def compute_residual(self, save_dir,
multi_processing=False,
n_processors=1):
'''
'''
if not os.path.exists(save_dir):
os.makedirs(save_dir)
batch_ids = []
fnames_seg = []
for batch_id in range(self.reader.n_batches):
batch_ids.append(batch_id)
fnames_seg.append(
os.path.join(save_dir,
'residual_seg{}.npy'.format(batch_id)))
#self.logger.info("computing residuals")
if multi_processing:
batches_in = np.array_split(batch_ids, n_processors)
fnames_in = np.array_split(fnames_seg, n_processors)
parmap.starmap(self.subtract_parallel,
list(zip(batches_in, fnames_in)),
processes=n_processors,
pm_pbar=True)
else:
for ctr in range(len(batch_ids)):
self.subtract_parallel(
[batch_ids[ctr]], [fnames_seg[ctr]])
self.fnames_seg = fnames_seg
def sen2cor_L2A_batch(res, L1Cdir):
"""
Batch processing of S1-L1C files in a directory to S1-L2A
Args:
res (str/num): resolution, accepts 10, 20, 60, or all
L1Cdir (str): location of S1 L1C products
"""
# Put S1 L1C directory names in list
L1C_files = filter(
re.compile(r'^S2.*L1C.*SAFE$').search, os.listdir(L1Cdir))
print("{} L1C files found in directory".format(str(len(L1C_files))))
l1cList = []
for L1C_file in L1C_files: # Iterate over directory names
# Check if the file exists
checker = r'S2._MSIL2A_' + L1C_file[11:]
checker_list = filter(re.compile(checker).search, os.listdir(L1Cdir))
checker2 = r'S2._USER_PRD_MSIL2A_' + L1C_file[20:]
checker_list = checker_list + filter(
re.compile(checker2).search, os.listdir(L1Cdir))
if len(checker_list) == 0:
# Call sen2cor function for individual product
print("{} is set for processing".format(L1C_file))
#sen2cor_L2A(res, L1Cdir+L1C_file)
l1cList.append((res, L1Cdir + L1C_file))
else:
print("{} was already processed, removing from list".format(
L1C_file))
parmap.starmap(sen2cor_L2A, l1cList, pm_chunksize=12)
def download_hub(download_dir,
file_path,
accounts_file,
start_date='NOW-30DAYS',
end_date='NOW',
downloads_per_account=1, # maximum allowed is 2
max_downloads=10):
start_date = str(start_date)
end_date = str(end_date)
if not os.path.isdir(download_dir):
raise ValueError('download_dir: '+ download_dir + ' does not exist or is inaccesible. Your current working directory is '+os.getcwd()+'.')
if not os.path.exists(file_path):
raise ValueError('file_path: '+ file_path + ' does not exist or is inaccesible. Your current working directory is '+os.getcwd()+'.')
if not os.path.exists(accounts_file):
raise ValueError('accounts_file: '+ accounts_file + ' does not exist or is inaccesible. Your current working directory is '+os.getcwd()+'.')
products, credentials = get_products_aoi(file_path, accounts_file, start_date=start_date, end_date=end_date)
# Creates directory for download files
owd = os.getcwd() # original working directory (owd)
new_dir = download_dir+'/hub%s' % time.strftime('%a%d%b%Y%H%M%S')
os.mkdir(new_dir)
os.chdir(new_dir)
n_accounts = len(credentials)
n_products = len(products)
n_threads = n_products // n_accounts * downloads_per_account
if n_threads < 1:
n_threads = 1
if n_threads >= max_downloads:
n_threads = max_downloads
div_products = dict_divider(products, n_threads)
if n_products > 1:
div_credentials = credentials.values() * (n_accounts//n_threads)
else:
div_credentials = [credentials.values()[0]]
parmap.starmap(download, izip(div_products, div_credentials))
# try zip
#for elem in products:
# print elem
# print products
# os.system('unzip ' + products[elem]['title'] + '.zip')
# os.remove(products[elem]['title'] + '.zip')
os.chdir(owd)
text_file = open(download_dir+'/TIME_DIR.txt','w')
text_file.write(new_dir)
text_file.close()
return new_dir
def run_voltage_treshold(standardized_path,
standardized_dtype,
output_directory,
run_chunk_sec='full'):
"""Run detection that thresholds on amplitude
"""
logger = logging.getLogger(__name__)
CONFIG = read_config()
# get data reader
#n_sec_chunk = CONFIG.resources.n_sec_chunk*CONFIG.resources.n_processors
batch_length = CONFIG.resources.n_sec_chunk
n_sec_chunk = 0.5
print(" batch length to (sec): ", batch_length,
" (longer increase speed a bit)")
print(" length of each seg (sec): ", n_sec_chunk)
buffer = CONFIG.spike_size
if run_chunk_sec == 'full':
chunk_sec = None
else:
chunk_sec = run_chunk_sec
reader = READER(standardized_path, standardized_dtype, CONFIG,
batch_length, buffer, chunk_sec)
# number of processed chunks
n_mini_per_big_batch = int(np.ceil(batch_length / n_sec_chunk))
total_processing = int(reader.n_batches * n_mini_per_big_batch)
# neighboring channels
channel_index = make_channel_index(CONFIG.neigh_channels,
CONFIG.geom,
steps=2)
if CONFIG.resources.multi_processing:
parmap.starmap(run_voltage_threshold_parallel,
list(zip(np.arange(reader.n_batches))),
reader,
n_sec_chunk,
CONFIG.detect.threshold,
channel_index,
output_directory,
processes=CONFIG.resources.n_processors,
pm_pbar=True)
else:
for batch_id in range(reader.n_batches):
run_voltage_threshold_parallel(batch_id, reader, n_sec_chunk,
CONFIG.detect.threshold,
channel_index, output_directory)
def apply_heuristics(self,
heuristics,
primitive_matrix,
feat_combos,
beta_opt,
mode=None):
"""
Apply given heuristics to given feature matrix X and abstain by beta
heuristics: list of pre-trained logistic regression models
feat_combos: primitive indices to apply heuristics to
beta: best beta value for associated heuristics
"""
if f'{heuristics[0].__class__}' == "<class 'program_synthesis.synthesizer.dummyKneighborClassifier'>":
if self.cuda:
#########gpu
L = np.zeros((np.shape(primitive_matrix)[0], len(heuristics)))
for i, hf in enumerate(heuristics):
L[:, i] = marginals_to_labels_cuda(
hf, beta_opt[i], feat_combos[i],
primitive_matrix[:, feat_combos[i]], self)
return L
else:
########using parmap
L_ = parmap.starmap(marginals_to_labels,
list(zip(heuristics, beta_opt,
feat_combos)),
primitive_matrix,
self,
pm_pbar=True)
return np.transpose(np.array(L_))
########single-core
# L = np.zeros((np.shape(primitive_matrix)[0], len(heuristics)))
# for i, hf in enumerate(heuristics):
# L[:, i] = marginals_to_labels(hf, beta_opt[i], feat_combos[i], primitive_matrix,
# self, mode=mode)
return L
else:
########using parmap
L_ = parmap.starmap(marginals_to_labels,
list(zip(heuristics, beta_opt, feat_combos)),
primitive_matrix,
self,
mode=mode,
pm_pbar=True)
return np.transpose(np.array(L_))
def append_price():
codes = pd.read_excel('코드리스트_test.xlsx', converters={'종목코드': str})['종목코드']
# codes = ['095570']
pages = range(1, 1000)
for code in codes:
csv = pd.DataFrame(
columns=['날짜', '종가', '전일비', '시가', '고가', '저가', '거래량'])
path = "./temp/" + str(code) + ".db"
con = sqlite3.connect(path)
csv.to_sql('price', con, if_exists='replace')
input_list = list(itertools.product([code], pages))
parmap.starmap(get_price, input_list, pm_pbar=True)
def get_evoked_map(mouse):
lofiles, lofilenames = get_file_list(base_dir, mouse)
print lofilenames
lop = get_distance_var(lofiles)
all_frames = get_video_frames(lofiles)
print "Alligning all video frames..."
all_frames = parmap.starmap(shift_frames, zip(all_frames, lop))
all_frames = np.asarray(all_frames, dtype=np.float32)
print np.shape(all_frames)
new_all_frames = parmap.map(process_frames_evoked, all_frames)
all_frames = np.reshape(all_frames,
(all_frames.shape[0]*all_frames.shape[1],
all_frames.shape[2],
all_frames.shape[3]))
save_to_file("conc_RAW.raw", all_frames, np.float32)
print "Creating array.."
new_all_frames = np.asarray(new_all_frames, dtype=np.float32)
print "Averaging together..."
new_all_frames = np.mean(new_all_frames, axis=0)
print np.shape(new_all_frames)
save_to_file("evoked_trial_noBP_GSR.raw", new_all_frames, np.float32)
def get_location(self):
"""
Extracts the location of each pixel in the satellite image
"""
self.ncols = self.satellite_gdal.RasterXSize / 2
self.nrows = self.satellite_gdal.RasterYSize / 2
self.length_df = self.nrows * self.ncols
print 'Columns, rows', self.ncols, self.nrows
cols_grid, rows_grid = np.meshgrid(range(0, self.ncols),
range(0, self.nrows))
self.cols_grid = cols_grid.flatten()
self.rows_grid = rows_grid.flatten()
print 'Checking the meshgrid procedure works'
# getting a series of lat lon points for each pixel
self.geotransform = self.satellite_gdal.GetGeoTransform()
print 'Getting locations'
self.location_series = np.array(
parmap.starmap(pixel_to_coordinates,
zip(self.cols_grid, self.rows_grid),
self.geotransform,
processes=self.processes))
print 'Converting to Points'
pool = Pool(self.processes)
self.location_series = pool.map(point_wrapper, self.location_series)
def get_location(self):
"""
Extracts the location of each pixel in the satellite image
"""
self.ncols = self.satellite_gdal.RasterXSize / 2
self.nrows = self.satellite_gdal.RasterYSize / 2
self.length_df = self.nrows * self.ncols
print 'Columns, rows', self.ncols, self.nrows
cols_grid, rows_grid = np.meshgrid(
range(0, self.ncols),
range(0, self.nrows))
self.cols_grid = cols_grid.flatten()
self.rows_grid = rows_grid.flatten()
print 'Checking the meshgrid procedure works'
# getting a series of lat lon points for each pixel
self.geotransform = self.satellite_gdal.GetGeoTransform()
print 'Getting locations'
self.location_series = np.array(parmap.starmap(
pixel_to_coordinates,
zip(self.cols_grid, self.rows_grid),
self.geotransform,
processes = self.processes))
print 'Converting to Points'
pool = Pool(self.processes)
self.location_series = pool.map(
point_wrapper,
self.location_series)
def pairwise(self, pairwiseList, nprocs=1, **kwargs):
"""Pairwise read mapping based on list of references and reads to map.
Args:
pairwiseList -- list of tuples (i,j,refIndex,readFile)
'i' and 'j' are comparison indices
nprocs -- max number of parallel mapper calls
kwargs -- passsed to mapper method
"""
# adding kwargs to function
new_mapper = partial(self, **kwargs)
# making trimmed list of tuples
trimmed = [(i[2],i[3],) for i in pairwiseList]
# calling mapper
samFiles = parmap.starmap(new_mapper, trimmed, processes=nprocs)
# creating a numpy array for output
#simSamFiles = np.array([['' for i in range(n_refs)] for j in range(n_refs)], dtype=object)
# appending samFiles to tuple
pairwiseList2 = []
for i,samFile in enumerate(samFiles):
pairwiseList2.append(pairwiseList[i] + (samFile,))
# return
return pairwiseList2
def parallel(self, names, mg, nprocs=1, **kwargs):
"""Calling mapper using multiple processors.
