def findMCF(name):
# FOCUS NAME
focus_name = name
# Cosine distance [True, False]
all = {
'title' : True,
'coauthors' : False,
'institutions' : True,
'journals' : False,
'year' : False,
'subjects' : False,
'keywords' : False,
'ref_authors' : False,
'ref_journals' : True
}
# Graphs [True, False]
graphs = {
"coauthorship" : True,
"references" : True,
"subjects" : False,
"keywords" : False
}
#get objects from data
library,catalog = get_library(focus_name)
#initialize matrices
final_matrix = np.zeros((len(library),len(library)))
mask_matrix = np.identity(len(library))
graph_mat = np.identity(len(library))
#get optimal matrix DISTANCES
mat = 1-get_string_distance_matrix(library,catalog,'author_id','exact_match')
#get mask
mask_matrix = get_string_distance_matrix(library,catalog,'author_name','syllab_jaccard')
#plt.figure(k)
#plt.imshow(mask_matrix, cmap=cm.coolwarm, interpolation='none', vmin=0, vmax=1)
#plt.colorbar()
#get distance matrices aggregated by sum (counter increases each time)
counter = 0
mat_temp = []
mat_list = []
for (k,v) in all.items():
if v:
mat_temp = get_cosine_distance_matrix(library,catalog,k,'cosine',mask_matrix)
"""plt.subplot(140+counter+1)
plt.imshow(mat_temp, cmap=cm.coolwarm, interpolation='none', vmin=0, vmax=1)
plt.title(k)
plt.colorbar()"""
mat_list.append(mat_temp)
counter = counter + 1
#get graph matrices aggregated by max (counter increases only of 1)
graph_mat = None
for (k,v) in graphs.items():
if v:
if graph_mat is None:
graph_mat = np.zeros((len(library), len(library)))
mat_temp = get_graph(library, catalog, focus_name, k)
graph_mat = np.maximum(mat_temp, graph_mat)
if graph_mat is not None:
counter = counter + 1
#get final matrix, apply also mask to graph matrix
mat_list.append(graph_mat * mask_matrix)
"""plt.figure()
plt.imshow(graph_mat, cmap=cm.coolwarm, interpolation='none', vmin=0, vmax=1)
plt.title("Graphs")
plt.colorbar()"""
#IMPORTANT BEFORE CLUSTERING
#normalize final matrix and convert to DISTANCE matrix
final_matrix = 1 - np.median(mat_list, axis=0)
#correct negative values due to floating point precision to zero
final_matrix[final_matrix<0] = 0
#statistics
final_matrix_mean = np.mean(final_matrix[(final_matrix>0)&(final_matrix<1)])
print("Mean", final_matrix_mean)
#print("Median", np.median(final_matrix[final_matrix<2]))
#print("Variance", np.var(final_matrix[final_matrix<2]))
"""final_matrix[final_matrix>0.7] = 1
final_matrix[final_matrix<=0.7] = 0 """
"""plt.figure()
plt.subplot(121)
plt.title("Optimal distance matrix")
plt.imshow(mat, cmap=cm.coolwarm, interpolation='none')
plt.colorbar()
plt.subplot(122)
plt.title("Obtained distance matrix")
plt.imshow(final_matrix, cmap=cm.coolwarm, interpolation='none')
plt.colorbar()"""
#save to file
np.savetxt("distance_matrix_{0}.csv".format(focus_name.split()[0]), final_matrix, fmt="%.2e", delimiter=",")
#plot distance matrices
#Compute optimal clustering
ground_truth = {}
for p1 in library:
flag = 0
if p1.author_id not in ground_truth:
ground_truth[p1.author_id] = []
ground_truth[p1.author_id].append(p1.unique_identifier)
#print("Ground truth: ", str(list(ground_truth.values())))
#Hierarchical clustering on final_mat
#single linkage = minimum spanning tree
#complete linkage, average distance, etc...
