def create_model(data, n_clusters, method, random):
model = SpectralBiclustering(n_clusters=n_clusters,
method=method,
random_state=random)
model.fit(data)
return model
def test_fit_best_piecewise():
model = SpectralBiclustering(random_state=0)
vectors = np.array([[0, 0, 0, 1, 1, 1],
[2, 2, 2, 3, 3, 3],
[0, 1, 2, 3, 4, 5]])
best = model._fit_best_piecewise(vectors, n_best=2, n_clusters=2)
assert_array_equal(best, vectors[:2])
def test_project_and_cluster():
model = SpectralBiclustering(random_state=0)
data = np.array([[1, 1, 1], [1, 1, 1], [3, 6, 3], [3, 6, 3]])
vectors = np.array([[1, 0], [0, 1], [0, 0]])
for mat in (data, csr_matrix(data)):
labels = model._project_and_cluster(data, vectors, n_clusters=2)
assert_array_equal(labels, [0, 0, 1, 1])
def test_spectral_biclustering():
"""Test Kluger methods on a checkerboard dataset."""
param_grid = {'method': ['scale', 'bistochastic', 'log'],
'svd_method': ['randomized', 'arpack'],
'n_svd_vecs': [None, 20],
'mini_batch': [False, True],
'init': ['k-means++'],
'n_init': [10],
'n_jobs': [1]}
random_state = 0
S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5,
random_state=random_state)
for mat in (S, csr_matrix(S)):
for kwargs in ParameterGrid(param_grid):
model = SpectralBiclustering(n_clusters=3,
random_state=random_state,
**kwargs)
if issparse(mat) and kwargs['method'] == 'log':
# cannot take log of sparse matrix
assert_raises(ValueError, model.fit, mat)
continue
else:
model.fit(mat)
assert_equal(model.rows_.shape, (9, 30))
assert_equal(model.columns_.shape, (9, 30))
assert_array_equal(model.rows_.sum(axis=0),
np.repeat(3, 30))
assert_array_equal(model.columns_.sum(axis=0),
np.repeat(3, 30))
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
def run(self, data):
bc = SpectralBiclustering(n_clusters=(self.n_gene_classes,
self.n_classes))
bc.fit(data)
gene_clusters = bc.row_labels_
cell_clusters = bc.column_labels_
return cell_clusters
def biclustering(matrix, distance, callback=None):
if min(matrix.shape) <= 2:
return np.arange(matrix.shape[0]), np.arange(matrix.shape[1])
best_score = np.iinfo(np.dtype('uint16')).max
best_model = None
# find the best biclusters (needs revision)
limit = int(min(matrix.shape) / 2) - 1
limit = 3 if limit < 3 else limit
for i in range(2, limit):
if callback is not None:
callback(0.2 + (i - 2) / (limit - 2) * 0.8)
# perform biclustering
model = SpectralBiclustering(
n_clusters=i, method='log', random_state=0)
model.fit(matrix)
fit_data = matrix[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
# calculate score and save the lowest one
score = distance(fit_data)
if score < best_score:
best_score = score
best_model = model
return np.argsort(best_model.row_labels_), np.argsort(best_model.column_labels_)
def fit_predict(self, D):
"""Run ConsensusClustering algorithm on data D.
Return partition of input data and consensus matrix for best k.
"""
# number of samples
n = D.shape[0]
# AUC score for each k
AUC_scores = np.zeros(len(self.num_clusters))
i = 0
for k in self.num_clusters:
M = self.calc_consensus(n, D, k)
AUC_scores[i] = self.calc_auc(M)
i = i + 1
# find best number of clusters (k_best)
idx_k_best = np.argmax(AUC_scores)
k_best = K[idx_k_best]
# uncomment to see the best k for given input data
#print("Best number of clusters (k): ", k_best)
M_k_best = self.calc_consensus(n, D, k_best)
# partition D into K-best clusters based on M_k_best using
# SpectralBiclustering
model = SpectralBiclustering(n_clusters=k_best, method='bistochastic')
model.fit(M_k_best)
P = model.row_labels_
return P, M_k_best
def test_fit_best_piecewise():
model = SpectralBiclustering(random_state=0)
vectors = np.array([[0, 0, 0, 1, 1, 1],
[2, 2, 2, 3, 3, 3],
[0, 1, 2, 3, 4, 5]])
best = model._fit_best_piecewise(vectors, n_best=2, n_clusters=2)
assert_array_equal(best, vectors[:2])
def test_spectral_biclustering():
"""Test Kluger methods on a checkerboard dataset."""