Args:
names -- NameFile instance with iter_names() method
mg -- MetaFile instance with readFile attrib
nprocs -- number of parallel calls
kwargs -- passed to mapper call
Return:
refSamFile attrib set for each name in names
"""
# making list of tuples (indexFile, readFile)
lt = [(name.get_indexFile(), mg.get_readFile())
for name in names.iter_names()]
# altering function kwargs
new_mapper = partial(self, **kwargs)
# calling mapper
samFiles = parmap.starmap(new_mapper, lt, processes=nprocs)
# adding samFile attrib to name instances
for i, name in enumerate(names.iter_names()):
name.set_refSamFile(samFiles[i])
def NCC_best_template_search(c1,
c2,
im1,
im2,
width=60,
c1_init=None,
c2_init=None,
search_w=100):
''' Finds the best center according to block matching search
'''
searchx, searchy = find_search_pixel(c1, c2, search_w)
if c1_init is None:
xv, yv = find_template_pixel(c1, c2, width, im1.shape[1], im1.shape[0])
else:
xv, yv = find_template_pixel(c1_init, c2_init, width, im1.shape[1],
im1.shape[0])
NCC_all = parmap.starmap(get_NCC,
zip(np.ravel(searchx), np.ravel(searchy)),
im1,
im2,
width,
yv,
xv,
pm_parallel=True,
pm_processes=2)
maxNCC = np.max(NCC_all)
if np.sum(NCC_all == -1) > 0:
print('Number of weird NCC {}'.format(np.sum(NCC_all == -1)))
if maxNCC == -1:
print('VERY WEIRD ALL NCC ARE -1')
idx = np.argmax(NCC_all)
best_c1, best_c2 = np.ravel(searchx)[idx], np.ravel(searchy)[idx]
return best_c1, best_c2, maxNCC
def compute_norm_dist_to_tissue_parallel(locs, tissue_mat_new, target_df,
result_df):
num_cores = mp.cpu_count()
if num_cores > math.floor(target_df.shape[0] / 2):
num_cores = int(math.floor(target_df.shape[0] / 2))
ttt = np.array_split(target_df, num_cores, axis=0)
tuples = [(l, d, u, c) for l, d, u, c in zip(
repeat(locs, num_cores), repeat(tissue_mat_new, num_cores), ttt,
repeat(result_df, num_cores))]
# repeat(p_cutoff, num_cores))]
dist_results = parmap.starmap(compute_norm_dist_to_tissue,
tuples,
pm_processes=num_cores,
pm_pbar=True)
dd = [dist_results[i][0] for i in range(len(dist_results))]
dist_val = reduce(operator.add, dd)
gg = [dist_results[i][1] for i in range(len(dist_results))]
genes = reduce(operator.add, gg)
re = [dist_results[i][2] for i in range(len(dist_results))]
recal_genes = reduce(operator.add, re)
dist_norm = [val / max(dist_val) for val in dist_val]
dist_df = pd.DataFrame([genes, dist_val, dist_norm]).T
dist_df.columns = ['geneID', 'dist', 'norm_dist']
dist_df.index = genes
return dist_df, recal_genes
def pairwise(self, pairwiseList, nprocs=1, **kwargs):
"""Pairwise read mapping based on list of references and reads to map.
Args:
pairwiseList -- list of tuples (i,j,refIndex,readFile)
'i' and 'j' are comparison indices
nprocs -- max number of parallel mapper calls
kwargs -- passsed to mapper method
"""
# adding kwargs to function
new_mapper = partial(self, **kwargs)
# making trimmed list of tuples
trimmed = [(
i[2],
i[3],
) for i in pairwiseList]
# calling mapper
samFiles = parmap.starmap(new_mapper, trimmed, processes=nprocs)
# creating a numpy array for output
#simSamFiles = np.array([['' for i in range(n_refs)] for j in range(n_refs)], dtype=object)
# appending samFiles to tuple
pairwiseList2 = []
for i, samFile in enumerate(samFiles):
pairwiseList2.append(pairwiseList[i] + (samFile, ))
# return
return pairwiseList2
def recalc_dist_to_tissue_parallel(locs,
data_norm,
cellGraph,
gmmDict,
test_genes,
tissue_mat_new,
add_sf=30,
unary_scale_factor=100,
label_cost=10,
algorithm='expansion'):
num_cores = mp.cpu_count()
if num_cores > math.floor(len(test_genes) / 2):
num_cores = int(math.floor(len(test_genes) / 2))
ttt = np.array_split(test_genes, num_cores, axis=0)
tuples = [
(l, d, c, g, t, ts, a, u, ll, al)
for l, d, c, g, t, ts, a, u, ll, al in zip(
repeat(locs, num_cores), repeat(data_norm, num_cores),
repeat(cellGraph, num_cores), repeat(gmmDict, num_cores), ttt,
repeat(tissue_mat_new, num_cores), repeat(add_sf, num_cores),
repeat(unary_scale_factor, num_cores), repeat(
label_cost, num_cores), repeat(algorithm, num_cores))
]
dist_results = parmap.starmap(recalc_dist_to_tissue,
tuples,
pm_processes=num_cores,
pm_pbar=True)
result_df_new = pd.DataFrame()
best_dist_df = pd.DataFrame()
for i in range(len(dist_results)):
result_df_new = pd.concat([result_df_new, dist_results[i][0]])
best_dist_df = pd.concat([best_dist_df, dist_results[i][1]])
return result_df_new, best_dist_df
def identify_spatial_genes(locs, data_norm, cellGraph, gmmDict, smooth_factor=10,
unary_scale_factor=100, label_cost=10, algorithm='expansion'):
# pool = mp.Pool()
'''
main function to identify spatially variable genes
:param file:locs: spatial coordinates (n, 2); data_norm: normalized gene expression;
smooth_factor=10; unary_scale_factor=100; label_cost=10; algorithm='expansion'
:rtype: prediction: a dataframe
'''
num_cores = mp.cpu_count()
if num_cores > math.floor(data_norm.shape[1]/2):
num_cores=int(math.floor(data_norm.shape[1]/2))
ttt = np.array_split(data_norm,num_cores,axis=1)
tuples = [(l, d, c, g, s, u, b, a) for l, d, c, g, s, u, b, a in zip(
repeat(locs, num_cores),
ttt,
repeat(cellGraph, num_cores),
repeat(gmmDict, num_cores),
repeat(smooth_factor, num_cores),
repeat(unary_scale_factor, num_cores),
repeat(label_cost, num_cores),
repeat(algorithm, num_cores))]
results = parmap.starmap(compute_spatial_genomewise_optimize_gmm, tuples,
pm_processes=num_cores, pm_pbar=True)
# pool.close()
# p_values, genes, diff_p_values, exp_diff, smooth_factors, pred_labels
nnn = [results[i][0] for i in np.arange(len(results))]
nodes = reduce(operator.add, nnn)
ppp = [results[i][1] for i in np.arange(len(results))]
p_values=reduce(operator.add, ppp)
ggg = [results[i][2] for i in np.arange(len(results))]
genes = reduce(operator.add, ggg)
# exp_ppp = [results[i][3] for i in np.arange(len(results))]
# exp_pvalues = reduce(operator.add, exp_ppp)
# exp_ddd = [results[i][4] for i in np.arange(len(results))]
# exp_diffs = reduce(operator.add, exp_ddd)
fff = [results[i][3] for i in np.arange(len(results))]
s_factors = reduce(operator.add, fff)
lll = [results[i][4] for i in np.arange(len(results))]
pred_labels = reduce(operator.add, lll)
best_p_values=[min(i) for i in p_values]
fdr = multi.multipletests(np.array(best_p_values), method='fdr_bh')[1]
#exp_fdr = multi.multipletests(np.array(exp_pvalues), method='fdr_bh')[1]
labels_array = np.array(pred_labels).reshape(len(genes), pred_labels[0].shape[0])
data_array = np.array((genes, p_values, fdr,s_factors, nodes), dtype=object).T
t_array = np.hstack((data_array, labels_array))
c_labels = ['p_value', 'fdr', 'smooth_factor', 'nodes']
for i in np.arange(labels_array.shape[1]) + 1:
temp_label = 'label_cell_' + str(i)
c_labels.append(temp_label)
result_df = pd.DataFrame(t_array[:,1:], index=t_array[:,0],
columns=c_labels)
return result_df
def sampling(self):
"""
Constructs a weighted sample of images from the GeoDataFrame
Returns:
(array) sample_idx: index of sampled images
Note: Keras uses the last x percent of data in cross validation
Have to shuffle here to ensure that the last ten percent isn't just
the southern most rows of information
"""
# Getting the sum of urban pixels for each patch
self.pop_array = self.df_image['pop_density'].fillna(0)
self.pop_array = np.array(
self.pop_array).reshape((self.nrows, self.ncols))
print 'extract patches'
self.image_slicer(self.pop_array)
print 'get locations for individual frames'
pool = Pool(self.processes)
cols_grid = pool.map(adder, self.indices[:,0])
rows_grid = pool.map(adder, self.indices[:,1])
print 'Max of cols grid after slicing:', max(cols_grid)
print 'Max of rows grid after slicing:', max(rows_grid)
self.frame_location_series = parmap.starmap(
pixel_to_coordinates,
zip(cols_grid, rows_grid), self.geotransform,
processes=self.processes)
print 'converting locations to Points'
self.frame_location_series = \
pool.map(Point, self.frame_location_series)
pop_count = np.array([np.mean(patch) for patch in self.patches])
self.df_sample = pd.DataFrame(pop_count, columns=['pop_ave'])
# Getting the locations
self.df_sample['location'] = self.frame_location_series
# Creating sample weights
seed =1975
self.pop_mean_sample = self.df_sample.sample(
frac=self.sample_rate,
replace=True,
weights='pop_ave',
random_state = seed)
self.sample_idx = np.array(self.pop_mean_sample.index.values)
# ensuring that we get some values with zero population
self.df_zero_sample = self.df_sample[self.df_sample['pop_ave']==0]
self.pop_mean_sample_zero = self.df_zero_sample.sample(
frac = self.sample_rate,
replace = True,
random_state = seed)
self.sample_idx_zero = np.array(self.pop_mean_sample_zero.index.values)
# combining with >0 sample
self.sample_idx = np.concatenate([self.sample_idx,
self.sample_idx_zero])
self.pop_output_data = np.concatenate([self.pop_mean_sample,
self.pop_mean_sample_zero]).T[0]
# shuffling so that we don't have the zero pop areas at end of sample
p = np.random.permutation(len(self.sample_idx))
self.sample_idx = self.sample_idx[p]
self.pop_output_data = np.array(
self.pop_output_data[p]).reshape((len(self.pop_output_data),1))
def exec18():
#date 불러오기
dates = pd.read_excel('workingdays_201912.xlsx', converters={'영업일': str})
date1 = dates['영업일']
# date1 = ['2020.06.03'] ####테스트용
#code 불러오기
codes = pd.read_excel('코드리스트.xlsx', converters={'종목코드': str})
code = codes['종목코드']
# code = ['011790'] ####테스트용
#불러온 date 라인별로 실행
for date in date1:
print(date)
date = [date]
input_list = list(itertools.product(date, code))
parmap.starmap(excute, input_list, pm_pbar=True)
def parallel_starmap(f, args, processes = 1):
"""
Wrapper function for 'parmap.starmap': Parallises the computations in
'starmap' form if required. If only one process is needed, computations
are performed serially
"""
if processes == 1:
return [f(*arg) for arg in args]
return parmap.starmap(f, args, processes = processes)
def sampling(self):
"""
Constructs a weighted sample of images from the GeoDataFrame
Returns:
(array) sample_idx: index of sampled images
Note: Keras uses the last x percent of data in cross validation
Have to shuffle here to ensure that the last ten percent isn't just
the southern most rows of information
"""
# Getting the sum of urban pixels for each patch
self.pop_array = self.df_image['pop_density'].fillna(0)
