class SOMMap(object):
def __init__(self,
data,
neighborhood,
normalizer=None,
mapsize=None,
mask=None,
mapshape='planar',
lattice='rect',
initialization='pca',
training='batch',
radius_train='linear',
name='sompy',
component_names=None,
components_to_plot=None,
isNormalized=False):
"""
Self Organizing Map
:param data: data to be clustered, represented as a matrix of n rows,
as inputs and m cols as input features
:param neighborhood: neighborhood object calculator.
:param normalizer: normalizer object calculator.
:param mapsize: tuple/list defining the dimensions of the som. If
single number is provided is considered as the number of nodes.
:param mask: mask
:param mapshape: shape of the som.
:param lattice: type of lattice.
:param initialization: method to be used for initialization of the som.
:param name: name used to identify the som
:param training: Training mode (seq, batch)
"""
if data is not None: # ajout LB
if normalizer and isNormalized == False:
for i in range(len(normalizer)):
data[:, i] = normalizer[i].normalize(data[:, i])
self._data = data
else:
self._data = data
self._data = self._data.astype('double') # ajout LB
self._dim = data.shape[1]
self._dlen = data.shape[0]
else:
self._data = None
self._dim = None
self._dlen = None
self._normalizer = normalizer
self._dlabel = None
self._bmu = None
self.name = name
self.data_raw = data
self.neighborhood = neighborhood
self.mapshape = mapshape
self.initialization = initialization
self.mask = mask
mapsize = self.calculate_map_size(lattice) if not mapsize else mapsize
self.mapsize = mapsize
self.codebook = Codebook(mapsize, lattice)
self.training = training
self.radius_train = radius_train
self._component_names = self.build_component_names(
) if component_names is None else [component_names]
self._distance_matrix = self.calculate_map_dist()
self.components_to_plot = components_to_plot
def __str__(self):
return f'mapsize={self.mapsize}\nname={self.name}\nNormaliser={self._normalizer.name}\nMap shape={self.mapshape}'
def attach_data(self, data, comp_names): # ajout LB
self.data_raw = data
self._dim = data.shape[1]
self._dlen = data.shape[0]
self.component_names = comp_names
self._data = self._normalizer.normalize(data)
self._data = self._data.astype('double')
self._bmu = self.find_bmu(data, njb=1)
def save(self, file):
dico = {
'name': self.name,
'codebook': self.codebook.matrix,
'lattice': self.codebook.lattice,
'mapsize': self.codebook.mapsize,
'normalization': self._normalizer,
'norm_params': self._normalizer.params,
'comp_names': self._component_names[0],
'mask': self.mask,
'neighborhood': self.neighborhood.name,
'codebookinitialized': self.codebook.initialized,
'initialization': self.initialization,
'bmu': self._bmu,
'mapshape': self.mapshape,
'training': self.training,
'radius_train': self.radius_train,
'dim': self._dim
}
import pickle
pickle.dump(dico, open(file, 'wb'))
#def plotepoch(self,comp0,comp1):
def plotplanes(self): # ajout LB
if self.components_to_plot is None:
return
comps = self.components_to_plot
n = {1: (1, 1), 2: (1, 2), 3: (2, 2), 4: (2, 2)} # lignes, colonnes
nplots = len(comps)
if nplots > 4:
raise ValueError(
'Le nombre de comp doit etre inferieur ou égal à 4')
nl = n[nplots][0]
nc = n[nplots][1]
neighbours_nodes = self.calculate_neighbours_nodes()
edges = []
for i in range(self.codebook.nnodes):
for j in range(len(neighbours_nodes[i])):
edges.append((i, neighbours_nodes[i][j]))
nodes = [i for i in range(self.codebook.nnodes)]
G = nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)
plt.clf()
for i in range(nplots):
refs = [self.codebook.matrix[:, comps[i][0]]
], [self.codebook.matrix[:, comps[i][1]]]
pos = {}
for k in range(self.codebook.nnodes):
name = int(k)
pos[name] = (refs[0][0][k], refs[1][0][k])
plt.subplot(nl, nc, i + 1)
plt.scatter(self._data[:, comps[i][0]],
self._data[:, comps[i][1]],
marker='x',
c='b')
#plt.scatter([self.codebook.matrix[:,comps[i][0]]],[self.codebook.matrix[:,comps[i][1]]],marker='o',c='r')
