def input_task(self,task_name,is_read_feature_data = True):
"""
Read the required data from a HCTSA_loc.mat file
Parameters:
-----------
task_name : string
the name of the classification task to be imported
is_read_feature_data : bool
if true, the feature data matrix will be read (default is True)
Returns:
--------
data : ndarray
Array containing the data. Each row corresponds to a timeseries and each column to an operation.
ts : dict
dictionary containing the information for all timeseries (rows in data).
['keywords', 'n_samples', 'id', 'filename']
op : dict
dictionary containing the information for all contained operations.
Keys are ['keywords', 'master_id', 'id', 'code_string', 'name']
"""
# -- assemble the file path
mat_file_path = self.path_pattern.format(task_name)
# -- load the data,operations and timeseries from the matlab file
if is_read_feature_data:
data , op, ts = mIO.read_from_mat_file(mat_file_path,['TS_DataMat','Operations','TimeSeries'],is_from_old_matlab = True)
else:
op, ts = mIO.read_from_mat_file(mat_file_path,['Operations','TimeSeries'],is_from_old_matlab = True)
data = None
if is_read_feature_data:
return self.masking_method(data), ts, op
else:
return None, ts, op
def count_op_calc(mat_file_paths, is_from_old_matlab=False):
"""
Counts how many times every operation has been calculated successfully for each problem
represented by a HCTSA_loc.mat file in root_dir
Parameters:
----------
mat_file_paths : list
Paths to the HCTSA_loc.mat files corresponding to the problems considered.
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
count_op_calc_all_problems : ndarray
Array where each entry represents one operation and each value is the number of successful
calculations of the corresponding operation for the given problems.
"""
count_op_calc_all_problems = np.zeros(10000)
for mat_file_path in mat_file_paths:
op, = mIO.read_from_mat_file(mat_file_path, ['Operations'],
is_from_old_matlab=is_from_old_matlab)
print "Counting which operations calculated in: {:s}".format(
mat_file_path)
count_op_calc_all_problems[op['id']] += 1
return count_op_calc_all_problems
def count_op_calc(mat_file_paths,is_from_old_matlab = False):
"""
Counts how many times every operation has been calculated successfully for each problem
represented by a HCTSA_loc.mat file in root_dir
Parameters:
----------
mat_file_paths : list
Paths to the HCTSA_loc.mat files corresponding to the problems considered.
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
count_op_calc_all_problems : ndarray
Array where each entry represents one operation and each value is the number of successful
calculations of the corresponding operation for the given problems.
"""
count_op_calc_all_problems = np.zeros(10000)
for mat_file_path in mat_file_paths:
op, = mIO.read_from_mat_file(mat_file_path,['Operations'],is_from_old_matlab = is_from_old_matlab )
print "Counting which operations calculated in: {:s}".format(mat_file_path)
count_op_calc_all_problems[op['id']]+=1
return count_op_calc_all_problems
def best_noncorr_op_ind(ind_dict,mask,file_path,op = None,is_from_old_matlab = False):
"""
Compute the indices for the top features for a specific HCTSA_loc.mat file
and the corresponding operation ids
Parameters:
-----------
ind_dict : dict
Dictionary where keys are file paths and values are the indices in the data matrix
of HCTSA_loc.mat
mask : array like
Mask to reduce the indices given in ind_dict
file_path : string
Path to HCTSA_loc.mat file
op : dict,optional
Operations dictionary from HCTSA_loc.mat file at file_path
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
ind_top : array
Indices of the features combining the information ind_dict and mask for the HCTSA_loc.mat
file pointed to by file_path
op_id_top : array
Operation ids corresponding to ind_top
"""
ind = np.array(ind_dict[file_path])
if op == None:
op, = mIO.read_from_mat_file(file_path, ['Operations'],is_from_old_matlab = is_from_old_matlab)
op_id_top = np.array(op['id'])[ind][mask]
ind_top = ind[mask]
return ind_top,op_id_top
def input_task_master(self,
task_name,
is_read_feature_data=True,
old_matlab=False):
"""
Read the required data from a HCTSA_loc.mat file and master operations
Parameters:
-----------
task_name : string
the name of the classification task to be imported
is_read_feature_data : bool
if true, the feature data matrix will be read (default is True)
Returns:
--------
data : ndarray
Array containing the data. Each row corresponds to a timeseries and each column to an operation.
ts : dict
dictionary containing the information for all timeseries (rows in data).
