def plot_similarity_array(self):
abs_corr_array = self.workflow.redundancy_method.similarity_array
op_id_name_map = self.map_op_id_name_mult_task(self.workflow.tasks)
names = hlp.ind_map_subset(
op_id_name_map[0], op_id_name_map[1],
self.workflow.redundancy_method.similarity_array_op_ids)
measures = np.zeros((2, len(names)))
tmp_ind = hlp.ismember(
self.workflow.redundancy_method.similarity_array_op_ids,
self.workflow.good_op_ids)
# -- number of problems for which each good performing feature has been calculated
measures[0, :] = (~self.workflow.stats_good_op[:, tmp_ind].mask).sum(
axis=0)
# -- z scored u-stat(for all features) for top features
stats_good_op_z_score = fap.normalise_masked_array(
self.workflow.stats_good_op_comb, axis=0, norm_type='zscore')[0]
measures[1, :] = stats_good_op_z_score[tmp_ind]
fiplt.plot_arr_dendrogram(abs_corr_array,
names,
max_dist_cluster=self.max_dist_cluster,
measures=measures)
def plot_similarity_array(self):
abs_corr_array = self.workflow.redundancy_method.similarity_array
op_id_name_map = self.map_op_id_name_mult_task(self.workflow.tasks)
names = hlp.ind_map_subset(op_id_name_map[0],op_id_name_map[1],self.workflow.redundancy_method.similarity_array_op_ids)
measures = np.zeros((2,len(names)))
tmp_ind = hlp.ismember(self.workflow.redundancy_method.similarity_array_op_ids,
self.workflow.good_op_ids)
# -- number of problems for which each good performing feature has been calculated
measures[0,:] = (~self.workflow.stats_good_op[:,tmp_ind].mask).sum(axis=0)
# -- z scored u-stat(for all features) for top features
stats_good_op_z_score = fap.normalise_masked_array(self.workflow.stats_good_op_comb, axis= 0,norm_type = 'zscore')[0]
measures[1,:] = stats_good_op_z_score[tmp_ind]
fiplt.plot_arr_dendrogram(abs_corr_array,names,max_dist_cluster=self.max_dist_cluster,measures = measures)
def reduce_to_good_perf_ops(self,ops_base_vals,good_perf_op_ids,good_op_ids):
"""
Reduce the ops_base_vals by keeping only the columns corresponding to the op_ids in self.good_perf_op_ids
Parameters:
-----------
ops_base_vals : nd array
Array containing the values on which the similarity of the operations will be calculated
good_op_ids : ndarray
The op_ids of the columns in ops_base_vals
good_perf_op_ids : ndarray
The op_ids of the features we are interested in
Returns:
--------
ops_base_perf_vals : ndarray
ops_base_vals reduced to contain only operations with ids given in good_perf_op_ids with the same ordering.
"""
good_perf_ind = hlp.ismember(good_perf_op_ids,good_op_ids)
ops_base_perf_vals = ops_base_vals[:,good_perf_ind]
return ops_base_perf_vals
def reduce_to_good_perf_ops(self, ops_base_vals, good_perf_op_ids,
good_op_ids):
"""
Reduce the ops_base_vals by keeping only the columns corresponding to the op_ids in self.good_perf_op_ids
Parameters:
-----------
ops_base_vals : nd array
Array containing the values on which the similarity of the operations will be calculated
good_op_ids : ndarray
The op_ids of the columns in ops_base_vals
good_perf_op_ids : ndarray
The op_ids of the features we are interested in
Returns:
--------
ops_base_perf_vals : ndarray
ops_base_vals reduced to contain only operations with ids given in good_perf_op_ids with the same ordering.