nx.draw_networkx(G,
pos,
arrows=False,
with_labels=False,
node_size=50)
plt.xlabel(self._component_names[0][comps[i][0]])
plt.ylabel(self._component_names[0][comps[i][1]])
plt.title('comp. {} - {}'.format(comps[i][0], comps[i][1]))
plt.pause(0.1)
def plot_tsne(self, init="pca", perplexity=10, verbose=2):
T_SNE = TSNE(2, init=init, perplexity=perplexity, verbose=verbose)
x2d = T_SNE.fit_transform(self.codebook.matrix)
neighbours_nodes = self.calculate_neighbours_nodes()
edges = []
for i in range(self.codebook.nnodes):
for j in range(len(neighbours_nodes[i])):
edges.append((i, neighbours_nodes[i][j]))
nodes = [i for i in range(self.codebook.nnodes)]
G = nx.Graph()
G.add_nodes_from(nodes)
for i in range(self.codebook.nnodes):
G.nodes[i]['label'] = self._nlabel[i]
G.add_edges_from(edges)
pos = {}
for i in range(self.codebook.nnodes):
name = int(i)
pos[name] = (x2d[i][0], x2d[i][1])
colorlist = []
for i in range(len(np.unique(self._nlabel))):
colorlist.append('#%06X' % randint(0, 0xFFFFFF))
plt.figure()
for i in range(len(np.unique(self._nlabel))):
nodelist = []
for j in range(self.codebook.nnodes):
if G.nodes[j]['label'] == np.unique(self._nlabel)[i]:
nodelist.append(j)
nx.draw_networkx(G,
pos,
arrows=False,
nodelist=nodelist,
node_color=colorlist[i],
with_labels=False,
node_size=50,
label=np.unique(self._nlabel)[i])
plt.xlabel('tsne-2d-one')
plt.ylabel('tsne-2d-two')
plt.title('T-SNE des neurones référants')
plt.show()
@property
def component_names(self):
return self._component_names
@component_names.setter
def component_names(self, compnames):
if self._dim == len(compnames):
self._component_names = np.asarray(compnames)[np.newaxis, :]
else:
raise ComponentNamesError('Component names should have the same '
'size as the data dimension/features')
def build_component_names(self):
return ['Variable-' + str(i + 1) for i in range(0, self._dim)]
@property
def node_labels(self):
return self._nlabel
@node_labels.setter
def node_labels(self, labels):
"""
Set labels of the training data, it should be in the format of a list
of strings
"""
if labels.shape == (1, self.codebook.nnodes):
label = labels.T
elif labels.shape == (self.codebook.nnodes, 1):
label = labels
elif labels.shape == (self.codebook.nnodes, ):
label = labels[:, np.newaxis]
else:
raise LabelsError('wrong label format')
self._nlabel = label
def build_node_labels(self):
return ['nlabel-' + str(i) for i in range(0, self.codebook.nnodes)]
def node_labels_from_data(self, sData):
nlabels = []
for i in range(self.codebook.nnodes):
ind = get_index_positions(self._bmu[0], i)
if ind != []:
subData = [sData._dlabel[k] for k in ind]
nlabels.append(Counter(subData).most_common(1)[0][0])
else:
nlabels.append("Nan")
self._nlabel = nlabels
def calculate_map_dist(self): # CALCUL MATRICE DIST
"""
Calculates the grid distance, which will be used during the training
steps. It supports only planar grids for the moment
"""
nnodes = self.codebook.nnodes
distance_matrix = np.zeros((nnodes, nnodes))
for i in range(nnodes):
#distance_matrix[i] = self.codebook.grid_dist(i).reshape(1, nnodes)
distance_matrix[i] = self.codebook.grid_dist(i).T.reshape(
1, nnodes) #attention c'et la distance au carré
return distance_matrix
@timeit()
def train(self,
n_job=1,
shared_memory=False,
verbose='info',
train_rough_len=None,
train_rough_radiusin=None,
train_rough_radiusfin=None,
train_finetune_len=None,
train_finetune_radiusin=None,
train_finetune_radiusfin=None,
train_len_factor=1,
maxtrainlen=np.Inf,
alreadyinit=False,
watch_evolution=True):
"""
Trains the som
:param n_job: number of jobs to use to parallelize the traning
:param shared_memory: flag to active shared memory
:param verbose: verbosity, could be 'debug', 'info' or None
:param train_len_factor: Factor that multiply default training lenghts (similar to "training" parameter in the matlab version). (lbugnon)
"""
logging.root.setLevel(
getattr(logging, verbose.upper()) if verbose else logging.ERROR)
logging.info(" Training...")