param_grid = {'method': ['scale', 'bistochastic', 'log'],
'svd_method': ['randomized', 'arpack'],
'n_svd_vecs': [None, 20],
'mini_batch': [False, True],
'init': ['k-means++'],
'n_init': [3],
'n_jobs': [1]}
random_state = 0
S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5,
random_state=random_state)
for mat in (S, csr_matrix(S)):
for kwargs in ParameterGrid(param_grid):
model = SpectralBiclustering(n_clusters=3,
random_state=random_state,
**kwargs)
if issparse(mat) and kwargs['method'] == 'log':
# cannot take log of sparse matrix
assert_raises(ValueError, model.fit, mat)
continue
else:
model.fit(mat)
assert_equal(model.rows_.shape, (9, 30))
assert_equal(model.columns_.shape, (9, 30))
assert_array_equal(model.rows_.sum(axis=0),
np.repeat(3, 30))
assert_array_equal(model.columns_.sum(axis=0),
np.repeat(3, 30))
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
def fi_selection_algo(metadata, settings, X, target_atts_list=None):
fi_scores = get_fi_scores(X, target_atts_list, metadata)
n_clusters = (int(settings["selection"]["param"]), 2)
model = SpectralBiclustering(n_clusters=n_clusters, method="log")
model.fit(fi_scores)
cluster_labels = model.row_labels_
codes = labels_to_codes(cluster_labels, target_atts_list)
return codes
def plotBicluster(df, n_clusters):
model = SpectralBiclustering(n_clusters=n_clusters, method='log', random_state=0)
model.fit(df)
fitDf = df.iloc[np.argsort(model.row_labels_), :]
fitDf = fitDf.iloc[:, np.argsort(model.column_labels_)]
plotCorrHeatmap(dmat=fitDf)
return fitDf
def plotBicluster(df, n_clusters, col_labels=None):
model = SpectralBiclustering(n_clusters=n_clusters, method='log', random_state=0)
model.fit(df)
fitDf = df.iloc[np.argsort(model.row_labels_),:]
fitDf = fitDf.iloc[:, np.argsort(model.column_labels_)]
plotCorrHeatmap(dmat=fitDf, col_labels=col_labels)
return fitDf
def fit_data_to_model(self,shapey): model = SpectralBiclustering(n_clusters=shapey, method='log',random_state=0) model.fit(self.data) self.fit_data = self.data[np.argsort(model.row_labels_)] self.fit_data = self.fit_data[:, np.argsort(model.column_labels_)] self.rowl = model.row_labels_ self.coll = model.column_labels_ self.shapex = shapey
def get_bicluster(self, data):
# Biclustering
model = SpectralBiclustering(n_clusters=data.shape[1], random_state=0)
print(data.sum(axis=0))
print(data.sum(axis=1))
model.fit(data.fillna(0))
fit_data = data.iloc[np.argsort(model.row_labels_)]
fit_data = fit_data.iloc[:, np.argsort(model.column_labels_)]
return fit_data
def SpectralBiCluster(data, n_clusters=(4, 4)):
from sklearn.datasets import make_checkerboard
from matplotlib import pyplot as plt
from sklearn.cluster.bicluster import SpectralBiclustering
model = SpectralBiclustering(method='log', random_state=0)
data = np.array(data)
model.fit(data)
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
def fit_data_to_model(self, shapey):
model = SpectralBiclustering(n_clusters=shapey,
method='log',
random_state=0)
model.fit(self.data)
self.fit_data = self.data[np.argsort(model.row_labels_)]
self.fit_data = self.fit_data[:, np.argsort(model.column_labels_)]
self.rowl = model.row_labels_
self.coll = model.column_labels_
self.shapex = shapey
def test_errors():
data = np.arange(25).reshape((5, 5))
model = SpectralBiclustering(n_clusters=(3, 3, 3))
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_clusters='abc')
