def save_model(args, user_map, item_map, row_factor, col_factor):
"""Save the user map, item map, row factor and column factor matrices in numpy format.
These matrices together constitute the "recommendation model."
Args:
args: input args to training job
user_map: user map numpy array
item_map: item map numpy array
row_factor: row_factor numpy array
col_factor: col_factor numpy array
"""
model_dir = os.path.join(args['output_dir'], 'model')
# if our output directory is a GCS bucket, write model files to /tmp,
# then copy to GCS
gs_model_dir = None
if model_dir.startswith('gs://'):
gs_model_dir = model_dir
model_dir = '/tmp/{0}'.format(args['job_name'])
os.makedirs(model_dir)
np.save(os.path.join(model_dir, 'user'), user_map)
np.save(os.path.join(model_dir, 'item'), item_map)
np.save(os.path.join(model_dir, 'row'), row_factor)
np.save(os.path.join(model_dir, 'col'), col_factor)
if gs_model_dir:
sh.gsutil('cp', '-r', os.path.join(model_dir, '*'), gs_model_dir)
def save_model(args, user_map, item_map, row_factor, col_factor):
"""Save the user map, item map, row factor and column factor matrices in numpy format.
These matrices together constitute the "recommendation model."
Args:
args: input args to training job
user_map: user map numpy array
item_map: item map numpy array
row_factor: row_factor numpy array
col_factor: col_factor numpy array
"""
model_dir = os.path.join(args['output_dir'], 'model')
# if our output directory is a GCS bucket, write model files to /tmp,
# then copy to GCS
gs_model_dir = None
if model_dir.startswith('gs://'):
gs_model_dir = model_dir
model_dir = '/tmp/{0}'.format(args['job_name'])
os.makedirs(model_dir)
np.save(os.path.join(model_dir, 'user'), user_map)
np.save(os.path.join(model_dir, 'item'), item_map)
np.save(os.path.join(model_dir, 'row'), row_factor)
np.save(os.path.join(model_dir, 'col'), col_factor)
if gs_model_dir:
sh.gsutil('cp', '-r', os.path.join(model_dir, '*'), gs_model_dir)
def save_model(args, user_map, item_map, row_factor, col_factor):
"""Save the user map, item map, row factor and column factor matrices in numpy format.
These matrices together constitute the "recommendation model."
Args:
args: input args to training job
user_map: user map numpy array
item_map: item map numpy array
row_factor: row_factor numpy array
col_factor: col_factor numpy array
"""
model_dir = os.path.join(args.output_dir, 'model')
# write model files to /tmp, then copy to GCS
gs_model_dir = model_dir
model_dir = '/tmp/{0}'.format(args.job_name)
os.makedirs(model_dir)
np.save(os.path.join(model_dir, 'user'), user_map)
np.save(os.path.join(model_dir, 'item'), item_map)
np.save(os.path.join(model_dir, 'row'), row_factor)
np.save(os.path.join(model_dir, 'col'), col_factor)
sh.gsutil('cp', '-r', os.path.join(model_dir, '*'), gs_model_dir)
def save_model(args, user_map, item_map, row_factor, col_factor,
item_ID_mapping_dd):
"""
These matrices together constitute the "recommendation model."
Inputs:
args: input args to training job
user_map: user map numpy array
item_map: item map numpy array
row_factor: row_factor numpy array
col_factor: col_factor numpy array
item_ID_mapping_dd: original item ID to rebased item ID mapping
"""
model_dir = os.path.join(args.output_dir, 'model')
# if our output directory is a GCS bucket, write model files to /tmp,
# then copy to GCS
gs_model_dir = None
if model_dir.startswith('gs://'):
gs_model_dir = model_dir
model_dir = '/tmp/{0}'.format(args.job_name)
os.makedirs(model_dir)
np.save(os.path.join(model_dir, 'user'), user_map)
np.save(os.path.join(model_dir, 'item'), item_map)
np.save(os.path.join(model_dir, 'row'), row_factor)
np.save(os.path.join(model_dir, 'col'), col_factor)
item_ID_mapping_dd.to_csv(os.path.join(model_dir,
'item_ID_mapping_dd.csv'))
if gs_model_dir:
sh.gsutil('cp', '-r', os.path.join(model_dir, '*'), gs_model_dir)
def ensure_local_file(input_file):
"""
Ensure the training ratings file is stored locally.