print('Training ...')
logging.debug((
"--------------------------------------------------------------\n"
" details: \n"
" > data len is {data_len} and data dimension is {data_dim}\n"
" > map size is {mpsz0},{mpsz1}\n"
" > array size in log10 scale is {array_size}\n"
" > number of jobs in parallel: {n_job}\n"
" -------------------------------------------------------------\n"
).format(data_len=self._dlen,
data_dim=self._dim,
mpsz0=self.codebook.mapsize[0],
mpsz1=self.codebook.mapsize[1],
array_size=np.log10(self._dlen * self.codebook.nnodes *
self._dim),
n_job=n_job))
if self.initialization == 'random' and alreadyinit == False:
self.codebook.random_initialization(self._data)
elif self.initialization == 'custom' and alreadyinit == False:
self.codebook.custom_initialization(self._data)
elif self.initialization == 'pca' and alreadyinit == False:
self.codebook.pca_linear_initialization(self._data, self.mask)
elif alreadyinit == False:
raise AttributeError('initialisation inconnue')
if train_rough_len > 0:
self.rough_train(njob=n_job,
shared_memory=shared_memory,
trainlen=train_rough_len,
radiusin=train_rough_radiusin,
radiusfin=train_rough_radiusfin,
trainlen_factor=train_len_factor,
maxtrainlen=maxtrainlen,
watch_evolution=watch_evolution)
if train_finetune_len > 0:
self.finetune_train(njob=n_job,
shared_memory=shared_memory,
trainlen=train_finetune_len,
radiusin=train_finetune_radiusin,
radiusfin=train_finetune_radiusfin,
trainlen_factor=train_len_factor,
maxtrainlen=maxtrainlen,
watch_evolution=watch_evolution)
if self._bmu is None: # calcul bmu meme si pas entrainé
self._bmu = self.find_bmu(self._data, njb=n_job)
#self.plotplanes2()
logging.debug(
" --------------------------------------------------------------")
logging.info(" Final quantization error: %f" % np.mean(self._bmu[1]))
def _calculate_ms_and_mpd(self):
mn = np.min(self.codebook.mapsize)
max_s = max(self.codebook.mapsize[0], self.codebook.mapsize[1])
if mn == 1:
mpd = float(self.codebook.nnodes * 10) / float(self._dlen)
else:
mpd = float(self.codebook.nnodes) / float(self._dlen)
ms = max_s / 2.0 if mn == 1 else max_s
return ms, mpd
def rough_train(self,
njob=1,
shared_memory=False,
trainlen=None,
radiusin=None,
radiusfin=None,
trainlen_factor=1,
maxtrainlen=np.Inf,
watch_evolution=True):
logging.info(" Rough training...")
print(" Rough training...")
ms, mpd = self._calculate_ms_and_mpd()
#lbugnon: add maxtrainlen
trainlen = min(int(np.ceil(30 * mpd)),
maxtrainlen) if not trainlen else trainlen
#print("maxtrainlen %d",maxtrainlen)
#lbugnon: add trainlen_factor
trainlen = int(trainlen * trainlen_factor)
if self.initialization == 'random':
radiusin = max(1, np.ceil(ms / 3.)) if not radiusin else radiusin
radiusfin = max(1, radiusin / 6.) if not radiusfin else radiusfin
elif self.initialization == 'pca':
radiusin = max(1, np.ceil(ms / 8.)) if not radiusin else radiusin
radiusfin = max(1, radiusin / 4.) if not radiusfin else radiusfin
self._batchtrain(trainlen,
radiusin,
radiusfin,
njob,
shared_memory,
watch_evolution=watch_evolution)
def finetune_train(self,
njob=1,
shared_memory=False,
trainlen=None,
radiusin=None,
radiusfin=None,
trainlen_factor=1,
maxtrainlen=np.Inf,
watch_evolution=True):
logging.info(" Finetune training...")
print('Finetune training')
ms, mpd = self._calculate_ms_and_mpd()
#lbugnon: add maxtrainlen
if self.initialization == 'random':
trainlen = min(int(np.ceil(50 * mpd)),
maxtrainlen) if not trainlen else trainlen
radiusin = max(
1, ms / 12.