"""
good_perf_ind = hlp.ismember(good_perf_op_ids, good_op_ids)
ops_base_perf_vals = ops_base_vals[:, good_perf_ind]
return ops_base_perf_vals
def collect_stats_good_op_ids(self):
"""
Collect all combined stats for each task and take stats for good operations only
"""
#stats_good_op_ma = np.empty((data.shape[0],np.array(self.good_op_ids).shape[0]))
stats_good_op_tmp = []
#stats_good_op_ma[:] = np.NaN
for task in self.tasks:
# -- create tmp array for good stats for current task. For sake of simplicity when dealing with different
# dimensions of task.tot_stats we transpose stats_good_op_ma_tmp so row corresponds to feature temporarily
if task.tot_stats.ndim > 1:
stats_good_op_ma_tmp = np.empty((self.good_op_ids.shape[0],task.tot_stats.shape[0]))
else:
stats_good_op_ma_tmp = np.empty((self.good_op_ids.shape[0]))
stats_good_op_ma_tmp[:] = np.NaN
ind = hlp.ismember(task.op_ids,self.good_op_ids,is_return_masked_array = True,return_dtype = int)
# -- it is position in task.op_ids and i is position in self.good_op_ids
for it,i in enumerate(ind):
if i is not np.ma.masked: # -- that means the entry in task.op_ids is also in self.good_op_ids
stats_good_op_ma_tmp[i] = task.tot_stats[it].T
# -- We return to the usual ordering: column equals feature
stats_good_op_tmp.append(stats_good_op_ma_tmp.T)
self.stats_good_op = np.ma.masked_invalid(np.vstack(stats_good_op_tmp))
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 corelated_features_mask(data=None,
abs_corr_array=None,
calc_times=None,
op_ids=None):
"""
Computes a mask that, when applied, removes correlated features from the data array.
Parmeters:
----------
data : ndarray
A data matrix with rows represent training samples and columns represent features
abs_corr_array : ndarray
The correlation matrix of all features. Has to be given id data == none
calc_times : ndarray
Array where the first row corresponds to operation id's and the second row to calculation times
for these operation ids
op_ids :
The operation ids corresponding to the rows/columns in abs_corr_array
Returns:
-------
mask : ndarray,dtype=bool
1d array whose entries are one only for uncorrelated entries.
abs_corr_arrayabs_corr_array : ndarray
the correlation matrix
"""
if abs_corr_array == None:
abs_corr_array = np.abs(np.ma.corrcoef(data, rowvar=0))
# -- Vector containing 0 for all operations we don't want
mask = np.ones(abs_corr_array.shape[0], dtype='bool')
for i in range(abs_corr_array.shape[0]):
# -- if the current line represents an operation not yet eleminated
if mask[i]:
# -- remove operations which are highly correlated
mask[(abs_corr_array[i] > 0.8)] = 0
#----------------------------------------------------------
# -- Use fastest operation in a set of correlated operations
#----------------------------------------------------------
if calc_times != None and op_ids != None:
# -- find ind in abs_corr_array of correlated features
ind_corr = np.nonzero((abs_corr_array[i] > 0.8))[0]
# -- translate ind_corr to op ids
op_id_corr = hlp.ind_map_subset(range(abs_corr_array.shape[0]),
op_ids, ind_corr)
# -- get calculation time of correlated op ids
t_corr = hlp.ind_map_subset(calc_times[0], calc_times[1],
op_id_corr)
# -- check if all entries are None -> no timing information for any of the operations
if np.nonzero(t_corr)[0].shape[0] == 0:
# -- pick the first operation as fastest as there is no timing information
op_id_corr_fastest = op_id_corr[0]
# -- else pick the fastest operation
else:
# -- get op_id of fastest operation in this correlated set
op_id_corr_fastest = op_id_corr[np.nanargmin(t_corr)]
# -- get index of fastest operation in this correlated set
ind_corr_fastest = hlp.ind_map_subset(
op_ids, range(abs_corr_array.shape[0]), op_id_corr_fastest)
# -- add fastest index back in
mask[ind_corr_fastest] = 1