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_clusters=(3, 'abc'))
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(method='unknown')
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(svd_method='unknown')
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_components=0)
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_best=0)
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering(n_components=3, n_best=4)
assert_raises(ValueError, model.fit, data)
model = SpectralBiclustering()
data = np.arange(27).reshape((3, 3, 3))
assert_raises(ValueError, model.fit, data)
def plot_biclustering_with_pearson(time_ms, title):
sliced_matrix = slice_matrix(matrix, time_ms)
channels_data = calculate_n_columns(sliced_matrix)
z_score = stats.zscore(channels_data)
plt.title('Z Score Biclustering Over %i ms' % time_ms)
spectral_model = SpectralBiclustering()
spectral_model.fit(z_score)
fit_data = z_score[np.argsort(spectral_model.row_labels_)]
fit_data = fit_data[:, np.argsort(spectral_model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.savefig('z_score_%s_biclustering_all_ts_%i.svg' % (time_ms, title))
def test_project_and_cluster():
model = SpectralBiclustering(random_state=0)
data = np.array([[1, 1, 1],
[1, 1, 1],
[3, 6, 3],
[3, 6, 3]])
vectors = np.array([[1, 0],
[0, 1],
[0, 0]])
for mat in (data, csr_matrix(data)):
labels = model._project_and_cluster(data, vectors,
n_clusters=2)
assert_array_equal(labels, [0, 0, 1, 1])
def biclustering(filtered, checked) : ### over 2 if len(filtered['data']) >= 2 : n_clusters = (2, 2) else : n_clusters = (1, 1) model = SpectralBiclustering(n_clusters=n_clusters, method='log', random_state=0) data = np.asarray(filtered['data']) model.fit(data) #biclustering y_fit_data = data[np.argsort(model.row_labels_)] fit_data = y_fit_data[:, np.argsort(model.column_labels_)] #set y label y = np.argsort(model.row_labels_) y_label = [0 for i in range(len(y))] for n in range(len(y)) : y_label[y[n]] = n #set x label x = np.argsort(model.column_labels_) x_label = [0 for i in range(len(x))] for n in range(len(x)) : x_label[x[n]] = n d1 = bd.draw_graph(group1, group2, checked, x = x, x_label = x_label, y_label = y_label, fit_data = fit_data, genus_data = filtered['genus'], pvalue_label = filtered['pvalue'], title = "After biclustering") d1.draw() # biclustering of fixed x-axis domain d2 = bd.draw_graph(group1, group2, checked, x_label = [i for i in range(len(group1+group2))], y_label = y_label, x = [i for i in range(len(group1+group2))], fit_data = y_fit_data, genus_data = filtered['genus'], pvalue_label = filtered['pvalue'], title = "After biclustering; fixed x domins") d2.draw()
def spectral_biclust(E, ngenes=3, nconditions=1, spectral_method="bistochastic", n=6, n_best_ratio=0.5, **kwargs):
n_best = max([int(n*n_best_ratio), 1])
spectral = SpectralBiclustering(n_clusters=(nconditions,ngenes), method=spectral_method, n_components=n, n_best=n_best)
spectral.fit(standardize(E))
bics = []
for columns, rows in zip(spectral.columns_, spectral.rows_):
genes = E.columns[columns]
conditions = E.index[rows]
bics.append(Bicluster(genes, conditions))
return bics
def spectral_bi_cluster(data, n_clusters, para_jobs=1, random_state=None):
from sklearn.cluster.bicluster import SpectralBiclustering
assert len(
n_clusters
) == 2, "n_cluster should be a tuple or list that contains 2 integer!"