) if not radiusin else radiusin # from radius fin in rough training
radiusfin = max(1, radiusin / 25.) if not radiusfin else radiusfin
elif self.initialization == 'pca':
trainlen = min(int(np.ceil(40 * mpd)),
maxtrainlen) if not trainlen else trainlen
radiusin = max(1,
np.ceil(ms / 8.) / 4) if not radiusin else radiusin
radiusfin = 1 if not radiusfin else radiusfin # max(1, ms/128)
#print("maxtrainlen %d",maxtrainlen)
#lbugnon: add trainlen_factor
trainlen = int(trainlen_factor * trainlen)
self._batchtrain(trainlen,
radiusin,
radiusfin,
njob,
shared_memory,
watch_evolution=watch_evolution)
def _batchtrain(self,
trainlen,
radiusin,
radiusfin,
njob=1,
shared_memory=False,
watch_evolution=True):
if self.radius_train == 'linear':
radius = np.linspace(radiusin, radiusfin, trainlen)
elif self.radius_train == 'power_series':
radius = []
ratio = radiusfin / radiusin
for i in range(trainlen):
radius.append(radiusin * ((ratio)**(i / trainlen)))
elif self.radius_train == 'inverse_of_time':
radius = []
B = trainlen / ((radiusin / radiusfin) - 1)
A = B * radiusin
for i in range(trainlen):
radius.append(A / (i + B))
else:
raise AttributeError('évolution du radius inconnue')
if shared_memory:
data = self._data
data_folder = tempfile.mkdtemp()
data_name = os.path.join(data_folder, 'data')
dump(data, data_name)
data = load(data_name, mmap_mode='r')
else:
data = self._data
bmu = None
# X2 is part of euclidean distance (x-y)^2 = x^2 +y^2 - 2xy that we use
# for each data row in bmu finding.
# Since it is a fixed value we can skip it during bmu finding for each
# data point, but later we need it calculate quantification error
logging.info(" radius_ini: %f , radius_final: %f, trainlen: %d\n" %
(radiusin, radiusfin, trainlen))
print(
"radius_ini: {:.3f} , radius_final: {:.3f}, trainlen: {}\n".format(
radiusin, radiusfin, trainlen))
for i in range(trainlen):
t1 = time()
neighborhood = self.neighborhood.calculate(self._distance_matrix,
radius[i],
self.codebook.nnodes)
bmu = self.find_bmu(data, njb=njob)
self.codebook.matrix = self.update_codebook_voronoi(
data, bmu, neighborhood)
#print('nombre de neurones activés : ',len(np.unique(bmu[0])))
qerror = self.calculate_quantization_error()
terror = self.calculate_topographic_error()
print('Epoch : {} qErr : {:.4f} tErr : {:.4f}'.format(
i, qerror, terror))
logging.info(
" epoch: {} ---> elapsed time: {:2.2f}, quantization error: {:2.4f}"
.format(i,
time() - t1, qerror))
if np.any(np.isnan(qerror)):
logging.info("nan quantization error, exit train\n")
#sys.exit("quantization error=nan, exit train")
if i % 1 == 0 and watch_evolution == True:
self.plotplanes() # ajout LB
if watch_evolution == False:
self.plotplanes()
#print('bmu = {}'.format(bmu[1] + fixed_euclidean_x2))
bmu = self.find_bmu(data,
njb=njob) # ajout LB : il faut remettre à jour
#tmp= bmu[1] + fixed_euclidean_x2
#tmp[tmp<0]=0 # ajout LB
#bmu[1] = np.sqrt(bmu[1] + fixed_euclidean_x2)
#bmu[1]=tmp
self._bmu = bmu
@timeit(logging.DEBUG)
def find_bmu(self, input_matrix, njb=1, nth=1):
"""
Finds the best matching unit (bmu) for each input data from the input
matrix. It does all at once parallelizing the calculation instead of
going through each input and running it against the codebook.