#----------------------------------------------------------
# -- Use arbitrary operation in a set of correlated operations
#----------------------------------------------------------
else:
mask[i] = 1
return mask, abs_corr_array
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])
# -- Calculate the measures to be plotted
problems_succ = (~all_classes_avg_good[:, sort_good_ind[:max_feat]].mask).sum(
axis=0)
u_stat_mean = all_classes_avg_good_norm[:,
sort_good_ind[:max_feat]].mean(axis=0)
measures = np.vstack((problems_succ, u_stat_mean))
fiplt.plot_arr_dendrogram(abs_corr_array, names, measures=measures)
plt.savefig('/home/philip/Desktop/tmp/figure_tmp/corr_array.png')
plt.show()
def plot_stat_array(self):
fig = plt.figure(figsize = ((15,15)))
# -- plot layout ------------------------------------------------------
rect_ustat_arr = [0.25,0.175,.5,.5]
rect_dendr = [0.755,0.175,.145,.5]
rect_measures0 = [0.25,0.68,0.5,0.1]
rect_measures1 = [0.25,0.785,0.5,0.1]
rect_measures2 = [0.25,0.89,0.5,0.1]
ax_ustat_arr = fig.add_axes(rect_ustat_arr)
ax_dendr = fig.add_axes(rect_dendr)
ax_measures00 = fig.add_axes(rect_measures0)
ax_measures01 = plt.twinx(ax_measures00)
ax_measures10 = fig.add_axes(rect_measures1)
ax_measures10.set_xticklabels([])
ax_measures20 = fig.add_axes(rect_measures2)
ax_measures20.set_xticklabels([])
ax_measures21 = plt.twinx(ax_measures20)
# -- calculate and plot the dendrogram
dist_dendrogram = hierarchy.dendrogram(self.linkage, orientation='left',no_plot=True)
hierarchy.dendrogram(self.linkage, orientation='left',p=50,truncate_mode ='lastp',ax = ax_dendr)
ax_dendr.set_yticks([])
ax_dendr.axvline(self.max_dist_cluster,ls='--',c='k')
# -- plot sorted classification stat array ------------------------------------------
# -- create index that sort rows to correspond to dendrogram
feat_sort_ind = dist_dendrogram['leaves']
# -- sort the good performant features so they have the same order as the similarity array
sort_ind = hlp.ismember(self.workflow.redundancy_method.similarity_array_op_ids,self.workflow.redundancy_method.good_perf_op_ids)
self.ops_base_perf_vals = self.ops_base_perf_vals[:,sort_ind]
# -- create index that sort columns with respect to their mean value
task_sort_ind = np.argsort(self.ops_base_perf_vals[:,feat_sort_ind].mean(axis=1))
#all_classes_avg_top = ((all_classes_avg_top - np.ma.mean(all_classes_avg_top,axis=0)) / np.ma.std(all_classes_avg_top,axis=0))
#all_classes_avg_top = fap.normalise_masked_array(self.ops_base_perf_vals, axis= 1,norm_type = 'zscore')[0]
all_classes_avg_top = self.ops_base_perf_vals
# -- plot the operation names as y-axis tick labels
aspect = all_classes_avg_top.shape[0] / float(all_classes_avg_top.shape[1])
ax_ustat_arr.matshow(all_classes_avg_top[task_sort_ind,:][:,feat_sort_ind].T,aspect=aspect,origin='bottom')
ax_ustat_arr.set_yticks(range(len(feat_sort_ind)))
op_id_name_map = self.map_op_id_name_mult_task(self.workflow.tasks)
names = hlp.ind_map_subset(op_id_name_map[0],op_id_name_map[1],self.workflow.redundancy_method.similarity_array_op_ids)
ax_ustat_arr.set_yticklabels(np.array(names)[feat_sort_ind])
# -- plot the problem names as x axis labels
ax_ustat_arr.xaxis.tick_bottom()
ax_ustat_arr.set_xticks(range(all_classes_avg_top.shape[0]))
ax_ustat_arr.set_xticklabels(self.task_names[task_sort_ind],rotation='vertical')
# -- plot clusters ----------------------------------
cluster_bounds = np.nonzero(np.diff(self.workflow.redundancy_method.cluster_inds[feat_sort_ind]))[0]+0.5
for cluster_bound in cluster_bounds:
ax_ustat_arr.axhline(cluster_bound,c='w',lw=2)