model = SpectralBiclustering(n_clusters,
random_state=random_state,
n_jobs=para_jobs,
method='bistochastic',
n_best=20,
n_components=40)
model.fit(data)
row_labels = model.row_labels_
col_labels = model.column_labels_
return row_labels, col_labels
def _spectral_bicluster(self, n_clusters, interaction_matrix):
clustering = SpectralBiclustering(n_clusters=n_clusters,
random_state=0).fit(
self.interaction_matrix)
pdz_clusters = clustering.row_labels_
peptide_clusters = clustering.column_labels_
return pdz_clusters, peptide_clusters
def plot_biclustering_raw_data(time_ms, t=False):
# take the transpose of sliced matrix
if t:
channels_data = slice_matrix(matrix, time_ms).T
else:
channels_data = slice_matrix(matrix, time_ms)
print len(channels_data), len(channels_data[1])
z_score = stats.zscore(channels_data)
plt.title('Z Score Biclustering Over %i ms' % time_ms)
spectral_model = SpectralBiclustering()
spectral_model.fit(z_score)
fit_data = z_score[np.argsort(spectral_model.row_labels_)]
fit_data = fit_data[:, np.argsort(spectral_model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
# plt.savefig('z_score_raw_biclustering_all_ts_%i_T_%s.svg' % (time_ms, str(t)))
plt.show()
def spectral(dataset_name, full, preprocessing, mindf, k1, k2, ngram_min,
ngram_max, start, end, n):
if not spectral_directory_exists(dataset_name):
create_spectral_directory(dataset_name)
h, c = obtain_file_name_from_dataset(dataset_name, preprocessing)
corpus = obtain_full_corpus(h, c)
if full:
texts = corpus.text.values
docnames = corpus.text.index.values
if not tfidf_exists(dataset_name, preprocessing):
X, v = create_tfidf(texts, mindf, ngram_min, ngram_max)
words = v.get_feature_names()
store_data(dataset_name, preprocessing, X, docnames, words)
tfidf, documents, terms = load_data(dataset_name, preprocessing)
if not spectral_exists(get_directory_dataset(dataset_name),
dataset_name, preprocessing, mindf, k1, k2,
ngram_min, ngram_max):
start = time.time()
model = SpectralBiclustering(n_clusters=(k1, k2), random_state=0)
model.fit(tfidf)
end = time.time()
print("Biclustering process takes", int(round(end - start)),
"seconds")
save_clasification(get_directory_dataset(dataset_name),
dataset_name, preprocessing, mindf, k1, k2,
ngram_min, ngram_max, model)
else:
time_corpus = split_data_in_time_slices(corpus, start, end, n)
if not tfidf_periods_exists(dataset_name, preprocessing, start, end,
n):
os.makedirs(
get_directory_dataset_periods(dataset_name, preprocessing,
start, end, n))
for (s, e), corp in time_corpus.items():
texts = corp.text.values
docnames = corp.text.index.values
X, v = create_tfidf(texts, mindf, ngram_min, ngram_max)
words = v.get_feature_names()
store_data_periods(dataset_name, preprocessing, start, end, n,
s, e, X, docnames, words)
for s, e in time_corpus:
tfidf, documents, terms = load_data_periods(
dataset_name, preprocessing, start, end, n, s, e)
if not spectral_periods_exists(dataset_name, preprocessing, mindf,
k1, k2, ngram_min, ngram_max, start,
end, n, s, e):
st = time.time()
model = SpectralBiclustering(n_clusters=(k1, k2),
random_state=0)
model.fit(tfidf)
ed = time.time()
print("Biclustering process takes", int(round(ed - st)),
"seconds")
save_clasification_periods(dataset_name, preprocessing, mindf,
k1, k2, ngram_min, ngram_max, model,
start, end, n, s, e)
def plot_biclusters_n_intervals(n_intervals=30000):
channels_data = [[] for i in range(64)]
for row in range(64):
start, end = 0, n_intervals
row_data = matrix[row]
while end < len(row_data):
channels_data[row].append(float(sum(row_data[start:end])) / len(row_data[start:end]))
start = end
end += n_intervals
z_score = stats.zscore(np.array(channels_data))
plt.title('Z Score Biclustering')
spectral_model = SpectralBiclustering()
spectral_model.fit(z_score)
fit_data = z_score[np.argsort(spectral_model.row_labels_)]
fit_data = fit_data[:, np.argsort(spectral_model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.savefig('z_score_raw_biclustering_all_%is.svg' % (n_intervals / 1000))
def spectral_biclustering(cls, *args):
"""
Wrapper method for the spectral_biclustering algorithm
:param args: the arguments to be sent to the sci-kit implementation
:return: returns the Biclustering object
"""
model = SpectralBiclustering(*args)
return cls(model)
def spectral_cluster(dataframe, n_clusters=(30, 30), show_plots=False):
model = SpectralBiclustering(n_clusters=n_clusters,