['keywords', 'n_samples', 'id', 'filename']
op : dict
dictionary containing the information for all contained operations.
Keys are ['keywords', 'master_id', 'id', 'code_string', 'name']
"""
# -- assemble the file path
mat_file_path = self.path_pattern.format(task_name)
print "Reading file {}".format(mat_file_path)
# -- load the data,operations and timeseries from the matlab file
if is_read_feature_data:
data, op, ts, m_op = mIO.read_from_mat_file(
mat_file_path,
['TS_DataMat', 'Operations', 'TimeSeries', 'MasterOperations'],
is_from_old_matlab=old_matlab)
else:
op, ts, m_op = mIO.read_from_mat_file(
mat_file_path,
['Operations', 'TimeSeries', 'MasterOperations'],
is_from_old_matlab=old_matlab)
data = None
if is_read_feature_data:
return self.masking_method(data), ts, op, m_op
else:
return None, ts, op, m_op
def calculate_ustat_avg_mult_task(mat_file_paths,
u_stat_file_paths,
all_classes_avg_out_path='./',
is_from_old_matlab=False):
"""
For multiple tasks calculate the u statistics for each task averaged over all possible label pairs.
The results are saved to disk.
Parameters:
-----------
mat_file_paths : list
List of file paths to the MAT files containing the HCTSA data.
u_stat_file_paths : list
File paths of the saved u statistics data in binary npy files.
all_classes_avg_out_path : string
Path to the output folder in which the tasks average u statistics are saved.
is_from_old_matlab : boolean
Are the MAT files from older version of the comp engine
Returns:
--------
all_classes_avg : ndarray
ndarray where each row represents a task and column i represents operation with op_id = i.
"""
# -- initialise the array containing the average u-statistic values for all problems and features
all_classes_avg = np.ones((len(u_stat_file_paths), 10000)) * np.NAN
for i, (u_stat_file_path,
mat_file_path) in enumerate(zip(u_stat_file_paths,
mat_file_paths)):
# -- load the u statistic for every operation and label pairing
u_stat = np.load(u_stat_file_path)
# -- calculate the scaling factor for every label pairing of the current classification problem
u_scale = u_stat_norm_factor(mat_file_path,
is_from_old_matlab=is_from_old_matlab)
# -- calculate the average scaled u statistic over all label pairs in current problem
u_stat_avg = (u_stat.T / u_scale).transpose().mean(axis=0)
# -- save the average scaled u-statistic for all features to the all_classes_avg array.
# The column number corresponds with the operation id
op, = mIO.read_from_mat_file(mat_file_path, ['Operations'],
is_from_old_matlab=is_from_old_matlab)
all_classes_avg[i, op['id']] = u_stat_avg
np.save(all_classes_avg_out_path, all_classes_avg)
return all_classes_avg
def calculate_ustat_avg_mult_task(mat_file_paths,u_stat_file_paths,all_classes_avg_out_path = './',is_from_old_matlab = False):
"""
For multiple tasks calculate the u statistics for each task averaged over all possible label pairs.
The results are saved to disk.
Parameters:
-----------
mat_file_paths : list
List of file paths to the MAT files containing the HCTSA data.
u_stat_file_paths : list
File paths of the saved u statistics data in binary npy files.
all_classes_avg_out_path : string
Path to the output folder in which the tasks average u statistics are saved.
is_from_old_matlab : boolean
Are the MAT files from older version of the comp engine
Returns:
--------
all_classes_avg : ndarray
ndarray where each row represents a task and column i represents operation with op_id = i.
