def get_nclusters_logw_pairs(extents, npoints):
"""
Get sample statistics by agglomeratively clustering random points.
These sample statistics will be used for a null distribution.
@param extents: the length of each axis of the hypercube
@param npoints: sample this many points at a time
@return: (nclusters, logw) pairs for a single sampling of points
"""
# sample the points
pointlist = []
for i in range(npoints):
p = [random.uniform(0, x) for x in extents]
pointlist.append(p)
points = np.array(pointlist)
# do the clustering, recording the within group sum of squares
nclusters_wgss_pairs = []
allmeandist = kmeans.get_allmeandist(points)
cluster_map = agglom.get_initial_cluster_map(points)
b_ssd_map = agglom.get_initial_b_ssd_map(points)
w_ssd_map = agglom.get_initial_w_ssd_map(points)
q = agglom.get_initial_queue(b_ssd_map)
while len(cluster_map) > 2:
pair = agglom.get_pair_fast(cluster_map, q)
agglom.merge_fast(cluster_map, w_ssd_map, b_ssd_map, q, pair)
indices = cluster_map.keys()
wgss = sum(w_ssd_map[i] / float(len(cluster_map[i])) for i in indices)
nclusters_wgss_pairs.append((len(cluster_map), wgss))
return nclusters_wgss_pairs
def get_nclusters_logw_pairs(extents, npoints):
"""
Get sample statistics by agglomeratively clustering random points.
These sample statistics will be used for a null distribution.
@param extents: the length of each axis of the hypercube
@param npoints: sample this many points at a time
@return: (nclusters, logw) pairs for a single sampling of points
"""
# sample the points
pointlist = []
for i in range(npoints):
p = [random.uniform(0, x) for x in extents]
pointlist.append(p)
points = np.array(pointlist)
# do the clustering, recording the within group sum of squares
nclusters_wgss_pairs = []
allmeandist = kmeans.get_allmeandist(points)
cluster_map = agglom.get_initial_cluster_map(points)
b_ssd_map = agglom.get_initial_b_ssd_map(points)
w_ssd_map = agglom.get_initial_w_ssd_map(points)
q = agglom.get_initial_queue(b_ssd_map)
while len(cluster_map) > 2:
pair = agglom.get_pair_fast(cluster_map, q)
agglom.merge_fast(cluster_map, w_ssd_map, b_ssd_map, q, pair)
indices = cluster_map.keys()
wgss = sum(w_ssd_map[i] / float(len(cluster_map[i])) for i in indices)
nclusters_wgss_pairs.append((len(cluster_map), wgss))
return nclusters_wgss_pairs
def get_response_content(fs):
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
points = get_rtable_info(rtable, fs.annotation, fs.axes)
# do the clustering
cluster_map = agglom.get_initial_cluster_map(points)
w_ssd_map = agglom.get_initial_w_ssd_map(points)
b_ssd_map = agglom.get_initial_b_ssd_map(points)
q = agglom.get_initial_queue(b_ssd_map)
while len(cluster_map) > fs.k:
pair = agglom.get_pair_fast(cluster_map, q)
agglom.merge_fast(cluster_map, w_ssd_map, b_ssd_map, q, pair)
# create the map from a point index to a cluster index
point_to_cluster = {}
for cluster_index, point_indices in cluster_map.items():
for point_index in point_indices:
point_to_cluster[point_index] = cluster_index
# define the raw labels which may be big numbers
raw_labels = [point_to_cluster[i] for i, p in enumerate(points)]
# rename the labels with small numbers
raw_to_label = dict((b, a) for a, b in enumerate(sorted(set(raw_labels))))
labels = [raw_to_label[raw] for raw in raw_labels]
# get the response
lines = ['\t'.join(header_row + [fs.annotation])]
for i, (label, data_row) in enumerate(zip(labels, data_rows)):
row = data_row + [str(label)]
lines.append('\t'.join(row))
# return the response
return '\n'.join(lines) + '\n'