self.pop_array = np.array(self.pop_array).reshape(
(self.nrows, self.ncols))
print 'extract patches'
self.image_slicer(self.pop_array)
print 'get locations for individual frames'
pool = Pool(self.processes)
cols_grid = pool.map(adder, self.indices[:, 0])
rows_grid = pool.map(adder, self.indices[:, 1])
print 'Max of cols grid after slicing:', max(cols_grid)
print 'Max of rows grid after slicing:', max(rows_grid)
self.frame_location_series = parmap.starmap(pixel_to_coordinates,
zip(cols_grid, rows_grid),
self.geotransform,
processes=self.processes)
print 'converting locations to Points'
self.frame_location_series = \
pool.map(Point, self.frame_location_series)
pop_count = np.array([np.mean(patch) for patch in self.patches])
self.df_sample = pd.DataFrame(pop_count, columns=['pop_ave'])
# Getting the locations
self.df_sample['location'] = self.frame_location_series
# Creating sample weights
seed = 1975
self.pop_mean_sample = self.df_sample.sample(frac=self.sample_rate,
replace=True,
weights='pop_ave',
random_state=seed)
self.sample_idx = np.array(self.pop_mean_sample.index.values)
# ensuring that we get some values with zero population
self.df_zero_sample = self.df_sample[self.df_sample['pop_ave'] == 0]
self.pop_mean_sample_zero = self.df_zero_sample.sample(
frac=self.sample_rate, replace=True, random_state=seed)
self.sample_idx_zero = np.array(self.pop_mean_sample_zero.index.values)
# combining with >0 sample
self.sample_idx = np.concatenate(
[self.sample_idx, self.sample_idx_zero])
self.pop_output_data = np.concatenate(
[self.pop_mean_sample, self.pop_mean_sample_zero]).T[0]
# shuffling so that we don't have the zero pop areas at end of sample
p = np.random.permutation(len(self.sample_idx))
self.sample_idx = self.sample_idx[p]
self.pop_output_data = np.array(self.pop_output_data[p]).reshape(
(len(self.pop_output_data), 1))
def calculate_mean_utt_length(raw_wavs, sr):
n_worker = int(cpu_count() * cpu_rate)
logging.info(
"{}% resources ({} cpu core(s)) will be used for mean utterance length calculation"
.format(cpu_rate * 100, n_worker))
# return pool.starmap(_calculate_mean_utt_length, zip(raw_wavs, repeat(sr)))
return parmap.starmap(_calculate_mean_utt_length,
list(zip(raw_wavs, repeat(sr))),
pm_processes=n_worker,
pm_pbar=True)
def pre_landsat_batch(data_dir, out_dir, ref_raster):
# Get a list of S2 L2A product directory names
landsat_products = filter(
re.compile(r'^L.*[0-9]$').search, os.listdir(data_dir))
for key in landsat_products:
ldir = filter(re.compile('tif$').search, os.listdir(data_dir + key))
# Create directories in dest location
if not os.path.exists(out_dir + key):
os.makedirs(out_dir + key)
# Put bands in list
bands = list(
map(
lambda x: (data_dir + key + '/' + x, out_dir + key + '/ref_' +
x.split('_')[-2] + x.split('_')[-1], ref_raster),
ldir))
parmap.starmap(pre_process_landsat, bands)
def uncompress_files(data_dir, unzip_dir=None):
"""
Unzips every zipfile in the path, and stores in directory with zipfile name+.SAFE
Args:
eo_dir (str): directory where zipfiles are located
unzip_dir (str): directory where files will be unzipped, default is data_dir
"""
# List all zip files in directory
eo_zip_files = filter(re.compile('zip$').search, os.listdir(data_dir))
# List all tar files files in directory
eo_tar_files = filter(re.compile('tar.gz$').search, os.listdir(data_dir))
# Check if a data folder exist
if unzip_dir is None:
unzip_direc = data_dir
else:
unzip_direc = unzip_dir
if not os.path.exists(unzip_direc):
os.makedirs(unzip_direc)
print('New directory {} was created'.format(unzip_direc))
# Make sure uncompress path ends with slash
#if unzip_direc[-1] != '/':
# unzip_direc = unzip_direc + '/'
# Put parameter sets in tuples
eo_zip_files = list(map(lambda x: (unzip_direc, data_dir, x),
eo_zip_files))
eo_tar_files = list(map(lambda x: (unzip_direc, data_dir, x),
eo_tar_files))
parmap.starmap(unzip_eo, eo_zip_files)
parmap.starmap(untar_eo, eo_tar_files)
def multiGMM(data_norm):
num_cores = mp.cpu_count()
if num_cores > math.floor(data_norm.shape[1]/2):
num_cores=int(math.floor(data_norm.shape[1]/2))
# print(num_cores)
ttt = np.array_split(data_norm,num_cores,axis=1)
#print(ttt)
tuples = [d for d in zip(ttt)]
gmmDict_=parmap.starmap(gmm_model, tuples,
pm_processes=num_cores, pm_pbar=True)
gmmDict={}
for i in np.arange(len(gmmDict_)):
gmmDict.update(gmmDict_[i]) ## dict.update() add dict to dict
return gmmDict
def calculate_speaking_rate(raw_wavs, texts, sr, dictionary):
n_worker = int(cpu_count() * cpu_rate)
logging.info(
"{}% resources ({} cpu core(s)) will be used for speaking_rate calculation"
.format(cpu_rate * 100, n_worker))
# rates = pool.starmap(_calculate_speaking_rate, zip(raw_wavs, texts, repeat(sr), repeat(dictionary)))
rates = parmap.starmap(_calculate_speaking_rate,
list(
zip(raw_wavs, texts, repeat(sr),
repeat(dictionary))),
pm_processes=n_worker,
pm_pbar=True)
return rates
def sen2_batch (res, dir): # Creates a list of arguments based on number of files to run
os.chdir(dir)
datafiles = os.listdir(dir)
slist = []
for files in datafiles: # checks for L1C folders
checker1 = "L1C"
if files[7:10] == checker1 or files[16:19] == checker1:
slist.append((res, files))
checker2 = "L2A" # checks for unfinished L2A folders an deletes it
if files[7:10] == checker2 or files[16:19] == checker2:
each_folder_dir = str(os.listdir(dir)) + '/' + str(files)
for subf in each_folder_dir:
if len(subf) <= 8:
shutil.rmtree(files, ignore_errors=True) #resolving the bug that shows error after deleting files
if not os.path.isdir(dir):
raise ValueError('working_directory: '+ dir + ' does not exist or is inaccesible. Your current working directory is '+os.getcwd()+'.')
parmap.starmap(sen2_single, slist) #goes for optimal processing setup since GIPP parallelizing is set to AUTO
dir_L1C = dir
return dir_L1C
def find_optimal_beta(self, heuristics, X, feat_combos, ground):
"""
Returns optimal beta for given heuristics
heuristics: list of pre-trained logistic regression models
X: primitive matrix
feat_combos: feature indices to apply heuristics to
ground: ground truth associated with X data
"""
if f'{heuristics[0].__class__}' == "<class 'program_synthesis.synthesizer.dummyKneighborClassifier'>":
########single-core, gpu
if self.cuda:
beta_opt = []
# for i, hf in enumerate(heuristics):
# X_.append(all_pair_dist_cuda(self.val_primitive_matrix[:, feat_combos[i]], X[:, feat_combos[i]], feat_combos[i]))
#
# marginals = parmap.map(heuristics[0].predict_proba, X_, self.val_ground)
#
# for i, hf in enumerate(heuristics):
# beta_opt.append((self.beta_optimizer(marginals[i], ground)))
for i, hf in enumerate(heuristics):
X_ = all_pair_dist_cuda(cp.array(self.val_primitive_matrix[:, feat_combos[i]]), cp.array(X[:, feat_combos[i]]), feat_combos[i])
marginals = hf.predict_proba_cuda(X_, self.val_ground)
beta_opt.append(self.beta_optimizer_cuda(marginals, ground))
self.betas.append = beta_opt[-1]
else:
#######with parmap
# beta_opt = parmap.starmap(self.find_dist_and_proba_and_beta, list(zip(heuristics, feat_combos)), X, ground,
# pm_pbar=True)
# self.betas = beta_opt
#######single-core, numba
beta_opt = []
for i, hf in enumerate(heuristics):
#print(i, end='\r')
X_ = all_pair_dist(self.val_primitive_matrix[:, feat_combos[i]], X[:, feat_combos[i]], feat_combos[i])
marginals = hf.predict_proba(X_, self.val_ground)
beta_opt.append(self.beta_optimizer(marginals, ground))
self.betas.append = beta_opt[-1]
else:
#######with parmap
beta_opt = parmap.starmap(self.find_proba_and_beta, list(zip(heuristics, feat_combos)), X, ground, pm_pbar=True)
self.betas = beta_opt
return beta_opt
def cross_val_proba_score_parmap(estimator,
X,
y,
scoring=multilabel_prec,
scoring_arg1=1,
scoring_arg2=5,
n_splits=5):
kf = KFold(n_splits=n_splits, shuffle=True)
scores = parmap.starmap(parmap_wrap,
kf.split(X),
X,
y,
estimator,
scoring=scoring,
scoring_arg1=scoring_arg1,
scoring_arg2=scoring_arg2)
cv_score = np.asarray(scores).mean()
return cv_score
def calc_silhouette_per_gene(genes=None,
expr=None,
dissim=None,
examine_top=0.1,
seed=-1,
num_cores=8,
bar=True):
if genes is None or expr is None or dissim is None:
sys.stderr.write("Need genes, expr, dissim\n")
return
if seed != -1 and seed >= 0:
np.random.seed(seed)
sys.stdout.write("Started 2 " + "\n")
sil = []
ncell = expr.shape[1]
ex = int((1.0 - examine_top) * 100.0)
subexpr = np.array_split(expr, num_cores, axis=0)
subgenes = np.array_split(np.array(genes), num_cores)
subgenes = [i.tolist() for i in subgenes]
print('number of cores : ' + str(num_cores))
dis_list = []
for i in range(num_cores):
dis_list.append(dissim)
assert len(subexpr) == len(subgenes)
tuples = [(expr, ncell, genes, exa, dis)
for expr, ncell, genes, exa, dis in zip(
subexpr, repeat(ncell, num_cores), subgenes,
repeat(examine_top, num_cores), dis_list)]
results = parmap.starmap(process,
tuples,
pm_processes=num_cores,
pm_pbar=bar)
for i in np.arange(len(results)):
sil += results[i]
res = []
for ig, g in enumerate(genes):
this_avg = sil[ig][1]
this_sil = sil[ig][2]
res.append((g, this_sil))
res.sort(key=itemgetter(1), reverse=True)
return res
def apply_rotation_correction(SHAPE_ROTATION_RESULTS_PATH,
APPLY_SHAPE_ROTATION_RESULTS_PATH):
'''
> 2.4
SHAPE_ROTATION_RESULTS_PATH - source
APPLY_SHAPE_ROTATION_RESULTS_PATH - export
'''
import parmap
## read correction results
df_corrections = pd.read_csv(SHAPE_ROTATION_RESULTS_PATH)
#display(df_corrections.head())
#display(df_corrections.Rotate.value_counts())
# apply only once
if not os.path.exists(APPLY_SHAPE_ROTATION_RESULTS_PATH):
print(APPLY_SHAPE_ROTATION_RESULTS_PATH,
' does not exist -> apply rotation on all images')
'''
apply rotation to ALL files
tskes 2 min (threadded with setting below)
'''
N_CPU = multiprocessing.cpu_count() - 10
args_1 = df_corrections.file.to_list()
args_2 = df_corrections.Rotate.to_list()
all_args = list(zip(args_1, args_2))
results = parmap.starmap(correct_img_rotation,
all_args,
pm_pbar=True,
pm_parallel=True,
pm_processes=N_CPU)
pd.DataFrame(list(zip(args_1, args_2, results)),
columns=['file', 'was_rotated', 'outcome'
]).to_csv(APPLY_SHAPE_ROTATION_RESULTS_PATH,
index=False)
else:
print('already applied rotation correction to data!')