"""
# -- initialise the array containing the average u-statistic values for all problems and features
all_classes_avg = np.ones((len(u_stat_file_paths),10000))*np.NAN
for i,(u_stat_file_path, mat_file_path) in enumerate(zip(u_stat_file_paths,mat_file_paths)):
# -- load the u statistic for every operation and label pairing
u_stat = np.load(u_stat_file_path)
# -- calculate the scaling factor for every label pairing of the current classification problem
u_scale = u_stat_norm_factor(mat_file_path,is_from_old_matlab = is_from_old_matlab)
# -- calculate the average scaled u statistic over all label pairs in current problem
u_stat_avg = (u_stat.T/u_scale).transpose().mean(axis=0)
# -- save the average scaled u-statistic for all features to the all_classes_avg array.
# The column number corresponds with the operation id
op, = mIO.read_from_mat_file(mat_file_path,['Operations'],is_from_old_matlab = is_from_old_matlab )
all_classes_avg[i,op['id']] = u_stat_avg
np.save(all_classes_avg_out_path,all_classes_avg)
return all_classes_avg
def u_stat_norm_factor(file_name, is_from_old_matlab=False):
"""
Return the u statisitc scaling factor n_1*n_2 where n_i is the
number of time series with label i. Every entry corresponds to
one label pairing in the classification problem pointed to
by file_name.
Parameters:
-----------
file_name : string
Filename of HCTSA_loc.mat file containing data of the current problem
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
u_scale : array
Scaling factor for u statistic for every label pairing in the current
classification problem
"""
ts, = mIO.read_from_mat_file(file_name, ['TimeSeries'],
is_from_old_matlab=is_from_old_matlab)
labels = [int(x.split(',')[-1]) for x in ts['keywords']]
labels_unique = list(set(labels))
labels = np.array(labels)
n_labels = len(labels_unique)
nr_items_label = []
# -- for every label calculate the number of time series with this label
for i, label in enumerate(labels_unique):
nr_items_label.append(np.nonzero((labels == label))[0].shape[0])
# -- initialise the u_scale array for all label pairings
u_scale = np.zeros(n_labels * (n_labels - 1) / 2)
# -- calculate the scaling factor for the u statistic for every label pairing
for i, (label_ind_0, label_ind_1) in enumerate(
itertools.combinations(range(n_labels), 2)):
u_scale[i] = nr_items_label[label_ind_0] * nr_items_label[label_ind_1]
return u_scale
def u_stat_norm_factor(file_name,is_from_old_matlab = False):
"""
Return the u statisitc scaling factor n_1*n_2 where n_i is the
number of time series with label i. Every entry corresponds to
one label pairing in the classification problem pointed to
by file_name.