def get_response_content(fs):
# read the table
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
Carbone.validate_headers(header_row)
# get the numpy array of conformant points
h_to_i = dict((h, i + 1) for i, h in enumerate(header_row))
axis_headers = fs.axes
if not axis_headers:
raise ValueError('no Euclidean axes were provided')
axis_set = set(axis_headers)
header_set = set(header_row)
bad_axes = axis_set - header_set
if bad_axes:
raise ValueError('invalid axes: ' + ', '.join(bad_axes))
axis_lists = []
for h in axis_headers:
index = h_to_i[h]
try:
axis_list = Carbone.get_numeric_column(data_rows, index)
except Carbone.NumericError:
raise ValueError('expected the axis column %s '
'to be numeric' % h)
axis_lists.append(axis_list)
points = np.array(zip(*axis_lists))
# do the clustering while computing the wgss at each merge
cluster_counts = []
wgss_values = []
allmeandist = kmeans.get_allmeandist(points)
cluster_map = agglom.get_initial_cluster_map(points)
w_ssd_map = agglom.get_initial_w_ssd_map(points)
b_ssd_map = agglom.get_initial_b_ssd_map(points)
q = agglom.get_initial_queue(b_ssd_map)
while len(cluster_map) > 2:
# do an agglomeration step
pair = agglom.get_pair_fast(cluster_map, q)
agglom.merge_fast(cluster_map, w_ssd_map, b_ssd_map, q, pair)
# compute the within group sum of squares
indices = cluster_map.keys()
wgss = sum(w_ssd_map[i] / float(len(cluster_map[i])) for i in indices)
# compute the between group sum of squares
bgss = allmeandist - wgss
# append to the lists
cluster_counts.append(len(cluster_map))
wgss_values.append(wgss)
# compute the log wgss values
wlogs = np.log(wgss_values)
# reverse the log values so that they are by increasing cluster size
wlogs = list(reversed(wlogs))
# sample from the null distribution
extents = np.max(points, axis=0) - np.min(points, axis=0)
nclusters_list, expectations, thresholds = do_sampling(
extents, len(points), fs.nsamples)
# get the gaps
gaps = np.array(expectations) - wlogs
# Get the best cluster count according to the gap statistic.
best_i = None
criteria = []
for i, ip1 in iterutils.pairwise(range(len(nclusters_list))):
k, kp1 = nclusters_list[i], nclusters_list[ip1]
criterion = gaps[i] - gaps[ip1] + thresholds[ip1]
criteria.append(criterion)
if criterion > 0:
if best_i is None:
best_i = i
best_k = nclusters_list[best_i]
# create the response
out = StringIO()
print >> out, 'best cluster count: k = %d' % best_k
if fs.verbose:
print >> out
print >> out, '(k, expected, observed, gap, threshold, criterion):'
n = len(nclusters_list)
for i, k in enumerate(nclusters_list):
row = [k, expectations[i], wlogs[i], gaps[i], thresholds[i]]
if i < n - 1:
row += [criteria[i]]
else:
row += ['-']
print >> out, '\t'.join(str(x) for x in row)
# return the response
return out.getvalue()
def get_response_content(fs):
# read the table
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
Carbone.validate_headers(header_row)
# get the numpy array of conformant points
h_to_i = dict((h, i+1) for i, h in enumerate(header_row))
axis_headers = fs.axes
if not axis_headers:
raise ValueError('no Euclidean axes were provided')
axis_set = set(axis_headers)
header_set = set(header_row)
bad_axes = axis_set - header_set
if bad_axes:
raise ValueError('invalid axes: ' + ', '.join(bad_axes))
axis_lists = []
for h in axis_headers:
index = h_to_i[h]
try:
axis_list = Carbone.get_numeric_column(data_rows, index)
except Carbone.NumericError:
raise ValueError(
'expected the axis column %s '
'to be numeric' % h)
axis_lists.append(axis_list)
