def agglo_logit_calc(Xps1, Yps1, nonmusic_subreddits):
""" Handles fitting and scoring of the agglomeration->logistic regression
machine learning scheme.
"""
Xps1 = Xps1.toarray()
logit = LogisticRegression()
(n_samples_1, _) = Xps1.shape
n_folds = 4
rand = 0
kf = KFold(n_samples_1, n_folds=n_folds, shuffle=True, random_state=rand)
logit_1 = 0.0
logit_20 = 0.0
n_lo = 1
n_hi = 155
step = 1
n_groups_gen = range(n_lo, n_hi + 1, step)
agglo_1s = [0.0 for _ in n_groups_gen]
agglo_20s = [0.0 for _ in n_groups_gen]
params = np.empty([len(n_groups_gen), n_folds], dtype=object)
logit_params = []
for i_fold, (train, test) in enumerate(kf):
print i_fold
logit.fit(Xps1[train], Yps1[train])
logit_params.append(logit.coef_)
logit_1 += 100.0 * logit.score(Xps1[test], Yps1[test])
(Xps20_test, Yps20_test) = prune_sparse_samples(Xps1[test], Yps1[test], threshold=20)
(Xps20_test, Yps20_test) = balance_data(Xps20_test, Yps20_test)
logit_20 += 100.0 * logit.score(Xps20_test, Yps20_test)
for j, n_groups in enumerate(n_groups_gen):
agglo = phi_agglomerate(N=n_groups).fit(Xps1[train], Yps1[train])
Xagglo_train_1, _ = agglo.transform(Xps1[train])
Xagglo_test_1, _ = agglo.transform(Xps1[test])
Xagglo_test_20, _ = agglo.transform(Xps20_test)
logit.fit(Xagglo_train_1, Yps1[train])
params[j][i_fold] = logit.coef_
agglo_1s[j] += 100.0 * logit.score(Xagglo_test_1, Yps1[test]) / n_folds
agglo_20s[j] += 100.0 * logit.score(Xagglo_test_20, Yps20_test) / n_folds
logit_1 /= n_folds
logit_20 /= n_folds
return (n_lo, n_hi, logit_1, logit_20, n_groups_gen, agglo_1s, agglo_20s, params, logit_params)
def graph_music_taste(Xps1, Yps1, nonmusic_subreddits, n_groups=20, node_cut=2000, edge_cut=0.15):
""" Creates a graph of connected subreddits, colour-coded by which rank
they come under.
Keyword args:
node_cut - # fans who need to post in a subreddit for it to be included
in visualisation
edge_cut - # weakest edge that will be included in visualisation
"""
(n_samples, n_features) = Xps1.shape
pickle_fn = "pickles/agglo_graph.pickle"
try:
with open(pickle_fn, "r") as graph_file:
g = pickle.load(graph_file)
except IOError:
print "No pickle of graph. Constructing."
Xps1 = Xps1.toarray()
(Xps1_agglo, sub_group) = phi_agglomerate(N=n_groups).fit(Xps1, Yps1).transform(Xps1)
coefs = LogisticRegression().fit(Xps1_agglo, Yps1).coef_[0]
colors = get_color_rgba(coefs)
# Create mask to only deal with subreddits above a threshold size
sub_size = Xps1.sum(axis=0)
# Create connections array to obtain number of users linking two arrays
n_connections = np.zeros([n_features, n_features], dtype=int)
for i_fan in range(n_samples):
subs = np.nonzero(Xps1[i_fan])[0]
for sub1, sub2 in combinations(subs, r=2):
n_connections[sub1, sub2] += 1
# Make vertices and assign properties
g = Graph(directed=False)
verts = g.add_vertex(n=n_features)
verts = list(verts)
sub_name = g.new_vertex_property("string")
group = g.new_vertex_property("int")
group_colour = g.new_vertex_property("vector<double>")
sub_size_v = g.new_vertex_property("float")
for i_vert in range(n_features):
sub_name[verts[i_vert]] = nonmusic_subreddits[i_vert]
group[verts[i_vert]] = sub_group[i_vert]
group_colour[verts[i_vert]] = colors[sub_group[i_vert]]
sub_size_v[verts[i_vert]] = sub_size[i_vert]
# Make edges and assign properties
connections = g.new_edge_property("int")
group_av_colour = g.new_edge_property("vector<double>")
group_av = g.new_edge_property("int")
for a, b in combinations(range(n_features), r=2):
e = g.add_edge(verts[a], verts[b])
connections[e] = n_connections[a][b]
group_av[e] = (sub_group[a] + sub_group[b]) / 2
group_av_colour[e] = colors[group_av[e]]
# Make all properties internal for pickling
g.vertex_properties["sub_name"] = sub_name
g.vertex_properties["sub_size"] = sub_size_v
g.vertex_properties["group"] = group
g.vertex_properties["group_colour"] = group_colour
g.edge_properties["connections"] = connections
g.edge_properties["group_av"] = group_av
g.edge_properties["group_color"] = group_av_colour
with open(pickle_fn, "w") as graph_file:
pickle.dump(g, graph_file)
# Mask small subreddits (less than node_cut users)
# Take log of subreddit size for size representations
vertex_filter = g.new_vertex_property("bool")
g.vp.sub_size_log = g.new_vertex_property("float")
biggest = 0
for vert in g.vertices():
vertex_filter[vert] = g.vp.sub_size[vert] > node_cut
g.vp.sub_size_log[vert] = np.log(g.vp.sub_size[vert])
# Track biggest node to use as root
if g.vp.sub_size[vert] > biggest:
root_vert = vert
biggest = g.vp.sub_size[vert]
g.set_vertex_filter(vertex_filter)
# Mask weakest edges (weight less than edge_cut)
# Divide through # connections to make line thickness
g.ep.line_thickness = g.new_edge_property("float")
g.ep.line_thick_log = g.new_edge_property("float")
edge_weight_threshold = g.new_edge_property("bool")
for edge in g.edges():
g.ep.line_thickness[edge] = g.ep.connections[edge] * 0.003
# g.ep.line_thick_log[edge] = np.log(g.ep.connections[edge])
a = edge.source()
b = edge.target()
edge_weight = min(
float(g.ep.connections[edge]) / g.vp.sub_size[a], float(g.ep.connections[edge]) / g.vp.sub_size[b]
)
edge_weight_threshold[edge] = edge_weight > edge_cut
g.set_edge_filter(edge_weight_threshold)
# Mask nodes with no edges (needs to converge)
for vert in g.vertices():
if len(list(vert.all_edges())) == 0:
vertex_filter[vert] = False
g.set_vertex_filter(vertex_filter)
pos = radial_tree_layout(g, root_vert)
graph_draw(
g,
pos=pos,
output_size=(1000, 800),
output="README_figs/top_subreddits_graph.svg",
vertex_font_size=10,
vertex_text=g.vp.sub_name,
vertex_fill_color=g.vp.group_colour,
vertex_size=g.vp.sub_size_log,
edge_pen_width=g.edge_properties.line_thickness,
edge_color=g.edge_properties.group_color,
)
