after_merge
stringlengths 28
79.6k
| before_merge
stringlengths 20
79.6k
| url
stringlengths 38
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| full_traceback
stringlengths 43
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| traceback_type
stringclasses 555
values |
|---|---|---|---|---|
def _update_tileable_and_its_chunk_shapes(self):
need_update_tileable_to_tiled = dict()
for tileable in self._chunk_graph_builder.prev_tileable_graph:
if tileable.key in self._target_tileable_finished:
tiled = self._tileable_key_opid_to_tiled[tileable.key, tileable.op.id][-1]
if not has_unknown_shape(tiled):
continue
need_update_tileable_to_tiled[tileable] = tiled
if len(need_update_tileable_to_tiled) == 0:
return
need_update_chunks = list(
c for t in need_update_tileable_to_tiled.values() for c in t.chunks
)
chunk_metas = self.chunk_meta.batch_get_chunk_meta(
self._session_id, list(c.key for c in need_update_chunks)
)
ops_to_restart = set()
keys = []
for chunk, chunk_meta in zip(need_update_chunks, chunk_metas):
if chunk_meta is None:
ops_to_restart.add(chunk.op.key)
keys.append(chunk.key)
else:
chunk.data._shape = chunk_meta.chunk_shape
if ops_to_restart:
for op_key in ops_to_restart:
self._get_operand_ref(op_key).start_operand(
OperandState.READY, _tell=True, _wait=False
)
raise RuntimeError(
f"Cannot find chunks {keys}. Operands {ops_to_restart} restarted."
)
for tileable, tiled in need_update_tileable_to_tiled.items():
chunk_idx_to_shape = OrderedDict((c.index, c.shape) for c in tiled.chunks)
nsplits = calc_nsplits(chunk_idx_to_shape)
tiled._nsplits = nsplits
if any(np.isnan(s) for s in tileable.shape):
shape = tuple(sum(ns) for ns in nsplits)
tileable._update_shape(shape)
tiled._update_shape(shape)
|
def _update_tileable_and_its_chunk_shapes(self):
need_update_tileable_to_tiled = dict()
for tileable in self._chunk_graph_builder.prev_tileable_graph:
if tileable.key in self._target_tileable_finished:
tiled = self._tileable_key_opid_to_tiled[tileable.key, tileable.op.id][-1]
if not has_unknown_shape(tiled):
continue
need_update_tileable_to_tiled[tileable] = tiled
if len(need_update_tileable_to_tiled) == 0:
return
need_update_chunks = list(
c for t in need_update_tileable_to_tiled.values() for c in t.chunks
)
chunk_metas = self.chunk_meta.batch_get_chunk_meta(
self._session_id, list(c.key for c in need_update_chunks)
)
for chunk, chunk_meta in zip(need_update_chunks, chunk_metas):
chunk.data._shape = chunk_meta.chunk_shape
for tileable, tiled in need_update_tileable_to_tiled.items():
chunk_idx_to_shape = OrderedDict((c.index, c.shape) for c in tiled.chunks)
nsplits = calc_nsplits(chunk_idx_to_shape)
tiled._nsplits = nsplits
if any(np.isnan(s) for s in tileable.shape):
shape = tuple(sum(ns) for ns in nsplits)
tileable._update_shape(shape)
tiled._update_shape(shape)
|
https://github.com/mars-project/mars/issues/1741
|
2020-12-02 11:19:40,309 mars.scheduler.operands.common 87 ERROR Attempt 1: Unexpected error KeyError occurred in executing operand 5c7a3b06d448300987640036d2f5a34e in 11.238.145.234:49708
Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.5.5.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/utils.py", line 377, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 564, in execute_graph
quota_request = self._prepare_quota_request(session_id, graph_key)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 249, in _prepare_quota_request
memory_estimations = self._estimate_calc_memory(session_id, graph_key)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 213, in _estimate_calc_memory
res = executor.execute_graph(graph_record.graph, graph_record.chunk_targets, mock=True)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 690, in execute_graph
res = graph_execution.execute(retval)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 574, in execute
return [self._chunk_results[key] for key in self._keys]
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 574, in <listcomp>
return [self._chunk_results[key] for key in self._keys]
KeyError: '3990ec90331559138b6ecbc6d76fbd0d'
|
KeyError
|
def append_graph(self, graph_key, op_info):
super().append_graph(graph_key, op_info)
if not self._is_terminal:
self._is_terminal = op_info.get("is_terminal")
if self.state in OperandState.STORED_STATES:
metas = self.chunk_meta.batch_get_chunk_meta(
self._session_id, self._io_meta.get("chunks")
)
if any(meta is None for meta in metas):
self.state = OperandState.UNSCHEDULED
if self.state not in OperandState.TERMINATED_STATES:
for in_key in self._pred_keys:
self._get_operand_actor(in_key).remove_finished_successor(
self._op_key, _tell=True, _wait=False
)
self.start_operand()
elif self.state in OperandState.STORED_STATES:
for out_key in self._succ_keys:
self._get_operand_actor(out_key).add_finished_predecessor(
self._op_key,
self.worker,
output_sizes=self._data_sizes,
output_shapes=self._data_shapes,
_tell=True,
_wait=False,
)
# require more chunks to execute if the completion caused no successors to run
if self._is_terminal:
# update records in GraphActor to help decide if the whole graph finished execution
self._add_finished_terminal()
|
def append_graph(self, graph_key, op_info):
super().append_graph(graph_key, op_info)
if not self._is_terminal:
self._is_terminal = op_info.get("is_terminal")
if self.state not in OperandState.TERMINATED_STATES:
for in_key in self._pred_keys:
self._get_operand_actor(in_key).remove_finished_successor(
self._op_key, _tell=True, _wait=False
)
self.start_operand()
elif self.state in OperandState.STORED_STATES:
for out_key in self._succ_keys:
self._get_operand_actor(out_key).add_finished_predecessor(
self._op_key,
self.worker,
output_sizes=self._data_sizes,
output_shapes=self._data_shapes,
_tell=True,
_wait=False,
)
# require more chunks to execute if the completion caused no successors to run
if self._is_terminal:
# update records in GraphActor to help decide if the whole graph finished execution
self._add_finished_terminal()
|
https://github.com/mars-project/mars/issues/1741
|
2020-12-02 11:19:40,309 mars.scheduler.operands.common 87 ERROR Attempt 1: Unexpected error KeyError occurred in executing operand 5c7a3b06d448300987640036d2f5a34e in 11.238.145.234:49708
Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.5.5.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/utils.py", line 377, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 564, in execute_graph
quota_request = self._prepare_quota_request(session_id, graph_key)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 249, in _prepare_quota_request
memory_estimations = self._estimate_calc_memory(session_id, graph_key)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 213, in _estimate_calc_memory
res = executor.execute_graph(graph_record.graph, graph_record.chunk_targets, mock=True)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 690, in execute_graph
res = graph_execution.execute(retval)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 574, in execute
return [self._chunk_results[key] for key in self._keys]
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 574, in <listcomp>
return [self._chunk_results[key] for key in self._keys]
KeyError: '3990ec90331559138b6ecbc6d76fbd0d'
|
KeyError
|
def create_reader(
self,
session_id,
data_key,
source_devices,
packed=False,
packed_compression=None,
_promise=True,
):
"""
Create a data reader from existing data and return in a Promise.
If no readers can be created, will try copying the data into a
readable storage.
:param session_id: session id
:param data_key: data key
:param source_devices: devices to read from
:param packed: create a reader to read packed data format
:param packed_compression: compression format to use when reading as packed
:param _promise: return a promise
"""
source_devices = self._normalize_devices(source_devices)
stored_devs = set(self._manager_ref.get_data_locations(session_id, [data_key])[0])
for src_dev in source_devices:
if src_dev not in stored_devs:
continue
handler = self.get_storage_handler(src_dev)
try:
logger.debug(
"Creating %s reader for (%s, %s) on %s",
"packed" if packed else "bytes",
session_id,
data_key,
handler.storage_type,
)
return handler.create_bytes_reader(
session_id,
data_key,
packed=packed,
packed_compression=packed_compression,
_promise=_promise,
)
except AttributeError: # pragma: no cover
raise IOError(f"Device {src_dev} does not support direct reading.")
if _promise:
return self.copy_to(session_id, [data_key], source_devices).then(
lambda *_: self.create_reader(
session_id, data_key, source_devices, packed=packed
)
)
else:
raise IOError(
f"Cannot return a non-promise result for key {data_key}, stored_devs {stored_devs!r}"
)
|
def create_reader(
self,
session_id,
data_key,
source_devices,
packed=False,
packed_compression=None,
_promise=True,
):
"""
Create a data reader from existing data and return in a Promise.
If no readers can be created, will try copying the data into a
readable storage.
:param session_id: session id
:param data_key: data key
:param source_devices: devices to read from
:param packed: create a reader to read packed data format
:param packed_compression: compression format to use when reading as packed
:param _promise: return a promise
"""
source_devices = self._normalize_devices(source_devices)
stored_devs = set(self._manager_ref.get_data_locations(session_id, [data_key])[0])
for src_dev in source_devices:
if src_dev not in stored_devs:
continue
handler = self.get_storage_handler(src_dev)
try:
logger.debug(
"Creating %s reader for (%s, %s) on %s",
"packed" if packed else "bytes",
session_id,
data_key,
handler.storage_type,
)
return handler.create_bytes_reader(
session_id,
data_key,
packed=packed,
packed_compression=packed_compression,
_promise=_promise,
)
except AttributeError: # pragma: no cover
raise IOError(f"Device {src_dev} does not support direct reading.")