points = np.array(zip(*axis_lists))
# do the clustering while computing the calinski index at each merge
cluster_counts = []
wgss_values = []
neg_calinskis = []
allmeandist = kmeans.get_allmeandist(points)
cluster_map = agglom.get_initial_cluster_map(points)
w_ssd_map = agglom.get_initial_w_ssd_map(points)
b_ssd_map = agglom.get_initial_b_ssd_map(points)
q = agglom.get_initial_queue(b_ssd_map)
while len(cluster_map) > 2:
# do an agglomeration step
pair = agglom.get_pair_fast(cluster_map, q)
agglom.merge_fast(cluster_map, w_ssd_map, b_ssd_map, q, pair)
# compute the within group sum of squares
indices = cluster_map.keys()
wgss = sum(w_ssd_map[i] / float(len(cluster_map[i])) for i in indices)
# compute the between group sum of squares
bgss = allmeandist - wgss
# get the calinksi index
n = len(points)
k = len(cluster_map)
numerator = bgss / float(k - 1)
denominator = wgss / float(n - k)
calinski = numerator / denominator
# append to the lists
cluster_counts.append(k)
wgss_values.append(wgss)
neg_calinskis.append(-calinski)
# Get the best cluster count according to the calinski index.
# Do this trickery with negs so that it breaks ties
# using the smallest number of clusters.
neg_calinksi, best_k = min(zip(neg_calinskis, cluster_counts))
# create the response
out = StringIO()
print >> out, 'best cluster count: k = %d' % best_k
if fs.verbose:
print >> out
print >> out, '(k, wgss, calinski):'
for k, wgss, neg_calinski in zip(
cluster_counts, wgss_values, neg_calinskis):
row = (k, wgss, -neg_calinski)
print >> out, '\t'.join(str(x) for x in row)
# return the response
return out.getvalue()
def get_response_content(fs):
# read the table
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
Carbone.validate_headers(header_row)
# get the numpy array of conformant points
h_to_i = dict((h, i+1) for i, h in enumerate(header_row))
axis_headers = fs.axes
if not axis_headers:
raise ValueError('no Euclidean axes were provided')
axis_set = set(axis_headers)
header_set = set(header_row)
bad_axes = axis_set - header_set
if bad_axes:
raise ValueError('invalid axes: ' + ', '.join(bad_axes))
axis_lists = []
for h in axis_headers:
index = h_to_i[h]
try:
axis_list = Carbone.get_numeric_column(data_rows, index)
except Carbone.NumericError:
raise ValueError(
'expected the axis column %s '
'to be numeric' % h)
axis_lists.append(axis_list)
points = np.array(zip(*axis_lists))
# do the clustering while computing the wgss at each merge
cluster_counts = []
wgss_values = []
allmeandist = kmeans.get_allmeandist(points)
cluster_map = agglom.get_initial_cluster_map(points)
w_ssd_map = agglom.get_initial_w_ssd_map(points)
b_ssd_map = agglom.get_initial_b_ssd_map(points)
q = agglom.get_initial_queue(b_ssd_map)
while len(cluster_map) > 2:
# do an agglomeration step
pair = agglom.get_pair_fast(cluster_map, q)
agglom.merge_fast(cluster_map, w_ssd_map, b_ssd_map, q, pair)
# compute the within group sum of squares
indices = cluster_map.keys()
wgss = sum(w_ssd_map[i] / float(len(cluster_map[i])) for i in indices)
# compute the between group sum of squares
bgss = allmeandist - wgss
# append to the lists
cluster_counts.append(len(cluster_map))
wgss_values.append(wgss)
# compute the log wgss values
wlogs = np.log(wgss_values)
# reverse the log values so that they are by increasing cluster size
wlogs = list(reversed(wlogs))
# sample from the null distribution
extents = np.max(points, axis=0) - np.min(points, axis=0)
nclusters_list, expectations, thresholds = do_sampling(
extents, len(points), fs.nsamples)
