from matplotlib_venn_wordcloud import venn2_wordcloud
x = {'sincerely', 'department', 'usa', 'usa nation'}
y = {'sincerely', 'security', 'usa democracy'}
s = (x, y)
v = venn2_wordcloud(s)
def ex5():
"""
Issue #4:
https://github.com/paulbrodersen/matplotlib_venn_wordcloud/issues/4
Handle non-overlapping sets gracefully.
"""
from matplotlib import pyplot as plt
from matplotlib_venn_wordcloud import venn2_wordcloud
x = set('abcd')
y = set('efgh')
s2 = (x, y)
v2 = venn2_wordcloud(s2)
z = set('ijkl')
s3 = (x, y, z)
v3 = venn3_wordcloud(s3)