# --------------------------------------------------------------------------------
# -- calculate and plot measures -------------------------------------------------
# --------------------------------------------------------------------------------
# -- nr samples and nr labels --------------------------------------------------
n_samples_avg = [np.array(self.workflow.tasks[i].ts['n_samples']).mean() for i in task_sort_ind]
n_classes = [len(set(self.workflow.tasks[i].labels)) for i in task_sort_ind]
x_loc = np.arange(0,len(self.workflow.tasks))+0.5
ax_measures00.scatter(x_loc,n_classes,c='b',s=40)
ax_measures00.plot(x_loc,n_classes,c='b')
[label.set_color('b') for label in ax_measures00.get_yticklabels()]
ax_measures00.set_ylabel('nr classes')
ax_measures00.yaxis.label.set_color('b')
ax_measures00.set_ylim([0,max(n_classes)+1])
ax_measures00.set_xticklabels([])
ax_measures01.scatter(x_loc,n_samples_avg,c='r',s=40)
ax_measures01.plot(x_loc,n_samples_avg,c='r')
[label.set_color('r') for label in ax_measures01.get_yticklabels()]
ax_measures01.set_ylabel('avg samples')
ax_measures01.yaxis.label.set_color('r')
ax_measures01.set_ylim([0,max(n_samples_avg)+100])
ax_measures00.set_xlim([0,len(self.workflow.tasks)])
ax_measures00.set_xticklabels([])
# -- Classification stat measures --------------------------------------------------
# -- minimum average classification stat for all features
ax_measures10.plot(x_loc,np.min(self.workflow.stats_good_op[task_sort_ind,:],axis=1),marker='o',label='min. avg. classification stat all')
# -- minimum average classification stat for top features
ax_measures10.plot(x_loc,np.ma.min(self.ops_base_perf_vals[task_sort_ind,:],axis=1),marker='o',label='min. avg. classification stat top')
# -- average minimum (for each class pair) classification stat for top features
# XXX This would require task.pair_stats to be available (not saved as intermediate at the moment); then it is trivial to implement
ax_measures10.legend(loc=2,fontsize='small',labelspacing=.1)
ax_measures10.set_ylabel('classification stat')
ax_measures10.set_xlim([0,len(self.workflow.tasks)])
ax_measures10.set_ylim([0,0.5])
# -- Classification stat measures and avg operations working--------------------------------------------------
# -- mean average classification stat for all features
ax_measures20.plot(x_loc,np.ma.mean(self.workflow.stats_good_op[task_sort_ind,:],axis=1),marker='o')
[label.set_color('b') for label in ax_measures20.get_yticklabels()]
ax_measures20.set_ylabel('avrg classification stat all feat')
ax_measures20.yaxis.label.set_color('b')
# -- number of successfully calculated features
ax_measures21.plot(x_loc,[len(self.workflow.tasks[i].op['id']) for i in task_sort_ind],c='r',marker='o')
[label.set_color('r') for label in ax_measures21.get_yticklabels()]
ax_measures21.set_ylabel('nr calc feat')
ax_measures21.yaxis.label.set_color('r')
ax_measures20.set_xlim([0,len(self.workflow.tasks)])
def plot_stat_array(self):
fig = plt.figure(figsize = ((15,15)))
# -- plot layout ------------------------------------------------------
rect_ustat_arr = [0.25,0.175,.5,.5]
rect_dendr = [0.755,0.175,.145,.5]
rect_measures0 = [0.25,0.68,0.5,0.1]
rect_measures1 = [0.25,0.785,0.5,0.1]
rect_measures2 = [0.25,0.89,0.5,0.1]
ax_ustat_arr = fig.add_axes(rect_ustat_arr)
ax_dendr = fig.add_axes(rect_dendr)
ax_measures00 = fig.add_axes(rect_measures0)
ax_measures01 = plt.twinx(ax_measures00)
ax_measures10 = fig.add_axes(rect_measures1)
ax_measures10.set_xticklabels([])
ax_measures20 = fig.add_axes(rect_measures2)
ax_measures20.set_xticklabels([])
ax_measures21 = plt.twinx(ax_measures20)
# -- calculate and plot the dendrogram
dist_dendrogram = hierarchy.dendrogram(self.linkage, orientation='left',no_plot=True)
hierarchy.dendrogram(self.linkage, orientation='left',p=50,truncate_mode ='lastp',ax = ax_dendr)
ax_dendr.set_yticks([])
ax_dendr.axvline(self.max_dist_cluster,ls='--',c='k')