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-ann_dir", help="Annotation directory")
parser.add_argument("-clueweb_dir", help="Clueweb directory")
parser.add_argument("-output_dir", help="Output directory")
parser.add_argument("-num_processes", help="Number of processes to run")
args = parser.parse_args()
num_processes = int(args.num_processes)
# Iterate over each subdirectory in the clueweb dir
for subdir, dirs, files in os.walk(args.clueweb_dir):
for folder in dirs:
ann_dir = os.path.join(args.ann_dir, folder)
clueweb_dir = os.path.join(args.clueweb_dir, folder)
output_dir = os.path.join(args.output_dir, folder)
ann_iter = (sorted(os.listdir(ann_dir)))
clueweb_iter = (sorted(os.listdir(clueweb_dir)))
# Ann_dir and clueweb_dir sometimes have different number of files
# Loop through, and create new ann list with correct files
ann_list = []
for cw_file in clueweb_iter:
for ann_file in ann_iter:
# Split to only get filename, and not file extensions
if cw_file.split(".")[0] == ann_file.split(".")[0]:
ann_list.append(ann_file)
kwargs = {'processes': num_processes}
start = time.time()
# Read and clean files in parallel
results = parmap.starmap(read_and_clean_files, zip(clueweb_iter, ann_list),
clueweb_dir, ann_dir, **kwargs)
end = time.time()
print "Time used reading and cleaning all files", end - start
start = time.time()
# Initiate indexer
indexer = Indexer(output_dir)
# Index all the cleaned records
indexer.index_files(results)
end = time.time()
print "Time used indexing all files", end - start
def calculate_f0(raw_wavs, sr, frame_period=5, parallel=True):
f0s = []
logging.info("Calculating f0 ...")
if not parallel:
for wav in raw_wavs:
f0s.append(_calculate_f0(wav, sr, frame_period))
else:
n_worker = int(cpu_count() * cpu_rate)
logging.info(
"{}% resources ({} cpu core(s)) will be used for f0 calculation".
format(cpu_rate * 100, n_worker))
# f0s = pool.starmap(_calculate_f0, zip(raw_wavs, repeat(sr), repeat(frame_period)))
f0s = parmap.starmap(_calculate_f0,
list(
zip(raw_wavs, repeat(sr),
repeat(frame_period))),
pm_processes=n_worker,
pm_pbar=True)
return f0s
def max_posterior(gmm, U, coords, covar=None):
import multiprocessing, parmap
pool = multiprocessing.Pool()
n_chunks, chunksize = gmm._mp_chunksize()
log_p = [[] for k in range(gmm.K)]
log_S = np.zeros(len(coords))
H = np.zeros(len(coords), dtype="bool")
k = 0
for log_p[k], U[k], _ in \
parmap.starmap(pygmmis._Estep, zip(range(gmm.K), U), gmm, data, covar, None, pool=pool, pm_chunksize=chunksize):
log_S[U[k]] += np.exp(log_p[k]) # actually S, not logS
H[U[k]] = 1
k += 1
log_S[H] = np.log(log_S[H])
max_q = np.zeros(len(coords))
max_k = np.zeros(len(coords), dtype='uint32')
for k in range(gmm.K):
q_k = np.exp(log_p[k] - log_S[U[k]])
max_k[U[k]] = np.where(max_q[U[k]] < q_k, k, max_k[U[k]])
max_q[U[k]] = np.maximum(max_q[U[k]],q_k)
return max_k
def parallel(self, names, mg, nprocs=1, **kwargs):
"""Calling mapper using multiple processors.
Args:
names -- NameFile instance with iter_names() method
mg -- MetaFile instance with readFile attrib
nprocs -- number of parallel calls
kwargs -- passed to mapper call
Return:
refSamFile attrib set for each name in names
"""
# making list of tuples (indexFile, readFile)
lt = [(name.get_indexFile(), mg.get_readFile()) for name in names.iter_names()]
# altering function kwargs
new_mapper = partial(self, **kwargs)
# calling mapper
samFiles = parmap.starmap(new_mapper, lt, processes=nprocs)
# adding samFile attrib to name instances
for i,name in enumerate(names.iter_names()):
name.set_refSamFile(samFiles[i])
def test_warn_wrong_argument_starmap(self):
with warnings.catch_warnings(record=True) as w:
parmap.starmap(range, [(0,2), (2,5)], processes=-3)
assert len(w) == 1
def training_and_testing(ARGS):
# Check conditions
if ARGS['list_columns']:
list_columns = list(sorted(ARGS['list_columns']))
if not ARGS['list_columns']:
list_columns = [
'CADD_phred', 'SIFTval', 'VEST4_score', 'gnomAD_exomes_AF'
]
if ARGS['flag']:
flag = list(sorted(ARGS['flag']))
if not ARGS['flag']:
flag = [
"REVEL_score",
"ClinPred_score",
"M-CAP_score",
"fathmm-XF_coding_score",
"Eigen-raw_coding",
"PrimateAI_score",
]
if not os.path.exists(ARGS['output_dir'] +
'/TRAIN/training.csv.gz') or not os.path.exists(
ARGS['output_dir'] + '/TEST/testing.csv.gz'):
logger.warn(
'--train_and_test mode selected but training and testing file not found, creation with following parameters :'
'--ratio : ' + str(ARGS['ratio']) + ', --proportion : ' +
str(ARGS['proportion']))
ARGS['force_datasets'] = True
if os.path.exists(ARGS['output_dir'] +
'/TRAIN/training.csv.gz') or os.path.exists(
ARGS['output_dir'] + '/TEST/testing.csv.gz'):
logger.info('Training and testing file found')
if ARGS['combinatory'] is True:
pass
# if enable, erase previously generated training and testing file from a global dataframe to creating other ones
if ARGS['force_datasets'] is True:
utils.mkdir(ARGS['output_dir'])
utils.mkdir(ARGS['output_dir'] + '/TRAIN')
utils.mkdir(ARGS['output_dir'] + '/TEST')
logger.warn('Creating new files or overwriting old ones')
prop = ARGS['proportion']
t = float(round(prop / (1 - prop), 2))
ratio = ARGS['ratio']
tmp = pd.read_csv(filepath_or_buffer=ARGS['input'],
sep='\t',
compression='gzip',
encoding='utf-8',
low_memory=False)
if list_columns and flag:
# Selection of specific columns to be used from a global dataframe
# Example : df with 10 columns, --list_columns column1 column2 column5
tmp = select_columns_pandas.select_columns_pandas(
tmp, list_columns, flag)
logger.info(tmp)
# Use of input parameters to build training and testing dataframes (proportion, ratio of data between train and test)
# Special attention is paid to remove overlap between evaluation|test sets and training dataset to prevent any overfitting
complete_data_path = tmp.loc[tmp['True_Label'] == 1]
complete_data_path = complete_data_path.sample(frac=1)
complete_data_begn = tmp.loc[tmp['True_Label'] == -1]
complete_data_begn = complete_data_begn.sample(frac=1)
max_size = max(complete_data_path.shape[0],
complete_data_begn.shape[0])
min_size = min(complete_data_path.shape[0],
complete_data_begn.shape[0])
if max_size > (t * min_size):
max_size = min_size * t
elif max_size < (t * min_size):
min_size = max_size / t
if min_size < 1000 and min(complete_data_path.shape[0], complete_data_begn.shape[0]) == \
complete_data_path.shape[0]:
logger.warn(
'CAREFUL : Size of the pathogenic dataset will be < 1000 samples'
)
eval_test_size = ratio
train_path = complete_data_path.head(
n=int(round(min_size * (1 - eval_test_size))))
train_begn = complete_data_begn.head(
n=int(round(max_size * (1 - eval_test_size))))
eval_path = complete_data_path.tail(
n=int(round(min_size * eval_test_size)))
eval_begn = complete_data_begn.tail(
n=int(round(min_size * eval_test_size)))
eval_path.dropna(inplace=True)
eval_begn.dropna(inplace=True)
complete_training = pd.concat([train_path, train_begn
]).drop_duplicates(keep='first')
complete_training = complete_training[complete_training.columns.drop(
list(complete_training.filter(regex='pred|flag')))]
complete_training.dropna(inplace=True)
# Some stats on Pathogenic and Benign variant numbers in both training and testing dataframes
logger.info('Training - Path : ' + str(complete_training[
complete_training['True_Label'] == 1].shape[0]))
logger.info('Training - Benign : ' + str(complete_training[
complete_training['True_Label'] == -1].shape[0]))
min_size_eval = min(eval_path.shape[0], eval_begn.shape[0])
complete_eval = pd.concat([
eval_path.sample(frac=1).head(min_size_eval),
eval_begn.sample(frac=1).head(min_size_eval)
]).drop_duplicates(keep='first')
logger.info(
'Testing - Path : ' +
str(complete_eval[complete_eval['True_Label'] == 1].shape[0]))
logger.info(
'Testing - Benign : ' +
str(complete_eval[complete_eval['True_Label'] == -1].shape[0]))
# Dumping data
complete_training.to_csv(path_or_buf=ARGS['output_dir'] +
'/TRAIN/training.csv.gz',
sep='\t',
compression='gzip',
encoding='utf-8',
index=False)
complete_eval.to_csv(path_or_buf=ARGS['output_dir'] +
'/TEST/testing.csv.gz',
sep='\t',
compression='gzip',
encoding='utf-8',
index=False)
check_dir_train = False
if os.path.isdir(ARGS['output_dir'] + '/TRAIN/Models'):
check_dir_train = True
if (ARGS['force_training'] is True) or (check_dir_train is False):
# Training model
# TrainingClassification(input_data=ARGS['output_dir'] + '/TRAIN/training.csv.gz',
# classifiers=classifiers,
# standardize=ARGS['standardize'],
# output=ARGS["output_dir"],
# logger=logger,
# cv=ARGS['cross_validation']
# )
TestingClassification(input_data=ARGS['output_dir'] +
'/TEST/testing.csv.gz',
standardize=ARGS['standardize'],
output_dir=ARGS["output_dir"],
model_dir=ARGS['model'],
logger=logger,
threshold=ARGS['threshold'])
# Generation of a histogram to see most important features used in builded model
# histo_weights.histo_and_metrics(folder=ARGS['output_dir'], logger=logger)
# This parameter, if enabled, will build all possible combinations from a single dataframe if sources are mentionned
# Example : A global dataframe based on 3 databases (2 pathogenic : Clinvar and HGMD and 1 benign : gnomAD) was generated
# The following lines will generate 2 evaluation sets : (clinvar|gnomAD) and (HGMD|gnomAD) with various MAF thresholds (<0.01, <0.001, 0.0001, AC=1(singleton), AF=0)
# and each of these combinations will be tested with the previously generated outputs. (Overlapping is checked between these combinations and training dataset)
if ARGS['eval'] and ARGS['eval'].endswith('.csv.gz'):
# TODO : CHANGE NAME
print('\n\n')
logger.info('--BUILDING & TESTING ON EVALUATION SETS--')
output_dir = ARGS['output_dir']
eval_output_dir = output_dir
eval_output_dir = eval_output_dir.split('/')
eval_output_dir[-1] = 'EVALUATION_SETS_' + eval_output_dir[-1]
eval_output_dir = "/".join(eval_output_dir)
if os.path.isdir(eval_output_dir):
pass
else:
utils.mkdir(eval_output_dir)
# if ARGS['list_columns'] and ARGS['flag']:
combination_pandas.combination_pandas(
ARGS['eval'],
output_dir + '/TRAIN/training.csv.gz',
eval_output_dir,
logger,
list_columns,
flag,
CV=ARGS['cross_validation_evaluation'])
# else:
# combination_pandas.combination_pandas(ARGS['eval'], ARGS['output_dir'] + '/TRAIN/training.csv.gz', output_dir, CV=ARGS['cross_validation_evaluation'])
l_dir = os.listdir(eval_output_dir)
print(list(zip(l_dir)))
parmap.starmap(test_eval_mp,
list(zip(l_dir)),
pm_pbar=True,
pm_processes=ARGS['threads'])
# Plots are automatically generated to visualize performance across various scenario for the different combinations
print('\n\n')
logger.info('--GENERATING PLOTS & STATS--')
utils.mkdir(eval_output_dir + '/PLOTS_AND_MEAN_TABLE')
# maf_plot.violin_plot_scores(eval_output_dir, logger)
# maf_plot.maf_plot_maf_0(eval_output_dir, ARGS['cross_validation_evaluation'], logger)
maf_plot.maf_plot_others(eval_output_dir,
ARGS['cross_validation_evaluation'], logger)
dt = [0.2] N = [1] sigma = [0.5, 1, 2] mu = [0] corr_time = [1000] repetitions = 1000 n_tau = 10 tau_list = [np.linspace(1, 20, n_tau)] # seed_list = np.arange(n_tau * repetitions).reshape((repetitions, n_tau)) seed_list = np.arange(repetitions) values = list(product([H], tau_list, dt, N, mu, sigma, corr_time, seed_list)) results = parmap.starmap(dd_wrapper, values, pm_chunksize=3, pm_pbar=True) results = np.array(results) print(results.shape) # Adapt results to input results = results.reshape((3, repetitions, n_tau)) results_mean = results.mean(axis=-2) results_std = results.std(axis=-2) / np.sqrt(repetitions - 1) plt.errorbar(tau_list[0], results_mean[0, :], results_std[0, :]) plt.errorbar(tau_list[0], results_mean[1, :], results_std[1, :]) plt.errorbar(tau_list[0], results_mean[2, :], results_std[1, :]) plt # plt.errorbar(tau_list[0], results_mean, results_std)
def test_starmap(self):
items = [(1, 2), (3, 4), (5, 6)]
pfalse = parmap.starmap(_identity, items, 5, 6, pm_parallel=False)
ptrue = parmap.starmap(_identity, items, 5, 6, pm_parallel=True)
self.assertEqual(pfalse, ptrue)
def main():
"""
Demostration Options
"""
logging.basicConfig(level = logging.DEBUG)
# If using parallel functionality, you must call this to set the appropriate
# logging level
gp.classifier.set_multiclass_logging_level(logging.DEBUG)
# np.random.seed(50)
# Feature Generation Parameters and Demonstration Options
SAVE_OUTPUTS = True # We don't want to make files everywhere for a demo.