Parameters:
-----------
file_name : string
Filename of HCTSA_loc.mat file containing data of the current problem
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
u_scale : array
Scaling factor for u statistic for every label pairing in the current
classification problem
"""
ts, = mIO.read_from_mat_file(file_name,['TimeSeries'],is_from_old_matlab = is_from_old_matlab )
labels = [int(x.split(',')[-1]) for x in ts['keywords']]
labels_unique = list(set(labels))
labels = np.array(labels)
n_labels = len(labels_unique)
nr_items_label = []
# -- for every label calculate the number of time series with this label
for i,label in enumerate(labels_unique):
nr_items_label.append(np.nonzero((labels == label))[0].shape[0])
# -- initialise the u_scale array for all label pairings
u_scale = np.zeros(n_labels * (n_labels-1)/2 )
# -- calculate the scaling factor for the u statistic for every label pairing
for i,(label_ind_0,label_ind_1) in enumerate(itertools.combinations(range(n_labels),2)):
u_scale[i] = nr_items_label[label_ind_0]*nr_items_label[label_ind_1]
return u_scale
def best_noncorr_op_ind(ind_dict,
mask,
file_path,
op=None,
is_from_old_matlab=False):
"""
Compute the indices for the top features for a specific HCTSA_loc.mat file
and the corresponding operation ids
Parameters:
-----------
ind_dict : dict
Dictionary where keys are file paths and values are the indices in the data matrix
of HCTSA_loc.mat
mask : array like
Mask to reduce the indices given in ind_dict
file_path : string
Path to HCTSA_loc.mat file
op : dict,optional
Operations dictionary from HCTSA_loc.mat file at file_path
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
ind_top : array
Indices of the features combining the information ind_dict and mask for the HCTSA_loc.mat
file pointed to by file_path
op_id_top : array
Operation ids corresponding to ind_top
"""
ind = np.array(ind_dict[file_path])
if op == None:
op, = mIO.read_from_mat_file(file_path, ['Operations'],
is_from_old_matlab=is_from_old_matlab)
op_id_top = np.array(op['id'])[ind][mask]
ind_top = ind[mask]
return ind_top, op_id_top
def cat_data_op_subset(file_paths,
op_id_top,
is_from_old_matlab=False,
is_return_masked=True):
"""
Concatenate the features where op_id is in op_id_top for all HCTSA_loc.m files pointed to by file_paths.
Warning, this can take a while and the returned data matrix can be very large.
XXX WARNING XXX This only works correctly if all HCTSA_loc.mat files come from the same
database. Meaning op_ids are the same. Otherwise one would have to go through operation names which is
only a little more work to implement. XXX
Parameters:
-----------
file_paths : list
list of file paths pointing to the files containing the data
op_id_top : list,ndarray
list of operation ids wanted in the concatenated data array
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
is_return_masked : boolean
Saving large masked arrays to disk can lead to memory errors while pickling. If this is false funtion
returns a normal ndarray with unknown entires are set to NaN. This can be converted to a masked array with
data_all = np.ma.masked_invalid(data_all)
Returns:
--------
data_all : ndarray/masked ndarray
Concatenated data array
"""
is_first = True
data_all = None
for file_path in file_paths:
print "Adding data from {:s} \n to complete data matrix".format(
file_path)
data, op = mIO.read_from_mat_file(
file_path, ['TS_DataMat', 'Operations'],
is_from_old_matlab=is_from_old_matlab)
# -- find the indices in the data for for op_id_top
ind = hlp.ismember(op['id'],
op_id_top,
is_return_masked_array=True,
return_dtype=int)
# -- if any of the operations was not calculated for this problem
# -- create a masked array and copy only valid data and mask
# -- invalid data
if ind.data != op_id_top:
# -- create an masked array filled with NaN.
# -- This makes later masking of non-existent entries easier
# -- each column of data_ma corresponds to the op_id in op_id_top with the
# -- same index (column i in data_ma corresponds to op_id_top[i])
data_ma = np.empty((data.shape[0], np.array(op_id_top).shape[0]))
data_ma[:] = np.NaN
for it, i in enumerate(ind):
# -- if i is masked in ind that means that the current operation in data
# -- is not part of op_id_top. We therefore do not need this operation to
# -- be included in data_ma.
if i is not np.ma.masked:
data_ma[:, i] = data[:, it]
# -- otherwise pick all relevant features and also automatically sort them correctly (if necessary)
else:
data_ma = np.array(data[:, ind])
# -- mask all NaN (not calculated) entries and stick them together
#data_ma = np.ma.masked_invalid(data_ma)
if is_first == True:
data_all = data_ma
is_first = False
else:
data_all = np.vstack((data_all, data_ma))
