def venue_menu(self):
"""
Menu for venue visualizations.
"""
print (u"\n░▒▓█ ■ Select a visualization of the inter-venue semantic distances.\n")
print (u"░▒▓█ (1) 30 Venue Heatmap - top 30 venues")
print (u"░▒▓█ (2) 30 Venue MDS - top 30 venues")
print (u"░▒▓█ (3) 600 Venue MDS - top 600 venues")
print (u"░▒▓█ (4) MDS - top 40 venues from each of top 5 categories")
print (u"░▒▓█ (5) Temporal view. - top 2 venues from top 5 categories")
print (u"░▒▓█ (6) ■ back to main menu ■\n")
pick_vis = get_input("> ", int, 1, 6)
if pick_vis is 1:
print u"\n░░▒▒▓█ ■ You must close graph in order to continue using program."
self.driver.vis_heatmap(self.driver.dist_matrix, [ven.name for ven in self.driver.vens])
self.venue_menu()
elif pick_vis is 2:
print u"\n░░▒▒▓█ ■ You must close graph in order to continue using program."
self.driver.vis_MDS(self.driver.dist_matrix, [ven.name for ven in self.driver.vens])
self.venue_menu()
elif pick_vis is 3:
print u"\n░░▒▒▓█ ■ You must close graph in order to continue using program."
super_dist_matrix = self.driver.compare_venues(self.driver.vens)
self.driver.vis_super_MDS(super_dist_matrix)
self.venue_menu()
elif pick_vis is 4:
print u"\n░░▒▒▓█ ■ You must close graph in order to continue using program."
self.driver.vis_super_cat_MDS(5)
self.venue_menu()
elif pick_vis is 5:
cat_list, ven_list = sq.venues_from_top_n_categories(2, 5)
for v in ven_list:
self.driver.temporal_weekday_single_ven(v.id)
self.venue_menu()
elif pick_vis is 6:
self.main_menu()
def vis_super_cat_MDS(self, n=5, fout=None):
"""
Displays MDS graph of venues in the top n categories
:param n: number of top categories to include
:type n: int
:param fout: save graph to file
:type fout: str
:return: None
:rtype: None
"""
category_list, venue_list = sq.venues_from_top_n_categories(200 / n, n)
ven_dmatrix = self.compare_venues(venue_list)
# make dict to assign numbers to category names for coloring in graph
category_name2num = {}
for i, category_name in enumerate(category_list):
category_name2num[category_name] = i
with plt.style.context("ggplot"):
# setup plot figure
fig, ax = plt.subplots(subplot_kw=dict(axisbg="#EEEEEE"))
ax.grid(color="white", linestyle="solid", linewidth=2)
fig = plt.gcf()
fig.set_dpi(100)
fig.set_size_inches((8.0, 8.0), forward=True)
plt.subplots_adjust(left=0.10, bottom=0.10, right=0.95, top=0.95)
plt.gca().grid(True)
plt.axis([-1.0, 1.0, -1.0, 1.0])
ttl = plt.title("MDS: Venues from Top {} Categories".format(n))
plt.xlabel("Click on dots to toggle labels.\nDot size depends on total # of shouts.")
# get MDS coordinates for venues
myMDS = MDS(2, verbose=0, n_jobs=-1, dissimilarity="precomputed")
myMDS.fit(ven_dmatrix)
points = myMDS.embedding_
# add column to points to hold venue categories
# TODO: higher-level categories from Foursquare's categories json
points = np.c_[points, np.zeros(len(points))]
# for each venue, normalize category number between 0-1 and store in new column.
for i, point in enumerate(points):
point[2] = float(category_name2num[venue_list[i].cat_name]) / len(category_list)
# plot the points
x_vals = points[:, 0]
y_vals = points[:, 1]
point_colors = points[:, 2]
sizes = [(30 * np.sqrt(ven.shout_count)) for ven in venue_list]
annotes = [u"{name}\n{category}".format(name=v.name, category=v.cat_name) for v in venue_list]
my_plot = plt.scatter(
x_vals,
y_vals,
marker="o",
facecolor=point_colors,
s=sizes,
cmap=plt.get_cmap("prism"),
edgecolor="#303030",
linewidth=0.5,
alpha=0.45,
)
# connect mouse-click action
af = AnnoteFinder(x_vals, y_vals, annotes)
plt.connect("button_press_event", af)
# adjust tick labels
plt.tick_params(axis="both", which="major", labelsize=7, color="gray")
plt.tick_params(axis="both", which="minor", labelsize=7, color="gray")
# turn off ticks
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if fout is None:
plt.show()
else:
fig.savefig(fout)
