def state_estimation(weight_matrices, file_name, state_estimation_mode):
X = weight_matrices.T
# X = StandardScaler().fit_transform(X)
if(state_estimation_mode == 'clustering_DBSCAN'):
model = DBSCAN().fit(X) #eps=0.3, min_samples=10
hard_states = model.labels_
n_clusters = len(set(hard_states)) - (1 if -1 in hard_states else 0)
elif(state_estimation_mode == 'NMF'):
model = NMF(n_components=min(10,num_windows-1)) #alpha = 0.1, l1_ratio =0.1 #n_components=min(10,num_windows)
try:
W = model.fit_transform(X-np.min(X))
H = model.components_ #array, [n_components, n_features]
# print(H.shape)
# print(W.shape)
# print(np.argmax(H,axis=0))
# print(np.argmax(H,axis=1))
# print(np.argmax(W,axis=0))
# print(np.argmax(W,axis=1))
hard_states = np.argmax(H,axis=0)
except Exception as e:
hard_states = np.zeros((weight_matrices.shape[0],))
n_states = len(set(hard_states))
print(file_name)
print(' Estimated number of states: %d' % n_states)
print(' state trajectory: ', hard_states)
return hard_states
# clusters assignments for different parameter values of min_samples and eps
mglearn.plots.plot_dbsacn()
# after data scaling using StandardScaler or MinMaxScaler may help to find a good setting for eps
X, y = make_moons(n_samples=200, noise=0.05, random_state=0)
# rescale the training data to zero mean and unit variance
scaler = StandardScaler()
scaler.fit(X)
X_scaled = scaler.transform(X)
dbscan = DBSCAN()
clusters = dbscan.fit_transform(X_scaled)
plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=clusters, cmap=mglearn.cm2, s=60)
plt.xlabel('feature 0')
plt.ylabel('feature 1')
### caompare and evaluate clustering algorithms
# ARI(adjusted rand index) and NMI(normalized mutual information) are measurements to assess the outcome of a clustering algorithms relative to a ground truth clustering
from sklearn.metrics.cluster import adjusted_rand_score
X, y = make_moons(n_samples=200, noise=0.05, random_state=0)
# rescale the training data to zero mean and unit variance
scaler = StandardScaler()
scaler.fit(X)
class Clusterer:
"""
TODO - Take into account graph data
- Take into account locked and unlocked papers
- Add an option to lock entire slots?
:type papers: list[ClusterPaper]
:type data_list : list[list[string]]
:type slots: list[ClusterSlot]
"""
bandwith_factor = 0
func = ""
eps = 0.02
clusters_merged = 0
using_dbs = False
paper_graph = []
papers = []
data = []
schedule = []
schedule_settings = []
slots = []
current_cluster = 1
cluster_function = None
first_clustering = True
num_clusters = 6
graph_dataset = None
using_graph_data = False
using_abstracts = False
using_titles = False
cluster_function = ""
vocab = []
nd_data = []
def __init__(self, papers: list, schedule: list, schedule_settings: list, func):
self.slots = []
self.vocab = []
self.cluster_function = func
self.papers = []
self.active_papers = []
self.data = []
self.schedule = []
self.schedule_settings = []
self.current_cluster = 1
self.num_clusters = 12
self.graph_dataset = None
self.add_papers(papers)
self.reset_papers()
self.schedule = schedule
self.schedule_settings = schedule_settings
self.first_clustering = True
self.get_slots()
# Needed, since clustering cannot be performed if n_samples < n_clusters
def set_custom_vocabulary(self, vocab_string):
if vocab_string != '':
words = vocab_string.split(' ')
for word in words:
self.vocab.append(word)
def set_cluster_function(self, func):
#print("num clusters: ", self.num_clusters)
self.func = func
self.using_dbs = False
if len(self.papers) < self.num_clusters:
self.num_clusters = len(self.papers)
if func == 'aff':
self.clusters_merged = 0
self.cluster_function = AffinityPropagation()
#print("AFF: ",self.cluster_function.preferences)
if func =='dbs':
self.using_dbs = True
self.cluster_function = DBSCAN(eps=0.02, min_samples=2)
if func == 'hie':
self.cluster_function = AgglomerativeClustering(n_clusters=self.num_clusters)
if func == 'kme':
self.cluster_function = KMeans(n_clusters=self.num_clusters)
if func == 'msh':
self.cluster_function = MeanShift()
if func == 'kmm':
self.cluster_function = MiniBatchKMeans(n_clusters=self.num_clusters)
def add_papers(self, papers: list):
for paper in papers:
paper_to_add = ClusterPaper(paper)
self.papers.append(paper_to_add)
self.active_papers = self.papers
def add_slot_times(self, schedule_settings: list):
self.schedule_settings = schedule_settings
def add_graph(self, con_str):
"""
Constructs a graph based on the papers currently in the class, and a string describing connections in the graph.
The graph is stored in self.paper_graph.