#http://pages.cs.wisc.edu/~jerryzhu/cs769/clustering.pdf
vector_distances = squareform(final_matrix, force='tovector', checks=False)
print("Mat shape: {0} Vector shape: {1}".format(final_matrix.shape, vector_distances.shape))
#return if cluster is individual
if len(vector_distances) == 0:
return 1, 0, 1
linkage_matrix = hac.linkage(vector_distances, method='average', metric='euclidean') #single, average
#print("Linkage matrix:\n", linkage_matrix)
relevant_thresholds = np.concatenate(([0],np.array(linkage_matrix[:,2])), axis=0)
year_entropy_history = []
coauthor_entropy_history = []
country_entropy_history = []
subject_entropy_history = []
journal_entropy_history = []
concensus_history = []
within_history = []
between_history = []
variance_history = []
truth_fmeasure = 0
truth_cut = 0
for x in relevant_thresholds:
clusters_list = hac.fcluster(linkage_matrix, x, criterion='distance') #'maxclust'
#mapping structure and evaluation structure
clusters = {}
for k,v in enumerate(clusters_list):
#print(library[k].author_name + " - " + str(library[k].author_id) + " - Cluster: " + str(clusters_list[k]))
#print(k, v)
if v not in clusters:
clusters[v] = []
#like assigning Id to clusters... library[k].unique_identifier would be better
clusters[v].append(library[k].unique_identifier)
#num of clusters for current threshold
num_clusters = len(clusters)
#evaluation
concensus_history.append(concensus(1 -final_matrix, catalog, clusters))
within_history.append(within_clust_sim(1-final_matrix, catalog, clusters))
between_history.append(between_clust_sim(1-final_matrix, catalog, clusters))
#print("Within clusters: {0}".format(within_clust_sim(final_matrix, catalog, clusters)))
#print("GT:", ground_truth)
precision, recall, f_measure = evaluate(clusters.values(), ground_truth.values())
#print("Precision: {0}".format(precision))
#print("Recall: {0}".format(recall))
if f_measure > truth_fmeasure:
truth_fmeasure = f_measure
truth_cut = x
#variance
#best cut
best_cut_index = concensus_history.index(max(concensus_history))
best_cut = relevant_thresholds[best_cut_index]
print("`\nBEst cut found index: {0} Value: {1}".format(best_cut_index,best_cut))
print("Ground truth best cut value {0} Fmeasure: {1}".format(truth_cut, truth_fmeasure))
if np.abs(final_matrix_mean) is np.inf:
final_matrix_mean = 0
#TEST!!!!!!!
return final_matrix_mean, truth_cut, truth_fmeasure
#plot the overall results
plt.figure("Optimal cut representation")
plt.plot(relevant_thresholds, variance_diff, 'r', label="Variance")
#plot clustering according to the best cut
fig, ax = plt.subplots()
fig.canvas.set_window_title('Hierarchical clustering')
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.10, top=0.95)
ax.set_title("Dendrogram")
hac.dendrogram(linkage_matrix, color_threshold=best_cut, orientation='top', leaf_font_size=10,
leaf_label_func=lambda id: library[id].author_name + " - " + str(library[id].author_id))
thismanager = plt.get_current_fig_manager()
thismanager.window.setGeometry(850,45,800, 500)
def main(name):
# FOCUS NAME
focus_name = name
print("\nFOCUS NAME: {0}".format(focus_name))
# Cosine distance [True, False]
all = {
'title' : False,
'coauthors' : False,
'institutions' : True,
'journals' : False,
'year' : False,
'subjects' : False,
'keywords' : False,
'ref_authors' : False,
'ref_journals' : False
}
# Graphs [True, False]
graphs = {
"coauthorship" : True,
"references" : True,
"subjects" : False,
"keywords" : False
}
#get objects from data
library,catalog = get_library(focus_name, DEBUG)
#initialize matrices
final_matrix = np.zeros((len(library),len(library)))
mask_matrix = np.identity(len(library))
graph_mat = np.identity(len(library))
#get optimal matrix DISTANCES
mat = 1 - get_string_distance_matrix(library,catalog,'author_id','exact_match')
#get mask
mask_matrix = get_string_distance_matrix(library,catalog,'author_name','syllab_jaccard')
#plots
"""
fig = plt.figure()
ax = fig.add_subplot(111)
plt.title("Mask with initials", fontdict={'fontsize': 18})