:param input_matrix: numpy matrix representing inputs as rows and
features/dimension as cols
:param njb: number of jobs to parallelize the search
:returns: the best matching unit for each input
"""
dlen = input_matrix.shape[0]
if self.mask is not None: # ajout LB
codebookmask = self.codebook.matrix * self.mask.squeeze()
datamask = input_matrix * self.mask
else:
codebookmask = self.codebook.matrix
datamask = input_matrix
y2 = np.einsum('ij,ij->i', self.codebook.matrix,
codebookmask) # somme des carrés Y**2
x2 = np.einsum('ij,ij->i', datamask, input_matrix)
if njb == -1:
njb = cpu_count()
pool = Pool(njb)
chunk_bmu_finder = _chunk_based_bmu_find
def row_chunk(part):
return part * dlen // njb
def col_chunk(part):
return min((part + 1) * dlen // njb, dlen)
chunks = [input_matrix[row_chunk(i):col_chunk(i)] for i in range(njb)]
#chunk_bmu_finder(input_matrix, self.codebook.matrix, y2, x2,nth=nth,mask=self.mask)
b = pool.map(lambda chk: chunk_bmu_finder(
chk, self.codebook.matrix, y2, x2, nth=nth, mask=self.mask),
chunks) # modif LB
pool.close()
pool.join()
bmu = np.asarray(list(itertools.chain(*b))).T
del b
return bmu
@timeit(logging.DEBUG)
def update_codebook_voronoi(self, training_data, bmu, neighborhood):
"""
Updates the weights of each node in the codebook that belongs to the
bmu's neighborhood.
First finds the Voronoi set of each node. It needs to calculate a
smaller matrix.
Super fast comparing to classic batch training algorithm, it is based
on the implemented algorithm in som toolbox for Matlab by Helsinky
University.
:param training_data: input matrix with input vectors as rows and
vector features as cols
:param bmu: best matching unit for each input data. Has shape of
(2, dlen) where first row has bmu indexes
:param neighborhood: matrix representing the neighborhood of each bmu
:returns: An updated codebook that incorporates the learnings from the
input data
"""
row = bmu[0].astype(
int) # pour chape individu le numero du codebook associé
col = np.arange(self._dlen)
val = np.tile(1, self._dlen) # autant de 1 que d'individus
P = csr_matrix(
(val, (row, col)), shape=(self.codebook.nnodes, self._dlen)
) # nbr codebook x nbr individus avec des 1 lorsque un individu voisinage du codebook, 0 sinon
S = P.dot(
training_data
) # nbr codebook x dim codebook : formule 5 page 11 de somtoolbox 5 matlab
# neighborhood has nnodes*nnodes and S has nnodes*dim
# ---> Nominator has nnodes*dim
nom = neighborhood.T.dot(S)
nV = P.sum(axis=1).reshape(1, self.codebook.nnodes)
denom = nV.dot(neighborhood.T).reshape(self.codebook.nnodes,
1) # role de la transposée ???
new_codebook = np.divide(nom, denom)
if (denom == 0.0).sum() > 0:
print('denominateur nul', denom)
raise
#return np.around(new_codebook, decimals=6)
return np.asarray(new_codebook) # modif LB
def project_data(self, data, normalize=False): # modif LB
"""
Projects a data set to a trained SOM. It is based on nearest
neighborhood search module of scikitlearn, but it is not that fast.