method='log',
random_state=0)
data = dataframe.fillna(0.0).values
model.fit(data)
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
if show_plots:
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
plt.matshow(np.outer(
np.sort(model.row_labels_) + 1,
np.sort(model.column_labels_) + 1),
cmap=plt.cm.Blues)
plt.title("Checkerboard structure of rearranged data")
return model
def test_perfect_checkerboard():
model = SpectralBiclustering(3, svd_method="arpack", random_state=0)
S, rows, cols = make_checkerboard((30, 30), 3, noise=0, random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1)
S, rows, cols = make_checkerboard((40, 30), 3, noise=0, random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1)
S, rows, cols = make_checkerboard((30, 40), 3, noise=0, random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1)
def Spectral_BiClustering(M, args):
'''Function to perform bipartite clustering'''
# Create model
try:
if args.arpack:
model = SpectralBiclustering(
n_clusters=args.nClusters, svd_method='arpack')
else:
model = SpectralBiclustering(
n_clusters=args.nClusters)
except:
print '-r 1 may cause problems when svd_method has been set to arpack'
print('Running biclustering')
model.fit(M.tocsc())
print('Biclustering done')
# Fit to data
# fit_data = M[np.argsort(model.row_labels_)]
# fit_data = fit_data[:, np.argsort(model.column_labels_)]
fit_data = M.tocoo()
fit_data.row = invert_permutation(np.argsort(model.row_labels_))[fit_data.row]
fit_data.col = invert_permutation(np.argsort(model.column_labels_))[fit_data.col]
save_clusters(model, fit_data, args, '_BiClustering', 1)
return model, fit_data
def test_perfect_checkerboard():
raise SkipTest("This test is failing on the buildbot, but cannot"
" reproduce. Temporarily disabling it until it can be"
" reproduced and fixed.")
model = SpectralBiclustering(3, svd_method="arpack", random_state=0)
S, rows, cols = make_checkerboard((30, 30), 3, noise=0, random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1)
S, rows, cols = make_checkerboard((40, 30), 3, noise=0, random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1)
S, rows, cols = make_checkerboard((30, 40), 3, noise=0, random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_, (rows, cols)), 1)
def _get_clusters_using_spectrals(corrarr, n_clusters=5, mode='co'):
if mode=='co':
model = SpectralCoclustering(n_clusters, random_state=0)
model.fit(corrarr)
indices = np.arange(corrarr.columns.size)
clusters = [indices[x].tolist() for x in model.columns_]
return clusters
elif mode=='bi':
model = SpectralBiclustering(n_clusters, random_state=0)
model.fit(corrarr)
indices = np.arange(corrarr.columns.size)
clusters = [indices[x].tolist() for x in model.columns_]
repetition_start = clusters[1:].index(clusters[0]) + 1
return clusters[:repetition_start]
else:
raise("Mode wrong?")
def test_spectral_biclustering():
# Test Kluger methods on a checkerboard dataset.
S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5,
random_state=0)
non_default_params = {'method': ['scale', 'log'],
'svd_method': ['arpack'],
'n_svd_vecs': [20],
'mini_batch': [True]}
for mat in (S, csr_matrix(S)):
for param_name, param_values in non_default_params.items():
for param_value in param_values:
model = SpectralBiclustering(
n_clusters=3,
n_init=3,
init='k-means++',
random_state=0,
)
model.set_params(**dict([(param_name, param_value)]))
if issparse(mat) and model.get_params().get('method') == 'log':
# cannot take log of sparse matrix
with pytest.raises(ValueError):
model.fit(mat)
continue
else:
model.fit(mat)
assert model.rows_.shape == (9, 30)
assert model.columns_.shape == (9, 30)
assert_array_equal(model.rows_.sum(axis=0),
np.repeat(3, 30))
assert_array_equal(model.columns_.sum(axis=0),
np.repeat(3, 30))
assert consensus_score(model.biclusters_,
(rows, cols)) == 1
_test_shape_indices(model)
def cocluster(self, mx, blockdiag=False):
logging.info('Co-clustering Tade..')
if blockdiag:
logging.info('blockdiag')
clusser = SpectralCoclustering(n_jobs=-1)
else: # checkerboard
logging.info('checkerboard')
clusser = SpectralBiclustering(n_jobs=-1, n_clusters=(4, 3))
#n_clusters=3, svd_method='randomized',
clusser.fit(mx)
logging.info('Argsorting mx rows..')
mx = mx[np.argsort(clusser.row_labels_)]
self.prev = self.prev[np.argsort(clusser.row_labels_)]
logging.info('Argsorting mx cases..')