# -- Saving a large masked array to disk can lead to Memory errors while using the pickle module.
if is_return_masked == True:
data_all = np.ma.masked_invalid(data_all)
return data_all
all_classes_avg_out_path = intermediate_data_root + '/all_classes_avg.npy'
op_id_good_path = intermediate_data_root + '/op_id_good.npy'
op_id_order_path = intermediate_data_root + '/op_id_order.npy'
sort_good_ind_path = intermediate_data_root + 'sort_good_ind.npy'
# -- Load the data
all_classes_avg = np.load(all_classes_avg_out_path)
op_id_good = np.load(op_id_good_path)
# -- Mask NaN entires
all_classes_avg_good = np.ma.masked_invalid(all_classes_avg[:, op_id_good])
# -- load a reference HCTSA_loc.mat containing all op_ids
import modules.misc.PK_matlab_IO as mIO
op_ref_HCTSA_path = '/home/philip/work/OperationImportanceProject/results/done/HCTSA_Beef.mat'
op, = mIO.read_from_mat_file(op_ref_HCTSA_path, ['Operations'],
is_from_old_matlab=True)
max_feat = 50
# -- calculate the correlation
abs_corr_array, sort_good_ind, all_classes_avg_good_norm = idtop.calc_perform_corr_mat(
all_classes_avg_good, norm='z-score', max_feat=max_feat)
# -- save the op id's in order of performance (first entry = best performance)
np.save(op_id_order_path, op_id_good[sort_good_ind])
# -- sort the permutation vector that would sort the data array containing the good operations only
np.save(sort_good_ind_path, sort_good_ind)
# -- extract the top feature names
names = hlp.ind_map_subset(op['id'], op['name'],
op_id_good[sort_good_ind][:max_feat])
# all_classes_avg_top = np.ma.masked_invalid(all_classes_avg[:,op_id_order[:100]])
# # -- calculate the z-score of the u stat array
# all_classes_avg_top = ((all_classes_avg_top.T - np.ma.mean(all_classes_avg_top,axis=1)) / np.ma.std(all_classes_avg_top,axis=1)).T
# abs_corr_array = np.abs(np.ma.corrcoef(all_classes_avg_top, rowvar=0))
# -- calculate the correlation array with respect to performance and mask nan.
abs_corr_array,sort_good_ind,all_classes_avg_good_norm = idtop.calc_perform_corr_mat(all_classes_avg[:,op_id_good],norm='z-score', max_feat = max_feat)
all_classes_avg_top = np.ma.masked_invalid(all_classes_avg[:,op_id_good][:,sort_good_ind[:max_feat]])
# -- calculate the linkage for the correlation
corr_linkage = idtop.calc_linkage(abs_corr_array)[0]
# -- extract operation names --- ------------------------------------------
# -- load a reference HCTSA_loc.mat containing all op_ids
op_ref_HCTSA_path = '/home/philip/work/OperationImportanceProject/results/done/HCTSA_Beef.mat'
op, = mIO.read_from_mat_file(op_ref_HCTSA_path,['Operations'],is_from_old_matlab = True)
top_id = op_id_good[sort_good_ind][:max_feat]
names = hlp.ind_map_subset(op['id'], op['name'], op_id_good[sort_good_ind][:max_feat])
# -- extract problem names --- ------------------------------------------
reg_ex = re.compile('.*\/HCTSA_(.*)_N_70_100_reduced.mat')
problem_paths = np.load(problem_names_path)
problem_names = np.array([reg_ex.match(problem_path).group(1) for problem_path in problem_paths])
# ---------------------------------------------------------------------
# -- Plot -------------------------------------------------------------
# ---------------------------------------------------------------------
def cat_data_op_subset(file_paths,op_id_top,is_from_old_matlab = False,is_return_masked = True):
"""
Concatenate the features where op_id is in op_id_top for all HCTSA_loc.m files pointed to by file_paths.
Warning, this can take a while and the returned data matrix can be very large.