# -- plot sorted U-Stat array ------------------------------------------
# -- create index that sort rows to correspond to dendrogram
feat_sort_ind = dist_dendrogram['leaves']
# -- sort the good performant features so they have the same order as the similarity array
sort_ind = hlp.ismember(self.workflow.redundancy_method.similarity_array_op_ids,self.workflow.redundancy_method.good_perf_op_ids)
self.ops_base_perf_vals = self.ops_base_perf_vals[:,sort_ind]
# -- create index that sort columns with respect to their mean value
task_sort_ind = np.argsort(self.ops_base_perf_vals[:,feat_sort_ind].mean(axis=1))
#all_classes_avg_top = ((all_classes_avg_top - np.ma.mean(all_classes_avg_top,axis=0)) / np.ma.std(all_classes_avg_top,axis=0))
#all_classes_avg_top = fap.normalise_masked_array(self.ops_base_perf_vals, axis= 1,norm_type = 'zscore')[0]
all_classes_avg_top = self.ops_base_perf_vals
# -- plot the operation names as y-axis tick labels
aspect = all_classes_avg_top.shape[0] / float(all_classes_avg_top.shape[1])
ax_ustat_arr.matshow(all_classes_avg_top[task_sort_ind,:][:,feat_sort_ind].T,aspect=aspect,origin='bottom')
ax_ustat_arr.set_yticks(range(len(feat_sort_ind)))
op_id_name_map = self.map_op_id_name_mult_task(self.workflow.tasks)
names = hlp.ind_map_subset(op_id_name_map[0],op_id_name_map[1],self.workflow.redundancy_method.similarity_array_op_ids)
ax_ustat_arr.set_yticklabels(np.array(names)[feat_sort_ind])
# -- plot the problem names as x axis labels
ax_ustat_arr.xaxis.tick_bottom()
ax_ustat_arr.set_xticks(range(all_classes_avg_top.shape[0]))
ax_ustat_arr.set_xticklabels(self.task_names[task_sort_ind],rotation='vertical')
# -- plot clusters ----------------------------------
cluster_bounds = np.nonzero(np.diff(self.workflow.redundancy_method.cluster_inds[feat_sort_ind]))[0]+0.5
for cluster_bound in cluster_bounds:
ax_ustat_arr.axhline(cluster_bound,c='w',lw=2)
# --------------------------------------------------------------------------------
# -- calculate and plot measures -------------------------------------------------
# --------------------------------------------------------------------------------
# -- nr samples and nr labels --------------------------------------------------
n_samples_avg = [np.array(self.workflow.tasks[i].ts['n_samples']).mean() for i in task_sort_ind]
n_classes = [len(set(self.workflow.tasks[i].labels)) for i in task_sort_ind]
x_loc = np.arange(0,len(self.workflow.tasks))+0.5
ax_measures00.scatter(x_loc,n_classes,c='b',s=40)
ax_measures00.plot(x_loc,n_classes,c='b')
[label.set_color('b') for label in ax_measures00.get_yticklabels()]
ax_measures00.set_ylabel('nr classes')
ax_measures00.yaxis.label.set_color('b')
ax_measures00.set_ylim([0,max(n_classes)+1])
ax_measures00.set_xticklabels([])
ax_measures01.scatter(x_loc,n_samples_avg,c='r',s=40)
ax_measures01.plot(x_loc,n_samples_avg,c='r')
[label.set_color('r') for label in ax_measures01.get_yticklabels()]
ax_measures01.set_ylabel('avg samples')
ax_measures01.yaxis.label.set_color('r')
ax_measures01.set_ylim([0,max(n_samples_avg)+100])
ax_measures00.set_xlim([0,len(self.workflow.tasks)])
ax_measures00.set_xticklabels([])
# -- U-stat measures --------------------------------------------------
# -- minimum average U-score for all features
ax_measures10.plot(x_loc,np.min(self.workflow.stats_good_op[task_sort_ind,:],axis=1),marker='o',label='min. avg. U-score all')
# -- minimum average U-score for top features
ax_measures10.plot(x_loc,np.ma.min(self.ops_base_perf_vals[task_sort_ind,:],axis=1),marker='o',label='min. avg. U-score top')
# -- average minimum (for each class pair) U-score for top features
# XXX This would require task.pair_stats to be available (not saved as intermediate at the moment); then it is trivial to implement
ax_measures10.legend(loc=2,fontsize='small',labelspacing=.1)