SHOW_RAW_BINARY = True
test_range_min = -2.0
test_range_max = +2.0
test_ranges = (test_range_min, test_range_max)
n_train = 100
n_query = 1000
n_dims = 2 # <- Must be 2 for vis
n_cores = None # number of cores for multi-class (None -> default: c-1)
walltime = 300.0
approxmethod = 'laplace' # 'laplace' or 'pls'
multimethod = 'OVA' # 'AVA' or 'OVA', ignored for binary problem
fusemethod = 'EXCLUSION' # 'MODE' or 'EXCLUSION', ignored for binary
responsename = 'probit' # 'probit' or 'logistic'
batch_start = False
entropy_threshold = None
n_draws = 6
n_draws_est = 2500
rows_subplot = 2
cols_subplot = 3
assert rows_subplot * cols_subplot >= n_draws
# Decision boundaries
db1 = lambda x1, x2: (((x1 - 1)**2 + x2**2/4) *
(0.9*(x1 + 1)**2 + x2**2/2) < 1.6) & \
((x1 + x2) < 1.5)
db2 = lambda x1, x2: (((x1 - 1)**2 + x2**2/4) *
(0.9*(x1 + 1)**2 + x2**2/2) > 0.3)
db3 = lambda x1, x2: ((x1 + x2) < 2) & ((x1 + x2) > -2.2)
db4 = lambda x1, x2: ((x1 - 0.75)**2 + (x2 + 0.8)**2 > 0.3**2)
db5 = lambda x1, x2: ((x1/2)**2 + x2**2 > 0.3)
db6 = lambda x1, x2: (((x1)/8)**2 + (x2 + 1.5)**2 > 0.2**2)
db7 = lambda x1, x2: (((x1)/8)**2 + ((x2 - 1.4)/1.25)**2 > 0.2**2)
db4a = lambda x1, x2: ((x1 - 1.25)**2 + (x2 - 1.25)**2 > 0.5**2) & ((x1 - 0.75)**2 + (x2 + 1.2)**2 > 0.6**2) & ((x1 + 0.75)**2 + (x2 + 1.2)**2 > 0.3**2) & ((x1 + 1.3)**2 + (x2 - 1.3)**2 > 0.4**2)
db5a = lambda x1, x2: ((x1/2)**2 + x2**2 > 0.3) & (x1 > 0)
db5b = lambda x1, x2: ((x1/2)**2 + x2**2 > 0.3) & (x1 < 0) & ((x1 + 0.75)**2 + (x2 - 1.2)**2 > 0.6**2)
db1a = lambda x1, x2: (((x1 - 1)**2 + x2**2/4) *
(0.9*(x1 + 1)**2 + x2**2/2) < 1.6) & \
((x1 + x2) < 1.6) | ((x1 + 0.75)**2 + (x2 + 1.2)**2 < 0.6**2)
db1b = lambda x1, x2: (((x1 - 1)**2 + x2**2/4) *
(0.9*(x1 + 1)**2 + x2**2/2) < 1.6) & ((x1/2)**2 + (x2)**2 > 0.4**2) & \
((x1 + x2) < 1.5) | ((x1 + 0.75)**2 + (x2 - 1.5)**2 < 0.4**2) | ((x1 + x2) > 2.25) & (x1 < 1.75) & (x2 < 1.75) # | (((x1 + 0.25)/4)**2 + (x2 + 1.5)**2 < 0.32**2) # & (((x1 + 0.25)/4)**2 + (x2 + 1.5)**2 > 0.18**2)
db1c = lambda x1, x2: (((x1 - 1)**2 + x2**2/4) *
(0.9*(x1 + 1)**2 + x2**2/2) < 1.6) & ((x1/2)**2 + (x2)**2 > 0.4**2) & \
((x1 + x2) < 1.5) | ((x1 + 0.75)**2 + (x2 - 1.5)**2 < 0.4**2) | ((x1 + x2) > 2.25) & (x1 < 1.75) & (x2 < 1.75) | (((x1 + 0.25)/4)**2 + (x2 + 1.75)**2 < 0.32**2) & (((x1 + 0.25)/4)**2 + (x2 + 1.75)**2 > 0.18**2)
db8 = lambda x1, x2: (np.sin(2*x1 + 3*x2) > 0) | (((x1 - 1)**2 + x2**2/4) *
(0.9*(x1 + 1)**2 + x2**2/2) < 1.4) & \
((x1 + x2) < 1.5) | (x1 < -1.9) | (x1 > +1.9) | (x2 < -1.9) | (x2 > +1.9) | ((x1 + 0.75)**2 + (x2 - 1.5)**2 < 0.3**2)
# db9 = lambda x1, x2: ((x1)**2 + (x2)**2 < 0.3**2) | ((x1)**2 + (x2)**2 > 0.5**2) |
decision_boundary = [db5b, db1c, db4a] # [db5b, db1c, db4a, db8, db6, db7]
"""
Data Generation
"""
# # # Training Points
# shrink = 0.8
# test_range_min *= shrink
# test_range_max *= shrink
# X1 = np.random.normal(loc = np.array([test_range_min, test_range_min]), scale = 0.9*np.ones(n_dims), size = (int(n_train/8), n_dims))
# X2 = np.random.normal(loc = np.array([test_range_min, test_range_max]), scale = 0.9*np.ones(n_dims), size = (int(n_train/8), n_dims))
# X3 = np.random.normal(loc = np.array([test_range_max, test_range_min]), scale = 0.9*np.ones(n_dims), size = (int(n_train/8), n_dims))
# X4 = np.random.normal(loc = np.array([test_range_max, test_range_max]), scale = 0.9*np.ones(n_dims), size = (int(n_train/8), n_dims))
# X5 = np.random.normal(loc = np.array([0, test_range_min]), scale = 0.9*np.ones(n_dims), size = (int(n_train/8), n_dims))
# X6 = np.random.normal(loc = np.array([test_range_min, 0]), scale = 0.9*np.ones(n_dims), size = (int(n_train/8), n_dims))
# X7 = np.random.normal(loc = np.array([test_range_max, 0]), scale = 0.9*np.ones(n_dims), size = (int(n_train/8), n_dims))
# X8 = np.random.normal(loc = np.array([0, test_range_max]), scale = 0.9*np.ones(n_dims), size = (int(n_train/8), n_dims))
# test_range_min /= shrink
# test_range_max /= shrink
# X = np.concatenate((X1, X2, X3, X4, X5, X6, X7, X8), axis = 0)
# X = np.random.uniform(test_range_min, test_range_max,
# size = (n_train, n_dims))
X_s = np.array([[0.0, 0.0], [-0.2, 0.3], [-0.1, -0.1], [0.05, 0.25], [-1.1, 0.0], [-0.5, 0.0], [-0.4, -0.7], [-0.1, -0.1], [test_range_min, test_range_min], [test_range_min, test_range_max], [test_range_max, test_range_max], [test_range_max, test_range_min]])
X_f = np.array([[1.4, 1.6], [1.8, 1.2], [-1.24, 1.72], [-1.56, -1.9], [-1.9, 1.0], [-0.5, -1.2], [-1.4, -1.9], [0.4, -1.2], [test_range_min, test_range_max], [test_range_max, test_range_max], [test_range_max, test_range_min], [test_range_min, test_range_min]])
n_track = 25
X_s = np.random.uniform(test_range_min, test_range_max, size = (n_track, n_dims))
X_f = test_range_max * np.random.standard_cauchy(n_track * n_dims).reshape(n_track, n_dims)
X_f = np.random.uniform(test_range_min, test_range_max, size = (n_track, n_dims))
X = generate_line_paths(X_s, X_f, n_points = 15)
x1 = X[:, 0]
x2 = X[:, 1]
# Query Points
Xq = np.random.uniform(test_range_min, test_range_max,
size = (n_query, n_dims))
xq1 = Xq[:, 0]
xq2 = Xq[:, 1]
n_train = X.shape[0]
n_query = Xq.shape[0]
logging.info('Training Points: %d' % n_train)
# Training Labels
y = gp.classifier.utils.make_decision(X, decision_boundary)
y_unique = np.unique(y)
assert y_unique.dtype == int
if y_unique.shape[0] == 2:
mycmap = cm.get_cmap(name = 'bone', lut = None)
mycmap2 = cm.get_cmap(name = 'BrBG', lut = None)
else:
mycmap = cm.get_cmap(name = 'gist_rainbow', lut = None)
mycmap2 = cm.get_cmap(name = 'gist_rainbow', lut = None)
"""
Classifier Training
"""
# Training
fig = plt.figure()
gp.classifier.utils.visualise_decision_boundary(
test_range_min, test_range_max, decision_boundary)
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.title('Training Labels')
plt.xlabel('x1')
plt.ylabel('x2')
cbar = plt.colorbar()
cbar.set_ticks(y_unique)
cbar.set_ticklabels(y_unique)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
plt.gca().patch.set_facecolor('gray')
print('Plotted Training Set')
plt.show()
# Training
print('===Begin Classifier Training===')
optimiser_config = gp.OptConfig()
optimiser_config.sigma = gp.auto_range(kerneldef)
optimiser_config.walltime = walltime
# User can choose to batch start each binary classifier with different
# initial hyperparameters for faster training
if batch_start:
if y_unique.shape[0] == 2:
initial_hyperparams = [100, 0.1, 0.1]
elif multimethod == 'OVA':
initial_hyperparams = [ [356.468, 0.762, 0.530], \
[356.556, 0.836, 0.763], \
[472.006, 1.648, 1.550], \
[239.720, 1.307, 0.721] ]
elif multimethod == 'AVA':
initial_hyperparams = [ [14.9670, 0.547, 0.402], \
[251.979, 1.583, 1.318], \
[420.376, 1.452, 0.750], \
[780.641, 1.397, 1.682], \
[490.353, 2.299, 1.526], \
[73.999, 1.584, 0.954]]
else:
raise ValueError
batch_config = gp.batch_start(optimiser_config, initial_hyperparams)
else:
batch_config = optimiser_config
# Obtain the response function
responsefunction = gp.classifier.responses.get(responsename)
# Train the classifier!
learned_classifier = gp.classifier.learn(X, y, kerneldef,
responsefunction, batch_config,
multimethod = multimethod, approxmethod = approxmethod,
train = True, ftol = 1e-6, processes = n_cores)
# Print learned kernels
print_function = gp.describer(kerneldef)
gp.classifier.utils.print_learned_kernels(print_function,
learned_classifier, y_unique)
# Print the matrix of learned classifier hyperparameters
logging.info('Matrix of learned hyperparameters')
gp.classifier.utils.print_hyperparam_matrix(learned_classifier)
"""
Classifier Prediction
"""
# Prediction
yq_prob = gp.classifier.predict(Xq, learned_classifier,
fusemethod = fusemethod)
yq_pred = gp.classifier.classify(yq_prob, y)
yq_entropy = gp.classifier.entropy(yq_prob)
logging.info('Caching Predictor...')