im = ax.imshow(mask_matrix, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
fig.colorbar(im)
return
"""
"""
mat_title = 1 - get_cosine_distance_matrix(library,catalog,'title','cosine',mask_matrix)
mat_coauthors = 1- get_cosine_distance_matrix(library,catalog,'coauthors','cosine',mask_matrix)
mat_institutions = 1-get_cosine_distance_matrix(library,catalog,'institutions','cosine',mask_matrix)
mat_journals = 1-get_cosine_distance_matrix(library,catalog,'journals','cosine',mask_matrix)
mat_year = 1-get_cosine_distance_matrix(library,catalog,'year','cosine',mask_matrix)
mat_subjects = 1-get_cosine_distance_matrix(library,catalog,'subjects','cosine',mask_matrix)
mat_keywords = 1-get_cosine_distance_matrix(library,catalog,'keywords','cosine',mask_matrix)
mat_ref_authors = 1-get_cosine_distance_matrix(library,catalog,'ref_authors','cosine',mask_matrix)
mat_ref_journals = 1-get_cosine_distance_matrix(library,catalog,'ref_journals','cosine',mask_matrix)
"""
"""
fig = plt.figure()
#("Cosine similarity matrices", fontdict={'fontsize': 18})
ax = fig.add_subplot(251)
plt.title("Optimal")
ax.imshow(mat, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(252)
ax.set_title("Title")
ax.imshow(mat_title, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(253)
ax.set_title("Coauthors")
ax.imshow(mat_coauthors, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(254)
ax.set_title("Institutions")
ax.imshow(mat_institutions, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(255)
ax.set_title("Journals")
ax.imshow(mat_journals, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(256)
ax.set_title("Year")
ax.imshow(mat_year, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(257)
ax.set_title("Subjects")
ax.imshow(mat_subjects, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(258)
ax.set_title("Keywords")
ax.imshow(mat_keywords, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(259)
ax.set_title("ReferenceAuthor")
ax.imshow(mat_ref_authors, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(250)
ax.set_title("ReferenceJournal")
ax.imshow(mat_ref_journals, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
return
"""
mat_coauthorship = 1 - (get_graph(library, catalog, focus_name, 'coauthorship') * mask_matrix)
mat_references = 1 - (get_graph(library, catalog, focus_name, 'references') * mask_matrix)
"""
fig = plt.figure()
ax = fig.add_subplot(121)
ax.set_title("Coauthorship")
ax.imshow(mat_coauthorship, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(122)
ax.set_title("References")
ax.imshow(mat_references, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
return
"""
#get distance matrices aggregated by sum (counter increases each time)
counter = 0
mat_temp = []
mat_list = []
for (k,v) in all.items():
if v:
mat_temp = get_cosine_distance_matrix(library,catalog,k,'cosine',mask_matrix)
"""plt.subplot(250+counter+2)
plt.imshow(mat_temp, cmap=cm.coolwarm, interpolation='none', vmin=0, vmax=1)
plt.title(k)
plt.colorbar()"""
mat_list.append(mat_temp)
counter = counter + 1
#get graph matrices aggregated by max (counter increases only of 1)
graph_mat = None
for (k,v) in graphs.items():
#mat_temp = get_graph(library, catalog, focus_name, k)
#mat_list.append(mat_temp)
#counter = counter + 1
if v:
if graph_mat is None:
graph_mat = np.zeros((len(library), len(library)))
mat_temp = get_graph(library, catalog, focus_name, k)
graph_mat = np.maximum(mat_temp, graph_mat)
if graph_mat is not None:
counter = counter + 1
#get final matrix, apply also mask to graph matrix
mat_list.append(graph_mat * mask_matrix)
"""plt.figure()
plt.imshow(graph_mat, cmap=cm.coolwarm, interpolation='none', vmin=0, vmax=1)
plt.title("Graphs")
plt.colorbar()"""
#IMPORTANT BEFORE CLUSTERING
#normalize final matrix and convert to DISTANCE matrix
final_matrix = 1 - np.median(mat_list, axis=0)
#correct negative values due to floating point precision to zero
final_matrix[final_matrix<0] = 0
#print(final_matrix)
if final_matrix.size == 0 or final_matrix.size == 1:
print("Only one paper found.")