XXX WARNING XXX This only works correctly if all HCTSA_loc.mat files come from the same
database. Meaning op_ids are the same. Otherwise one would have to go through operation names which is
only a little more work to implement. XXX
Parameters:
-----------
file_paths : list
list of file paths pointing to the files containing the data
op_id_top : list,ndarray
list of operation ids wanted in the concatenated data array
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
is_return_masked : boolean
Saving large masked arrays to disk can lead to memory errors while pickling. If this is false funtion
returns a normal ndarray with unknown entires are set to NaN. This can be converted to a masked array with
data_all = np.ma.masked_invalid(data_all)
Returns:
--------
data_all : ndarray/masked ndarray
Concatenated data array
"""
is_first = True
data_all = None
for file_path in file_paths:
print "Adding data from {:s} \n to complete data matrix".format(file_path)
data,op = mIO.read_from_mat_file(file_path, ['TS_DataMat','Operations'],is_from_old_matlab = is_from_old_matlab)
# -- find the indices in the data for for op_id_top
ind = hlp.ismember(op['id'],op_id_top,is_return_masked_array = True,return_dtype = int)
# -- if any of the operations was not calculated for this problem
# -- create a masked array and copy only valid data and mask
# -- invalid data
if ind.data != op_id_top:
# -- create an masked array filled with NaN.
# -- This makes later masking of non-existent entries easier
# -- each column of data_ma corresponds to the op_id in op_id_top with the
# -- same index (column i in data_ma corresponds to op_id_top[i])
data_ma = np.empty((data.shape[0],np.array(op_id_top).shape[0]))
data_ma[:] = np.NaN
for it,i in enumerate(ind):
# -- if i is masked in ind that means that the current operation in data
# -- is not part of op_id_top. We therefore do not need this operation to
# -- be included in data_ma.
if i is not np.ma.masked:
data_ma[:,i] = data[:,it]
# -- otherwise pick all relevant features and also automatically sort them correctly (if necessary)
else:
data_ma = np.array(data[:,ind])
# -- mask all NaN (not calculated) entries and stick them together
#data_ma = np.ma.masked_invalid(data_ma)
if is_first == True:
data_all = data_ma
is_first = False
else:
data_all = np.vstack((data_all,data_ma))
# -- Saving a large masked array to disk can lead to Memory errors while using the pickle module.
if is_return_masked == True:
data_all = np.ma.masked_invalid(data_all)
return data_all
def u_stat_all_label_file_name(file_name, mask=None, is_from_old_matlab=False):
"""
Calculate the U-statistic for all label pairings in a HCTSA_loc.mat file. The operations can be masked
by a boolean array.
Parameters:
-----------
file_name : string
File name of the HCTSA_loc.mat file containing as least the matrices 'TimeSeries' and 'TS_DataMat'
mask : ndarray dtype='bool', optional
If given this acts as mask to which features are included in the calculation
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
ranks : ndarray of dim (n_labels * (n_labels-1) / 2, nr_timeseries)
The U-statistic for all features and label pairs, where a row represents a pair of labels.