if _promise:
return self.copy_to(session_id, [data_key], source_devices).then(
lambda *_: self.create_reader(
session_id, data_key, source_devices, packed=packed
)
)
else:
raise IOError("Cannot return a non-promise result")
|
https://github.com/mars-project/mars/issues/1741
|
2020-12-02 11:19:40,309 mars.scheduler.operands.common 87 ERROR Attempt 1: Unexpected error KeyError occurred in executing operand 5c7a3b06d448300987640036d2f5a34e in 11.238.145.234:49708
Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.5.5.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/utils.py", line 377, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 564, in execute_graph
quota_request = self._prepare_quota_request(session_id, graph_key)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 249, in _prepare_quota_request
memory_estimations = self._estimate_calc_memory(session_id, graph_key)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/worker/execution.py", line 213, in _estimate_calc_memory
res = executor.execute_graph(graph_record.graph, graph_record.chunk_targets, mock=True)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 690, in execute_graph
res = graph_execution.execute(retval)
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 574, in execute
return [self._chunk_results[key] for key in self._keys]
File "/home/admin/work/_public-mars-0.5.5.zip/mars/executor.py", line 574, in <listcomp>
return [self._chunk_results[key] for key in self._keys]
KeyError: '3990ec90331559138b6ecbc6d76fbd0d'
|
KeyError
|
def execute(cls, ctx, op):
def _base_concat(chunk, inputs):
# auto generated concat when executing a DataFrame, Series or Index
if chunk.op.output_types[0] == OutputType.dataframe:
return _auto_concat_dataframe_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.series:
return _auto_concat_series_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.index:
return _auto_concat_index_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.categorical:
return _auto_concat_categorical_chunks(chunk, inputs)
else: # pragma: no cover
raise TypeError(
"Only DataFrameChunk, SeriesChunk, IndexChunk, "
"and CategoricalChunk can be automatically concatenated"
)
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = (
pd
if isinstance(inputs[0], (pd.DataFrame, pd.Series)) or cudf is None
else cudf
)
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
n_rows = len(set(inp.index[0] for inp in chunk.inputs))
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) or cudf is None else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
def _auto_concat_index_chunks(chunk, inputs):
if len(inputs) == 1:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
concat_df = xdf.DataFrame(index=inputs[0])
else:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
concat_df = xdf.concat(empty_dfs, axis=0)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat_df.sort_index(inplace=True)
return concat_df.index
def _auto_concat_categorical_chunks(_, inputs):
if len(inputs) == 1: # pragma: no cover
return inputs[0]
else:
# convert categorical into array
arrays = [np.asarray(inp) for inp in inputs]
array = np.concatenate(arrays)
return pd.Categorical(
array, categories=inputs[0].categories, ordered=inputs[0].ordered
)
chunk = op.outputs[0]
inputs = [ctx[input.key] for input in op.inputs]
if isinstance(inputs[0], tuple):
ctx[chunk.key] = tuple(
_base_concat(chunk, [input[i] for input in inputs])
for i in range(len(inputs[0]))
)
else:
ctx[chunk.key] = _base_concat(chunk, inputs)
|
def execute(cls, ctx, op):
def _base_concat(chunk, inputs):
# auto generated concat when executing a DataFrame, Series or Index
if chunk.op.output_types[0] == OutputType.dataframe:
return _auto_concat_dataframe_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.series:
return _auto_concat_series_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.index:
return _auto_concat_index_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.categorical:
return _auto_concat_categorical_chunks(chunk, inputs)
else: # pragma: no cover
raise TypeError(
"Only DataFrameChunk, SeriesChunk, IndexChunk, "
"and CategoricalChunk can be automatically concatenated"
)
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = (
pd
if isinstance(inputs[0], (pd.DataFrame, pd.Series)) or cudf is None
else cudf
)
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
max_rows = max(inp.index[0] for inp in chunk.inputs)
min_rows = min(inp.index[0] for inp in chunk.inputs)
n_rows = max_rows - min_rows + 1
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) or cudf is None else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
def _auto_concat_index_chunks(chunk, inputs):
if len(inputs) == 1:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
concat_df = xdf.DataFrame(index=inputs[0])
else:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
concat_df = xdf.concat(empty_dfs, axis=0)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat_df.sort_index(inplace=True)
return concat_df.index
def _auto_concat_categorical_chunks(_, inputs):
if len(inputs) == 1: # pragma: no cover
return inputs[0]
else:
# convert categorical into array
arrays = [np.asarray(inp) for inp in inputs]
array = np.concatenate(arrays)
return pd.Categorical(
array, categories=inputs[0].categories, ordered=inputs[0].ordered
)
chunk = op.outputs[0]
inputs = [ctx[input.key] for input in op.inputs]
if isinstance(inputs[0], tuple):
ctx[chunk.key] = tuple(
_base_concat(chunk, [input[i] for input in inputs])
for i in range(len(inputs[0]))
)
else:
ctx[chunk.key] = _base_concat(chunk, inputs)
|
https://github.com/mars-project/mars/issues/1740
|
Error
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 377, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 646, in prepare_graph
cur_chunk_graph = chunk_graph_builder.build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 347, in build
chunk_graph = super().build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/Users/wenjun.swj/Code/mars/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 257, in tile
return cls._tile_single_output(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 227, in _tile_single_output
data_chunk = concat_chunks(chunks[0])
File "/Users/wenjun.swj/Code/mars/mars/learn/utils/core.py", line 33, in concat_chunks
tileable = chunks[0].op.create_tileable_from_chunks(chunks)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 212, in create_tileable_from_chunks
params = cls._calc_dataframe_params(chunk_index_to_chunk, chunk_shape)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in _calc_dataframe_params
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in <listcomp>
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
KeyError: (2, 0)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/tests/integrated/test_distributed_xgboost.py", line 71, in testDistributedXGBClassifier
classifier.fit(X, y, eval_set=[(X, y)], session=sess, run_kwargs=run_kwargs)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/classifier.py", line 55, in fit
result = train(params, dtrain, num_boost_round=self.get_num_boosting_rounds(),
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/train.py", line 200, in train
ret = t.execute(session=session, **run_kwargs).fetch(session=session)
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 379, in execute
return run()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 374, in run
session.run(self, **kw)
File "/Users/wenjun.swj/Code/mars/mars/session.py", line 499, in run
result = self._sess.run(*tileables, **kw)
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 214, in run
if self._check_response_finished(graph_url, timeout_val):
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 174, in _check_response_finished
raise ExecutionFailed('Graph execution failed.') from exc
mars.errors.ExecutionFailed: 'Graph execution failed.'
|
KeyError
|
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = (
pd if isinstance(inputs[0], (pd.DataFrame, pd.Series)) or cudf is None else cudf
)
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
n_rows = len(set(inp.index[0] for inp in chunk.inputs))
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
|
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = (
pd if isinstance(inputs[0], (pd.DataFrame, pd.Series)) or cudf is None else cudf
)
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
max_rows = max(inp.index[0] for inp in chunk.inputs)
min_rows = min(inp.index[0] for inp in chunk.inputs)
n_rows = max_rows - min_rows + 1
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
|
https://github.com/mars-project/mars/issues/1740
|
Error
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 377, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 646, in prepare_graph
cur_chunk_graph = chunk_graph_builder.build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 347, in build
chunk_graph = super().build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/Users/wenjun.swj/Code/mars/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 257, in tile
return cls._tile_single_output(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 227, in _tile_single_output
data_chunk = concat_chunks(chunks[0])
File "/Users/wenjun.swj/Code/mars/mars/learn/utils/core.py", line 33, in concat_chunks
tileable = chunks[0].op.create_tileable_from_chunks(chunks)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 212, in create_tileable_from_chunks
params = cls._calc_dataframe_params(chunk_index_to_chunk, chunk_shape)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in _calc_dataframe_params
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in <listcomp>
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
KeyError: (2, 0)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/tests/integrated/test_distributed_xgboost.py", line 71, in testDistributedXGBClassifier
classifier.fit(X, y, eval_set=[(X, y)], session=sess, run_kwargs=run_kwargs)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/classifier.py", line 55, in fit
result = train(params, dtrain, num_boost_round=self.get_num_boosting_rounds(),
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/train.py", line 200, in train
ret = t.execute(session=session, **run_kwargs).fetch(session=session)
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 379, in execute
return run()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 374, in run
session.run(self, **kw)
File "/Users/wenjun.swj/Code/mars/mars/session.py", line 499, in run
result = self._sess.run(*tileables, **kw)
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 214, in run
if self._check_response_finished(graph_url, timeout_val):
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 174, in _check_response_finished
raise ExecutionFailed('Graph execution failed.') from exc
mars.errors.ExecutionFailed: 'Graph execution failed.'
|
KeyError
|
def _calc_dataframe_params(cls, chunk_index_to_chunks, chunk_shape):
dtypes = pd.concat(
[
chunk_index_to_chunks[0, i].dtypes
for i in range(chunk_shape[1])
if (0, i) in chunk_index_to_chunks
]
)
columns_value = parse_index(dtypes.index, store_data=True)
pd_indexes = [
chunk_index_to_chunks[i, 0].index_value.to_pandas()
for i in range(chunk_shape[0])
if (i, 0) in chunk_index_to_chunks
]
pd_index = reduce(lambda x, y: x.append(y), pd_indexes)
index_value = parse_index(pd_index)
return {
"dtypes": dtypes,
"columns_value": columns_value,
"index_value": index_value,
}
|
def _calc_dataframe_params(cls, chunk_index_to_chunks, chunk_shape):
dtypes = pd.concat(
[chunk_index_to_chunks[0, i].dtypes for i in range(chunk_shape[1])]
)
columns_value = parse_index(dtypes.index, store_data=True)
pd_indxes = [
chunk_index_to_chunks[i, 0].index_value.to_pandas()
for i in range(chunk_shape[0])
]
pd_index = reduce(lambda x, y: x.append(y), pd_indxes)
index_value = parse_index(pd_index)
return {
"dtypes": dtypes,
"columns_value": columns_value,
"index_value": index_value,
}
|
https://github.com/mars-project/mars/issues/1740
|
Error
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 377, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 646, in prepare_graph
cur_chunk_graph = chunk_graph_builder.build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 347, in build
chunk_graph = super().build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/Users/wenjun.swj/Code/mars/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 257, in tile
return cls._tile_single_output(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 227, in _tile_single_output
data_chunk = concat_chunks(chunks[0])
File "/Users/wenjun.swj/Code/mars/mars/learn/utils/core.py", line 33, in concat_chunks
tileable = chunks[0].op.create_tileable_from_chunks(chunks)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 212, in create_tileable_from_chunks
params = cls._calc_dataframe_params(chunk_index_to_chunk, chunk_shape)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in _calc_dataframe_params
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in <listcomp>
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
KeyError: (2, 0)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/tests/integrated/test_distributed_xgboost.py", line 71, in testDistributedXGBClassifier
classifier.fit(X, y, eval_set=[(X, y)], session=sess, run_kwargs=run_kwargs)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/classifier.py", line 55, in fit
result = train(params, dtrain, num_boost_round=self.get_num_boosting_rounds(),
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/train.py", line 200, in train
ret = t.execute(session=session, **run_kwargs).fetch(session=session)
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 379, in execute
return run()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 374, in run
session.run(self, **kw)
File "/Users/wenjun.swj/Code/mars/mars/session.py", line 499, in run
result = self._sess.run(*tileables, **kw)
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 214, in run
if self._check_response_finished(graph_url, timeout_val):
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 174, in _check_response_finished
raise ExecutionFailed('Graph execution failed.') from exc
mars.errors.ExecutionFailed: 'Graph execution failed.'
|
KeyError
|
def parse_args(self, parser, argv, environ=None):
args = super().parse_args(parser, argv)
environ = environ or os.environ
args.disable_failover = args.disable_failover or bool(
int(environ.get("MARS_DISABLE_FAILOVER", "0"))
)
options.scheduler.dump_graph_data = bool(
int(environ.get("MARS_DUMP_GRAPH_DATA", "0"))
)
return args
|
def parse_args(self, parser, argv, environ=None):
args = super().parse_args(parser, argv)
environ = environ or os.environ
args.disable_failover = args.disable_failover or bool(
int(environ.get("MARS_DISABLE_FAILOVER", "0"))
)
return args
|
https://github.com/mars-project/mars/issues/1740
|
Error
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 377, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 646, in prepare_graph
cur_chunk_graph = chunk_graph_builder.build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 347, in build
chunk_graph = super().build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/Users/wenjun.swj/Code/mars/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 257, in tile
return cls._tile_single_output(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 227, in _tile_single_output
data_chunk = concat_chunks(chunks[0])
File "/Users/wenjun.swj/Code/mars/mars/learn/utils/core.py", line 33, in concat_chunks
tileable = chunks[0].op.create_tileable_from_chunks(chunks)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 212, in create_tileable_from_chunks
params = cls._calc_dataframe_params(chunk_index_to_chunk, chunk_shape)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in _calc_dataframe_params
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in <listcomp>
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
KeyError: (2, 0)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/tests/integrated/test_distributed_xgboost.py", line 71, in testDistributedXGBClassifier
classifier.fit(X, y, eval_set=[(X, y)], session=sess, run_kwargs=run_kwargs)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/classifier.py", line 55, in fit
result = train(params, dtrain, num_boost_round=self.get_num_boosting_rounds(),
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/train.py", line 200, in train
ret = t.execute(session=session, **run_kwargs).fetch(session=session)
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 379, in execute
return run()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 374, in run
session.run(self, **kw)
File "/Users/wenjun.swj/Code/mars/mars/session.py", line 499, in run
result = self._sess.run(*tileables, **kw)
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 214, in run
if self._check_response_finished(graph_url, timeout_val):
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 174, in _check_response_finished
raise ExecutionFailed('Graph execution failed.') from exc
mars.errors.ExecutionFailed: 'Graph execution failed.'