mx = mx[:, np.argsort(clusser.column_labels_)]
self.case = self.case[np.argsort(clusser.column_labels_)]
return mx
def test_perfect_checkerboard():
model = SpectralBiclustering(3, svd_method="arpack", random_state=0)
S, rows, cols = make_checkerboard((30, 30), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
S, rows, cols = make_checkerboard((40, 30), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
S, rows, cols = make_checkerboard((30, 40), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
def test_perfect_checkerboard():
raise SkipTest("This test is failing on the buildbot, but cannot"
" reproduce. Temporarily disabling it until it can be"
" reproduced and fixed.")
model = SpectralBiclustering(3, svd_method="arpack", random_state=0)
S, rows, cols = make_checkerboard((30, 30), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
S, rows, cols = make_checkerboard((40, 30), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
S, rows, cols = make_checkerboard((30, 40), 3, noise=0,
random_state=0)
model.fit(S)
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
def test_spectral_biclustering():
# Test Kluger methods on a checkerboard dataset.
S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5,
random_state=0)
non_default_params = {'method': ['scale', 'log'],
'svd_method': ['arpack'],
'n_svd_vecs': [20],
'mini_batch': [True]}
for mat in (S, csr_matrix(S)):
for param_name, param_values in non_default_params.items():
for param_value in param_values:
model = SpectralBiclustering(
n_clusters=3,
n_init=3,
init='k-means++',
random_state=0,
)
model.set_params(**dict([(param_name, param_value)]))
if issparse(mat) and model.get_params().get('method') == 'log':
# cannot take log of sparse matrix
assert_raises(ValueError, model.fit, mat)
continue
else:
model.fit(mat)
assert_equal(model.rows_.shape, (9, 30))
assert_equal(model.columns_.shape, (9, 30))
assert_array_equal(model.rows_.sum(axis=0),
np.repeat(3, 30))
assert_array_equal(model.columns_.sum(axis=0),
np.repeat(3, 30))
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
_test_shape_indices(model)
##0.780023781213
###############################################################################
## Draw dendrogram
Z = linkage(data, 'ward')
plt.figure(figsize=(25, 10))
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('sample index')
plt.ylabel('distance')
dendrogram(
Z,
leaf_rotation=90., # rotates the x axis labels
leaf_font_size=8., # font size for the x axis labels
labels=np.array(authors)
)
plt.show()
###############################################################################
## Biclustering
data = data.astype('float')
bc = SpectralBiclustering(n_clusters=(n_authors,5))
bc.fit(data)
## TODO : sort the rows and columns
bc_data = data[np.argsort(bc.row_labels_)]
bc_data = bc_data[:, np.argsort(bc.column_labels_)]
## How to annotate the words?
plt.matshow(data, cmap = plt.cm.Blues)
plt.title("Original dataset")
plt.matshow(bc_data, cmap = plt.cm.Blues)
plt.title("After biclustering; rearrange to show biclusters")
l = int(sys.argv[4])
output_mat_name = sys.argv[5]
tfidf = load_sparse_mat(mat_name,mat_filename).astype(float32)
data = tfidf.A
im = plt.matshow(data, aspect='auto', cmap='jet')
vmax = amax(data)
vmin = amin(data)
plt.clim(vmin,vmax)
plt.colorbar(im)
m,n = tfidf.shape
print("Matrix dimensions: ",m,"x",n)
print("Row clusters:",k)
print("Column clusters:",l)
start = time.time()
model = SpectralBiclustering(n_clusters=(k,l),random_state=0)
model.fit(tfidf)
end = time.time()
print("Biclustering process takes",int(round(end-start)),"seconds")
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
im = plt.matshow(fit_data, aspect='auto', cmap='jet')
plt.clim(vmin,vmax)
plt.colorbar(im)
im = plt.matshow(np.outer(np.sort(model.row_labels_) + 1,
np.sort(model.column_labels_) + 1),
cmap='jet',aspect='auto')
plt.clim(vmin,vmax)
plt.colorbar(im)
plt.title("Checkerboard structure of rearranged data")
plt.show()
spectral_model.fit(z_score)
fit_data = z_score[np.argsort(spectral_model.row_labels_)]