On top of that, a dataset created from the graph is stored in self.graph_dataset
:param con_str: string
:return: None
"""
con_list = ast.literal_eval(con_str)
self.paper_graph = PaperGraph()
# First, add paper ids as nodes
for paper in self.papers:
self.paper_graph.add_node(paper.paper.id)
# Then add connections
for con in con_list:
id1 = con[0]
id2 = con[1]
weight = con[2]
# id1 and id2 are submission ids of a paper and need to be converted into regular ids
found1 = False
found2 = False
for paper in self.papers:
if paper.paper.submission_id == id1:
id1 = paper.paper.id
found1 = True
break
for paper in self.papers:
if paper.paper.submission_id == id2:
id2 = paper.paper.id
found2 = True
break
# Then we can add the connection
if found1 == True and found2 == True:
self.paper_graph.add_connection(id1, id2, weight)
# Create a probability matrix
#
self.paper_graph.create_probability_matrix()
"""
print(self.paper_graph.prob_matrix[0])
print(self.paper_graph.nodes[0])
print("sums")
"""
graph_dataset = []
for node in self.paper_graph.nodes:
pr = self.paper_graph.get_pr(node)
graph_dataset.append(pr.tolist())
self.graph_dataset = graph_dataset
def get_slots(self):
"""
Calculates the slots that will have to be filled based on the schedule structure and saves their lengths it into
self.slot_lengths. Also saves the amount of slots into self.num_slot. Also saves the type of slot into
self.parallel_slots
:return: None
"""
# Each unfilled schedule slot (a slot with no assigned papers) requires its own cluster
for d,day in enumerate(self.schedule):
for r,row in enumerate(day):
if(len(row) > 1):
is_parallel = True
parent_slot = ClusterSlot()
for c, col in enumerate(row):
if col == []:
sub_slot = ClusterSlot()
sub_slot.length = self.schedule_settings[d][r][c]
sub_slot.coords = [d,r,c]
sub_slot.is_parallel = is_parallel
parent_slot.is_parallel = True
parent_slot.sub_slots.append(sub_slot)
# The parent slot length can be defined as the sum of all subslot lengths
parent_slot.length += sub_slot.length
self.slots.append(parent_slot)
else:
is_parallel = False
for c,col in enumerate(row):
if col == []:
slot_to_add = ClusterSlot()
slot_to_add.length = self.schedule_settings[d][r][c]
slot_to_add.coords = [d,r,c]
slot_to_add.is_parallel = is_parallel
self.slots.append(slot_to_add)
def simple_get_slots(self):
for d,day in enumerate(self.schedule):
for r,row in enumerate(day):
if(len(row) > 1):
is_parallel = True
else:
is_parallel = False
for c,col in enumerate(row):
if col == []:
slot_to_add = ClusterSlot()
slot_to_add.length = self.schedule_settings[d][r][c]
slot_to_add.coords = [d,r,c]
slot_to_add.is_parallel = is_parallel
self.slots.append(slot_to_add)
def create_dataset(self):
self.data_list = []
abstracts = []
titles = []
#print("VOCAB: ", self.vocab)
if self.vocab == []:
count_vectorizer = CountVectorizer(stop_words='english')
else:
count_vectorizer = CountVectorizer(vocabulary=self.vocab)
tfid_transformer = TfidfTransformer()
abstract_data = None
title_data = None
graph_data = None
for paper in self.papers:
abstracts.append(paper.paper.abstract)
titles.append(paper.paper.title)
if self.using_abstracts == True:
#print("using abstract data")
abstract_count = count_vectorizer.fit_transform(abstracts)
abstract_tfid = tfid_transformer.fit_transform(abstract_count)
abstract_data = abstract_tfid
#print(abstract_data)
if self.using_titles == True:
#print("using title data")
title_count = count_vectorizer.fit_transform(titles)
abstract_tfid = tfid_transformer.fit_transform(title_count)
title_data = abstract_tfid
#print(title_data.toarray(), len(self.papers))
if self.using_graph_data == True:
#print("using graph data")
graph_data = scipy.sparse.csr_matrix(np.matrix(self.graph_dataset))
#print(graph_data)
self.data = []
for paper in self.papers:
self.data.append([1])
self.data=scipy.sparse.csr_matrix(self.data)
#print("MAT ", self.data)
if abstract_data != None:
self.data = scipy.sparse.hstack([self.data, abstract_data])
if title_data != None:
self.data = scipy.sparse.hstack([self.data, title_data])
if graph_data != None:
self.data = scipy.sparse.hstack([self.data, graph_data])
# Reduce data to two dimensions
#print("nd data: ", self.data)
self.nd_data = self.data.toarray()
#print("SHAPE ", self.data.shape[1])
if self.data.shape[1] > 50:
svd_data = TruncatedSVD(n_components=50).fit_transform(self.data)
tsne_data = TSNE(n_components=2, metric='cosine').fit_transform(svd_data)
elif self.data.shape[1] < 10:
#print("PCA")
svd_data = PCA(n_components=2).fit_transform(self.data.toarray())
tsne_data = svd_data
if len(tsne_data) == 1:
tsne_data = [[1,0]]
else:
svd_data = self.data
tsne_data = TSNE(n_components=2, metric='cosine').fit_transform(svd_data)
self.data = scipy.sparse.csr_matrix(tsne_data)
self.data = self.data.toarray()
# Normalize data to max x and y == 1
#print("before normalization: ", self.data)
max_x = 0
max_y = 0
min_x = 0
min_y = 0
for row in self.data:
x = row[0]
y = row[1]
if x > max_x:
max_x = x
if y > max_y:
max_y = y
if x < min_x:
min_x = x
if y < min_y:
min_y = y
for row in self.data:
if(max_x-min_x != 0):
row[0] = (row[0] - min_x) / (max_x - min_x)
else:
row[0] = 0
if(max_y-min_y != 0):
row[1] = (row[1] - min_y) / (max_y - min_y)
else:
row[1] = 0
if(self.func == "msh"):
try:
band = estimate_bandwidth(self.data)
except ValueError:
return False
band = band*(self.bandwith_factor/100)
if band == 0:
return False
self.cluster_function = MeanShift(bandwidth=band)
return True
def basic_clustering(self):
data = self.data
#print("2d data: ", data)
cluster_values = self.cluster_function.fit_predict(data).tolist()
if self.func == 'kmm' or self.func == 'kme':
cluster_distances = self.cluster_function.fit_transform(data)
else:
cluster_distances = []
for paper in self.papers:
d = []