|
KeyError
|
def add_finished_predecessor(
self, op_key, worker, output_sizes=None, output_shapes=None
):
super().add_finished_predecessor(
op_key, worker, output_sizes=output_sizes, output_shapes=output_shapes
)
from ..chunkmeta import WorkerMeta
chunk_key = next(iter(output_sizes.keys()))[0]
self._mapper_op_to_chunk[op_key] = chunk_key
if op_key not in self._worker_to_mappers[worker]:
self._worker_to_mappers[worker].add(op_key)
self.chunk_meta.add_worker(self._session_id, chunk_key, worker, _tell=True)
shuffle_keys_to_op = self._shuffle_keys_to_op
if not self._reducer_workers:
self._reducer_workers = self._graph_refs[0].assign_operand_workers(
self._succ_keys, input_chunk_metas=self._reducer_to_mapper
)
reducer_workers = self._reducer_workers
data_to_addresses = dict()
unused_keys = []
for (chunk_key, shuffle_key), data_size in output_sizes.items() or ():
if shuffle_key not in shuffle_keys_to_op:
# outputs may be pruned, hence those keys become useless
unused_keys.append((chunk_key, shuffle_key))
continue
succ_op_key = shuffle_keys_to_op[shuffle_key]
meta = self._reducer_to_mapper[succ_op_key][op_key] = WorkerMeta(
chunk_size=data_size,
workers=(worker,),
chunk_shape=output_shapes.get((chunk_key, shuffle_key)),
)
reducer_worker = reducer_workers.get(succ_op_key)
if reducer_worker and reducer_worker != worker:
data_to_addresses[(chunk_key, shuffle_key)] = [reducer_worker]
meta.workers += (reducer_worker,)
if unused_keys:
self._free_data_in_worker(unused_keys, [(worker,)] * len(unused_keys))
if data_to_addresses:
try:
with rewrite_worker_errors():
self._get_raw_execution_ref(address=worker).send_data_to_workers(
self._session_id, data_to_addresses, _tell=True
)
except WorkerDead:
self._resource_ref.detach_dead_workers([worker], _tell=True)
if all(k in self._finish_preds for k in self._pred_keys):
self._start_successors()
|
def add_finished_predecessor(
self, op_key, worker, output_sizes=None, output_shapes=None
):
super().add_finished_predecessor(
op_key, worker, output_sizes=output_sizes, output_shapes=output_shapes
)
from ..chunkmeta import WorkerMeta
chunk_key = next(iter(output_sizes.keys()))[0]
self._mapper_op_to_chunk[op_key] = chunk_key
if op_key not in self._worker_to_mappers[worker]:
self._worker_to_mappers[worker].add(op_key)
self.chunk_meta.add_worker(self._session_id, chunk_key, worker, _tell=True)
shuffle_keys_to_op = self._shuffle_keys_to_op
if not self._reducer_workers:
self._reducer_workers = self._graph_refs[0].assign_operand_workers(
self._succ_keys, input_chunk_metas=self._reducer_to_mapper
)
reducer_workers = self._reducer_workers
data_to_addresses = dict()
for (chunk_key, shuffle_key), data_size in output_sizes.items() or ():
succ_op_key = shuffle_keys_to_op[shuffle_key]
meta = self._reducer_to_mapper[succ_op_key][op_key] = WorkerMeta(
chunk_size=data_size,
workers=(worker,),
chunk_shape=output_shapes.get((chunk_key, shuffle_key)),
)
reducer_worker = reducer_workers.get(succ_op_key)
if reducer_worker and reducer_worker != worker:
data_to_addresses[(chunk_key, shuffle_key)] = [reducer_worker]
meta.workers += (reducer_worker,)
if data_to_addresses:
try:
with rewrite_worker_errors():
self._get_raw_execution_ref(address=worker).send_data_to_workers(
self._session_id, data_to_addresses, _tell=True
)
except WorkerDead:
self._resource_ref.detach_dead_workers([worker], _tell=True)
if all(k in self._finish_preds for k in self._pred_keys):
self._start_successors()
|
https://github.com/mars-project/mars/issues/1740
|
Error
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 377, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 646, in prepare_graph
cur_chunk_graph = chunk_graph_builder.build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 347, in build
chunk_graph = super().build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/Users/wenjun.swj/Code/mars/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 257, in tile
return cls._tile_single_output(op)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/dmatrix.py", line 227, in _tile_single_output
data_chunk = concat_chunks(chunks[0])
File "/Users/wenjun.swj/Code/mars/mars/learn/utils/core.py", line 33, in concat_chunks
tileable = chunks[0].op.create_tileable_from_chunks(chunks)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 212, in create_tileable_from_chunks
params = cls._calc_dataframe_params(chunk_index_to_chunk, chunk_shape)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in _calc_dataframe_params
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/operands.py", line 228, in <listcomp>
pd_indxes = [chunk_index_to_chunks[i, 0].index_value.to_pandas()
KeyError: (2, 0)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/tests/integrated/test_distributed_xgboost.py", line 71, in testDistributedXGBClassifier
classifier.fit(X, y, eval_set=[(X, y)], session=sess, run_kwargs=run_kwargs)
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/classifier.py", line 55, in fit
result = train(params, dtrain, num_boost_round=self.get_num_boosting_rounds(),
File "/Users/wenjun.swj/Code/mars/mars/learn/contrib/xgboost/train.py", line 200, in train
ret = t.execute(session=session, **run_kwargs).fetch(session=session)
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 379, in execute
return run()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 374, in run
session.run(self, **kw)
File "/Users/wenjun.swj/Code/mars/mars/session.py", line 499, in run
result = self._sess.run(*tileables, **kw)
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 214, in run
if self._check_response_finished(graph_url, timeout_val):
File "/Users/wenjun.swj/Code/mars/mars/web/session.py", line 174, in _check_response_finished
raise ExecutionFailed('Graph execution failed.') from exc
mars.errors.ExecutionFailed: 'Graph execution failed.'
|
KeyError
|
def all(a, axis=None, out=None, keepdims=None, combine_size=None):
"""
Test whether all array elements along a given axis evaluate to True.
Parameters
----------
a : array_like
Input tensor or object that can be converted to a tensor.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical AND reduction is performed.
The default (`axis` = `None`) is to perform a logical AND over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : Tensor, optional
Alternate output tensor in which to place the result.
It must have the same shape as the expected output and its
type is preserved (e.g., if ``dtype(out)`` is float, the result
will consist of 0.0's and 1.0's). See `doc.ufuncs` (Section
"Output arguments") for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input tensor.
If the default value is passed, then `keepdims` will not be
passed through to the `all` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
all : Tensor, bool
A new boolean or tensor is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
Tensor.all : equivalent method
any : Test whether any element along a given axis evaluates to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to `True` because these are not equal to zero.
Examples
--------
>>> import mars.tensor as mt
>>> mt.all([[True,False],[True,True]]).execute()
False
>>> mt.all([[True,False],[True,True]], axis=0).execute()
array([ True, False])
>>> mt.all([-1, 4, 5]).execute()
True
>>> mt.all([1.0, mt.nan]).execute()
True
"""
a = astensor(a)
if a.dtype == np.object_:
dtype = a.dtype
else:
dtype = np.dtype(bool)
op = TensorAll(axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size)
return op(a, out=out)
|
def all(a, axis=None, out=None, keepdims=None, combine_size=None):
"""
Test whether all array elements along a given axis evaluate to True.
Parameters
----------
a : array_like
Input tensor or object that can be converted to a tensor.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical AND reduction is performed.
The default (`axis` = `None`) is to perform a logical AND over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : Tensor, optional
Alternate output tensor in which to place the result.
It must have the same shape as the expected output and its
type is preserved (e.g., if ``dtype(out)`` is float, the result
will consist of 0.0's and 1.0's). See `doc.ufuncs` (Section
"Output arguments") for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input tensor.
If the default value is passed, then `keepdims` will not be
passed through to the `all` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
all : Tensor, bool
A new boolean or tensor is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
Tensor.all : equivalent method
any : Test whether any element along a given axis evaluates to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to `True` because these are not equal to zero.
Examples
--------
>>> import mars.tensor as mt
>>> mt.all([[True,False],[True,True]]).execute()
False
>>> mt.all([[True,False],[True,True]], axis=0).execute()
array([ True, False])
>>> mt.all([-1, 4, 5]).execute()
True
>>> mt.all([1.0, mt.nan]).execute()
True
"""
a = astensor(a)
op = TensorAll(
axis=axis, dtype=np.dtype(bool), keepdims=keepdims, combine_size=combine_size
)
return op(a, out=out)
|
https://github.com/mars-project/mars/issues/1743
|
In [5]: a = mt.tensor(['a', 'b', 'c'], dtype=object)
In [6]: a.max().execute()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-d9ebfaf2dc7b> in <module>
----> 1 a.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
641
642 if wait:
--> 643 return run()
644 else:
645 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
637
638 def run():
--> 639 self.data.execute(session, **kw)
640 return self
641
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
377
378 if wait:
--> 379 return run()
380 else:
381 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
372 def run():
373 # no more fetch, thus just fire run
--> 374 session.run(self, **kw)
375 # return Tileable or ExecutableTuple itself
376 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
497 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
498 for t in tileables)
--> 499 result = self._sess.run(*tileables, **kw)
500
501 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/tensor/reduction/core.py in execute(cls, ctx, op)
288 return cls.execute_agg(ctx, op)
289 else:
--> 290 return cls.execute_one_chunk(ctx, op)
291
292
~/Workspace/mars/mars/tensor/reduction/core.py in execute_one_chunk(cls, ctx, op)
277 @classmethod
278 def execute_one_chunk(cls, ctx, op):
--> 279 cls.execute_agg(ctx, op)
280
281 @classmethod
~/Workspace/mars/mars/tensor/reduction/core.py in execute_agg(cls, ctx, op)
273 keepdims=bool(op.keepdims))
274
--> 275 ctx[out.key] = ret.astype(op.dtype, order=out.order.value, copy=False)
276
277 @classmethod
AttributeError: 'str' object has no attribute 'astype'
|
AttributeError
|
def any(a, axis=None, out=None, keepdims=None, combine_size=None):
"""
Test whether any tensor element along a given axis evaluates to True.
Returns single boolean unless `axis` is not ``None``
Parameters
----------
a : array_like
Input tensor or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical OR reduction is performed.
The default (`axis` = `None`) is to perform a logical OR over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : Tensor, optional
Alternate output tensor in which to place the result. It must have
the same shape as the expected output and its type is preserved
(e.g., if it is of type float, then it will remain so, returning
1.0 for True and 0.0 for False, regardless of the type of `a`).
See `doc.ufuncs` (Section "Output arguments") for details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input tensor.
If the default value is passed, then `keepdims` will not be
passed through to the `any` method of sub-classes of
`Tensor`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
any : bool or Tensor
A new boolean or `Tensor` is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
Tensor.any : equivalent method
all : Test whether all elements along a given axis evaluate to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity evaluate
to `True` because these are not equal to zero.
Examples
--------
>>> import mars.tensor as mt
>>> mt.any([[True, False], [True, True]]).execute()
True
>>> mt.any([[True, False], [False, False]], axis=0).execute()
array([ True, False])
>>> mt.any([-1, 0, 5]).execute()
True
>>> mt.any(mt.nan).execute()
True
"""
a = astensor(a)
if a.dtype == np.object_:
dtype = a.dtype
else:
dtype = np.dtype(bool)
op = TensorAny(axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size)
return op(a, out=out)
|
def any(a, axis=None, out=None, keepdims=None, combine_size=None):
"""
Test whether any tensor element along a given axis evaluates to True.
Returns single boolean unless `axis` is not ``None``
Parameters
----------
a : array_like
Input tensor or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical OR reduction is performed.
The default (`axis` = `None`) is to perform a logical OR over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : Tensor, optional
Alternate output tensor in which to place the result. It must have
the same shape as the expected output and its type is preserved
(e.g., if it is of type float, then it will remain so, returning
1.0 for True and 0.0 for False, regardless of the type of `a`).
See `doc.ufuncs` (Section "Output arguments") for details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input tensor.
If the default value is passed, then `keepdims` will not be
passed through to the `any` method of sub-classes of
`Tensor`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
any : bool or Tensor
A new boolean or `Tensor` is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
Tensor.any : equivalent method
all : Test whether all elements along a given axis evaluate to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity evaluate
to `True` because these are not equal to zero.