fit_data = fit_data[:, np.argsort(spectral_model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.savefig('z_score_raw_biclustering_all_%is.svg' % (n_intervals / 1000))
def dump_raw_z_scores():
np.array(stats.zscore(np.array(matrix))).dump('raw_z_npdump.dump')
if __name__ == '__main__':
<<<<<<< HEAD
z_scores = np.load('raw_z_npdump.dump')
plt.title('Z Score Biclustering')
spectral_model = SpectralBiclustering()
spectral_model.fit(z_scores)
fit_data = z_scores[np.argsort(spectral_model.row_labels_)]
fit_data = fit_data[:, np.argsort(spectral_model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.savefig('z_score_bicluster.svg')
=======
# plot_biclustering_with_pearson(30000000000)
# plot_biclustering_raw_data(60000)
# plot_biclustering_raw_data(60000, t=True)
# plot_coclusters_raw_data(60000)
# plot_coclusters_raw_data(60000, t=True)
# plot_biclusters_n_intervals(15000)
dump_raw_z_scores()
print(z_score)
>>>>>>> 51502dc598c9e79326407b5d15302c706bb6cdf2
# train_features[user]['average_set_score'] = sum_set_scores / float(num_sets) # # s average score # sum_s_scores = 0 # for i in range(0, num_sets): # sum_s_scores += grades_rowdict[key]['s' + str(i)] # train_features[user]['average_s_score'] = sum_set_scores / float(num_sets) # # rest of the features # train_features[user]['course_score'] = grades_rowdict[key]['course'] # train_features[user]['final_exam_score'] = grades_rowdict[key]['final'] # train_features[user]['hw_score'] = grades_rowdict[key]['hw'] # train_features[user]['letter'] = grades_rowdict[key]['letter'] # train_features[user]['demerit'] = grades_rowdict[key]['demerit'] # else: # pass # MACHINE LEARNING CLUSTERING import numpy as np from sklearn.cluster import KMeans, DBSCAN kmeans = KMeans(init='k-means++', n_clusters=5, n_init=10) kmeans.fit(train_features) from sklearn.cluster.bicluster import SpectralBiclustering model = SpectralBiclustering(n_clusters=5, method='log', random_state=0) model.fit(train_features) train_features.loc['bf7aa87b-444a-4eff-9f81-b4078e6dccd3'] model.row_labels_
except:
print('FAYOL!')
media_id_num = picsdict[media_id]
m[media_id_num, usersdict[db[media_id][3]]] = True
for user in temp:
try:
m[media_id_num, usersdict[user.username]] = True
except:
# print(':3 ', user.username)
other_users.add(user.username)
import pickle
pickle.dump( m, open( "save.p", "wb" ), protocol = 2 )
m = pickle.load( open( "save.p", "rb" ) )
import numpy as np
from matplotlib import pyplot as plt
plt.matshow(m, cmap=plt.cm.Blues)
from sklearn.cluster.bicluster import SpectralBiclustering
from sklearn.metrics import consensus_score
model = SpectralBiclustering(method='bistochastic', n_jobs = -1)
model.fit(m)
fit_data = m[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
from sklearn.manifold import TSNE # C1 = sklearn.cluster.AgglomerativeClustering(n_clusters=5, affinity='precomputed') # # R1 = C1.fit_predict(Gram) # n = len(Gram) Di = np.reshape(np.diag(Gram),(n,1)) M = Di.dot(np.ones((1,n))) D = M + M.T - 2*Gram C2 = AffinityPropagation(affinity='precomputed') C1 = KMeans(n_clusters = 5) C3 = AgglomerativeClustering(n_clusters=5, affinity='precomputed',linkage='average') C4 = SpectralClustering(n_clusters=5,affinity='precomputed') C5 = SpectralBiclustering(n_clusters=(5,5)) R1 = C1.fit_predict(D) R2 = C2.fit_predict(D) R3 = C3.fit_predict(D) R4 = C4.fit_predict(Gram +11) R5 = C5.fit(D) print(R4) modèle = TSNE(n_components=2,metric='precomputed') Trans = modèle.fit_transform(D) G_ACP = ACP(Gram,precomputed=True) trace_ACP(G_ACP,[10]*5)
mplpyplot.show()
# nodebox section end
n_clusters = (4, 3)
data, rows, columns = make_checkerboard(
shape=(300, 300), n_clusters=n_clusters, noise=10,
shuffle=False, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")
data, row_idx, col_idx = sg._shuffle(data, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")
model = SpectralBiclustering(n_clusters=n_clusters, method='log',
random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))
print("consensus score: {:.1f}".format(score))
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
plt.matshow(np.outer(np.sort(model.row_labels_) + 1,
np.sort(model.column_labels_) + 1),
cmap=plt.cm.Blues)