predictors = gp.classifier.query(learned_classifier, Xq)
logging.info('Computing Expectance...')
yq_exp_list = gp.classifier.expectance(learned_classifier, predictors)
logging.info('Computing Covariance...')
yq_cov_list = gp.classifier.covariance(learned_classifier, predictors)
logging.info('Drawing from GP...')
yq_draws = gp.classifier.draws(n_draws, yq_exp_list, yq_cov_list,
learned_classifier)
logging.info('Computing Linearised Entropy...')
yq_linearised_entropy = gp.classifier.linearised_entropy(
yq_exp_list, yq_cov_list, learned_classifier)
logging.info('Linearised Entropy is {0}'.format(yq_linearised_entropy))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
THE GAP BETWEEN ANALYSIS AND PLOTS
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
"""
Classifier Prediction Results (Plots)
"""
logging.info('Plotting... please wait')
Xq_plt = gp.classifier.utils.query_map(test_ranges, n_points = 250)
yq_truth_plt = gp.classifier.utils.make_decision(Xq_plt, decision_boundary)
"""
Plot: Ground Truth
"""
# Training
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(yq_truth_plt, test_ranges, cmap = mycmap)
plt.title('Ground Truth')
plt.xlabel('x1')
plt.ylabel('x2')
cbar = plt.colorbar()
cbar.set_ticks(y_unique)
cbar.set_ticklabels(y_unique)
gp.classifier.utils.visualise_decision_boundary(
test_range_min, test_range_max, decision_boundary)
logging.info('Plotted Prediction Labels')
"""
Plot: Training Set
"""
# Training
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_decision_boundary(
test_range_min, test_range_max, decision_boundary)
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.title('Training Labels')
plt.xlabel('x1')
plt.ylabel('x2')
cbar = plt.colorbar()
cbar.set_ticks(y_unique)
cbar.set_ticklabels(y_unique)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
plt.gca().patch.set_facecolor('gray')
logging.info('Plotted Training Set')
"""
Plot: Query Computations
"""
# Compute Linearised and True Entropy for plotting
logging.info('Plot: Caching Predictor...')
predictor_plt = gp.classifier.query(learned_classifier, Xq_plt)
logging.info('Plot: Computing Expectance...')
expectance_latent_plt = \
gp.classifier.expectance(learned_classifier, predictor_plt)
logging.info('Plot: Computing Variance...')
variance_latent_plt = \
gp.classifier.variance(learned_classifier, predictor_plt)
logging.info('Plot: Computing Linearised Entropy...')
entropy_linearised_plt = gp.classifier.linearised_entropy(
expectance_latent_plt, variance_latent_plt, learned_classifier)
logging.info('Plot: Computing Equivalent Standard Deviation')
eq_sd_plt = gp.classifier.equivalent_standard_deviation(
entropy_linearised_plt)
logging.info('Plot: Computing Prediction Probabilities...')
yq_prob_plt = gp.classifier.predict_from_latent(
expectance_latent_plt, variance_latent_plt, learned_classifier,
fusemethod = fusemethod)
logging.info('Plot: Computing True Entropy...')
yq_entropy_plt = gp.classifier.entropy(yq_prob_plt)
logging.info('Plot: Computing Class Predicitons')
yq_pred_plt = gp.classifier.classify(yq_prob_plt, y_unique)
if isinstance(learned_classifier, list):
logging.info('Plot: Computing Naive Linearised Entropy...')
args = [(expectance_latent_plt[i], variance_latent_plt[i],
learned_classifier[i]) for i in range(len(learned_classifier))]
entropy_linearised_naive_plt = \
np.array(parmap.starmap(gp.classifier.linearised_entropy,
args)).sum(axis = 0)
Xq_meas = gp.classifier.utils.query_map(test_ranges, n_points = 10)
predictor_meas = gp.classifier.query(learned_classifier, Xq_meas)
exp_meas = gp.classifier.expectance(learned_classifier, predictor_meas)
cov_meas = gp.classifier.covariance(learned_classifier, predictor_meas)
logging.info('Objective Measure: Computing Joint Linearised Entropy...')
entropy_linearised_meas = gp.classifier.linearised_entropy(
exp_meas, cov_meas, learned_classifier)
logging.info('Objective Measure: Computing Monte Carlo Joint Entropy...')
# start_time = time.clock()
# entropy_monte_carlo_meas = gp.classifier.monte_carlo_joint_entropy(exp_meas, cov_meas, learned_classifier, n_draws = n_draws_est)
# logging.info('Sampling took %.4f seconds' % (time.clock() - start_time))
entropy_linearised_mean_meas = entropy_linearised_plt.mean()
entropy_true_mean_meas = yq_entropy_plt.mean()
mistake_ratio = (yq_truth_plt - yq_pred_plt).nonzero()[0].shape[0] / yq_truth_plt.shape[0]
if isinstance(learned_classifier, list) & False:
"""
Plot: Latent Function Expectance
"""
for i in range(len(expectance_latent_plt)):
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(
expectance_latent_plt[i], test_ranges,
levels = [0.0],
vmin = -np.max(np.abs(expectance_latent_plt[i])),
vmax = np.max(np.abs(expectance_latent_plt[i])),
cmap = cm.coolwarm)
plt.title('Latent Funtion Expectance %s'
% gp.classifier.utils.binary_classifier_name(
learned_classifier[i], y_unique))
plt.xlabel('x1')
plt.ylabel('x2')
plt.colorbar()
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
logging.info('Plotted Latent Function Expectance on Training Set')
"""
Plot: Latent Function Variance
"""
for i in range(len(variance_latent_plt)):
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(
variance_latent_plt[i], test_ranges,
cmap = cm.coolwarm)
plt.title('Latent Funtion Variance %s'
% gp.classifier.utils.binary_classifier_name(
learned_classifier[i], y_unique))
plt.xlabel('x1')
plt.ylabel('x2')
plt.colorbar()
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
logging.info('Plotted Latent Function Variance on Training Set')
"""
Plot: Prediction Probabilities
"""
for i in range(len(yq_prob_plt)):
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(yq_prob_plt[i], test_ranges,
levels = [0.5], cmap = cm.coolwarm)
plt.title('Prediction Probabilities (Class %d)' % y_unique[i])
plt.xlabel('x1')
plt.ylabel('x2')
plt.colorbar()
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
logging.info('Plotted Prediction Probabilities on Training Set')
"""
Plot: Prediction Labels
"""
# Query (Prediction Map)
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(yq_pred_plt, test_ranges,
boundaries = True, cmap = mycmap)
plt.title('Prediction [Miss Ratio: %.3f %s]' % (100 * mistake_ratio, '%'))
plt.xlabel('x1')
plt.ylabel('x2')
cbar = plt.colorbar()
cbar.set_ticks(y_unique)
cbar.set_ticklabels(y_unique)
logging.info('Plotted Prediction Labels')
"""
Plot: Prediction Entropy onto Training Set
"""
# Query (Prediction Entropy)
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(yq_entropy_plt, test_ranges,
threshold = entropy_threshold, cmap = cm.coolwarm)
plt.title('Prediction Entropy [ACE = %.4f]'
% (entropy_true_mean_meas))
plt.xlabel('x1')
plt.ylabel('x2')
plt.colorbar()
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
logging.info('Plotted Prediction Entropy on Training Set')
"""
Plot: Linearised Prediction Entropy onto Training Set
"""
# Query (Linearised Entropy)
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(entropy_linearised_plt, test_ranges,
threshold = entropy_threshold, cmap = cm.coolwarm)
plt.title('Linearised Prediction Entropy [FLE = %.4f, ALE = %.4f]'
% (entropy_linearised_meas, entropy_linearised_mean_meas))
plt.xlabel('x1')
plt.ylabel('x2')
plt.colorbar()
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
logging.info('Plotted Linearised Prediction Entropy on Training Set')
"""
Plot: Exponentiated Linearised Prediction Entropy onto Training Set
"""
# Query (Linearised Entropy)
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(eq_sd_plt, test_ranges,
threshold = entropy_threshold, cmap = cm.coolwarm)
plt.title('Equivalent standard deviation')
plt.xlabel('x1')
plt.ylabel('x2')
plt.colorbar()
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
logging.info('Plotted Exponentiated Linearised Prediction Entropy (Equivalent Standard Deviation) on Training Set')
"""
Plot: Naive Linearised Prediction Entropy onto Training Set
"""
if isinstance(learned_classifier, list):
# Query (Naive Linearised Entropy)
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_map(
entropy_linearised_naive_plt, test_ranges,
threshold = entropy_threshold, cmap = cm.coolwarm)
plt.title('Naive Linearised Prediction Entropy')
plt.xlabel('x1')
plt.ylabel('x2')
plt.colorbar()
plt.scatter(x1, x2, c = y, marker = 'x', cmap = mycmap)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
logging.info('Plotted Naive Linearised Prediction Entropy on Training Set')
"""
Plot: Sample Query Predictions
"""
# Visualise Predictions
fig = plt.figure(figsize = (15, 15))
gp.classifier.utils.visualise_decision_boundary(
test_range_min, test_range_max, decision_boundary)
plt.scatter(xq1, xq2, c = yq_pred, marker = 'x', cmap = mycmap)
plt.title('Predicted Query Labels')
plt.xlabel('x1')
plt.ylabel('x2')
cbar = plt.colorbar()
cbar.set_ticks(y_unique)
cbar.set_ticklabels(y_unique)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
plt.gca().patch.set_facecolor('gray')
logging.info('Plotted Sample Query Labels')
"""
Plot: Sample Query Draws
"""
# Visualise Predictions
fig = plt.figure(figsize = (19.2, 10.8))
for i in range(n_draws):
plt.subplot(rows_subplot, cols_subplot, i + 1)
gp.classifier.utils.visualise_decision_boundary(
test_range_min, test_range_max, decision_boundary)
plt.scatter(xq1, xq2, c = yq_draws[i], marker = 'x', cmap = mycmap)
plt.title('Query Label Draws')
plt.xlabel('x1')
plt.ylabel('x2')
cbar = plt.colorbar()
cbar.set_ticks(y_unique)
cbar.set_ticklabels(y_unique)
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
plt.gca().patch.set_facecolor('gray')
logging.info('Plotted Sample Query Draws')
"""
Save Outputs
"""
# Save all the figures
if SAVE_OUTPUTS:
save_directory = "response_%s_approxmethod_%s" \
"_training_%d_query_%d_walltime_%d" \
"_method_%s_fusemethod_%s/" \
% ( responsename, approxmethod,
n_train, n_query, walltime,
multimethod, fusemethod)
full_directory = gp.classifier.utils.create_directories(
save_directory, home_directory = 'Figures/', append_time = True)
gp.classifier.utils.save_all_figures(full_directory)
shutil.copy2('./receding_horizon_path_planning.py', full_directory)
logging.info('Modeling Done')
"""
Path Planning
"""
""" Setup Path Planning """
xq_now = np.array([[0., 0.]])