return
#else:
# #print("Number of papers: {0}".format(len(library)))
# #print("Number of authors: {0}".format(len(set([x.author_id for x in library]))))
#statistics
if DEBUG:
print("Mean", np.mean(final_matrix[final_matrix<1]))
print("Median", np.median(final_matrix[final_matrix<1]))
print("Variance", np.var(final_matrix[final_matrix<1]))
#save to file
np.savetxt("distance_matrix_{0}.csv".format(focus_name.split()[0]), final_matrix, fmt="%.2e", delimiter=",")
#plot distance matrices
if plots:
plt.figure()
plt.subplot(121)
plt.title("Optimal distance matrix")
plt.imshow(mat, cmap=cm.GnBu, interpolation='none')
plt.colorbar()
plt.subplot(122)
plt.title("Obtained distance matrix")
plt.imshow(final_matrix, cmap=cm.GnBu, interpolation='none')
plt.colorbar()
thismanager = plt.get_current_fig_manager()
thismanager.window.setGeometry(15,45,800, 500)
#other plots for the report
"""fig = plt.figure()
ax = fig.add_subplot(151)
ax.set_title("Optimal", fontdict={'fontsize': 15})
ax.imshow(mat, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(152)
ax.set_title("Institutions", fontdict={'fontsize': 15})
ax.imshow(mat_institutions, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(153)
ax.set_title("Coauthorship", fontdict={'fontsize': 15})
ax.imshow(mat_coauthorship, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(154)
ax.set_title("References", fontdict={'fontsize': 15})
ax.imshow(mat_references, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax = fig.add_subplot(155)
ax.set_title("Aggregated", fontdict={'fontsize': 15})
im = ax.imshow(final_matrix, cmap=cm.GnBu, interpolation='none', vmin=0, vmax=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
return
"""
#Compute optimal clustering
ground_truth = {}
for p1 in library:
flag = 0
if p1.author_id not in ground_truth:
ground_truth[p1.author_id] = []
ground_truth[p1.author_id].append(p1.unique_identifier)
#print("Ground truth: ", str(list(ground_truth.values())))
#Hierarchical clustering on final_mat
#single linkage = minimum spanning tree
#complete linkage, average distance, etc...
