def write_numpy_to_dense_tensor(file, array, labels=None):
"""Writes a numpy array to a dense tensor
Args:
file:
array:
labels:
"""
# Validate shape of array and labels, resolve array and label types
if not len(array.shape) == 2:
raise ValueError("Array must be a Matrix")
if labels is not None:
if not len(labels.shape) == 1:
raise ValueError("Labels must be a Vector")
if labels.shape[0] not in array.shape:
raise ValueError(
"Label shape {} not compatible with array shape {}".format(
labels.shape, array.shape))
resolved_label_type = _resolve_type(labels.dtype)
resolved_type = _resolve_type(array.dtype)
# Write each vector in array into a Record in the file object
record = Record()
for index, vector in enumerate(array):
record.Clear()
_write_feature_tensor(resolved_type, record, vector)
if labels is not None:
_write_label_tensor(resolved_label_type, record, labels[index])
_write_recordio(file, record.SerializeToString())
def write_spmatrix_to_sparse_tensor(file, array, labels=None):
"""Writes a scipy sparse matrix to a sparse tensor
Args:
file:
array:
labels:
"""
try:
import scipy
except ImportError as e:
logging.warning(
"scipy failed to import. Sparse matrix functions will be impaired or broken."
)
# Any subsequent attempt to use scipy will raise the ImportError
scipy = DeferredError(e)
if not scipy.sparse.issparse(array):
raise TypeError("Array must be sparse")
# Validate shape of array and labels, resolve array and label types
if not len(array.shape) == 2:
raise ValueError("Array must be a Matrix")
if labels is not None:
if not len(labels.shape) == 1:
raise ValueError("Labels must be a Vector")
if labels.shape[0] not in array.shape:
raise ValueError(
"Label shape {} not compatible with array shape {}".format(
labels.shape, array.shape))
resolved_label_type = _resolve_type(labels.dtype)
resolved_type = _resolve_type(array.dtype)
csr_array = array.tocsr()
n_rows, n_cols = csr_array.shape
record = Record()
for row_idx in range(n_rows):
record.Clear()
row = csr_array.getrow(row_idx)
# Write values
_write_feature_tensor(resolved_type, record, row.data)
# Write keys
_write_keys_tensor(resolved_type, record,
row.indices.astype(np.uint64))
# Write labels
if labels is not None:
_write_label_tensor(resolved_label_type, record, labels[row_idx])
# Write shape
_write_shape(resolved_type, record, n_cols)
_write_recordio(file, record.SerializeToString())
def write_spmatrix_to_sparse_tensor(file, array, labels=None):
"""Writes a scipy sparse matrix to a sparse tensor
Args:
file:
array:
labels:
"""
if not issparse(array):
raise TypeError("Array must be sparse")
# Validate shape of array and labels, resolve array and label types
if not len(array.shape) == 2:
raise ValueError("Array must be a Matrix")
if labels is not None:
if not len(labels.shape) == 1:
raise ValueError("Labels must be a Vector")
if labels.shape[0] not in array.shape:
raise ValueError(
"Label shape {} not compatible with array shape {}".format(
labels.shape, array.shape))
resolved_label_type = _resolve_type(labels.dtype)
resolved_type = _resolve_type(array.dtype)
csr_array = array.tocsr()
n_rows, n_cols = csr_array.shape
record = Record()
for row_idx in range(n_rows):
record.Clear()
row = csr_array.getrow(row_idx)
# Write values
_write_feature_tensor(resolved_type, record, row.data)
# Write keys
_write_keys_tensor(resolved_type, record,
row.indices.astype(np.uint64))
# Write labels
if labels is not None:
_write_label_tensor(resolved_label_type, record, labels[row_idx])
# Write shape
_write_shape(resolved_type, record, n_cols)
_write_recordio(file, record.SerializeToString())
def write_record(file, features=None, metadata=None):
record = Record()
record.Clear()
"""
print(record)
print(dir(record))
print(record.features)
print("data: {}, {}".format(type(data), len(data)))
print(dir(record.features["values"].bytes.value))
"""
if features:
for k, v in features.items():
write_feature_tensor(record, v, k)
if metadata:
if not isinstance(metadata, str):
metadata = json.dumps(metadata)
record.metadata = metadata
# print(record.metadata)
# print(dir(record.metadata))
# record.metadata.val
_write_recordio(file, record.SerializeToString())
def transformation():
"""Do an inference on a single batch of data. In this server, we take data as JSON, convert
it to a sparse array for internal use and then convert the predictions back to json.
Input format is:
'{"instances": [{"keys": ["User","1","2"], "values": ["a","b","c"]}, {"keys": ["User","5","6"], "values": ["d","e","f"]}]}'
"""
# Convert from json to numpy
te_row_ind = []
te_col_ind = []
te_data = []
te_idx = 0
headers = ScoringService.get_headers()
if flask.request.content_type == 'application/json':
print("Working with JSON input")
s = flask.request.data.decode('utf-8')
inputs = json.loads(s)
for instance in inputs['instances']:
# The column index has to be found from the headers
for col_idx in range(0, len(instance['keys'])):
key = instance['keys'][col_idx]
val = instance['values'][col_idx]
item_to_find = "{0}_{1}".format(key, val)
try:
te_col_ind.append(headers.index(item_to_find))
te_data.append(1.0)
te_row_ind.append(te_idx)
except Exception as e:
te_col_ind.append(1)
te_data.append(0.0)
te_row_ind.append(te_idx)
print("Couldn't find header for {0}".format(item_to_find))
te_idx = te_idx + 1
elif flask.request.content_type == 'application/x-recordio-protobuf':
print("Working with Protobuf input")
#print("{0}".format(flask.request.stream))
#s = flask.request.data.decode('latin-1')
#print("Data: {}".format(s))
test_records = smac.read_records(StringIO.StringIO(flask.request.data))
num_test_samples = len(test_records)
for test_record in test_records:
te_row_ind.extend([te_idx] * len(test_record.features['values'].float32_tensor.values))
te_col_ind.extend(test_record.features['values'].float32_tensor.keys)
te_data.extend(test_record.features['values'].float32_tensor.values)
te_idx = te_idx + 1
else:
return flask.Response(response='This predictor only supports JSON or Protobuf data', status=415, mimetype='text/plain')
X_te_sparse = sp.csr_matrix( (np.array(te_data),(np.array(te_row_ind),np.array(te_col_ind))), shape=(te_idx,ScoringService.get_num_features()) )
print('Invoked with {} records'.format(X_te_sparse.shape))
# Do the prediction
predictions = ScoringService.predict(X_te_sparse)
# Convert from array back to json
result = None
if flask.request.content_type == 'application/json':
js = {'predictions': []}
for pred_value in predictions:
js['predictions'].append({'score': str(pred_value)})
result = json.dumps(js)
else:
# convert to protobuf
buf = io.BytesIO()
record = Record()
for pred_value in predictions:
record.Clear()
#smac._write_label_tensor('Float32', record, pred_value)
record.label["score"].float32_tensor.values.extend([pred_value])
smac._write_recordio(buf, record.SerializeToString())
buf.seek(0)
result = buf.getvalue()
return flask.Response(response=result, status=200, mimetype=flask.request.content_type)