horizon = (test_range_max - test_range_min) + 0.5
n_steps = 30
theta_bound = np.deg2rad(30)
theta_add_init = -np.deg2rad(10) * np.ones(n_steps)
theta_add_init[0] = np.deg2rad(180)
theta_add_low = -theta_bound * np.ones(n_steps)
theta_add_high = theta_bound * np.ones(n_steps)
theta_add_low[0] = 0.0
theta_add_high[0] = 2 * np.pi
r = horizon/n_steps
choice_walltime = 1500.0
xtol_rel = 1e-2
ftol_rel = 1e-4
k_step = 1
""" Initialise Values """
# The observed data till now
X_now = X.copy()
y_now = y.copy()
# Observe the current location
yq_now = gp.classifier.utils.make_decision(xq_now[[-1]],
decision_boundary)
# Add the observed data to the training set
X_now = np.concatenate((X_now, xq_now[[-1]]), axis = 0)
y_now = np.append(y_now, yq_now)
# Add the new location to the array of travelled coordinates
xq1_nows = xq_now[:, 0]
xq2_nows = xq_now[:, 1]
yq_nows = yq_now.copy()
# Plot the current situation
fig1 = plt.figure(figsize = (15, 15))
fig2 = plt.figure(figsize = (15, 15))
fig3 = plt.figure(figsize = (15, 15))
fig4 = plt.figure(figsize = (15, 15))
fig5 = plt.figure(figsize = (20, 20))
# Start exploring
i_trials = 0
n_trials = 2000
entropy_linearised_array = np.nan * np.ones(n_trials)
# entropy_monte_carlo_array = np.nan * np.ones(n_trials)
entropy_linearised_mean_array = np.nan * np.ones(n_trials)
entropy_true_mean_array = np.nan * np.ones(n_trials)
entropy_opt_array = np.nan * np.ones(n_trials)
mistake_ratio_array = np.nan * np.ones(n_trials)
m_step = 0
while i_trials < n_trials:
""" Path Planning """
print(m_step)
print(k_step)
if m_step <= k_step:
# Propose a place to observe
xq_abs_opt, theta_add_opt, entropy_opt = \
go_optimised_path(theta_add_init, xq_now[-1], r,
learned_classifier, test_ranges,
theta_add_low = theta_add_low, theta_add_high = theta_add_high,
walltime = choice_walltime, xtol_rel = xtol_rel,
ftol_rel = ftol_rel, globalopt = False, objective = 'LE',
n_draws = n_draws_est)
logging.info('Optimal Joint Entropy: %.5f' % entropy_opt)
m_step = keep_going_until_surprise(xq_abs_opt, learned_classifier,
decision_boundary)
logging.info('Taking %d steps' % m_step)
else:
m_step -= 1
theta_add_opt = theta_add_init.copy()
xq_abs_opt = forward_path_model(theta_add_init, r, xq_now[-1])
logging.info('%d steps left' % m_step)
xq_now = xq_abs_opt[:k_step]
theta_add_init = initiate_with_continuity(theta_add_opt,
k_step = k_step)
np.clip(theta_add_init, theta_add_low + 1e-4, theta_add_high - 1e-4,
out = theta_add_init)
# Observe the current location
yq_now = gp.classifier.utils.make_decision(xq_now,
decision_boundary)
# Add the observed data to the training set
X_now = np.concatenate((X_now, xq_now), axis = 0)
y_now = np.append(y_now, yq_now)
# Add the new location to the array of travelled coordinates
xq1_nows = np.append(xq1_nows, xq_now[:, 0])
xq2_nows = np.append(xq2_nows, xq_now[:, 1])
yq_nows = np.append(yq_nows, yq_now)
# Update that into the model
logging.info('Learning Classifier...')
batch_config = \
gp.classifier.batch_start(optimiser_config, learned_classifier)
try:
learned_classifier = gp.classifier.learn(X_now, y_now, kerneldef,
responsefunction, batch_config,
multimethod = multimethod, approxmethod = approxmethod,
train = True, ftol = 1e-6, processes = n_cores)
except Exception as e:
logging.warning(e)
try:
learned_classifier = gp.classifier.learn(X_now, y_now, kerneldef,
responsefunction, batch_config,
multimethod = multimethod, approxmethod = approxmethod,
train = False, ftol = 1e-6, processes = n_cores)
except Exception as e:
logging.warning(e)
pass
logging.info('Finished Learning')
# This is the finite horizon optimal route
xq1_proposed = xq_abs_opt[:, 0][k_step:]
xq2_proposed = xq_abs_opt[:, 1][k_step:]
yq_proposed = gp.classifier.classify(gp.classifier.predict(xq_abs_opt,
learned_classifier), y_unique)[k_step:]
""" Computing Analysis Maps """
# Compute Linearised and True Entropy for plotting
logging.info('Plot: Caching Predictor...')
predictor_plt = gp.classifier.query(learned_classifier, Xq_plt)
logging.info('Plot: Computing Expectance...')
expectance_latent_plt = \
gp.classifier.expectance(learned_classifier, predictor_plt)
logging.info('Plot: Computing Variance...')
variance_latent_plt = \
gp.classifier.variance(learned_classifier, predictor_plt)
logging.info('Plot: Computing Linearised Entropy...')
entropy_linearised_plt = gp.classifier.linearised_entropy(
expectance_latent_plt, variance_latent_plt, learned_classifier)
logging.info('Plot: Computing Equivalent Standard Deviation')
eq_sd_plt = gp.classifier.equivalent_standard_deviation(
entropy_linearised_plt)
logging.info('Plot: Computing Prediction Probabilities...')
yq_prob_plt = gp.classifier.predict_from_latent(
expectance_latent_plt, variance_latent_plt, learned_classifier,
fusemethod = fusemethod)
logging.info('Plot: Computing True Entropy...')
yq_entropy_plt = gp.classifier.entropy(yq_prob_plt)
logging.info('Plot: Computing Class Predicitons')
yq_pred_plt = gp.classifier.classify(yq_prob_plt, y_unique)
predictor_meas = gp.classifier.query(learned_classifier, Xq_meas)
exp_meas = gp.classifier.expectance(learned_classifier, predictor_meas)
cov_meas = gp.classifier.covariance(learned_classifier, predictor_meas)
logging.info('Objective Measure: Computing Linearised Joint Entropy')
start_time = time.clock()
entropy_linearised_meas = gp.classifier.linearised_entropy(
exp_meas, cov_meas, learned_classifier)
logging.info('Computation took %.4f seconds' % (time.clock() - start_time))
logging.info('Linearised Joint Entropy: %.4f' % entropy_linearised_meas)
# logging.info('Objective Measure: Computing Monte Carlo Joint Entropy...')
# start_time = time.clock()
# entropy_monte_carlo_meas = gp.classifier.monte_carlo_joint_entropy(exp_meas, cov_meas, learned_classifier, n_draws = n_draws_est)
# logging.info('Computation took %.4f seconds' % (time.clock() - start_time))
# logging.info('Monte Carlo Joint Entropy: %.4f' % entropy_monte_carlo_meas)
entropy_linearised_mean_meas = entropy_linearised_plt.mean()
entropy_true_mean_meas = yq_entropy_plt.mean()
mistake_ratio = (yq_truth_plt - yq_pred_plt).nonzero()[0].shape[0] / yq_truth_plt.shape[0]
entropy_linearised_array[i_trials] = entropy_linearised_meas
# entropy_monte_carlo_array[i_trials] = entropy_monte_carlo_meas
entropy_linearised_mean_array[i_trials] = entropy_linearised_mean_meas
entropy_true_mean_array[i_trials] = entropy_true_mean_meas
entropy_opt_array[i_trials] = entropy_opt
mistake_ratio_array[i_trials] = mistake_ratio
# Find the bounds of the entropy predictions
vmin1 = entropy_linearised_plt.min()
vmax1 = entropy_linearised_plt.max()
vmin2 = yq_entropy_plt.min()
vmax2 = yq_entropy_plt.max()
vmin3 = eq_sd_plt.min()
vmax3 = eq_sd_plt.max()
""" Linearised Entropy Map """
# Prepare Figure 1
plt.figure(fig1.number)
plt.clf()
plt.title('Linearised entropy [horizon = %.2f, FLE = %.2f, ALE = %.2f, ACE = %.2f, TPE = %.2f]'
% (horizon, entropy_linearised_meas, entropy_linearised_mean_meas, entropy_true_mean_meas, entropy_opt))
plt.xlabel('x1')
plt.ylabel('x2')
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
# Plot linearised entropy
gp.classifier.utils.visualise_map(entropy_linearised_plt, test_ranges,
cmap = cm.coolwarm, vmin = vmin1, vmax = vmax1)
plt.colorbar()
# Plot training set on top
plt.scatter(x1, x2, c = y, s = 40, marker = 'x', cmap = mycmap)
# Plot the path on top
plt.scatter(xq1_nows, xq2_nows, c = yq_nows, s = 60,
facecolors = 'none',
vmin = y_unique[0], vmax = y_unique[-1], cmap = mycmap)
plt.plot(xq1_nows, xq2_nows, c = 'w')
plt.scatter(xq_now[:, 0], xq_now[:, 1], c = yq_now, s = 120,
vmin = y_unique[0], vmax = y_unique[-1],
cmap = mycmap)
# Plot the proposed path
plt.scatter(xq1_proposed, xq2_proposed, c = yq_proposed,
s = 60, marker = 'D',
vmin = y_unique[0], vmax = y_unique[-1], cmap = mycmap)
plt.plot(xq1_proposed, xq2_proposed, c = 'w')
# Plot the horizon
gp.classifier.utils.plot_circle(xq_now[-1], horizon, c = 'k',
marker = '.')
# Save the plot
plt.gca().set_aspect('equal', adjustable = 'box')
plt.savefig('%sentropy_linearised_step%d.png'
% (full_directory, i_trials + 1))
""" Equivalent Standard Deviation Map """
# Prepare Figure 2
plt.figure(fig2.number)
plt.clf()
plt.title('Equivalent SD [horizon = %.2f, FLE = %.2f, ALE = %.2f, ACE = %.2f, TPE = %.2f]'
% (horizon, entropy_linearised_meas, entropy_linearised_mean_meas, entropy_true_mean_meas, entropy_opt))
plt.xlabel('x1')
plt.ylabel('x2')
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
# Plot linearised entropy
gp.classifier.utils.visualise_map(eq_sd_plt, test_ranges,
cmap = cm.coolwarm, vmin = vmin3, vmax = vmax3)
plt.colorbar()
# Plot training set on top
plt.scatter(x1, x2, c = y, s = 40, marker = 'x', cmap = mycmap)
# Plot the path on top
plt.scatter(xq1_nows, xq2_nows, c = yq_nows, s = 60,
facecolors = 'none',
vmin = y_unique[0], vmax = y_unique[-1], cmap = mycmap)
plt.plot(xq1_nows, xq2_nows, c = 'w')
plt.scatter(xq_now[:, 0], xq_now[:, 1], c = yq_now, s = 120,
vmin = y_unique[0], vmax = y_unique[-1],
cmap = mycmap)
# Plot the proposed path
plt.scatter(xq1_proposed, xq2_proposed, c = yq_proposed,
s = 60, marker = 'D',
vmin = y_unique[0], vmax = y_unique[-1], cmap = mycmap)
plt.plot(xq1_proposed, xq2_proposed, c = 'w')
# Plot the horizon
gp.classifier.utils.plot_circle(xq_now[-1], horizon, c = 'k',
marker = '.')
# Save the plot
plt.gca().set_aspect('equal', adjustable = 'box')
plt.savefig('%seq_sd_step%d.png'
% (full_directory, i_trials + 1))
""" True Entropy Map """
# Prepare Figure 3
plt.figure(fig3.number)
plt.clf()
plt.title('True entropy [horizon = %.2f, FLE = %.2f, ALE = %.2f, ACE = %.2f, TPE = %.2f]'
% (horizon, entropy_linearised_meas, entropy_linearised_mean_meas, entropy_true_mean_meas, entropy_opt))
plt.xlabel('x1')
plt.ylabel('x2')
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
# Plot true entropy
gp.classifier.utils.visualise_map(yq_entropy_plt, test_ranges,
cmap = cm.coolwarm, vmin = vmin2, vmax = vmax2)
plt.colorbar()
# Plot training set on top
plt.scatter(x1, x2, c = y, s = 40, marker = 'x', cmap = mycmap)
# Plot the path on top
plt.scatter(xq1_nows, xq2_nows, c = yq_nows, s = 60,
facecolors = 'none',
vmin = y_unique[0], vmax = y_unique[-1], cmap = mycmap)
plt.plot(xq1_nows, xq2_nows, c = 'w')
plt.scatter(xq_now[:, 0], xq_now[:, 1], c = yq_now, s = 120,
vmin = y_unique[0], vmax = y_unique[-1],
cmap = mycmap)
# Plot the proposed path
plt.scatter(xq1_proposed, xq2_proposed, c = yq_proposed,
s = 60, marker = 'D',
vmin = y_unique[0], vmax = y_unique[-1], cmap = mycmap)
plt.plot(xq1_proposed, xq2_proposed, c = 'w')
# Plot the horizon
gp.classifier.utils.plot_circle(xq_now[-1], horizon, c = 'k',
marker = '.')