# get the gaps
gaps = np.array(expectations) - wlogs
# Get the best cluster count according to the gap statistic.
best_i = None
criteria = []
for i, ip1 in iterutils.pairwise(range(len(nclusters_list))):
k, kp1 = nclusters_list[i], nclusters_list[ip1]
criterion = gaps[i] - gaps[ip1] + thresholds[ip1]
criteria.append(criterion)
if criterion > 0:
if best_i is None:
best_i = i
best_k = nclusters_list[best_i]
# create the response
out = StringIO()
print >> out, 'best cluster count: k = %d' % best_k
if fs.verbose:
print >> out
print >> out, '(k, expected, observed, gap, threshold, criterion):'
n = len(nclusters_list)
for i, k in enumerate(nclusters_list):
row = [k, expectations[i], wlogs[i], gaps[i], thresholds[i]]
if i < n-1:
row += [criteria[i]]
else:
row += ['-']
print >> out, '\t'.join(str(x) for x in row)
# return the response
return out.getvalue()
def get_response_content(fs):
# read the table
rtable = RUtil.RTable(fs.table.splitlines())
header_row = rtable.headers
data_rows = rtable.data
Carbone.validate_headers(header_row)
# get the numpy array of conformant points
h_to_i = dict((h, i + 1) for i, h in enumerate(header_row))
axis_headers = fs.axes
if not axis_headers:
raise ValueError('no Euclidean axes were provided')
axis_set = set(axis_headers)
header_set = set(header_row)
bad_axes = axis_set - header_set
if bad_axes:
raise ValueError('invalid axes: ' + ', '.join(bad_axes))
axis_lists = []
for h in axis_headers:
index = h_to_i[h]
try:
axis_list = Carbone.get_numeric_column(data_rows, index)
except Carbone.NumericError:
raise ValueError('expected the axis column %s '
'to be numeric' % h)
axis_lists.append(axis_list)
points = np.array(zip(*axis_lists))
# do the clustering while computing the calinski index at each merge
cluster_counts = []
wgss_values = []
neg_calinskis = []
allmeandist = kmeans.get_allmeandist(points)
cluster_map = agglom.get_initial_cluster_map(points)
w_ssd_map = agglom.get_initial_w_ssd_map(points)
b_ssd_map = agglom.get_initial_b_ssd_map(points)
q = agglom.get_initial_queue(b_ssd_map)
while len(cluster_map) > 2:
# do an agglomeration step
pair = agglom.get_pair_fast(cluster_map, q)
agglom.merge_fast(cluster_map, w_ssd_map, b_ssd_map, q, pair)
# compute the within group sum of squares
indices = cluster_map.keys()
wgss = sum(w_ssd_map[i] / float(len(cluster_map[i])) for i in indices)
# compute the between group sum of squares
bgss = allmeandist - wgss
# get the calinksi index
n = len(points)
k = len(cluster_map)
numerator = bgss / float(k - 1)
denominator = wgss / float(n - k)
calinski = numerator / denominator
# append to the lists
cluster_counts.append(k)
wgss_values.append(wgss)
neg_calinskis.append(-calinski)
# Get the best cluster count according to the calinski index.
# Do this trickery with negs so that it breaks ties
# using the smallest number of clusters.
neg_calinksi, best_k = min(zip(neg_calinskis, cluster_counts))
# create the response
out = StringIO()
print >> out, 'best cluster count: k = %d' % best_k
if fs.verbose:
print >> out
print >> out, '(k, wgss, calinski):'
for k, wgss, neg_calinski in zip(cluster_counts, wgss_values,
neg_calinskis):
row = (k, wgss, -neg_calinski)
print >> out, '\t'.join(str(x) for x in row)
# return the response
return out.getvalue()