Examples
--------
>>> import mars.tensor as mt
>>> mt.any([[True, False], [True, True]]).execute()
True
>>> mt.any([[True, False], [False, False]], axis=0).execute()
array([ True, False])
>>> mt.any([-1, 0, 5]).execute()
True
>>> mt.any(mt.nan).execute()
True
"""
a = astensor(a)
op = TensorAny(
axis=axis, dtype=np.dtype(bool), keepdims=keepdims, combine_size=combine_size
)
return op(a, out=out)
|
https://github.com/mars-project/mars/issues/1743
|
In [5]: a = mt.tensor(['a', 'b', 'c'], dtype=object)
In [6]: a.max().execute()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-d9ebfaf2dc7b> in <module>
----> 1 a.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
641
642 if wait:
--> 643 return run()
644 else:
645 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
637
638 def run():
--> 639 self.data.execute(session, **kw)
640 return self
641
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
377
378 if wait:
--> 379 return run()
380 else:
381 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
372 def run():
373 # no more fetch, thus just fire run
--> 374 session.run(self, **kw)
375 # return Tileable or ExecutableTuple itself
376 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
497 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
498 for t in tileables)
--> 499 result = self._sess.run(*tileables, **kw)
500
501 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/tensor/reduction/core.py in execute(cls, ctx, op)
288 return cls.execute_agg(ctx, op)
289 else:
--> 290 return cls.execute_one_chunk(ctx, op)
291
292
~/Workspace/mars/mars/tensor/reduction/core.py in execute_one_chunk(cls, ctx, op)
277 @classmethod
278 def execute_one_chunk(cls, ctx, op):
--> 279 cls.execute_agg(ctx, op)
280
281 @classmethod
~/Workspace/mars/mars/tensor/reduction/core.py in execute_agg(cls, ctx, op)
273 keepdims=bool(op.keepdims))
274
--> 275 ctx[out.key] = ret.astype(op.dtype, order=out.order.value, copy=False)
276
277 @classmethod
AttributeError: 'str' object has no attribute 'astype'
|
AttributeError
|
def execute_agg(cls, ctx, op):
(input_chunk,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
axis = cls.get_axis(op.axis)
func_name = getattr(cls, "_func_name", None)
reduce_func = getattr(xp, func_name)
out = op.outputs[0]
with device(device_id):
if "dtype" in inspect.getfullargspec(reduce_func).args:
ret = reduce_func(
input_chunk, axis=axis, dtype=op.dtype, keepdims=bool(op.keepdims)
)
else:
ret = reduce_func(input_chunk, axis=axis, keepdims=bool(op.keepdims))
if hasattr(ret, "astype"):
# for non-object dtype
ret = ret.astype(op.dtype, order=out.order.value, copy=False)
ctx[out.key] = ret
|
def execute_agg(cls, ctx, op):
(input_chunk,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
axis = cls.get_axis(op.axis)
func_name = getattr(cls, "_func_name", None)
reduce_func = getattr(xp, func_name)
out = op.outputs[0]
with device(device_id):
if "dtype" in inspect.getfullargspec(reduce_func).args:
ret = reduce_func(
input_chunk, axis=axis, dtype=op.dtype, keepdims=bool(op.keepdims)
)
else:
ret = reduce_func(input_chunk, axis=axis, keepdims=bool(op.keepdims))
ctx[out.key] = ret.astype(op.dtype, order=out.order.value, copy=False)
|
https://github.com/mars-project/mars/issues/1743
|
In [5]: a = mt.tensor(['a', 'b', 'c'], dtype=object)
In [6]: a.max().execute()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-d9ebfaf2dc7b> in <module>
----> 1 a.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
641
642 if wait:
--> 643 return run()
644 else:
645 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
637
638 def run():
--> 639 self.data.execute(session, **kw)
640 return self
641
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
377
378 if wait:
--> 379 return run()
380 else:
381 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
372 def run():
373 # no more fetch, thus just fire run
--> 374 session.run(self, **kw)
375 # return Tileable or ExecutableTuple itself
376 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
497 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
498 for t in tileables)
--> 499 result = self._sess.run(*tileables, **kw)
500
501 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/tensor/reduction/core.py in execute(cls, ctx, op)
288 return cls.execute_agg(ctx, op)
289 else:
--> 290 return cls.execute_one_chunk(ctx, op)
291
292
~/Workspace/mars/mars/tensor/reduction/core.py in execute_one_chunk(cls, ctx, op)
277 @classmethod
278 def execute_one_chunk(cls, ctx, op):
--> 279 cls.execute_agg(ctx, op)
280
281 @classmethod
~/Workspace/mars/mars/tensor/reduction/core.py in execute_agg(cls, ctx, op)
273 keepdims=bool(op.keepdims))
274
--> 275 ctx[out.key] = ret.astype(op.dtype, order=out.order.value, copy=False)
276
277 @classmethod
AttributeError: 'str' object has no attribute 'astype'
|
AttributeError
|
def execute_map(cls, ctx, op):
arg_axis = cls.get_arg_axis(op.axis, op.inputs[0].ndim)
(in_chunk,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
func_name = getattr(cls, "_func_name")
agg_func_name = getattr(cls, "_agg_func_name")
arg_func = getattr(xp, func_name)
agg_func_name = getattr(xp, agg_func_name)
offset = op.offset
chunk = op.outputs[0]
with device(device_id):
vals = agg_func_name(in_chunk, axis=arg_axis)
if hasattr(vals, "reshape"):
vals = vals.reshape(chunk.shape)
try:
arg = arg_func(in_chunk, axis=arg_axis)
if hasattr(arg, "reshape"):
arg = arg.reshape(chunk.shape)
except ValueError:
# handle all NaN
arg = arg_func(
xp.where(xp.isnan(in_chunk), np.inf, in_chunk), axis=arg_axis
).reshape(chunk.shape)
if arg_axis is None:
if xp == cp:
# we need to copy to do cpu computation, then copy back to gpu
# cuz unravel_index and ravel_multi_index are not implemented in cupy
in_chunk = in_chunk.get()
total_shape = op.total_shape
ind = np.unravel_index(arg.ravel()[0], in_chunk.shape)
total_ind = tuple(o + i for (o, i) in zip(offset, ind))
res = np.ravel_multi_index(total_ind, total_shape)
if xp == cp:
# copy back
with xp.cuda.Device(in_chunk.device.id):
arg[:] = xp.asarray(res)
else:
arg[:] = res
else:
arg += offset
ctx[op.outputs[0].key] = (vals, arg)
|
def execute_map(cls, ctx, op):
arg_axis = cls.get_arg_axis(op.axis, op.inputs[0].ndim)
(in_chunk,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
func_name = getattr(cls, "_func_name")
agg_func_name = getattr(cls, "_agg_func_name")
arg_func = getattr(xp, func_name)
agg_func_name = getattr(xp, agg_func_name)
offset = op.offset
chunk = op.outputs[0]
with device(device_id):
vals = agg_func_name(in_chunk, axis=arg_axis).reshape(chunk.shape)
try:
arg = arg_func(in_chunk, axis=arg_axis).reshape(chunk.shape)
except ValueError:
# handle all NaN
arg = arg_func(
xp.where(xp.isnan(in_chunk), np.inf, in_chunk), axis=arg_axis
).reshape(chunk.shape)
if arg_axis is None:
if xp == cp:
# we need to copy to do cpu computation, then copy back to gpu
# cuz unravel_index and ravel_multi_index are not implemented in cupy
in_chunk = in_chunk.get()
total_shape = op.total_shape
ind = np.unravel_index(arg.ravel()[0], in_chunk.shape)
total_ind = tuple(o + i for (o, i) in zip(offset, ind))
res = np.ravel_multi_index(total_ind, total_shape)
if xp == cp:
# copy back
with xp.cuda.Device(in_chunk.device.id):
arg[:] = xp.asarray(res)
else:
arg[:] = res
else:
arg += offset
ctx[op.outputs[0].key] = (vals, arg)
|
https://github.com/mars-project/mars/issues/1743
|
In [5]: a = mt.tensor(['a', 'b', 'c'], dtype=object)
In [6]: a.max().execute()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-d9ebfaf2dc7b> in <module>
----> 1 a.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
641
642 if wait:
--> 643 return run()
644 else:
645 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
637
638 def run():
--> 639 self.data.execute(session, **kw)
640 return self
641
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
377
378 if wait:
--> 379 return run()
380 else:
381 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
372 def run():
373 # no more fetch, thus just fire run
--> 374 session.run(self, **kw)
375 # return Tileable or ExecutableTuple itself
376 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
497 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
498 for t in tileables)
--> 499 result = self._sess.run(*tileables, **kw)
500
501 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/tensor/reduction/core.py in execute(cls, ctx, op)
288 return cls.execute_agg(ctx, op)
289 else:
--> 290 return cls.execute_one_chunk(ctx, op)
291
292
~/Workspace/mars/mars/tensor/reduction/core.py in execute_one_chunk(cls, ctx, op)
277 @classmethod
278 def execute_one_chunk(cls, ctx, op):
--> 279 cls.execute_agg(ctx, op)
280
281 @classmethod
~/Workspace/mars/mars/tensor/reduction/core.py in execute_agg(cls, ctx, op)
273 keepdims=bool(op.keepdims))
274
--> 275 ctx[out.key] = ret.astype(op.dtype, order=out.order.value, copy=False)
276
277 @classmethod
AttributeError: 'str' object has no attribute 'astype'
|
AttributeError
|
def tile(cls, op):
from ..indexing.slice import TensorSlice
in_tensor = op.inputs[0]
out_tensor = op.outputs[0]
axis = op.axis
if not isinstance(axis, int):
raise ValueError("axis must be a integer")
axis = validate_axis(in_tensor.ndim, axis)
if axis is None:
raise NotImplementedError
op_type, bin_op_type = getattr(op, "_get_op_types")()
chunks = []
for c in in_tensor.chunks:
chunk_op = op_type(axis=op.axis, dtype=op.dtype)
chunks.append(
chunk_op.new_chunk(
[c], shape=c.shape, index=c.index, order=out_tensor.order
)
)
inter_tensor = copy.copy(in_tensor)
inter_tensor._chunks = chunks
slc = tuple(
slice(None) if i != axis else slice(-1, None) for i in range(in_tensor.ndim)
)
output_chunks = []
for chunk in chunks:
if chunk.index[axis] == 0:
output_chunks.append(chunk)
continue
to_cum_chunks = []
for i in range(chunk.index[axis]):
to_cum_index = chunk.index[:axis] + (i,) + chunk.index[axis + 1 :]
shape = chunk.shape[:axis] + (1,) + chunk.shape[axis + 1 :]
to_cum_chunk = inter_tensor.cix[to_cum_index]
slice_op = TensorSlice(slices=slc, dtype=chunk.dtype)
sliced_chunk = slice_op.new_chunk(
[to_cum_chunk], shape=shape, index=to_cum_index, order=out_tensor.order
)
to_cum_chunks.append(sliced_chunk)
to_cum_chunks.append(chunk)
bin_op = bin_op_type(dtype=chunk.dtype)
output_chunk = bin_op.new_chunk(
to_cum_chunks, shape=chunk.shape, index=chunk.index, order=out_tensor.order
)
output_chunks.append(output_chunk)
new_op = op.copy()
return new_op.new_tensors(
op.inputs,
in_tensor.shape,
order=out_tensor.order,
chunks=output_chunks,
nsplits=in_tensor.nsplits,
)
|
def tile(cls, op):
from ..indexing.slice import TensorSlice
in_tensor = op.inputs[0]
out_tensor = op.outputs[0]
axis = op.axis
if not isinstance(axis, int):
raise ValueError("axis must be a integer")
axis = validate_axis(in_tensor.ndim, axis)
if axis is None:
raise NotImplementedError
op_type, bin_op_type = getattr(op, "_get_op_types")()
chunks = []
for c in in_tensor.chunks:
chunk_op = op_type(axis=op.axis, dtype=op.dtype)
chunks.append(
chunk_op.new_chunk(
[c], shape=c.shape, index=c.index, order=out_tensor.order
)
)
inter_tensor = copy.copy(in_tensor)
inter_tensor._chunks = chunks
slc = tuple(
slice(None) if i != axis else slice(-1, None) for i in range(in_tensor.ndim)
)
output_chunks = []
for chunk in chunks:
if chunk.index[axis] == 0:
output_chunks.append(chunk)
continue
to_cum_chunks = [chunk]
for i in range(chunk.index[axis]):
to_cum_index = chunk.index[:axis] + (i,) + chunk.index[axis + 1 :]
shape = chunk.shape[:axis] + (1,) + chunk.shape[axis + 1 :]
to_cum_chunk = inter_tensor.cix[to_cum_index]
slice_op = TensorSlice(slices=slc, dtype=chunk.dtype)
sliced_chunk = slice_op.new_chunk(
[to_cum_chunk], shape=shape, index=to_cum_index, order=out_tensor.order
)
to_cum_chunks.append(sliced_chunk)
bin_op = bin_op_type(dtype=chunk.dtype)
output_chunk = bin_op.new_chunk(
to_cum_chunks, shape=chunk.shape, index=chunk.index, order=out_tensor.order
)
output_chunks.append(output_chunk)
new_op = op.copy()
return new_op.new_tensors(
op.inputs,
in_tensor.shape,
order=out_tensor.order,
chunks=output_chunks,
nsplits=in_tensor.nsplits,
)
|
https://github.com/mars-project/mars/issues/1743
|
In [5]: a = mt.tensor(['a', 'b', 'c'], dtype=object)
In [6]: a.max().execute()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-d9ebfaf2dc7b> in <module>
----> 1 a.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
641
642 if wait:
--> 643 return run()
644 else:
645 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
637
638 def run():
--> 639 self.data.execute(session, **kw)
640 return self
641
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
377
378 if wait:
--> 379 return run()
380 else:
381 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
372 def run():
373 # no more fetch, thus just fire run
--> 374 session.run(self, **kw)
375 # return Tileable or ExecutableTuple itself
376 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
497 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
498 for t in tileables)
--> 499 result = self._sess.run(*tileables, **kw)
500
501 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/tensor/reduction/core.py in execute(cls, ctx, op)
288 return cls.execute_agg(ctx, op)
289 else:
--> 290 return cls.execute_one_chunk(ctx, op)
291
292
~/Workspace/mars/mars/tensor/reduction/core.py in execute_one_chunk(cls, ctx, op)
277 @classmethod
278 def execute_one_chunk(cls, ctx, op):
--> 279 cls.execute_agg(ctx, op)
280
281 @classmethod
~/Workspace/mars/mars/tensor/reduction/core.py in execute_agg(cls, ctx, op)
273 keepdims=bool(op.keepdims))
274
--> 275 ctx[out.key] = ret.astype(op.dtype, order=out.order.value, copy=False)
276
277 @classmethod
AttributeError: 'str' object has no attribute 'astype'
|
AttributeError
|
def sum(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None):
"""
Sum of tensor elements over a given axis.