for a in range(0,len(cluster_values)):
d.append(0)
cluster_distances.append(d)
if self.using_dbs:
#print("USING DBS")
ok_clusters = cluster_values.count(1)
if ok_clusters == 0:
self.eps += 0.002
self.cluster_function = DBSCAN(eps=self.eps, min_samples=5)
if self.eps > 1:
print("DBS ERROR")
return
return self.basic_clustering()
for index,distance in enumerate(cluster_distances):
self.papers[index].cluster_distances = distance
for index,value in enumerate(cluster_values):
self.papers[index].cluster = value
# Assign basic clusters to papers
if self.first_clustering == True:
#print("first clustering")
for index,paper in enumerate(self.papers):
#print("assigned ", paper.cluster, "to paper " ,paper.paper.title)
paper.paper.simple_cluster = paper.cluster+1
paper.paper.simple_visual_x = self.data[index][0]
paper.paper.simple_visual_y = self.data[index][1]
paper.paper.visual_x = self.data[index][0]
paper.paper.visual_y = self.data[index][1]
paper.paper.save()
self.first_clustering = False
# Get coordinates for visualization
self.get_coords()
#for i in range(0,len(self.cluster_distances)):
# print(self.papers[i].title, "-", self.cluster_distances[i])
for index,paper in enumerate(self.papers):
pass
#print(self.num_clusters, " assigned ", paper.cluster, "to paper " ,paper.paper.title)
def find_papers_with_sum(self, set, subset, desired_sum, curr_index, result):
# return after finding 10 combinations
if len(result) >= 10:
return
lens = [p.paper.length for p in subset]
if sum(lens) == desired_sum:
indexes = []
for paper in subset:
indexes.append(self.papers.index(paper))
result.append(indexes)
return
if sum(lens) > desired_sum:
return
for i in range(curr_index, len(set)):
self.find_papers_with_sum(set, subset + [set[i]], desired_sum, i+1, result)
def get_coords(self):
if(len(self.papers) == 1):
visual_coords_x = [0]
visual_coords_y = [0]
else:
self.create_dataset()
#print("--------------COORDS------------")
#print(pca_data[:,0])
#print(pca_data[:,1])
visual_coords_x = self.data[:,0]
visual_coords_y = self.data[:,1]
for index, x in enumerate(visual_coords_x):
#self.papers[index].paper.visual_x = x
self.papers[index].paper.save()
for index, y in enumerate(visual_coords_y):
#self.papers[index].paper.visual_y = y
self.papers[index].paper.save()
def reset_papers(self):
for paper in self.papers:
paper.paper.add_to_day = -1
paper.paper.add_to_row = -1
paper.paper.add_to_col = -1
paper.paper.cluster = 0
paper.paper.simple_cluster = 0
paper.paper.save()
def fit_to_schedule2(self):
#print("started fitting")
"""
An alternate approach approach:
1.) Perform clustering with basic_clustering - done manually in the view
2.) Select a cluster with papers most similar to one another and fill a slot
3.) Fill slots in the following order: single slots, parallel slots
4.) With parallel slots, each slot should be filled with papers from a different cluster - prefferebly the
cluster centroids of such clusters should be as far away as possible
5.) Repeat untill all slots are filled
"""
# The following is repeated for every cluster independently
# Slot lengths are initialized in __init__
previous_clusters = [] # Used when picking clusters for parallel slots
slots_to_delete = []
while self.slots != []:
#print("slots")
#for slot in self.slots:
#print(slot.length, slot.is_parallel, slot.sub_slots)
do_break = False
# Get biggest empty slot - only select parallel slots once all non-parallel slots have already been filled
slot_length = 0
slot_index = 0
sub_slot = None
num_nonparallel = 0
for slot in self.slots:
if slot.is_parallel == False:
num_nonparallel += 1
for index,slot in enumerate(self.slots):
if slot.length > slot_length:
# Skip parallel slots until there are no other options
if num_nonparallel > 0 and slot.is_parallel == True:
continue
if slot.is_parallel:
# Ignore empty slots - they will be deleted later
if(len(slot.sub_slots) == 0):
previous_clusters = []
slots_to_delete.append(index)
continue
# For parallel slots, pick the first unfilled subslot. If all subslots have been filled,
# delete the slot and continue
else:
sub_slot = slot.sub_slots[0]
slot_length = sub_slot.length
else: # slot is not parallel
slot_length = slot.length
slot_index = index
# If slot is not parallel, select a single cluster
if slot_length == 0:
break
if not self.slots[slot_index].is_parallel:
# Select biggest cluster
cluster_values = [paper.cluster for paper in self.papers]
max_cluster_index = max(cluster_values)
#print("values ", cluster_values)
cluster_sizes = [cluster_values.count(i) for i in range(0, max_cluster_index+1)]
max_cluster = cluster_sizes.index(max(cluster_sizes))
# Get papers from that cluster
cluster_papers = [p for p in self.papers if p.cluster == max_cluster]
else:
# If the slot is parallel, then consider previous clusters
cluster_values = [paper.cluster for paper in self.papers]
cluster_sizes = [cluster_values.count(i) for i in range(0, len(self.papers))]
max_size = 0
max_cluster = None
for index,size in enumerate(cluster_sizes):
if index in previous_clusters:
#print("PREVIOUS CLUSTER")
continue
if size > max_size:
max_size = size
max_cluster = index
if max_cluster == None:
max_cluster = cluster_sizes.index(max(cluster_sizes))
# This means that there is not enough clusters to ignore previous clusters, so we don't
pass
cluster_papers = [p for p in self.papers if p.cluster == max_cluster]
#print(cluster_sizes)
#print("SELECTED ", max_cluster, " with size", len(cluster_papers))
# If slot is parallel, then the previous clusters must also be considered - simultaneous parallel slots should
# be filled whith papers from different clusters
# Select papers that fit into the slot
papers = []
#print("finding papers")
self.find_papers_with_sum(cluster_papers, [], slot_length, 0, papers)
#print("found papers")
#print(papers)
if papers == []:
# This happens when there are no papers, that can completely fill a slot in the largest cluster.
# In this case, it makes sense to rerun clustering with less clusters, as that should produce clusters
# with more papers.
# If even that doesnt help, then the function should end end report this to the user
if self.func=="msh":
cond = (self.bandwith_factor >= 300)
if self.func=="hie" or self.func=="kmm" or self.func == "kme":
cond = (self.num_clusters == 0)
if self.func=="aff":
#print("starting merge")
merged = []
# This one is problematic - manually merge clusters
cond = False
centers = self.cluster_function.cluster_centers_
self.clusters_merged += 1
if self.clusters_merged >= 20:
print("failed to cluster")
return False
for x in range(self.clusters_merged):
# Merge 2 nearest clusters
min_dist = 1000000
cluster1 = 0
cluster2 = 0
for i in range(len(centers)):
coord1 = np.array(centers[i])
if i in merged:
continue
for j in range(i+1,len(centers)):
if j in merged:
continue
coord2 = np.array(centers[j])
if np.linalg.norm(coord1-coord2) < min_dist:
cluster1 = i
cluster2 = j
min_dist = np.linalg.norm(coord1-coord2)
#print("merged ", cluster1, cluster2)
merged.append(cluster1)
# Change paper clusters
for paper in self.papers:
if paper.cluster == cluster1:
paper.cluster = cluster2
return self.fit_to_schedule2()
if cond:
print("failed to cluster")
return False
else:
# Needed, since clustering cannot be performed if n_samples < n_clusters
self.num_clusters -= 1
if self.num_clusters == 0 and (self.func=="hie" or self.func=="kmm" or self.func == "kme"):
print("failed to cluster")
return False
self.bandwith_factor += 10
self.set_cluster_function(self.func)
if not self.create_dataset():
print("failed to cluster")
return False
self.basic_clustering()
#print("NO SUITABLE COMBINATION FOUND2")
return self.fit_to_schedule2()
else:
# if there are multiple fitting groups in the same cluster, select the group with the smallest error
selected_index = 0
if len(papers) > 1:
min_error = 9999999999999
for index,subset in enumerate(papers):
error = 0
for paper in subset:
error += self.papers[paper].cluster_distances[max_cluster]*self.papers[paper].cluster_distances[max_cluster]
if error < min_error:
selected_index = index
min_error = error
# Update the papers' add_to_day/row/col fields. This fields will then be used to add the papers into the schedule
# Also update the papers' cluster field
ids = [self.papers[i].paper.id for i in papers[selected_index]]
papers_to_update = [(self.papers[i],i) for i in papers[selected_index]]
#print("PAPERS TO UPATE: ", papers_to_update)
# Return the cluster coordinates - used for visualization
for paper, index in papers_to_update:
paper.paper.cluster = self.current_cluster
if not self.slots[slot_index].is_parallel:
coords = self.slots[slot_index].coords
else:
coords = sub_slot.coords
paper.paper.add_to_day = coords[0]
paper.paper.add_to_row = coords[1]
paper.paper.add_to_col = coords[2]
#print("COORD ", index, self.visual_coords_x[index], self.visual_coords_y[index])
paper.paper.save()
self.current_cluster += 1
# remove the assigned papers from this class, since they no longer need to be assigned
offset = 0
for paper, index in papers_to_update:
self.papers.remove(paper)
# Remove the relevant line from graph dataset
if self.using_graph_data == True:
graph = len(self.graph_dataset)
del self.graph_dataset[index - offset]
# since indexes are sorted in asending order, they must be updated, which is handled by offset
offset += 1
# also remove the information about the slot
if not self.slots[slot_index].is_parallel:
del self.slots[slot_index]
else:
previous_clusters.append(max_cluster)
del self.slots[slot_index].sub_slots[0]
# delete marked slot
#print("SLOTS TO DELETE: ", slots_to_delete, len(self.slots))
if len(slots_to_delete) > 0:
del self.slots[slots_to_delete[0]]
del slots_to_delete[0]
class Clusterer:
"""
TODO - Take into account graph data
- Take into account locked and unlocked papers
- Add an option to lock entire slots?