ax_measures10.set_ylabel('u-score')
ax_measures10.set_xlim([0,len(self.workflow.tasks)])
ax_measures10.set_ylim([0,0.5])
# -- U-stat measures and avg operations working--------------------------------------------------
# -- mean average U-score for all features
ax_measures20.plot(x_loc,np.ma.mean(self.workflow.stats_good_op[task_sort_ind,:],axis=1),marker='o')
[label.set_color('b') for label in ax_measures20.get_yticklabels()]
ax_measures20.set_ylabel('avrg u-scrore all feat')
ax_measures20.yaxis.label.set_color('b')
# -- number of successfully calculated features
ax_measures21.plot(x_loc,[len(self.workflow.tasks[i].op['id']) for i in task_sort_ind],c='r',marker='o')
[label.set_color('r') for label in ax_measures21.get_yticklabels()]
ax_measures21.set_ylabel('nr calc feat')
ax_measures21.yaxis.label.set_color('r')
ax_measures20.set_xlim([0,len(self.workflow.tasks)])
# 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 -------------------------------------------------------------
# ---------------------------------------------------------------------
fig = plt.figure(figsize = ((15,15)))
# -- plot layout ------------------------------------------------------
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 corelated_features_mask(data=None,abs_corr_array=None,calc_times=None,op_ids=None):
"""
Computes a mask that, when applied, removes correlated features from the data array.
Parmeters:
----------
data : ndarray
A data matrix with rows represent training samples and columns represent features
abs_corr_array : ndarray
The correlation matrix of all features. Has to be given id data == none
calc_times : ndarray
Array where the first row corresponds to operation id's and the second row to calculation times
for these operation ids
op_ids :
The operation ids corresponding to the rows/columns in abs_corr_array
Returns:
-------
mask : ndarray,dtype=bool
1d array whose entries are one only for uncorrelated entries.
abs_corr_arrayabs_corr_array : ndarray
the correlation matrix
"""
if abs_corr_array == None:
abs_corr_array = np.abs(np.ma.corrcoef(data, rowvar=0))
# -- Vector containing 0 for all operations we don't want
mask = np.ones(abs_corr_array.shape[0],dtype='bool')
for i in range(abs_corr_array.shape[0]):
# -- if the current line represents an operation not yet eleminated
if mask[i]:
# -- remove operations which are highly correlated
mask[(abs_corr_array[i] > 0.8)] = 0
#----------------------------------------------------------
# -- Use fastest operation in a set of correlated operations
#----------------------------------------------------------
if calc_times != None and op_ids != None:
# -- find ind in abs_corr_array of correlated features
ind_corr = np.nonzero((abs_corr_array[i] > 0.8))[0]
# -- translate ind_corr to op ids
op_id_corr = hlp.ind_map_subset(range(abs_corr_array.shape[0]),op_ids,ind_corr)
# -- get calculation time of correlated op ids
t_corr = hlp.ind_map_subset(calc_times[0],calc_times[1],op_id_corr)
# -- check if all entries are None -> no timing information for any of the operations
if np.nonzero(t_corr)[0].shape[0] == 0:
# -- pick the first operation as fastest as there is no timing information
op_id_corr_fastest = op_id_corr[0]
# -- else pick the fastest operation
else:
# -- get op_id of fastest operation in this correlated set
op_id_corr_fastest = op_id_corr[np.nanargmin(t_corr)]
# -- get index of fastest operation in this correlated set
ind_corr_fastest = hlp.ind_map_subset(op_ids,range(abs_corr_array.shape[0]),op_id_corr_fastest)
# -- add fastest index back in
mask[ind_corr_fastest] = 1
#----------------------------------------------------------
# -- Use arbitrary operation in a set of correlated operations
#----------------------------------------------------------