Parameters
----------
a : array_like
Elements to sum.
axis : None or int or tuple of ints, optional
Axis or axes along which a sum is performed. The default,
axis=None, will sum all of the elements of the input tensor. If
axis is negative it counts from the last to the first axis.
If axis is a tuple of ints, a sum is performed on all of the axes
specified in the tuple instead of a single axis or all the axes as
before.
dtype : dtype, optional
The type of the returned tensor and of the accumulator in which the
elements are summed. The dtype of `a` is used by default unless `a`
has an integer dtype of less precision than the default platform
integer. In that case, if `a` is signed then the platform integer
is used while if `a` is unsigned then an unsigned integer of the
same precision as the platform integer is used.
out : Tensor, optional
Alternative output tensor in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input tensor.
If the default value is passed, then `keepdims` will not be
passed through to the `sum` method of sub-classes of
`Tensor`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
sum_along_axis : Tensor
An array with the same shape as `a`, with the specified
axis removed. If `a` is a 0-d tensor, or if `axis` is None, a scalar
is returned. If an output array is specified, a reference to
`out` is returned.
See Also
--------
Tensor.sum : Equivalent method.
cumsum : Cumulative sum of tensor elements.
trapz : Integration of tensor values using the composite trapezoidal rule.
mean, average
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
The sum of an empty array is the neutral element 0:
>>> import mars.tensor as mt
>>> mt.sum([]).execute()
0.0
Examples
--------
>>> mt.sum([0.5, 1.5]).execute()
2.0
>>> mt.sum([0.5, 0.7, 0.2, 1.5], dtype=mt.int32).execute()
1
>>> mt.sum([[0, 1], [0, 5]]).execute()
6
>>> mt.sum([[0, 1], [0, 5]], axis=0).execute()
array([0, 6])
>>> mt.sum([[0, 1], [0, 5]], axis=1).execute()
array([1, 5])
If the accumulator is too small, overflow occurs:
>>> mt.ones(128, dtype=mt.int8).sum(dtype=mt.int8).execute()
-128
"""
a = astensor(a)
if dtype is None:
if a.dtype == np.object_:
dtype = a.dtype
else:
dtype = np.empty((1,), dtype=a.dtype).sum().dtype
else:
dtype = np.dtype(dtype)
op = TensorSum(axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size)
return op(a, out=out)
|
def sum(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None):
"""
Sum of tensor elements over a given axis.
Parameters
----------
a : array_like
Elements to sum.
axis : None or int or tuple of ints, optional
Axis or axes along which a sum is performed. The default,
axis=None, will sum all of the elements of the input tensor. If
axis is negative it counts from the last to the first axis.
If axis is a tuple of ints, a sum is performed on all of the axes
specified in the tuple instead of a single axis or all the axes as
before.
dtype : dtype, optional
The type of the returned tensor and of the accumulator in which the
elements are summed. The dtype of `a` is used by default unless `a`
has an integer dtype of less precision than the default platform
integer. In that case, if `a` is signed then the platform integer
is used while if `a` is unsigned then an unsigned integer of the
same precision as the platform integer is used.
out : Tensor, optional
Alternative output tensor in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input tensor.
If the default value is passed, then `keepdims` will not be
passed through to the `sum` method of sub-classes of
`Tensor`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
sum_along_axis : Tensor
An array with the same shape as `a`, with the specified
axis removed. If `a` is a 0-d tensor, or if `axis` is None, a scalar
is returned. If an output array is specified, a reference to
`out` is returned.
See Also
--------
Tensor.sum : Equivalent method.
cumsum : Cumulative sum of tensor elements.
trapz : Integration of tensor values using the composite trapezoidal rule.
mean, average
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
The sum of an empty array is the neutral element 0:
>>> import mars.tensor as mt
>>> mt.sum([]).execute()
0.0
Examples
--------
>>> mt.sum([0.5, 1.5]).execute()
2.0
>>> mt.sum([0.5, 0.7, 0.2, 1.5], dtype=mt.int32).execute()
1
>>> mt.sum([[0, 1], [0, 5]]).execute()
6
>>> mt.sum([[0, 1], [0, 5]], axis=0).execute()
array([0, 6])
>>> mt.sum([[0, 1], [0, 5]], axis=1).execute()
array([1, 5])
If the accumulator is too small, overflow occurs:
>>> mt.ones(128, dtype=mt.int8).sum(dtype=mt.int8).execute()
-128
"""
a = astensor(a)
if dtype is None:
dtype = np.empty((1,), dtype=a.dtype).sum().dtype
else:
dtype = np.dtype(dtype)
op = TensorSum(axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size)
return op(a, out=out)
|
https://github.com/mars-project/mars/issues/1743
|
In [5]: a = mt.tensor(['a', 'b', 'c'], dtype=object)
In [6]: a.max().execute()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-d9ebfaf2dc7b> in <module>
----> 1 a.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
641
642 if wait:
--> 643 return run()
644 else:
645 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
637
638 def run():
--> 639 self.data.execute(session, **kw)
640 return self
641
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
377
378 if wait:
--> 379 return run()
380 else:
381 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
372 def run():
373 # no more fetch, thus just fire run
--> 374 session.run(self, **kw)
375 # return Tileable or ExecutableTuple itself
376 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
497 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
498 for t in tileables)
--> 499 result = self._sess.run(*tileables, **kw)
500
501 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/tensor/reduction/core.py in execute(cls, ctx, op)
288 return cls.execute_agg(ctx, op)
289 else:
--> 290 return cls.execute_one_chunk(ctx, op)
291
292
~/Workspace/mars/mars/tensor/reduction/core.py in execute_one_chunk(cls, ctx, op)
277 @classmethod
278 def execute_one_chunk(cls, ctx, op):
--> 279 cls.execute_agg(ctx, op)
280
281 @classmethod
~/Workspace/mars/mars/tensor/reduction/core.py in execute_agg(cls, ctx, op)
273 keepdims=bool(op.keepdims))
274
--> 275 ctx[out.key] = ret.astype(op.dtype, order=out.order.value, copy=False)
276
277 @classmethod
AttributeError: 'str' object has no attribute 'astype'
|
AttributeError
|
def read_csv(
path,
names=None,
sep=",",
index_col=None,
compression=None,
header="infer",
dtype=None,
usecols=None,
nrows=None,
chunk_bytes="64M",
gpu=None,
head_bytes="100k",
head_lines=None,
incremental_index=False,
use_arrow_dtype=None,
storage_options=None,
**kwargs,
):
r"""
Read a comma-separated values (csv) file into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
Parameters
----------
path : str
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be: file://localhost/path/to/table.csv,
you can alos read from external resources using a URL like:
hdfs://localhost:8020/test.csv.
If you want to pass in a path object, pandas accepts any ``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method, such as
a file handler (e.g. via builtin ``open`` function) or ``StringIO``.
sep : str, default ','
Delimiter to use. If sep is None, the C engine cannot automatically detect
the separator, but the Python parsing engine can, meaning the latter will
be used and automatically detect the separator by Python's builtin sniffer
tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
different from ``'\s+'`` will be interpreted as regular expressions and
will also force the use of the Python parsing engine. Note that regex
delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, default ``None``
Alias for sep.
header : int, list of int, default 'infer'
Row number(s) to use as the column names, and the start of the
data. Default behavior is to infer the column names: if no names
are passed the behavior is identical to ``header=0`` and column
names are inferred from the first line of the file, if column
names are passed explicitly then the behavior is identical to
``header=None``. Explicitly pass ``header=0`` to be able to
replace existing names. The header can be a list of integers that
specify row locations for a multi-index on the columns
e.g. [0,1,3]. Intervening rows that are not specified will be
skipped (e.g. 2 in this example is skipped). Note that this
parameter ignores commented lines and empty lines if
``skip_blank_lines=True``, so ``header=0`` denotes the first line of
data rather than the first line of the file.
names : array-like, optional
List of column names to use. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, default ``None``
Column(s) to use as the row labels of the ``DataFrame``, either given as
string name or column index. If a sequence of int / str is given, a
MultiIndex is used.
Note: ``index_col=False`` can be used to force pandas to *not* use the first
column as the index, e.g. when you have a malformed file with delimiters at
the end of each line.
usecols : list-like or callable, optional
Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or strings
that correspond to column names provided either by the user in `names` or
inferred from the document header row(s). For example, a valid list-like
`usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
To instantiate a DataFrame from ``data`` with element order preserved use
``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
in ``['foo', 'bar']`` order or
``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
for ``['bar', 'foo']`` order.