:type papers: list[ClusterPaper]
:type data_list : list[list[string]]
:type slots: list[ClusterSlot]
"""
bandwith_factor = 0
func = ""
eps = 0.02
clusters_merged = 0
using_dbs = False
paper_graph = []
papers = []
data = []
schedule = []
schedule_settings = []
paper_schedule = [] # Needed when filling non-empty slots
slots = []
locked_slots = [] # Slots containing a locked paper
current_cluster = 1
first_clustering = True
num_clusters = 6
graph_dataset = None
using_graph_data = False
using_abstracts = False
using_titles = False
cluster_function = ""
vocab = []
nd_data = []
def __init__(self, papers: list, schedule: list, schedule_settings: list,
paper_schedule: list, func):
self.slots = []
self.vocab = []
self.cluster_function = func
self.papers = []
self.active_papers = []
self.data = []
self.schedule = []
self.schedule_settings = []
self.current_cluster = 1
self.num_clusters = 12
self.graph_dataset = None
self.add_papers(papers)
self.reset_papers()
self.schedule = schedule
self.schedule_settings = schedule_settings
self.paper_schedule = paper_schedule
self.first_clustering = True
self.get_slots()
# Needed, since clustering cannot be performed if n_samples < n_clusters
def set_custom_vocabulary(self, vocab_string):
if vocab_string != '':
words = vocab_string.split(' ')
for word in words:
self.vocab.append(word)
def set_cluster_function(self, func):
# print("num clusters: ", self.num_clusters)
self.func = func
self.using_dbs = False
if len(self.papers) < self.num_clusters:
self.num_clusters = len(self.papers)
if func == 'aff':
self.clusters_merged = 0
self.cluster_function = AffinityPropagation()
# print("AFF: ",self.cluster_function.preferences)
if func == 'dbs':
self.using_dbs = True
self.cluster_function = DBSCAN(eps=0.02, min_samples=2)
if func == 'hie':
self.cluster_function = AgglomerativeClustering(
n_clusters=self.num_clusters)
if func == 'kme':
self.cluster_function = KMeans(n_clusters=self.num_clusters)
if func == 'msh':
self.cluster_function = MeanShift()
if func == 'kmm':
self.cluster_function = MiniBatchKMeans(
n_clusters=self.num_clusters)
def add_papers(self, papers: list):
for paper in papers:
paper_to_add = ClusterPaper(paper)
self.papers.append(paper_to_add)
self.active_papers = self.papers
def add_slot_times(self, schedule_settings: list):
self.schedule_settings = schedule_settings
def add_graph(self, con_str):
"""
Constructs a graph based on the papers currently in the class, and a string describing connections in the graph.
The graph is stored in self.paper_graph.
On top of that, a dataset created from the graph is stored in self.graph_dataset
:param con_str: string
:return: None
"""
con_list = ast.literal_eval(con_str)
self.paper_graph = PaperGraph()
# First, add paper ids as nodes
for paper in self.papers:
self.paper_graph.add_node(paper.paper.id)
# Then add connections
for con in con_list:
id1 = con[0]
id2 = con[1]
weight = con[2]
# id1 and id2 are submission ids of a paper and need to be converted into regular ids
found1 = False
found2 = False
for paper in self.papers:
if paper.paper.submission_id == id1:
id1 = paper.paper.id
found1 = True
break
for paper in self.papers:
if paper.paper.submission_id == id2:
id2 = paper.paper.id
found2 = True
break
# Then we can add the connection
if found1 == True and found2 == True:
self.paper_graph.add_connection(id1, id2, weight)
# Create a probability matrix
#
self.paper_graph.create_probability_matrix()
"""
print(self.paper_graph.prob_matrix[0])
print(self.paper_graph.nodes[0])
print("sums")
"""
graph_dataset = []
for node in self.paper_graph.review_preferences_graph.nodes():
pr = self.paper_graph.get_pr(node)
graph_dataset.append(pr.tolist())
self.graph_dataset = graph_dataset
#nx.draw(self.paper_graph.review_preferences_graph, node_color='c', edge_color='k', with_labels=True)
#plt.draw()
#plt.show()
def get_slots(self):
"""
Calculates the slots that will have to be filled based on the schedule structure and saves their lengths it into
self.slot_lengths. Also saves the amount of slots into self.num_slot. Also saves the type of slot into
self.parallel_slots
:return: None
"""
# Each unfilled schedule slot (a slot with no assigned papers) requires its own cluster
for d, day in enumerate(self.schedule):
for r, row in enumerate(day):
if (len(row) > 1):
is_parallel = True
parent_slot = ClusterSlot()
for c, col in enumerate(row):
# Empty slots
if col == []:
sub_slot = ClusterSlot()
sub_slot.length = self.schedule_settings[d][r][c]
sub_slot.coords = [d, r, c]
sub_slot.is_parallel = is_parallel
parent_slot.is_parallel = True
parent_slot.sub_slots.append(sub_slot)
# The parent slot length can be defined as the sum of all subslot lengths
parent_slot.length += sub_slot.length
# filled slots
else:
sub_slot = ClusterSlot()
sub_slot.length = self.schedule_settings[d][r][c]
sub_slot.coords = [d, r, c]
self.locked_slots.append(sub_slot)
print("Locked slot " + str(sub_slot.coords))
self.slots.append(parent_slot)
else:
is_parallel = False
for c, col in enumerate(row):
if col == []:
slot_to_add = ClusterSlot()
slot_to_add.length = self.schedule_settings[d][r][
c]
slot_to_add.coords = [d, r, c]
slot_to_add.is_parallel = is_parallel
self.slots.append(slot_to_add)
else:
sub_slot = ClusterSlot()
sub_slot.length = self.schedule_settings[d][r][c]
sub_slot.coords = [d, r, c]
self.locked_slots.append(sub_slot)
print("Locked slot " + str(sub_slot.coords))
def simple_get_slots(self):
for d, day in enumerate(self.schedule):
for r, row in enumerate(day):
if (len(row) > 1):
is_parallel = True
else:
is_parallel = False
for c, col in enumerate(row):
if col == []:
slot_to_add = ClusterSlot()
slot_to_add.length = self.schedule_settings[d][r][c]
slot_to_add.coords = [d, r, c]
slot_to_add.is_parallel = is_parallel
self.slots.append(slot_to_add)
def create_dataset(self):
self.data_list = []
abstracts = []
titles = []
# print("VOCAB: ", self.vocab)
if self.vocab == []:
count_vectorizer = CountVectorizer(stop_words='english')
else:
count_vectorizer = CountVectorizer(vocabulary=self.vocab)
tfid_transformer = TfidfTransformer()
abstract_data = None
title_data = None
graph_data = None
for paper in self.papers:
abstracts.append(paper.paper.abstract)
titles.append(paper.paper.title)
if self.using_abstracts == True:
# print("using abstract data")
abstract_count = count_vectorizer.fit_transform(abstracts)
abstract_tfid = tfid_transformer.fit_transform(abstract_count)
abstract_data = abstract_tfid
# print(abstract_data)
if self.using_titles == True:
# print("using title data")
title_count = count_vectorizer.fit_transform(titles)
abstract_tfid = tfid_transformer.fit_transform(title_count)
title_data = abstract_tfid
# print(title_data.toarray(), len(self.papers))
if self.using_graph_data == True:
# print("using graph data")
graph_data = scipy.sparse.csr_matrix(np.matrix(self.graph_dataset))
# print(graph_data)
self.data = []
for paper in self.papers:
self.data.append([1])
self.data = scipy.sparse.csr_matrix(self.data)
# print("MAT ", self.data)
if abstract_data != None:
self.data = scipy.sparse.hstack([self.data, abstract_data])
if title_data != None:
self.data = scipy.sparse.hstack([self.data, title_data])
if graph_data != None:
self.data = scipy.sparse.hstack([self.data, graph_data])
# Reduce data to two dimensions
# print("nd data: ", self.data)
self.nd_data = self.data.toarray()
# print("SHAPE ", self.data.shape[1])
"""
if self.data.shape[1] > 50:
svd_data = TruncatedSVD(n_components=50).fit_transform(self.data)
tsne_data = TSNE(n_components=2, metric='cosine').fit_transform(svd_data)
elif self.data.shape[1] < 10:
# print("PCA")
svd_data = PCA(n_components=2).fit_transform(self.data.toarray())
tsne_data = svd_data
if len(tsne_data) == 1:
tsne_data = [[1, 0]]
else:
svd_data = self.data
tsne_data = TSNE(n_components=2, metric='cosine').fit_transform(svd_data)
self.data = scipy.sparse.csr_matrix(tsne_data)
"""
self.data = self.data.toarray()
# Normalize data to max x and y == 1
# print("before normalization: ", self.data)
max_x = 0
max_y = 0
min_x = 0
min_y = 0
for row in self.data:
x = row[0]
y = row[1]
if x > max_x:
max_x = x
if y > max_y:
max_y = y
if x < min_x:
min_x = x
if y < min_y:
min_y = y
for row in self.data:
if max_x - min_x != 0:
row[0] = (row[0] - min_x) / (max_x - min_x)
else:
row[0] = 0
if max_y - min_y != 0:
row[1] = (row[1] - min_y) / (max_y - min_y)
else:
row[1] = 0
if self.func == "msh":
try:
band = estimate_bandwidth(self.data)
except ValueError:
return False
band = band * (self.bandwith_factor / 100)
if band == 0:
return False
self.cluster_function = MeanShift(bandwidth=band)
return True
def basic_clustering(self):
data = self.data
# print("2d data: ", data)
cluster_values = self.cluster_function.fit_predict(data).tolist()
if self.func == 'kmm' or self.func == 'kme':
cluster_distances = self.cluster_function.fit_transform(data)
else:
cluster_distances = []
for paper in self.papers:
d = []
for a in range(0, len(cluster_values)):
d.append(0)
cluster_distances.append(d)
if self.using_dbs:
# print("USING DBS")
ok_clusters = cluster_values.count(1)
if ok_clusters == 0:
self.eps += 0.002
self.cluster_function = DBSCAN(eps=self.eps, min_samples=5)
if self.eps > 1:
print("DBS ERROR")
return
return self.basic_clustering()
for index, distance in enumerate(cluster_distances):
self.papers[index].cluster_distances = distance
for index, value in enumerate(cluster_values):
self.papers[index].cluster = value
# Assign basic clusters to papers
if self.first_clustering == True:
# print("first clustering")
for index, paper in enumerate(self.papers):
# print("assigned ", paper.cluster, "to paper " ,paper.paper.title)
paper.paper.simple_cluster = paper.cluster + 1
paper.paper.simple_visual_x = self.data[index][0]
paper.paper.simple_visual_y = self.data[index][1]
paper.paper.visual_x = self.data[index][0]
paper.paper.visual_y = self.data[index][1]
paper.paper.save()
self.first_clustering = False
# Get coordinates for visualization
self.get_coords()
# for i in range(0,len(self.cluster_distances)):
# print(self.papers[i].title, "-", self.cluster_distances[i])
for index, paper in enumerate(self.papers):
pass
# print(self.num_clusters, " assigned ", paper.cluster, "to paper " ,paper.paper.title)
def find_papers_with_sum(self, set, subset, desired_sum, curr_index,
result):
# return after finding 10 combinations
if len(result) >= 10:
return
lens = [p.paper.length for p in subset]
if sum(lens) == desired_sum:
indexes = []
for paper in subset:
indexes.append(self.papers.index(paper))
result.append(indexes)
return
if sum(lens) > desired_sum:
return
for i in range(curr_index, len(set)):
self.find_papers_with_sum(set, subset + [set[i]], desired_sum,
i + 1, result)
def get_coords(self):
if (len(self.papers) == 1):
visual_coords_x = [0]
visual_coords_y = [0]
else:
self.create_dataset()
# print("--------------COORDS------------")
# print(pca_data[:,0])
# print(pca_data[:,1])
visual_coords_x = self.data[:, 0]
visual_coords_y = self.data[:, 1]
for index, x in enumerate(visual_coords_x):
# self.papers[index].paper.visual_x = x
self.papers[index].paper.save()
for index, y in enumerate(visual_coords_y):
# self.papers[index].paper.visual_y = y
self.papers[index].paper.save()
def reset_papers(self):
for paper in self.papers:
paper.paper.add_to_day = -1
paper.paper.add_to_row = -1
paper.paper.add_to_col = -1
paper.paper.cluster = 0
paper.paper.simple_cluster = 0
paper.paper.save()
def fit_to_schedule2(self):
# print("started fitting")
"""
An alternate approach approach:
1.) Perform clustering with basic_clustering - done manually in the view
2.) Select a cluster with papers most similar to one another and fill a slot
3.) Fill slots in the following order: single slots, parallel slots
4.) With parallel slots, each slot should be filled with papers from a different cluster - prefferebly the
cluster centroids of such clusters should be as far away as possible
5.) Repeat untill all slots are filled
"""