# Save the plot
plt.gca().set_aspect('equal', adjustable = 'box')
plt.savefig('%sentropy_true_step%d.png'
% (full_directory, i_trials + 1))
""" Class Prediction Map """
# Prepare Figure 4
plt.figure(fig4.number)
plt.clf()
plt.title('Class predictions [Miss Ratio: %.3f %s]' % (100 * mistake_ratio, '%'))
plt.xlabel('x1')
plt.ylabel('x2')
plt.xlim((test_range_min, test_range_max))
plt.ylim((test_range_min, test_range_max))
# Plot class predictions
gp.classifier.utils.visualise_map(yq_pred_plt, test_ranges,
boundaries = True, cmap = mycmap2, vmin = y_unique[0], vmax = y_unique[-1])
cbar = plt.colorbar()
cbar.set_ticks(y_unique)
cbar.set_ticklabels(y_unique)
# Plot training set on top
plt.scatter(x1, x2, c = y, s = 40, marker = 'v', cmap = mycmap)
# Plot the path on top
plt.scatter(xq1_nows, xq2_nows, c = yq_nows, s = 60, marker = 'o',
vmin = y_unique[0], vmax = y_unique[-1], cmap = mycmap)
plt.plot(xq1_nows, xq2_nows, c = 'w')
plt.scatter(xq_now[:, 0], xq_now[:, 1], c = yq_now, s = 120,
vmin = y_unique[0], vmax = y_unique[-1],
cmap = mycmap)
# Plot the proposed path
plt.scatter(xq1_proposed, xq2_proposed, c = yq_proposed,
s = 60, marker = 'D',
vmin = y_unique[0], vmax = y_unique[-1], cmap = mycmap)
plt.plot(xq1_proposed, xq2_proposed, c = 'w')
# Plot the horizon
gp.classifier.utils.plot_circle(xq_now[-1], horizon, c = 'k',
marker = '.')
# Save the plot
plt.gca().set_aspect('equal', adjustable = 'box')
plt.savefig('%sclass_prediction_step%d.png'
% (full_directory, i_trials + 1))
# Prepare Figure 5
plt.figure(fig5.number)
plt.clf()
plt.subplot(5, 1, 1)
plt.plot(np.arange(i_trials + 1), entropy_linearised_array[:(i_trials + 1)])
plt.title('Field Linearised Entropy')
plt.ylabel('Field Linearised Entropy')
# plt.subplot(6, 1, 2)
# plt.plot(np.arange(i_trials + 1), entropy_monte_carlo_array[:(i_trials + 1)])
# plt.title('Field True Entropy through Monte Carlo')
# plt.ylabel('Field True Entropy')
plt.subplot(5, 1, 2)
plt.plot(np.arange(i_trials + 1), entropy_linearised_mean_array[:(i_trials + 1)])
plt.title('Average Linearised Entropy')
plt.ylabel('Average Linearised Entropy')
plt.subplot(5, 1, 3)
plt.plot(np.arange(i_trials + 1), entropy_true_mean_array[:(i_trials + 1)])
plt.title('Average True Entropy')
plt.ylabel('Average True Entropy')
plt.subplot(5, 1, 4)
plt.plot(np.arange(i_trials + 1), entropy_opt_array[:(i_trials + 1)])
plt.title('Joint entropy of path chosen each iteration')
plt.ylabel('Joint Entropy')
plt.subplot(5, 1, 5)
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.plot(np.arange(i_trials + 1), 100 * mistake_ratio_array[:(i_trials + 1)])
plt.title('Prediction Miss Ratio')
plt.ylabel('Prediction Miss Ratio (%)')
plt.xlabel('Steps')
# Save the plot
plt.savefig('%sentropy_history%d.png'
% (full_directory, i_trials + 1))
logging.info('Plotted and Saved Iteration')
# Move on to the next step
i_trials += 1
# Save the learned classifier
if i_trials % 50 == 0:
np.savez('%slearned_classifier_trial%d.npz'
% (full_directory, i_trials),
learned_classifier = learned_classifier)
np.savez('%sentropy_linearised_array_trial%d.npz'
% (full_directory, i_trials),
entropy_linearised_array = entropy_linearised_array)
np.savez('%sentropy_linearised_mean_array_trial%d.npz'
% (full_directory, i_trials),
entropy_linearised_mean_array = entropy_linearised_mean_array)
np.savez('%sentropy_true_mean_array_trial%d.npz'
% (full_directory, i_trials),
entropy_true_mean_array = entropy_true_mean_array)
np.savez('%sentropy_opt_array_trial%d.npz'
% (full_directory, i_trials),
entropy_opt_array = entropy_opt_array)
np.savez('%smistake_ratio_array_trial%d.npz'
% (full_directory, i_trials),
mistake_ratio_array = mistake_ratio_array)
# When finished, save the learned classifier
np.savez('%slearned_classifier_final.npz' % full_directory,
learned_classifier = learned_classifier)
# Show everything!
plt.show()
def main(fasta, output_dir, bacterial_query, email, viral_db,
keep_tmp=False, threads=20, evalue=0.001, outfmt=5):
verbose_logging(**locals())
fasta_name = op.basename(fasta)
sample = get_sample(fasta_name)
try:
tmpdir = tf.mkdtemp("_tmp", "%s_" % sample, tf.tempdir)
# rename the fa headers while writing to temp working dir
tmpfasta = preprocess_fasta(fasta, tmpdir)
blastp_xml = op.join(tmpdir, "%s.blastp.xml" % sample)
blast_db = op.join(tmpdir, "%s.blastdb" % sample)
tmp_viral_db = op.join(tmpdir, op.basename(viral_db))
copytree(viral_db, tmp_viral_db)
tmpquery = op.join(tmpdir, op.basename(bacterial_query))
shutil.copyfile(bacterial_query, tmpquery)
# prodigal
p_proteins, p_genes, p_genbank, p_score = prodigal(tmpfasta, sample, tmpdir)
# tRNAscan-SE
trna_output = os.path.join(tmpdir, "%s.trnascan" % sample)
trna_output = trnascan(tmpfasta, o=trna_output, B=None)
# gc content and skew
gc_output = gc_content(tmpfasta)
# tetramerPCA
tetramerPCA_output = tetramerPCA(tmpfasta, window=1600, step=200)
# blastp
blastp(query=p_proteins, db='nr', out=blastp_xml, evalue=evalue,
num_alignments=10, num_threads=threads, outfmt=outfmt)
# blast_db
makeblastdb(**{'in':p_proteins, 'parse_seqids':None, 'dbtype':'prot', 'out':blast_db})
# blastx; viral
viral_xmls, viral_queries = batch_blastx(blast_db, tmp_viral_db, tmpdir, evalue, threads, outfmt)
viral_bams = parmap.starmap(xml_to_bam, zip(viral_xmls, viral_queries), p_proteins, processes=12)
viral_coverages = parmap.map(per_contig_coverage, viral_bams, p_proteins, processes=12)
viral_pileups = parmap.map(samtools_mpileup, viral_bams, processes=12)
# blastx; bacterial
bacterial_xml = os.path.join(tmpdir, "%s.bacterial_fraction.xml" % sample)
blastx(query=tmpquery, db=blast_db, out=bacterial_xml, evalue=evalue,
max_target_seqs=1, num_threads=threads, outfmt=5, query_gencode=11)
bacterial_bam = xml_to_bam(bacterial_xml, bacterial_query, p_proteins)
bacterial_coverage = per_contig_coverage(bacterial_bam, p_proteins)
bacterial_pileup = samtools_mpileup(bacterial_bam)
except Exception as e:
print e.__doc__
print e.message
raise
finally:
# always remove viral fastas; don't copy back from ram
if op.exists(tmp_viral_db):
shutil.rmtree(tmp_viral_db)
# gzip all of the files in the temp dir
gzip_all(tmpdir, ignore=['pdf', 'bam', 'bai'])
# copy over the files
runcmd("cp -R -v {src}/* {dst}".format(src=tmpdir, dst=output_dir))
if not keep_tmp:
# delete the temp working directory
shutil.rmtree(tmpdir)
if email:
send_email(to=email, subject=op.basename(__file__),
message="finished processing %s; results were copied to %s" % (fasta, output_dir))
logging.info("Complete.")
arg.get("rows"),
arg.get("columns"),
arg.get("data_type") if op != "sm" else "float",
arg["values"]),
file=tf)
except Exception as e:
print("Failed to generate", test_file, "with error", traceback.format_exc())
try:
os.unlink(test_file)
except:
raise
if __name__ == "__main__":
op_to_args = defaultdict(list)
for dirpath, dirnames, filenames in os.walk("."):
if dirpath == ".":
continue
operation = os.path.basename(dirpath)
for i, output_filename in enumerate(filenames):
if ".out" not in output_filename:
continue
output_filepath = os.path.join(dirpath, output_filename)
args = get_args(output_filepath)
op_to_args[operation].append(args)
file_params = chain.from_iterable(
[(op, arg) for arg in args]
for op, args in op_to_args.items()
)
list(parmap.starmap(gen_test_file, file_params))
def align_frames(mouse, dir, freq):
lofiles, lofilenames = get_file_list(dir+"Videos/", mouse)
print lofilenames
lop = get_distance_var(lofiles)
all_frames = np.asarray(get_video_frames(lofiles), dtype=np.uint8)
print "Alligning all video frames..."
all_frames = parmap.starmap(shift_frames, zip(all_frames, lop))
## for i in range(len(lop)):
## for frame in all_frames[i]:
## frame = image_registration.fft_tools.shift2d(frame, lop[i].dx, lop[i].dy)
print np.shape(all_frames)
count = 0
new_all_frames = parmap.map(process_frames, all_frames, freq, mouse, dir)
'''
for frames in all_frames:
print np.shape(frames)
save_to_file("Green/"+lofilenames[count][:-4]+"_aligned.raw", frames, np.float32)
print "Calculating mean..."
avg_pre_filt = calculate_avg(frames)
print "Temporal filter..."
frames = cheby_filter(frames)
frames += avg_pre_filt
save_to_file("Green/Cheby/"+lofilenames[count][:-4]+"_BPFilter_0.1-1Hz.raw", frames, np.float32)
print "Calculating DF/F0..."
frames = calculate_df_f0(frames)
save_to_file("Green/DFF/"+lofilenames[count][:-4]+"_DFF.raw", frames, np.float32)
print "Applying MASKED GSR..."
#frames = gsr(frames)
frames = masked_gsr(frames, save_dir+"202_mask.raw")
save_to_file("Green/GSR/"+lofilenames[count][:-4]+"_GSR.raw", frames, np.float32)
print "Getting SD map..."
sd = standard_deviation(frames)
save_to_file("Green/SD_maps/"+lofilenames[count][:-4]+"_SD.raw", frames, np.float32)
new_all_frames.append(frames)
count += 1
'''
print "Creating array..."
new_all_frames = np.asarray(new_all_frames, dtype=np.float32)
all_frames = np.asarray(all_frames, dtype=np.float32)
print "Joining Files..."
new_all_frames = np.reshape(new_all_frames,
(new_all_frames.shape[0]*new_all_frames.shape[1],
new_all_frames.shape[2],
new_all_frames.shape[3]))
all_frames = np.reshape(all_frames,
(all_frames.shape[0]*all_frames.shape[1],
all_frames.shape[2],
all_frames.shape[3]))
print "Shapes: "
print np.shape(all_frames)
print np.shape(new_all_frames)
where_are_NaNs = np.isnan(new_all_frames)
new_all_frames[where_are_NaNs] = 0
save_to_file("FULL_conc.raw", new_all_frames, np.float32)
save_to_file("conc_RAW.raw", all_frames, np.float32)
sd = standard_deviation(new_all_frames)
save_to_file("FULL_SD.raw", sd, np.float32)
print "Displaying correlation map..."
mapper = CorrelationMapDisplayer(new_all_frames)
mapper.display('spectral', -0.3, 1.0)