If callable, the callable function will be evaluated against the column
names, returning names where the callable function evaluates to True. An
example of a valid callable argument would be ``lambda x: x.upper() in
['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
parsing time and lower memory usage.
squeeze : bool, default False
If the parsed data only contains one column then return a Series.
prefix : str, optional
Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
mangle_dupe_cols : bool, default True
Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
'X'...'X'. Passing in False will cause data to be overwritten if there
are duplicate names in the columns.
dtype : Type name or dict of column -> type, optional
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,
'c': 'Int64'}
Use `str` or `object` together with suitable `na_values` settings
to preserve and not interpret dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
engine : {'c', 'python'}, optional
Parser engine to use. The C engine is faster while the python engine is
currently more feature-complete.
converters : dict, optional
Dict of functions for converting values in certain columns. Keys can either
be integers or column labels.
true_values : list, optional
Values to consider as True.
false_values : list, optional
Values to consider as False.
skipinitialspace : bool, default False
Skip spaces after delimiter.
skiprows : list-like, int or callable, optional
Line numbers to skip (0-indexed) or number of lines to skip (int)
at the start of the file.
If callable, the callable function will be evaluated against the row
indices, returning True if the row should be skipped and False otherwise.
An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
skipfooter : int, default 0
Number of lines at bottom of file to skip (Unsupported with engine='c').
nrows : int, optional
Number of rows of file to read. Useful for reading pieces of large files.
na_values : scalar, str, list-like, or dict, optional
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values. By default the following values are interpreted as
NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
'1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',
'nan', 'null'.
keep_default_na : bool, default True
Whether or not to include the default NaN values when parsing the data.
Depending on whether `na_values` is passed in, the behavior is as follows:
* If `keep_default_na` is True, and `na_values` are specified, `na_values`
is appended to the default NaN values used for parsing.
* If `keep_default_na` is True, and `na_values` are not specified, only
the default NaN values are used for parsing.
* If `keep_default_na` is False, and `na_values` are specified, only
the NaN values specified `na_values` are used for parsing.
* If `keep_default_na` is False, and `na_values` are not specified, no
strings will be parsed as NaN.
Note that if `na_filter` is passed in as False, the `keep_default_na` and
`na_values` parameters will be ignored.
na_filter : bool, default True
Detect missing value markers (empty strings and the value of na_values). In
data without any NAs, passing na_filter=False can improve the performance
of reading a large file.
verbose : bool, default False
Indicate number of NA values placed in non-numeric columns.
skip_blank_lines : bool, default True
If True, skip over blank lines rather than interpreting as NaN values.
parse_dates : bool or list of int or names or list of lists or dict, default False
The behavior is as follows:
* boolean. If True -> try parsing the index.
* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
each as a separate date column.
* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as
a single date column.
* dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
result 'foo'
If a column or index cannot be represented as an array of datetimes,
say because of an unparseable value or a mixture of timezones, the column
or index will be returned unaltered as an object data type. For
non-standard datetime parsing, use ``pd.to_datetime`` after
``pd.read_csv``. To parse an index or column with a mixture of timezones,
specify ``date_parser`` to be a partially-applied
:func:`pandas.to_datetime` with ``utc=True``. See
:ref:`io.csv.mixed_timezones` for more.
Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, default False
If True and `parse_dates` is enabled, pandas will attempt to infer the
format of the datetime strings in the columns, and if it can be inferred,
switch to a faster method of parsing them. In some cases this can increase
the parsing speed by 5-10x.
keep_date_col : bool, default False
If True and `parse_dates` specifies combining multiple columns then
keep the original columns.
date_parser : function, optional
Function to use for converting a sequence of string columns to an array of
datetime instances. The default uses ``dateutil.parser.parser`` to do the
conversion. Pandas will try to call `date_parser` in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
string values from the columns defined by `parse_dates` into a single array
and pass that; and 3) call `date_parser` once for each row using one or
more strings (corresponding to the columns defined by `parse_dates`) as
arguments.
dayfirst : bool, default False
DD/MM format dates, international and European format.
cache_dates : bool, default True
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.
.. versionadded:: 0.25.0
iterator : bool, default False
Return TextFileReader object for iteration or getting chunks with
``get_chunk()``.
chunksize : int, optional
Return TextFileReader object for iteration.
See the `IO Tools docs
<https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
for more information on ``iterator`` and ``chunksize``.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
For on-the-fly decompression of on-disk data. If 'infer' and
`filepath_or_buffer` is path-like, then detect compression from the
following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
decompression). If using 'zip', the ZIP file must contain only one data
file to be read in. Set to None for no decompression.
thousands : str, optional
Thousands separator.
decimal : str, default '.'
Character to recognize as decimal point (e.g. use ',' for European data).
lineterminator : str (length 1), optional
Character to break file into lines. Only valid with C parser.
quotechar : str (length 1), optional
The character used to denote the start and end of a quoted item. Quoted
items can include the delimiter and it will be ignored.
quoting : int or csv.QUOTE_* instance, default 0
Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
doublequote : bool, default ``True``
When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
whether or not to interpret two consecutive quotechar elements INSIDE a
field as a single ``quotechar`` element.
escapechar : str (length 1), optional
One-character string used to escape other characters.
comment : str, optional
Indicates remainder of line should not be parsed. If found at the beginning
of a line, the line will be ignored altogether. This parameter must be a
single character. Like empty lines (as long as ``skip_blank_lines=True``),
fully commented lines are ignored by the parameter `header` but not by
`skiprows`. For example, if ``comment='#'``, parsing
``#empty\na,b,c\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
treated as the header.
encoding : str, optional
Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
standard encodings
<https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .
dialect : str or csv.Dialect, optional
If provided, this parameter will override values (default or not) for the
following parameters: `delimiter`, `doublequote`, `escapechar`,
`skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
override values, a ParserWarning will be issued. See csv.Dialect
documentation for more details.
error_bad_lines : bool, default True
Lines with too many fields (e.g. a csv line with too many commas) will by
default cause an exception to be raised, and no DataFrame will be returned.
If False, then these "bad lines" will dropped from the DataFrame that is
returned.
warn_bad_lines : bool, default True
If error_bad_lines is False, and warn_bad_lines is True, a warning for each
"bad line" will be output.
delim_whitespace : bool, default False
Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) will be
used as the sep. Equivalent to setting ``sep='\s+'``. If this option
is set to True, nothing should be passed in for the ``delimiter``
parameter.
low_memory : bool, default True
Internally process the file in chunks, resulting in lower memory use
while parsing, but possibly mixed type inference. To ensure no mixed
types either set False, or specify the type with the `dtype` parameter.
Note that the entire file is read into a single DataFrame regardless,
use the `chunksize` or `iterator` parameter to return the data in chunks.
(Only valid with C parser).
float_precision : str, optional
Specifies which converter the C engine should use for floating-point
values. The options are `None` for the ordinary converter,
`high` for the high-precision converter, and `round_trip` for the
round-trip converter.
chunk_bytes: int, float or str, optional
Number of chunk bytes.
gpu: bool, default False
If read into cudf DataFrame.
head_bytes: int, float or str, optional
Number of bytes to use in the head of file, mainly for data inference.
head_lines: int, optional
Number of lines to use in the head of file, mainly for data inference.
incremental_index: bool, default False
Create a new RangeIndex if csv doesn't contain index columns.
use_arrow_dtype: bool, default None
If True, use arrow dtype to store columns.
storage_options: dict, optional
Options for storage connection.
Returns
-------
DataFrame
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.
See Also
--------
to_csv : Write DataFrame to a comma-separated values (csv) file.
Examples
--------
>>> import mars.dataframe as md
>>> md.read_csv('data.csv') # doctest: +SKIP
>>> # read from HDFS
>>> md.read_csv('hdfs://localhost:8020/test.csv') # doctest: +SKIP
"""
# infer dtypes and columns
if isinstance(path, (list, tuple)):
file_path = path[0]
else:
file_path = glob(path)[0]
with open_file(
file_path, compression=compression, storage_options=storage_options
) as f:
if head_lines is not None:
b = b"".join([f.readline() for _ in range(head_lines)])
else:
head_bytes = int(parse_readable_size(head_bytes)[0])
head_start, head_end = _find_chunk_start_end(f, 0, head_bytes)
f.seek(head_start)
b = f.read(head_end - head_start)
mini_df = pd.read_csv(
BytesIO(b),
sep=sep,
index_col=index_col,
dtype=dtype,
names=names,
header=header,
)
if names is None:
names = list(mini_df.columns)
else:
# if names specified, header should be None
header = None
if usecols:
usecols = usecols if isinstance(usecols, list) else [usecols]
col_index = sorted(mini_df.columns.get_indexer(usecols))
mini_df = mini_df.iloc[:, col_index]
if isinstance(mini_df.index, pd.RangeIndex):
index_value = parse_index(pd.RangeIndex(-1))
else:
index_value = parse_index(mini_df.index)
columns_value = parse_index(mini_df.columns, store_data=True)
if index_col and not isinstance(index_col, int):
index_col = list(mini_df.columns).index(index_col)
op = DataFrameReadCSV(
path=path,
names=names,
sep=sep,
header=header,
index_col=index_col,
usecols=usecols,
compression=compression,
gpu=gpu,
incremental_index=incremental_index,
use_arrow_dtype=use_arrow_dtype,
storage_options=storage_options,
**kwargs,
)
chunk_bytes = chunk_bytes or options.chunk_store_limit
dtypes = mini_df.dtypes
if use_arrow_dtype is None:
use_arrow_dtype = options.dataframe.use_arrow_dtype
if not gpu and use_arrow_dtype:
dtypes = to_arrow_dtypes(dtypes, test_df=mini_df)
ret = op(
index_value=index_value,
columns_value=columns_value,
dtypes=dtypes,
chunk_bytes=chunk_bytes,
)
if nrows is not None:
return ret.head(nrows)
return ret
|
def read_csv(
path,
names=None,
sep=",",
index_col=None,
compression=None,
header="infer",
dtype=None,
usecols=None,
nrows=None,
chunk_bytes="64M",
gpu=None,
head_bytes="100k",
head_lines=None,
incremental_index=False,
use_arrow_dtype=None,
storage_options=None,
**kwargs,
):
r"""
Read a comma-separated values (csv) file into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
Parameters
----------
path : str
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be: file://localhost/path/to/table.csv,
you can alos read from external resources using a URL like:
hdfs://localhost:8020/test.csv.
If you want to pass in a path object, pandas accepts any ``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method, such as
a file handler (e.g. via builtin ``open`` function) or ``StringIO``.
sep : str, default ','
Delimiter to use. If sep is None, the C engine cannot automatically detect
the separator, but the Python parsing engine can, meaning the latter will
be used and automatically detect the separator by Python's builtin sniffer
tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
different from ``'\s+'`` will be interpreted as regular expressions and
will also force the use of the Python parsing engine. Note that regex
delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, default ``None``
Alias for sep.
header : int, list of int, default 'infer'
Row number(s) to use as the column names, and the start of the
data. Default behavior is to infer the column names: if no names
are passed the behavior is identical to ``header=0`` and column
names are inferred from the first line of the file, if column
names are passed explicitly then the behavior is identical to
``header=None``. Explicitly pass ``header=0`` to be able to
replace existing names. The header can be a list of integers that
specify row locations for a multi-index on the columns
e.g. [0,1,3]. Intervening rows that are not specified will be
skipped (e.g. 2 in this example is skipped). Note that this
parameter ignores commented lines and empty lines if
``skip_blank_lines=True``, so ``header=0`` denotes the first line of
data rather than the first line of the file.
names : array-like, optional
List of column names to use. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, default ``None``
Column(s) to use as the row labels of the ``DataFrame``, either given as
string name or column index. If a sequence of int / str is given, a
MultiIndex is used.
Note: ``index_col=False`` can be used to force pandas to *not* use the first
column as the index, e.g. when you have a malformed file with delimiters at
the end of each line.
usecols : list-like or callable, optional
Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or strings
that correspond to column names provided either by the user in `names` or
inferred from the document header row(s). For example, a valid list-like
`usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
To instantiate a DataFrame from ``data`` with element order preserved use
``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
in ``['foo', 'bar']`` order or
``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
for ``['bar', 'foo']`` order.