# The following is repeated for every cluster independently
# Slot lengths are initialized in __init__
previous_clusters = [] # Used when picking clusters for parallel slots
slots_to_delete = []
# First fill out slots with locked papers by filling them with similar papers
# Since there is no guarantee multiple locked papers will be from the same cluster only look at one of them
for locked_slot in self.locked_slots:
# Find first locked paper of the slot - all non locked are already removed at this point
day = locked_slot.coords[0]
row = locked_slot.coords[1]
col = locked_slot.coords[2]
locked_paper_id = self.paper_schedule[day][row][col][0]
# Find ths cluster containing the paper
containing_cluster = 0
for paper in self.papers:
if paper.paper.submission_id == locked_paper_id:
containing_cluster = paper.cluster
# The length of papers in the slot must be subtracted from the slot length
for paper_id in self.paper_schedule[day][row][col]:
for paper in self.papers:
if paper.paper.submission_id == paper_id:
locked_slot.length -= paper.paper.length
# Fit slot with papers from the same cluster, if able
cluster_papers = [
p for p in self.papers if p.cluster == containing_cluster
]
papers = []
self.find_papers_with_sum(cluster_papers, [], locked_slot.length,
0, papers)
# If the cluster does not contain enough papers, try with all papers
if len(papers) == 0:
self.find_papers_with_sum(self.papers, [], locked_slot.length,
0, papers)
# If a valid combination cannut be found even with this, the slot cannot be fitted
if len(papers) == 0:
continue
# if there are multiple fitting groups in the same cluster, select the group with the smallest error
selected_index = 0
if len(papers) > 1:
min_error = 9999999999999
for index, subset in enumerate(papers):
error = 0
for paper in subset:
error += self.papers[paper].cluster_distances[containing_cluster] * \
self.papers[paper].cluster_distances[containing_cluster]
if error < min_error:
selected_index = index
min_error = error
print(str(len(self.papers)))
print(str(len(papers)))
# ids = [self.papers[i].paper.id for i in papers[selected_index]]
papers_to_update = [(self.papers[i], i)
for i in papers[selected_index]]
print('papers_to_update in locked slot', papers_to_update)
# print("PAPERS TO UPATE: ", papers_to_update)
# Return the cluster coordinates - used for visualization
for paper, index in papers_to_update:
paper.paper.cluster = self.current_cluster
coords = locked_slot.coords
paper.paper.add_to_day = coords[0]
paper.paper.add_to_row = coords[1]
paper.paper.add_to_col = coords[2]
# print("COORD ", index, self.visual_coords_x[index], self.visual_coords_y[index])
paper.paper.save()
self.current_cluster += 1
# remove the assigned papers from this class, since they no longer need to be assigned
offset = 0
for paper, index in papers_to_update:
self.papers.remove(paper)
# Remove the relevant line from graph dataset
if self.using_graph_data == True:
graph = len(self.graph_dataset)
del self.graph_dataset[index - offset]
# since indexes are sorted in asending order, they must be updated, which is handled by offset
offset += 1
# also remove the information about the slot
del locked_slot
# delete marked slot
# print("SLOTS TO DELETE: ", slots_to_delete, len(self.slots))
if len(slots_to_delete) > 0:
del self.slots[slots_to_delete[0]]
del slots_to_delete[0]
while self.slots != []:
# print("slots")
# for slot in self.slots:
# print(slot.length, slot.is_parallel, slot.sub_slots)
do_break = False
# Get biggest empty slot - only select parallel slots once all non-parallel slots have already been filled
slot_length = 0
slot_index = 0
sub_slot = None
num_nonparallel = 0
for slot in self.slots:
if slot.is_parallel == False:
num_nonparallel += 1
for index, slot in enumerate(self.slots):
if slot.length > slot_length:
# Skip parallel slots until there are no other options
if num_nonparallel > 0 and slot.is_parallel == True:
continue
if slot.is_parallel:
# Ignore empty slots - they will be deleted later
if (len(slot.sub_slots) == 0):
previous_clusters = []
slots_to_delete.append(index)
continue
# For parallel slots, pick the first unfilled subslot. If all subslots have been filled,
# delete the slot and continue
else:
sub_slot = slot.sub_slots[0]
slot_length = sub_slot.length
else: # slot is not parallel
slot_length = slot.length
slot_index = index
# If slot is not parallel, select a single cluster
if slot_length == 0:
break
if not self.slots[slot_index].is_parallel:
# Select biggest cluster
cluster_values = [paper.cluster for paper in self.papers]
max_cluster_index = max(cluster_values)
# print("values ", cluster_values)
cluster_sizes = [
cluster_values.count(i)
for i in range(0, max_cluster_index + 1)
]
max_cluster = cluster_sizes.index(max(cluster_sizes))
# Get papers from that cluster
cluster_papers = [
p for p in self.papers if p.cluster == max_cluster
]
else:
# If the slot is parallel, then consider previous clusters
cluster_values = [paper.cluster for paper in self.papers]
cluster_sizes = [
cluster_values.count(i)
for i in range(0, len(self.papers))
]
max_size = 0
max_cluster = None
for index, size in enumerate(cluster_sizes):
if index in previous_clusters:
# print("PREVIOUS CLUSTER")
continue
if size > max_size:
max_size = size
max_cluster = index
if max_cluster == None:
max_cluster = cluster_sizes.index(max(cluster_sizes))
# This means that there is not enough clusters to ignore previous clusters, so we don't
pass
cluster_papers = [
p for p in self.papers if p.cluster == max_cluster
]
print('cluster_papers', cluster_papers)
# print(cluster_sizes)
print("SELECTED ", max_cluster, " with size", len(cluster_papers))
# If slot is parallel, then the previous clusters must also be considered - simultaneous parallel slots should
# be filled whith papers from different clusters
# Select papers that fit into the slot
papers = []
# print("finding papers")
self.find_papers_with_sum(cluster_papers, [], slot_length, 0,
papers)
print("papers_with_sum", papers)
# print(papers)
if papers == []:
# This happens when there are no papers, that can completely fill a slot in the largest cluster.
# In this case, it makes sense to rerun clustering with less clusters, as that should produce clusters
# with more papers.
# If even that doesnt help, then the function should end end report this to the user
if self.func == "msh":
cond = (self.bandwith_factor >= 300)
if self.func == "hie" or self.func == "kmm" or self.func == "kme":
cond = (self.num_clusters == 0)
if self.func == "aff":
# print("starting merge")
merged = []
# This one is problematic - manually merge clusters
cond = False
centers = self.cluster_function.cluster_centers_
self.clusters_merged += 1
if self.clusters_merged >= 20:
print("failed to cluster")
return False
for x in range(self.clusters_merged):
# Merge 2 nearest clusters
min_dist = 1000000
cluster1 = 0
cluster2 = 0
for i in range(len(centers)):
coord1 = np.array(centers[i])
if i in merged:
continue
for j in range(i + 1, len(centers)):
if j in merged:
continue
coord2 = np.array(centers[j])
if np.linalg.norm(coord1 - coord2) < min_dist:
cluster1 = i
cluster2 = j
min_dist = np.linalg.norm(coord1 - coord2)
# print("merged ", cluster1, cluster2)
merged.append(cluster1)
# Change paper clusters
for paper in self.papers:
if paper.cluster == cluster1:
paper.cluster = cluster2
return self.fit_to_schedule2()
if cond:
print("failed to cluster")
return False
else:
# Needed, since clustering cannot be performed if n_samples < n_clusters
self.num_clusters -= 1
if self.num_clusters == 0 and (self.func == "hie"
or self.func == "kmm"
or self.func == "kme"):
print("failed to cluster")
return False
self.bandwith_factor += 10
self.set_cluster_function(self.func)
if not self.create_dataset():
print("failed to cluster")
return False
self.basic_clustering()
# print("NO SUITABLE COMBINATION FOUND2")
return self.fit_to_schedule2()
else:
# if there are multiple fitting groups in the same cluster, select the group with the smallest error
selected_index = 0
if len(papers) > 1:
min_error = 9999999999999
for index, subset in enumerate(papers):
error = 0
for paper in subset:
error += self.papers[paper].cluster_distances[max_cluster] * \
self.papers[paper].cluster_distances[max_cluster]
if error < min_error:
selected_index = index
min_error = error
# Update the papers' add_to_day/row/col fields. This fields will then be used to add the papers into the schedule
# Also update the papers' cluster field
ids = [self.papers[i].paper.id for i in papers[selected_index]]
papers_to_update = [(self.papers[i], i)
for i in papers[selected_index]]
# print("PAPERS TO UPATE: ", papers_to_update)
# Return the cluster coordinates - used for visualization
for paper, index in papers_to_update:
paper.paper.cluster = self.current_cluster
if not self.slots[slot_index].is_parallel:
coords = self.slots[slot_index].coords
else:
coords = sub_slot.coords
paper.paper.add_to_day = coords[0]
paper.paper.add_to_row = coords[1]
paper.paper.add_to_col = coords[2]
# print("COORD ", index, self.visual_coords_x[index], self.visual_coords_y[index])
paper.paper.save()
self.current_cluster += 1
# remove the assigned papers from this class, since they no longer need to be assigned
offset = 0
for paper, index in papers_to_update:
self.papers.remove(paper)
# Remove the relevant line from graph dataset
if self.using_graph_data == True:
graph = len(self.graph_dataset)
del self.graph_dataset[index - offset]
# since indexes are sorted in asending order, they must be updated, which is handled by offset
offset += 1
# also remove the information about the slot
if not self.slots[slot_index].is_parallel:
del self.slots[slot_index]
else:
previous_clusters.append(max_cluster)
del self.slots[slot_index].sub_slots[0]
# delete marked slot
# print("SLOTS TO DELETE: ", slots_to_delete, len(self.slots))
if len(slots_to_delete) > 0:
del self.slots[slots_to_delete[0]]
del slots_to_delete[0]