#http://pages.cs.wisc.edu/~jerryzhu/cs769/clustering.pdf
vector_distances = squareform(final_matrix, force='tovector', checks=False)
#print("Mat shape: {0} Vector shape: {1}".format(final_matrix.shape, vector_distances.shape))
linkage_matrix = hac.linkage(vector_distances, method='average', metric='euclidean') #single, average
#print("Linkage matrix:\n", linkage_matrix)
relevant_thresholds = np.concatenate(([0],np.array(linkage_matrix[:,2]), [1]), axis=0)
year_entropy_history = []
coauthor_entropy_history = []
country_entropy_history = []
subject_entropy_history = []
journal_entropy_history = []
concensus_history = []
within_history = []
between_history = []
variance_history = []
truth_fmeasure = 0
truth_cut = 0
for x in relevant_thresholds:
x+=0.00001
clusters_list = hac.fcluster(linkage_matrix, x, criterion='distance') #'maxclust'
#mapping structure and evaluation structure
clusters = {}
for k,v in enumerate(clusters_list):
#print(library[k].author_name + " - " + str(library[k].author_id) + " - Cluster: " + str(clusters_list[k]))
#print(k, v)
if v not in clusters:
clusters[v] = []
#like assigning Id to clusters... library[k].unique_identifier would be better
clusters[v].append(library[k].unique_identifier)
#num of clusters for current threshold
num_clusters = len(clusters)
#evaluation
concensus_history.append(concensus(1 -final_matrix, catalog, clusters))
within_history.append(within_clust_sim(1-final_matrix, catalog, clusters))
between_history.append(between_clust_sim(1-final_matrix, catalog, clusters))
#print("Within clusters: {0}".format(within_clust_sim(final_matrix, catalog, clusters)))
precision, recall, f_measure = evaluate(clusters.values(), ground_truth.values())
if f_measure >= truth_fmeasure:
truth_fmeasure = f_measure
truth_cut = x
truth_precision = precision
truth_recall = recall
# entropies with respect to features, for each cluster:
# - first split the feature of interest (e.g. coauthors) and put them in the "entropy_feature" list, keep repetitions
# - then count the ocurrences of each class (e.g. each coauthor)
# - finally calculate the shannon entropy
# - sum up all the entropies
entropy_coauthors = {}
entropy_subjects = {}
entropy_countries = {}
entropy_years = {}
entropy_journals = {}
for clust, author_uids in clusters.items():
#entropy coauthors for the current cluster
coauthors_in_clusters = []
for uid in author_uids:
for coauthor in library[catalog[uid]].coauthors.split(" "):
if coauthor.strip(): coauthors_in_clusters.append(coauthor)
entropy_coauthors[clust] = get_clusters_shannon_entropy(coauthors_in_clusters)
#entropy test on subjects
subjects_in_clusters = []
for uid in author_uids:
for subject in library[catalog[uid]].subjects.split(" "):
if subject.strip(): subjects_in_clusters.append(subject)
entropy_subjects[clust] = get_clusters_shannon_entropy(subjects_in_clusters)
#entropy test on countries
countries_in_clusters = []
for uid in author_uids:
for country in library[catalog[uid]].countries.split(" "):
if country.strip(): countries_in_clusters.append(country)
entropy_countries[clust] = get_clusters_shannon_entropy(countries_in_clusters)
#entropy on years
years_in_clusters = []
for uid in author_uids:
for year in library[catalog[uid]].year.split(" "):
if year.strip(): years_in_clusters.append(year)
entropy_years[clust] = get_clusters_shannon_entropy(years_in_clusters)
#entropy on journal
journals_in_clusters = []
for uid in author_uids:
for journal in library[catalog[uid]].journals.split(" "):
if journal.strip(): journals_in_clusters.append(journal)
entropy_journals[clust] = get_clusters_shannon_entropy(journals_in_clusters)
#statistics for the current cluster
#sum up the entropies or average them
year_entropy_history.append(sum(entropy_years.values())/num_clusters)
coauthor_entropy_history.append(sum(entropy_coauthors.values())/num_clusters)
country_entropy_history.append(sum(entropy_countries.values())/num_clusters)
subject_entropy_history.append(sum(entropy_subjects.values())/num_clusters)
journal_entropy_history.append(sum(entropy_journals.values())/num_clusters)
#best cut
my_cut_index = concensus_history.index(max(concensus_history))