If callable, the callable function will be evaluated against the column
names, returning names where the callable function evaluates to True. An
example of a valid callable argument would be ``lambda x: x.upper() in
['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
parsing time and lower memory usage.
squeeze : bool, default False
If the parsed data only contains one column then return a Series.
prefix : str, optional
Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
mangle_dupe_cols : bool, default True
Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
'X'...'X'. Passing in False will cause data to be overwritten if there
are duplicate names in the columns.
dtype : Type name or dict of column -> type, optional
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,
'c': 'Int64'}
Use `str` or `object` together with suitable `na_values` settings
to preserve and not interpret dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
engine : {'c', 'python'}, optional
Parser engine to use. The C engine is faster while the python engine is
currently more feature-complete.
converters : dict, optional
Dict of functions for converting values in certain columns. Keys can either
be integers or column labels.
true_values : list, optional
Values to consider as True.
false_values : list, optional
Values to consider as False.
skipinitialspace : bool, default False
Skip spaces after delimiter.
skiprows : list-like, int or callable, optional
Line numbers to skip (0-indexed) or number of lines to skip (int)
at the start of the file.
If callable, the callable function will be evaluated against the row
indices, returning True if the row should be skipped and False otherwise.
An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
skipfooter : int, default 0
Number of lines at bottom of file to skip (Unsupported with engine='c').
nrows : int, optional
Number of rows of file to read. Useful for reading pieces of large files.
na_values : scalar, str, list-like, or dict, optional
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values. By default the following values are interpreted as
NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
'1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',
'nan', 'null'.
keep_default_na : bool, default True
Whether or not to include the default NaN values when parsing the data.
Depending on whether `na_values` is passed in, the behavior is as follows:
* If `keep_default_na` is True, and `na_values` are specified, `na_values`
is appended to the default NaN values used for parsing.
* If `keep_default_na` is True, and `na_values` are not specified, only
the default NaN values are used for parsing.
* If `keep_default_na` is False, and `na_values` are specified, only
the NaN values specified `na_values` are used for parsing.
* If `keep_default_na` is False, and `na_values` are not specified, no
strings will be parsed as NaN.
Note that if `na_filter` is passed in as False, the `keep_default_na` and
`na_values` parameters will be ignored.
na_filter : bool, default True
Detect missing value markers (empty strings and the value of na_values). In
data without any NAs, passing na_filter=False can improve the performance
of reading a large file.
verbose : bool, default False
Indicate number of NA values placed in non-numeric columns.
skip_blank_lines : bool, default True
If True, skip over blank lines rather than interpreting as NaN values.
parse_dates : bool or list of int or names or list of lists or dict, default False
The behavior is as follows:
* boolean. If True -> try parsing the index.
* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
each as a separate date column.
* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as
a single date column.
* dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
result 'foo'
If a column or index cannot be represented as an array of datetimes,
say because of an unparseable value or a mixture of timezones, the column
or index will be returned unaltered as an object data type. For
non-standard datetime parsing, use ``pd.to_datetime`` after
``pd.read_csv``. To parse an index or column with a mixture of timezones,
specify ``date_parser`` to be a partially-applied
:func:`pandas.to_datetime` with ``utc=True``. See
:ref:`io.csv.mixed_timezones` for more.
Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, default False
If True and `parse_dates` is enabled, pandas will attempt to infer the
format of the datetime strings in the columns, and if it can be inferred,
switch to a faster method of parsing them. In some cases this can increase
the parsing speed by 5-10x.
keep_date_col : bool, default False
If True and `parse_dates` specifies combining multiple columns then
keep the original columns.
date_parser : function, optional
Function to use for converting a sequence of string columns to an array of
datetime instances. The default uses ``dateutil.parser.parser`` to do the
conversion. Pandas will try to call `date_parser` in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
string values from the columns defined by `parse_dates` into a single array
and pass that; and 3) call `date_parser` once for each row using one or
more strings (corresponding to the columns defined by `parse_dates`) as
arguments.
dayfirst : bool, default False
DD/MM format dates, international and European format.
cache_dates : bool, default True
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.
.. versionadded:: 0.25.0
iterator : bool, default False
Return TextFileReader object for iteration or getting chunks with
``get_chunk()``.
chunksize : int, optional
Return TextFileReader object for iteration.
See the `IO Tools docs
<https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
for more information on ``iterator`` and ``chunksize``.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
For on-the-fly decompression of on-disk data. If 'infer' and
`filepath_or_buffer` is path-like, then detect compression from the
following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
decompression). If using 'zip', the ZIP file must contain only one data
file to be read in. Set to None for no decompression.
thousands : str, optional
Thousands separator.
decimal : str, default '.'
Character to recognize as decimal point (e.g. use ',' for European data).
lineterminator : str (length 1), optional
Character to break file into lines. Only valid with C parser.
quotechar : str (length 1), optional
The character used to denote the start and end of a quoted item. Quoted
items can include the delimiter and it will be ignored.
quoting : int or csv.QUOTE_* instance, default 0
Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
doublequote : bool, default ``True``
When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
whether or not to interpret two consecutive quotechar elements INSIDE a
field as a single ``quotechar`` element.
escapechar : str (length 1), optional
One-character string used to escape other characters.
comment : str, optional
Indicates remainder of line should not be parsed. If found at the beginning
of a line, the line will be ignored altogether. This parameter must be a
single character. Like empty lines (as long as ``skip_blank_lines=True``),
fully commented lines are ignored by the parameter `header` but not by
`skiprows`. For example, if ``comment='#'``, parsing
``#empty\na,b,c\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
treated as the header.
encoding : str, optional
Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
standard encodings
<https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .
dialect : str or csv.Dialect, optional
If provided, this parameter will override values (default or not) for the
following parameters: `delimiter`, `doublequote`, `escapechar`,
`skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
override values, a ParserWarning will be issued. See csv.Dialect
documentation for more details.
error_bad_lines : bool, default True
Lines with too many fields (e.g. a csv line with too many commas) will by
default cause an exception to be raised, and no DataFrame will be returned.
If False, then these "bad lines" will dropped from the DataFrame that is
returned.
warn_bad_lines : bool, default True
If error_bad_lines is False, and warn_bad_lines is True, a warning for each
"bad line" will be output.
delim_whitespace : bool, default False
Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) will be
used as the sep. Equivalent to setting ``sep='\s+'``. If this option
is set to True, nothing should be passed in for the ``delimiter``
parameter.
low_memory : bool, default True
Internally process the file in chunks, resulting in lower memory use
while parsing, but possibly mixed type inference. To ensure no mixed
types either set False, or specify the type with the `dtype` parameter.
Note that the entire file is read into a single DataFrame regardless,
use the `chunksize` or `iterator` parameter to return the data in chunks.
(Only valid with C parser).
float_precision : str, optional
Specifies which converter the C engine should use for floating-point
values. The options are `None` for the ordinary converter,
`high` for the high-precision converter, and `round_trip` for the
round-trip converter.
chunk_bytes: int, float or str, optional
Number of chunk bytes.
gpu: bool, default False
If read into cudf DataFrame.
head_bytes: int, float or str, optional
Number of bytes to use in the head of file, mainly for data inference.
head_lines: int, optional
Number of lines to use in the head of file, mainly for data inference.
incremental_index: bool, default False
Create a new RangeIndex if csv doesn't contain index columns.
use_arrow_dtype: bool, default None
If True, use arrow dtype to store columns.
storage_options: dict, optional
Options for storage connection.
Returns
-------
DataFrame
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.
See Also
--------
to_csv : Write DataFrame to a comma-separated values (csv) file.
Examples
--------
>>> import mars.dataframe as md
>>> md.read_csv('data.csv') # doctest: +SKIP
>>> # read from HDFS
>>> md.read_csv('hdfs://localhost:8020/test.csv') # doctest: +SKIP
"""
# infer dtypes and columns
if isinstance(path, (list, tuple)):
file_path = path[0]
else:
file_path = glob(path)[0]
with open_file(
file_path, compression=compression, storage_options=storage_options
) as f:
if head_lines is not None:
b = b"".join([f.readline() for _ in range(head_lines)])
else:
head_bytes = int(parse_readable_size(head_bytes)[0])
head_start, head_end = _find_chunk_start_end(f, 0, head_bytes)
f.seek(head_start)
b = f.read(head_end - head_start)
mini_df = pd.read_csv(
BytesIO(b),
sep=sep,
index_col=index_col,
dtype=dtype,
names=names,
header=header,
)
if isinstance(mini_df.index, pd.RangeIndex):
index_value = parse_index(pd.RangeIndex(-1))
else:
index_value = parse_index(mini_df.index)
columns_value = parse_index(mini_df.columns, store_data=True)
if index_col and not isinstance(index_col, int):
index_col = list(mini_df.columns).index(index_col)
names = list(mini_df.columns)
op = DataFrameReadCSV(
path=path,
names=names,
sep=sep,
header=header,
index_col=index_col,
usecols=usecols,
compression=compression,
gpu=gpu,
incremental_index=incremental_index,
use_arrow_dtype=use_arrow_dtype,
storage_options=storage_options,
**kwargs,
)
chunk_bytes = chunk_bytes or options.chunk_store_limit
dtypes = mini_df.dtypes
if use_arrow_dtype is None:
use_arrow_dtype = options.dataframe.use_arrow_dtype
if not gpu and use_arrow_dtype:
dtypes = to_arrow_dtypes(dtypes, test_df=mini_df)
ret = op(
index_value=index_value,
columns_value=columns_value,
dtypes=dtypes,
chunk_bytes=chunk_bytes,
)
if nrows is not None:
return ret.head(nrows)
return ret
|
https://github.com/mars-project/mars/issues/1736
|
In [20]: d.flag.execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-68cd215e82a2> in <module>
----> 1 d.flag.execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
641
642 if wait:
--> 643 return run()
644 else:
645 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
637
638 def run():
--> 639 self.data.execute(session, **kw)
640 return self
641
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
377
378 if wait:
--> 379 return run()
380 else:
381 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
372 def run():
373 # no more fetch, thus just fire run
--> 374 session.run(self, **kw)
375 # return Tileable or ExecutableTuple itself
376 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
497 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
498 for t in tileables)