"""
os.makedirs(input_path)
tmp_input_file = os.path.join(input_path, os.path.basename(input_file))
sh.gsutil("cp", "-r", input_file, tmp_input_file)
return tmp_input_file
def ensure_local_file(input_file):
"""
Ensure the training ratings file is stored locally.
"""
if input_file.startswith('gs:/'):
input_path = os.path.join('/tmp/', str(uuid.uuid4()))
os.makedirs(input_path)
tmp_input_file = os.path.join(input_path, os.path.basename(input_file))
sh.gsutil("cp", "-r", input_file, tmp_input_file)
return tmp_input_file
else:
return input_file
def ensure_local_file(input_file):
"""
Ensure the training ratings file is stored locally.
"""
if input_file.startswith('gs:/'):
input_path = os.path.join('/tmp/', str(uuid.uuid4()))
os.makedirs(input_path)
tmp_input_file = os.path.join(input_path, os.path.basename(input_file))
sh.gsutil("cp", "-r", input_file, tmp_input_file)
return tmp_input_file
else:
return input_file
def save_model(r_factor, c_factor, job_dir='.', job_name='myjob'):
model_dir = os.path.join(job_dir, 'model')
gs_model_dir = None
if model_dir.startswith('gs://'):
gs_model_dir = model_dir
model_dir = '/tmp/{0}'.format(job_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
np.save(os.path.join(model_dir, 'row'), r_factor)
np.save(os.path.join(model_dir, 'col'), c_factor)
if gs_model_dir:
import sh
sh.gsutil('cp', '-r', os.path.join(model_dir, '*'), gs_model_dir)
def save_model(r_factor, c_factor, job_dir = '.', job_name = 'myjob'):
model_dir = os.path.join(job_dir, 'model')
gs_model_dir = None
if model_dir.startswith('gs://'):
gs_model_dir = model_dir
model_dir = '/tmp/{0}'.format(job_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
np.save(os.path.join(model_dir, 'row'), r_factor)
np.save(os.path.join(model_dir, 'col'), c_factor)
if gs_model_dir:
import sh
sh.gsutil('cp', '-r', os.path.join(model_dir, '*'), gs_model_dir)
def save_user_items_w(args, user_items_w):
"""Save the user map, item map, row factor and column factor matrices in numpy format.
"""
model_dir = os.path.join(args['output_dir'], 'model')
# if our output directory is a GCS bucket, write model files to /tmp,
# then copy to GCS
gs_model_dir = None
if model_dir.startswith('gs://'):
gs_model_dir = model_dir
model_dir = '/tmp/{0}'.format(args['job_name'])
# os.makedirs(model_dir)
user_items_w.to_json(os.path.join(model_dir, 'user_item_w.json'),orient='records')
if gs_model_dir:
sh.gsutil('cp', '-r', os.path.join(model_dir, '*'), gs_model_dir)
def move(self, path_to_src, path_to_dest):
self.logger.info("moving %s to %s", path_to_src, path_to_dest)
path_to_dest = path_to_dest[1:] #remove / from beginning
path_to_src = path_to_src[1:]
k = self.bucket.get_key(path_to_src)
#assume that src is a directory, if the key does not exist
#a more secure way would be to check if the directory exists by appending a slash to the path and trying to get it
is_dir = k == None
if not is_dir:
k.copy(self.bucket, path_to_dest)
k.delete()
return
path_to_dest += '/'
path_to_src += '/'
listing = self.bucket.list(path_to_src, '/')
directories = [d for d in listing if self._is_dir(d)]
files = [f for f in listing if not self._is_dir(d)]
for key in directories:
new_path = path_to_dest+key.name.split(path_to_src,1)[1]
self.create_directory('/'+new_path[:-1])
for key in files:
new_path = path_to_dest+key.name.split(path_to_src,1)[1]
key.copy(self.bucket, new_path)
gsutil('rm', 'gs://%s/%s**' % (self.bucket_name, path_to_src) )
def get_used_space(self):
self.logger.debug("retrieving used space")
return int( awk(gsutil('du', '-s', 'gs://cloudfusion'), '{print $1}') )