"""
clf = neighbors.KNeighborsClassifier(n_neighbors=1)
labels = np.arange(0, self.codebook.matrix.shape[0])
clf.fit(self.codebook.matrix, labels)
# The codebook values are all normalized
# we can normalize the input data based on mean and std of
# original data
if normalize:
data = self._normalizer.normalize_by(self.data_raw, data)
return clf.predict(data)
def predict_by(self, data, target, k=5, wt='distance'):
# here it is assumed that target is the last column in the codebook
# and data has dim-1 columns
print('fonction predict_by est elle utilisée ?')
raise
dim = self.codebook.matrix.shape[1]
ind = np.arange(0, dim)
indX = ind[ind != target]
x = self.codebook.matrix[:, indX]
y = self.codebook.matrix[:, target]
n_neighbors = k
clf = neighbors.KNeighborsRegressor(n_neighbors, weights=wt)
clf.fit(x, y)
# The codebook values are all normalized
# we can normalize the input data based on mean and std of
# original data
dimdata = data.shape[1]
if dimdata == dim:
data[:, target] = 0
data = self._normalizer.normalize_by(self.data_raw, data)
data = data[:, indX]
elif dimdata == dim - 1:
data = self._normalizer.normalize_by(self.data_raw[:, indX], data)
predicted_values = clf.predict(data)
predicted_values = self._normalizer.denormalize_by(
self.data_raw[:, target], predicted_values)
return predicted_values
def predict(self, x_test, k=5, wt='distance'):
"""
Similar to SKlearn we assume that we have X_tr, Y_tr and X_test. Here
it is assumed that target is the last column in the codebook and data
has dim-1 columns
:param x_test: input vector
:param k: number of neighbors to use
:param wt: method to use for the weights
(more detail in KNeighborsRegressor docs)
:returns: predicted values for the input data
"""
target = self.data_raw.shape[1] - 1
x_train = self.codebook.matrix[:, :target]
y_train = self.codebook.matrix[:, target]
clf = neighbors.KNeighborsRegressor(k, weights=wt)
clf.fit(x_train, y_train)
# The codebook values are all normalized
# we can normalize the input data based on mean and std of
# original data
x_test = self._normalizer.normalize_by(self.data_raw[:, :target],
x_test)
predicted_values = clf.predict(x_test)
return self._normalizer.denormalize_by(self.data_raw[:, target],
predicted_values)
def find_k_nodes(self, data, k=5):
from sklearn.neighbors import NearestNeighbors
# we find the k most similar nodes to the input vector
neighbor = NearestNeighbors(n_neighbors=k)
neighbor.fit(self.codebook.matrix)
# The codebook values are all normalized
# we can normalize the input data based on mean and std of
# original data
return neighbor.kneighbors(
self._normalizer.normalize_by(self.data_raw, data))
def bmu_ind_to_xy(self, bmu_ind):
"""
Translates a best matching unit index to the corresponding
matrix x,y coordinates.
:param bmu_ind: node index of the best matching unit
(number of node from top left node)
:returns: corresponding (x,y) coordinate
"""
rows = self.codebook.mapsize[0]
cols = self.codebook.mapsize[1]
# bmu should be an integer between 0 to no_nodes
out = np.zeros((bmu_ind.shape[0], 3))
out[:, 2] = bmu_ind
out[:, 0] = rows - 1 - bmu_ind / cols
out[:, 0] = bmu_ind / cols
out[:, 1] = bmu_ind % cols
return out.astype(int)
def cluster(self, n_clusters=8):
import sklearn.cluster as clust
#cl_labels = clust.KMeans(n_clusters=n_clusters).fit_predict(
# self._normalizer.denormalize_by(self.data_raw,
# self.codebook.matrix))
cl_labels = clust.KMeans(n_clusters=n_clusters).fit_predict(
self.codebook.matrix)
#print(cl_labels)
self.cluster_labels = cl_labels
return cl_labels
def predict_probability(self, data, target, k=5):
"""
Predicts probability of the input data to be target
:param data: data to predict, it is assumed that 'target' is the last
column in the codebook, so data hould have dim-1 columns
:param target: target to predict probability