else:
mask[i] = 1
return mask,abs_corr_array
# -- 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 -------------------------------------------------------------
# ---------------------------------------------------------------------
fig = plt.figure(figsize=((15, 15)))
# -- plot layout ------------------------------------------------------
def plot_stat_array(self):
fig = plt.figure(figsize=((15, 15)))
# -- plot layout ------------------------------------------------------
#rect_ustat_arr = [0.01,0.01,0.75,0.75] #[0.25,0.175,.5,.5]
#rect_dendr = [0.76,0.01,.2175,.75] #[0.755,0.175,.145,.5]
rect_ustat_arr = fig.add_axes([0.15, 0.2, 0.7, 0.8])
#rect_dendr = fig.add_axes([0.7, 0.1, 0.145, 0.8])
rect_dendr = fig.add_axes([0.15, 0.8, 0.873, 0.2])
'''rect_measures0 = [0.25,0.68,0.5,0.1]
rect_measures1 = [0.25,0.785,0.5,0.1]
rect_measures2 = [0.25,0.89,0.5,0.1]'''
ax_ustat_arr = fig.add_axes(rect_ustat_arr)
ax_dendr = fig.add_axes(rect_dendr)
'''ax_measures00 = fig.add_axes(rect_measures0)
ax_measures01 = plt.twinx(ax_measures00)
ax_measures10 = fig.add_axes(rect_measures1)
ax_measures10.set_xticklabels([])
ax_measures20 = fig.add_axes(rect_measures2)
ax_measures20.set_xticklabels([])
ax_measures21 = plt.twinx(ax_measures20)'''
# -- calculate and plot the dendrogram
dist_dendrogram = hierarchy.dendrogram(self.linkage,
orientation='top',
no_plot=True)
hierarchy.dendrogram(self.linkage,
orientation='top',
p=50,
truncate_mode='lastp',
ax=ax_dendr)
ax_dendr.set_xticks([])
ax_dendr.axvline(self.max_dist_cluster, ls='--', c='k')
# -- plot sorted classification stat array ------------------------------------------
# -- create index that sort rows to correspond to dendrogram
feat_sort_ind = dist_dendrogram['leaves']
# -- sort the good performant features so they have the same order as the similarity array
sort_ind = hlp.ismember(
self.workflow.redundancy_method.similarity_array_op_ids,
self.workflow.redundancy_method.good_perf_op_ids)
self.ops_base_perf_vals = self.ops_base_perf_vals[:, sort_ind]
# -- create index that sort columns with respect to their mean value
task_sort_ind = np.argsort(
self.ops_base_perf_vals[:, feat_sort_ind].mean(axis=1))
#all_classes_avg_top = ((all_classes_avg_top - np.ma.mean(all_classes_avg_top,axis=0)) / np.ma.std(all_classes_avg_top,axis=0))
#all_classes_avg_top = fap.normalise_masked_array(self.ops_base_perf_vals, axis= 1,norm_type = 'zscore')[0]
all_classes_avg_top = self.ops_base_perf_vals
# -- plot the operation names as y-axis tick labels
aspect = all_classes_avg_top.shape[0] / float(
all_classes_avg_top.shape[1])
im = ax_ustat_arr.matshow(
all_classes_avg_top[task_sort_ind, :][:, feat_sort_ind],
aspect=aspect,
origin='bottom',
cmap='turbo')
ax_ustat_arr.set_yticks(range(all_classes_avg_top.shape[0]))
op_id_name_map = self.map_op_id_name_mult_task(self.workflow.tasks)
names = hlp.ind_map_subset(
op_id_name_map[0], op_id_name_map[1],
self.workflow.redundancy_method.similarity_array_op_ids)
ax_ustat_arr.set_yticklabels(self.task_names[task_sort_ind])
# -- plot the problem names as x axis labels
ax_ustat_arr.xaxis.tick_bottom()
ax_ustat_arr.set_xticks(range(len(feat_sort_ind)))
ax_ustat_arr.set_xticklabels(np.array(names)[feat_sort_ind],
rotation='vertical')
fig.colorbar(im)
# -- plot clusters ----------------------------------
'''cluster_bounds = np.nonzero(np.diff(self.workflow.redundancy_method.cluster_inds[feat_sort_ind]))[0]+0.5
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 -------------------------------------------------------------
# ---------------------------------------------------------------------
fig = plt.figure(figsize=((15, 15)))
# -- plot layout ------------------------------------------------------