Every column represents one feature
labels_unique : list
List of all unique labels
label_ind_list : list
List of lists where each sub-list i represents all row-indices in data containing timeseries
for labels_unique[i]
"""
# ---------------------------------------------------------------------
# load the data
# ---------------------------------------------------------------------
ts, data = mIO.read_from_mat_file(file_name, ['TimeSeries', 'TS_DataMat'],
is_from_old_matlab=is_from_old_matlab)
# ---------------------------------------------------------------------
# mask the data if required
# ---------------------------------------------------------------------
if mask != None:
data = data[:, mask]
# ---------------------------------------------------------------------
# extract the unique labels
# ---------------------------------------------------------------------
labels = [int(x.split(',')[-1]) for x in ts['keywords']]
labels_unique = list(set(labels))
labels = np.array(labels)
label_ind_list = []
# ---------------------------------------------------------------------
# get indices for all unique labels
# ---------------------------------------------------------------------
for i, label in enumerate(labels_unique):
label_ind_list.append(np.nonzero((labels == label))[0])
n_labels = len(label_ind_list)
# ---------------------------------------------------------------------
# calculate Mann-Whitney u-test
# ---------------------------------------------------------------------
ranks = np.zeros((n_labels * (n_labels - 1) / 2, data.shape[1]))
for i, (label_ind_0, label_ind_1) in enumerate(
itertools.combinations(range(n_labels), 2)):
# -- select the data for the current labels
data_0 = data[label_ind_list[label_ind_0], :]
data_1 = data[label_ind_list[label_ind_1], :]
print i + 1, '/', n_labels * (n_labels - 1) / 2
for k in range(0, data.shape[1]):
# -- in the case of same value for every feature in both arrays set max possible value
if np.ma.all((data_0[:, k] == data_0[0, k])) and np.ma.all(
(data_1[:, k] == data_0[0, k])):
ranks[i,
k] = data_0[:, k].shape[0] * data_1[:, k].shape[0] / 2.
else:
ranks[i, k] = stats.mannwhitneyu(data_0[:, k], data_1[:, k])[0]
return ranks, labels_unique, label_ind_list
def u_stat_all_label_file_name(file_name,mask = None, is_from_old_matlab=False):
"""
Calculate the U-statistic for all label pairings in a HCTSA_loc.mat file. The operations can be masked
by a boolean array.
Parameters:
-----------
file_name : string
File name of the HCTSA_loc.mat file containing as least the matrices 'TimeSeries' and 'TS_DataMat'
mask : ndarray dtype='bool', optional
If given this acts as mask to which features are included in the calculation
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
ranks : ndarray of dim (n_labels * (n_labels-1) / 2, nr_timeseries)
The U-statistic for all features and label pairs, where a row represents a pair of labels.
Every column represents one feature
labels_unique : list
List of all unique labels
label_ind_list : list
List of lists where each sub-list i represents all row-indices in data containing timeseries
for labels_unique[i]
"""
# ---------------------------------------------------------------------
# load the data
# ---------------------------------------------------------------------
ts,data = mIO.read_from_mat_file(file_name,['TimeSeries','TS_DataMat'],is_from_old_matlab = is_from_old_matlab )
# ---------------------------------------------------------------------
# mask the data if required
# ---------------------------------------------------------------------
if mask != None:
data = data[:,mask]
# ---------------------------------------------------------------------
# extract the unique labels
# ---------------------------------------------------------------------
labels = [int(x.split(',')[-1]) for x in ts['keywords']]
labels_unique = list(set(labels))
labels = np.array(labels)
label_ind_list = []
# ---------------------------------------------------------------------
# get indices for all unique labels
# ---------------------------------------------------------------------
for i,label in enumerate(labels_unique):
label_ind_list.append(np.nonzero((labels == label))[0])
n_labels = len(label_ind_list)
# ---------------------------------------------------------------------
# calculate Mann-Whitney u-test
# ---------------------------------------------------------------------
ranks = np.zeros((n_labels * (n_labels-1) / 2,data.shape[1]))
for i,(label_ind_0,label_ind_1) in enumerate(itertools.combinations(range(n_labels),2)):
# -- select the data for the current labels
data_0 = data[label_ind_list[label_ind_0],:]
data_1 = data[label_ind_list[label_ind_1],:]
print i+1,'/',n_labels * (n_labels-1) / 2
for k in range(0,data.shape[1]):
# -- in the case of same value for every feature in both arrays set max possible value
if np.ma.all((data_0[:,k] == data_0[0,k])) and np.ma.all((data_1[:,k] == data_0[0,k] )):
ranks[i,k] = data_0[:,k].shape[0] * data_1[:,k].shape[0]/2.
else:
ranks[i,k] = stats.mannwhitneyu(data_0[:,k], data_1[:,k])[0]
return ranks,labels_unique,label_ind_list