:param k: k parameter on KNeighborsRegressor
:returns: probability of data been target
"""
dim = self.codebook.matrix.shape[1]
ind = np.arange(0, dim)
indx = ind[ind != target]
x = self.codebook.matrix[:, indx]
y = self.codebook.matrix[:, target]
clf = neighbors.KNeighborsRegressor(k, weights='distance')
clf.fit(x, y)
# The codebook values are all normalized
# we can normalize the input data based on mean and std of
# original data
dimdata = data.shape[1]
if dimdata == dim:
data[:, target] = 0
data = self._normalizer.normalize_by(self.data_raw, data)
data = data[:, indx]
elif dimdata == dim - 1:
data = self._normalizer.normalize_by(self.data_raw[:, indx], data)
weights, ind = clf.kneighbors(data,
n_neighbors=k,
return_distance=True)
weights = 1. / weights
sum_ = np.sum(weights, axis=1)
weights = weights / sum_[:, np.newaxis]
labels = np.sign(self.codebook.matrix[ind, target])
labels[labels >= 0] = 1
# for positives
pos_prob = labels.copy()
pos_prob[pos_prob < 0] = 0
pos_prob *= weights
pos_prob = np.sum(pos_prob, axis=1)[:, np.newaxis]
# for negatives
neg_prob = labels.copy()
neg_prob[neg_prob > 0] = 0
neg_prob = neg_prob * weights * -1
neg_prob = np.sum(neg_prob, axis=1)[:, np.newaxis]
return np.concatenate((pos_prob, neg_prob), axis=1)
def node_activation(self, data, target=None, wt='distance'):
weights, ind = None, None
if not target:
clf = neighbors.KNeighborsClassifier(
n_neighbors=self.codebook.nnodes)
labels = np.arange(0, self.codebook.matrix.shape[0])
clf.fit(self.codebook.matrix, labels)
# The codebook values are all normalized
# we can normalize the input data based on mean and std of
# original data
data = self._normalizer.normalize_by(self.data_raw, data)
weights, ind = clf.kneighbors(data)
# Softmax function
weights = 1. / weights
return weights, ind
def calculate_topographic_error(self):
bmus1 = self.find_bmu(self.data_raw, njb=1, nth=1)
bmus2 = self.find_bmu(self.data_raw, njb=1, nth=2)
topographic_error = None
if self.codebook.lattice == "rect":
bmus_gap = np.abs(
(self.bmu_ind_to_xy(np.array(bmus1[0]))[:, 0:2] -
self.bmu_ind_to_xy(np.array(bmus2[0]))[:, 0:2]).sum(axis=1))
topographic_error = np.mean(bmus_gap != 1)
elif self.codebook.lattice == "hexa":
dist_matrix_1 = self.codebook.lattice_distances[bmus1[0].astype(
int)].reshape(len(bmus1[0]), -1)
topographic_error = (np.array([
distances[bmu2]
for bmu2, distances in zip(bmus2[0].astype(int), dist_matrix_1)
]) > 2).mean()
return (topographic_error)
def calculate_quantization_error(self):
neuron_values = self.codebook.matrix[self.find_bmu(
self._data)[0].astype(int)]
quantization_error = np.mean(
np.abs(neuron_values - self._data)) # norme L1 et pas L2 ????
return quantization_error
def calculate_map_size(self, lattice):
"""
Calculates the optimal map size given a dataset using eigenvalues and eigenvectors. Matlab ported
:lattice: 'rect' or 'hex'
:return: map sizes
"""
D = self.data_raw.copy()
dlen = D.shape[0]
dim = D.shape[1]
munits = np.ceil(5 * (dlen**0.5))
A = np.ndarray(shape=[dim, dim]) + np.Inf
for i in range(dim):
D[:, i] = D[:, i] - np.mean(D[np.isfinite(D[:, i]), i])
for i in range(dim):
for j in range(dim):
c = D[:, i] * D[:, j]
c = c[np.isfinite(c)]
A[i, j] = sum(c) / len(c)
A[j, i] = A[i, j]
VS = np.linalg.eig(A)
eigval = sorted(VS[0])
if eigval[-1] == 0 or eigval[-2] * munits < eigval[-1]:
ratio = 1
else:
ratio = np.sqrt(eigval[-1] / eigval[-2])
if lattice == "rect":
size1 = min(munits, round(np.sqrt(munits / ratio)))
else:
size1 = min(munits, round(np.sqrt(munits / ratio * np.sqrt(0.75))))
size2 = round(munits / size1)
return [int(size1), int(size2)]
def calculate_neighbours_nodes(self):
res = []
for i in range(self.codebook.nnodes):
current = []
for j in range(self.codebook.nnodes):
if (self._distance_matrix[i][j] < 1.5
and self._distance_matrix[i][j] != 0):
current.append(j)
res.append(current)
return res