my_cut = relevant_thresholds[my_cut_index]
my_clusters_list = hac.fcluster(linkage_matrix, my_cut, criterion='distance')
my_clusters = {}
for k,v in enumerate(my_clusters_list):
if v not in my_clusters:
my_clusters[v] = []
my_clusters[v].append(library[k].unique_identifier)
#print fmeasure if not in PRODUCTION mode
if not PRODUCTION:
my_precision, my_recall, my_f_measure = evaluate(my_clusters.values(), ground_truth.values())
print("My cut value: {0} F-Measure: {1} Precision: {2} Recall: {3}".format(my_cut, my_f_measure, my_precision, my_recall))
print("Ground truth cut value {0} F-Measure: {1} Precision: {2} Recall: {3}:".format(truth_cut, truth_fmeasure, truth_precision,truth_recall))
#print("{0:.3f} / {1:.3f} / {2:.3f}".format(my_precision,my_recall,my_f_measure))
#else print titles of the articles and clusters
else:
print("\nHIERARCHICAL CLUSTERING RESULTS")
for my_clust, my_author_uids in my_clusters.items():
print("Author {0} assigned to the following papers:".format(my_clust))
for uid in my_author_uids:
print(" - {0}".format(library[catalog[uid]].printout))
print("\n")
if plots:
#normalization for plotting
coauthor_entropy_history_norm = normalize(coauthor_entropy_history)
subject_entropy_history_norm = normalize(subject_entropy_history)
country_entropy_history_norm = normalize(country_entropy_history)
year_entropy_history_norm = normalize(year_entropy_history)
journal_entropy_history_norm = normalize(journal_entropy_history)
#plot the overall results
max_y = max(max(
year_entropy_history_norm,
subject_entropy_history_norm,
coauthor_entropy_history_norm,
country_entropy_history_norm,
journal_entropy_history_norm
))
min_y = min(min(
year_entropy_history_norm,
subject_entropy_history_norm,
coauthor_entropy_history_norm,
country_entropy_history_norm,
journal_entropy_history_norm
))
plt.figure("Optimal cut representation")
plt.title('Hierarchical agglomerative clustering thresholds and entropies', fontdict={'fontsize': 18})
plt.ylabel('Normalized information entropy', fontdict={'fontsize': 14})
plt.xlabel('HAC cutting threshold', fontdict={'fontsize': 14})
plt.axis([.5, 1 , 0, max_y])
plt.plot(relevant_thresholds, year_entropy_history_norm, 'r', label="Year")
plt.plot(relevant_thresholds, subject_entropy_history_norm, 'b', label="Subjects")
plt.plot(relevant_thresholds, coauthor_entropy_history_norm, 'g', label="Coauthors")
plt.plot(relevant_thresholds, country_entropy_history_norm, 'm', label="Countries")
plt.plot(relevant_thresholds, journal_entropy_history_norm, 'y', label="Journals")
plt.legend(loc=2)
plt.vlines(truth_cut, min_y, max_y)
plt.legend(loc=2)
thismanager = plt.get_current_fig_manager()
thismanager.window.setGeometry(15,600,800, 400)
plt.figure()
plt.title('Hierarchical agglomerative clustering thresholds and similarities', fontdict={'fontsize': 18})
plt.plot(relevant_thresholds, within_history, color='r', linewidth=1, label="Intra clusters")
plt.plot(relevant_thresholds, between_history, color='g', linewidth=1, label="Inter clusters")
plt.plot(relevant_thresholds, concensus_history, color='b', linewidth=1, label="Consensus")
plt.plot(my_cut, concensus_history[list(relevant_thresholds).index(my_cut)], 'k', marker='o', markersize=10)
min_y = min(min(concensus_history,within_history,between_history))
max_y = max(max(concensus_history,within_history,between_history))
plt.legend(loc=2)
plt.ylabel('Similarity', fontdict={'fontsize': 14})
plt.xlabel('HAC cutting threshold', fontdict={'fontsize': 14})
plt.vlines(truth_cut, min_y, max_y)
plt.axis([0.5, 1 , min_y, max_y])
#plot clustering according to the best cut
fig, ax = plt.subplots()
fig.canvas.set_window_title('Hierarchical clustering')
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.15, top=0.95)
ax.set_title("Hierarchical Agglomerative Clustering for {0}".format(focus_name.split()[0]), fontdict={'fontsize': 18})
hac.dendrogram(linkage_matrix, color_threshold=my_cut, orientation='top', leaf_font_size=12,
leaf_label_func=lambda id: library[id].author_name + " - " + str(library[id].author_id))
plt.ylabel("Threshold", fontdict={'fontsize': 14})
thismanager = plt.get_current_fig_manager()
thismanager.window.setGeometry(850,45,800, 500)
