def _handler(key, func, *args, **kwargs):
data = cache.get(key)
if data is None:
logger.warning('update cache %s' % key)
data = func(*args, **kwargs)
cache.set(key, data)
return data
return data
def calc_hyper_param_importance(df, hyper_param, target):
new_df = df[[hyper_param, target]]
no_missing_value_df = new_df.dropna()
# Can not calc pearson correlation coefficient when number of samples is less or equal than 2
if len(no_missing_value_df) <= 2:
logger.error("Number of samples is less or equal than 2.")
return 0
correlation = no_missing_value_df[target].corr(
no_missing_value_df[hyper_param])
if np.isnan(correlation):
logger.warning("Correlation is nan!")
return 0
return abs(correlation)
def add_embeddings(self,
tag,
mat=None,
metadata=None,
metadata_header=None,
walltime=None,
labels=None,
hot_vectors=None,
labels_meta=None):
"""Add embeddings to vdl record file.
Args:
tag (string): Data identifier
mat (numpy.array or list): A matrix which each row is
feature of labels.
metadata (numpy.array or list): A 1D or 2D matrix of labels
metadata_header (numpy.array or list): Meta data of labels.
walltime (int): Wall time of embeddings.
labels (numpy.array or list): Obsolete parameter, use `metadata` to
replace it.
hot_vectors (numpy.array or list): Obsolete parameter, use `mat` to
replace it.
labels_meta (numpy.array or list): Obsolete parameter, use
`metadata_header` to replace it.
Example 1:
mat = [
[1.3561076367500755, 1.3116267195134017, 1.6785401875616097],
[1.1039614644440658, 1.8891609992484688, 1.32030488587171],
[1.9924524852447711, 1.9358920727142739, 1.2124401279391606],
[1.4129542689796446, 1.7372166387197474, 1.7317806077076527],
[1.3913371800587777, 1.4684674577930312, 1.5214136352476377]]
metadata = ["label_1", "label_2", "label_3", "label_4", "label_5"]
# or like this
# metadata = [["label_1", "label_2", "label_3", "label_4", "label_5"]]
writer.add_embeddings(tag='default',
metadata=metadata,
mat=mat,
walltime=round(time.time() * 1000))
Example 2:
mat = [
[1.3561076367500755, 1.3116267195134017, 1.6785401875616097],
[1.1039614644440658, 1.8891609992484688, 1.32030488587171],
[1.9924524852447711, 1.9358920727142739, 1.2124401279391606],
[1.4129542689796446, 1.7372166387197474, 1.7317806077076527],
[1.3913371800587777, 1.4684674577930312, 1.5214136352476377]]
metadata = [["label_a_1", "label_a_2", "label_a_3", "label_a_4", "label_a_5"],
["label_b_1", "label_b_2", "label_b_3", "label_b_4", "label_b_5"]]
metadata_header = ["label_a", "label_2"]
writer.add_embeddings(tag='default',
metadata=metadata,
metadata_header=metadata_header,
mat=mat,
walltime=round(time.time() * 1000))
"""
if '%' in tag:
raise RuntimeError("% can't appear in tag!")
if (mat is None) and hot_vectors:
mat = hot_vectors
logger.warning('Parameter `hot_vectors` in function '
'`add_embeddings` will be deprecated in '
'future, use `mat` instead.')
if (metadata is None) and labels:
metadata = labels
logger.warning(
'Parameter `labels` in function `add_embeddings` will be '
'deprecated in future, use `metadata` instead.')
if (metadata_header is None) and labels_meta:
metadata_header = labels_meta
logger.warning(
'Parameter `labels_meta` in function `add_embeddings` will be'
' deprecated in future, use `metadata_header` instead.')
if isinstance(mat, np.ndarray):
mat = mat.tolist()
if isinstance(metadata, np.ndarray):
metadata = metadata.tolist()
if isinstance(metadata[0], list) and not metadata_header:
metadata_header = ["label_%d" % i for i in range(len(metadata))]
step = 0
walltime = round(time.time() * 1000) if walltime is None else walltime
self._get_file_writer().add_record(
embedding(tag=tag,
labels=metadata,
labels_meta=metadata_header,
hot_vectors=mat,
step=step,
walltime=walltime))