--> 499 result = self._sess.run(*tileables, **kw)
500
501 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
859 # build chunk graph, tile will be done during building
860 chunk_graph = chunk_graph_builder.build(
--> 861 tileables, tileable_graph=tileable_graph)
862 tileable_graph = chunk_graph_builder.prev_tileable_graph
863 temp_result_keys = set(result_keys)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph)
346
347 chunk_graph = super().build(
--> 348 tileables, tileable_graph=tileable_graph)
349 self._iterative_chunk_graphs.append(chunk_graph)
350 if len(self._interrupted_ops) == 0:
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph)
260 # for further execution
261 partial_tiled_chunks = \
--> 262 self._on_tile_failure(tileable_data.op, exc_info)
263 if partial_tiled_chunks is not None and \
264 len(partial_tiled_chunks) > 0:
~/Workspace/mars/mars/tiles.py in inner(op, exc_info)
299 on_tile_failure(op, exc_info)
300 else:
--> 301 raise exc_info[1].with_traceback(exc_info[2]) from None
302 return inner
303
~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph)
240 continue
241 try:
--> 242 tiled = self._tile(tileable_data, tileable_graph)
243 tiled_op.add(tileable_data.op)
244 for t, td in zip(tileable_data.op.outputs, tiled):
~/Workspace/mars/mars/tiles.py in _tile(self, tileable_data, tileable_graph)
335 if any(inp.op in self._interrupted_ops for inp in tileable_data.inputs):
336 raise TilesError('Tile fail due to failure of inputs')
--> 337 return super()._tile(tileable_data, tileable_graph)
338
339 @enter_mode(build=True, kernel=True)
~/Workspace/mars/mars/tiles.py in _tile(self, tileable_data, tileable_graph)
199 t._nsplits = o.nsplits
200 elif on_tile is None:
--> 201 tds[0]._inplace_tile()
202 else:
203 tds = on_tile(tileable_data.op.outputs, tds)
~/Workspace/mars/mars/core.py in _inplace_tile(self)
166
167 def _inplace_tile(self):
--> 168 return handler.inplace_tile(self)
169
170 def __getattr__(self, attr):
~/Workspace/mars/mars/tiles.py in inplace_tile(self, to_tile)
134 if not to_tile.is_coarse():
135 return to_tile
--> 136 dispatched = self.dispatch(to_tile.op)
137 self._assign_to([d.data for d in dispatched], to_tile.op.outputs)
138 return to_tile
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/tiles.py in dispatch(self, op)
117 else:
118 try:
--> 119 tiled = op_cls.tile(op)
120 except NotImplementedError as ex:
121 cause = ex
~/Workspace/mars/mars/dataframe/indexing/getitem.py in tile(cls, op)
269 def tile(cls, op):
270 if op.col_names is not None:
--> 271 return cls.tile_with_columns(op)
272 else:
273 return cls.tile_with_mask(op)
~/Workspace/mars/mars/dataframe/indexing/getitem.py in tile_with_columns(cls, op)
340 dtype = in_df.dtypes[col_names]
341 for i in range(in_df.chunk_shape[0]):
--> 342 c = in_df.cix[(i, column_index)]
343 op = DataFrameIndex(col_names=col_names)
344 out_chunks.append(op.new_chunk([c], shape=(c.shape[0],), index=(i,), dtype=dtype,
~/Workspace/mars/mars/core.py in __getitem__(self, item)
714 indexes = tuple(zip(*itertools.product(*slices)))
715
--> 716 flat_index = np.ravel_multi_index(indexes, self._tileable.chunk_shape)
717 if singleton:
718 return self._tileable._chunks[flat_index[0]]
<__array_function__ internals> in ravel_multi_index(*args, **kwargs)
ValueError: invalid entry in coordinates array
|
ValueError
|
def __call__(self, series, dtype):
if dtype is None:
inferred_dtype = None
if callable(self._arg):
# arg is a function, try to inspect the signature
sig = inspect.signature(self._arg)
return_type = sig.return_annotation
if return_type is not inspect._empty:
inferred_dtype = np.dtype(return_type)
else:
try:
# try to infer dtype by calling the function
inferred_dtype = (
build_series(series)
.map(self._arg, na_action=self._na_action)
.dtype
)
except: # noqa: E722 # nosec
pass
else:
if isinstance(self._arg, MutableMapping):
inferred_dtype = pd.Series(self._arg).dtype
else:
inferred_dtype = self._arg.dtype
if inferred_dtype is not None and np.issubdtype(inferred_dtype, np.number):
if np.issubdtype(inferred_dtype, np.inexact):
# for the inexact e.g. float
# we can make the decision,
# but for int, due to the nan which may occur,
# we cannot infer the dtype
dtype = inferred_dtype
else:
dtype = inferred_dtype
if dtype is None:
raise ValueError(
"cannot infer dtype, it needs to be specified manually for `map`"
)
else:
dtype = np.int64 if dtype is int else dtype
dtype = np.dtype(dtype)
inputs = [series]
if isinstance(self._arg, SERIES_TYPE):
inputs.append(self._arg)
return self.new_series(
inputs,
shape=series.shape,
dtype=dtype,
index_value=series.index_value,
name=series.name,
)
|
def __call__(self, series, dtype):
if dtype is None:
inferred_dtype = None
if callable(self._arg):
# arg is a function, try to inspect the signature
sig = inspect.signature(self._arg)
return_type = sig.return_annotation
if return_type is not inspect._empty:
inferred_dtype = np.dtype(return_type)
else:
if isinstance(self._arg, MutableMapping):
inferred_dtype = pd.Series(self._arg).dtype
else:
inferred_dtype = self._arg.dtype
if inferred_dtype is not None and np.issubdtype(inferred_dtype, np.number):
if np.issubdtype(inferred_dtype, np.inexact):
# for the inexact e.g. float
# we can make the decision,
# but for int, due to the nan which may occur,
# we cannot infer the dtype
dtype = inferred_dtype
else:
dtype = inferred_dtype
if dtype is None:
raise ValueError(
"cannot infer dtype, it needs to be specified manually for `map`"
)
else:
dtype = np.int64 if dtype is int else dtype
dtype = np.dtype(dtype)
inputs = [series]
if isinstance(self._arg, SERIES_TYPE):
inputs.append(self._arg)
return self.new_series(
inputs,
shape=series.shape,
dtype=dtype,
index_value=series.index_value,
name=series.name,
)
|
https://github.com/mars-project/mars/issues/1717
|
In [4]: import mars.dataframe as md
In [5]: md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-90507c117e4f> in <module>
----> 1 md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
~/Workspace/mars/mars/dataframe/base/map.py in map_(series, arg, na_action, dtype)
155 def map_(series, arg, na_action=None, dtype=None):
156 op = DataFrameMap(arg=arg, na_action=na_action)
--> 157 return op(series, dtype=dtype)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/dataframe/base/map.py in __call__(self, series, dtype)
93
94 if dtype is None:
---> 95 raise ValueError('cannot infer dtype, '
96 'it needs to be specified manually for `map`')
97 else:
ValueError: cannot infer dtype, it needs to be specified manually for `map`
|
ValueError
|
def tile(cls, op):
inp = op.input
out = op.outputs[0]
if len(inp.chunks) == 1:
chunk_op = op.copy().reset_key()
chunk_param = out.params
chunk_param["index"] = (0,)
chunk = chunk_op.new_chunk(inp.chunks, kws=[chunk_param])
new_op = op.copy()
param = out.params
param["chunks"] = [chunk]
param["nsplits"] = ((np.nan,),)
return new_op.new_seriess(op.inputs, kws=[param])
inp = Series(inp)
if op.dropna:
inp = inp.dropna()
inp = inp.groupby(inp).count(method=op.method)
if op.normalize:
if op.convert_index_to_interval:
check_chunks_unknown_shape([op.input], TilesError)
inp = inp.truediv(op.input.shape[0], axis=0)
else:
inp = inp.truediv(inp.sum(), axis=0)
if op.sort:
inp = inp.sort_values(ascending=op.ascending)
ret = recursive_tile(inp)
chunks = []
for c in ret.chunks:
chunk_op = DataFrameValueCounts(
convert_index_to_interval=op.convert_index_to_interval,
stage=OperandStage.map,
)
chunk_params = c.params
if op.convert_index_to_interval:
# convert index to IntervalDtype
chunk_params["index_value"] = parse_index(
pd.IntervalIndex([]), c, store_data=False
)
chunks.append(chunk_op.new_chunk([c], kws=[chunk_params]))
new_op = op.copy()
params = out.params
params["chunks"] = chunks
params["nsplits"] = ret.nsplits
return new_op.new_seriess(out.inputs, kws=[params])
|
def tile(cls, op):
inp = op.input
out = op.outputs[0]
if len(inp.chunks) == 1:
chunk_op = op.copy().reset_key()
chunk_param = out.params
chunk_param["index"] = (0,)
chunk = chunk_op.new_chunk(inp.chunks, kws=[chunk_param])
new_op = op.copy()
param = out.params
param["chunks"] = [chunk]
param["nsplits"] = ((np.nan,),)
return new_op.new_seriess(op.inputs, kws=[param])
inp = Series(inp)
if op.dropna:
inp = inp.dropna()
inp = inp.groupby(inp).count(method=op.method)
if op.normalize:
if op.convert_index_to_interval:
check_chunks_unknown_shape([op.input], TilesError)
inp = inp.truediv(op.input.shape[0], axis=0)
else:
inp = inp.truediv(inp.sum(), axis=0)
if op.sort:
inp = inp.sort_values(ascending=op.ascending)
ret = recursive_tile(inp)
if op.convert_index_to_interval:
# convert index to IntervalDtype
chunks = []
for c in ret.chunks:
chunk_op = DataFrameValueCounts(
convert_index_to_interval=True, stage=OperandStage.map
)
chunk_params = c.params
chunk_params["index_value"] = parse_index(
pd.IntervalIndex([]), c, store_data=False
)
chunks.append(chunk_op.new_chunk([c], kws=[chunk_params]))
new_op = op.copy()
params = out.params
params["chunks"] = chunks
params["nsplits"] = ret.nsplits
return new_op.new_seriess(out.inputs, kws=[params])
return [ret]
|
https://github.com/mars-project/mars/issues/1717
|
In [4]: import mars.dataframe as md
In [5]: md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-90507c117e4f> in <module>
----> 1 md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
~/Workspace/mars/mars/dataframe/base/map.py in map_(series, arg, na_action, dtype)
155 def map_(series, arg, na_action=None, dtype=None):
156 op = DataFrameMap(arg=arg, na_action=na_action)
--> 157 return op(series, dtype=dtype)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/dataframe/base/map.py in __call__(self, series, dtype)
93
94 if dtype is None:
---> 95 raise ValueError('cannot infer dtype, '
96 'it needs to be specified manually for `map`')
97 else:
ValueError: cannot infer dtype, it needs to be specified manually for `map`
|
ValueError
|
def execute(cls, ctx, op: "DataFrameValueCounts"):
if op.stage != OperandStage.map:
in_data = ctx[op.input.key]
if op.convert_index_to_interval:
result = in_data.value_counts(
normalize=False,
sort=op.sort,
ascending=op.ascending,
bins=op.bins,
dropna=op.dropna,
)
if op.normalize:
result /= in_data.shape[0]
else:
try:
result = in_data.value_counts(
normalize=op.normalize,
sort=op.sort,
ascending=op.ascending,
bins=op.bins,
dropna=op.dropna,
)
except ValueError:
in_data = in_data.copy()
result = in_data.value_counts(
normalize=op.normalize,
sort=op.sort,
ascending=op.ascending,
bins=op.bins,
dropna=op.dropna,
)
else:
result = ctx[op.input.key]
# set index name to None to keep consistency with pandas
result.index.name = None
if op.convert_index_to_interval:
# convert CategoricalDtype which generated in `cut`
# to IntervalDtype
result.index = result.index.astype("interval")
ctx[op.outputs[0].key] = result
|
def execute(cls, ctx, op: "DataFrameValueCounts"):
if op.stage != OperandStage.map:
in_data = ctx[op.input.key]
if op.convert_index_to_interval:
result = in_data.value_counts(
normalize=False,
sort=op.sort,
ascending=op.ascending,
bins=op.bins,
dropna=op.dropna,
)
if op.normalize:
result /= in_data.shape[0]
else:
try:
result = in_data.value_counts(
normalize=op.normalize,
sort=op.sort,
ascending=op.ascending,
bins=op.bins,
dropna=op.dropna,
)
except ValueError:
in_data = in_data.copy()
result = in_data.value_counts(
normalize=op.normalize,
sort=op.sort,
ascending=op.ascending,
bins=op.bins,
dropna=op.dropna,
)
else:
result = ctx[op.input.key]
if op.convert_index_to_interval:
# convert CategoricalDtype which generated in `cut`
# to IntervalDtype
result.index = result.index.astype("interval")
ctx[op.outputs[0].key] = result
|
https://github.com/mars-project/mars/issues/1717
|
In [4]: import mars.dataframe as md
In [5]: md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-90507c117e4f> in <module>
----> 1 md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
~/Workspace/mars/mars/dataframe/base/map.py in map_(series, arg, na_action, dtype)
155 def map_(series, arg, na_action=None, dtype=None):
156 op = DataFrameMap(arg=arg, na_action=na_action)
--> 157 return op(series, dtype=dtype)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/dataframe/base/map.py in __call__(self, series, dtype)
93
94 if dtype is None:
---> 95 raise ValueError('cannot infer dtype, '
96 'it needs to be specified manually for `map`')
97 else:
ValueError: cannot infer dtype, it needs to be specified manually for `map`
|
ValueError
|
def build_mock_groupby(self, **kwargs):
in_df = self.inputs[0]
if self.is_dataframe_obj:
empty_df = build_df(in_df, size=1)
obj_dtypes = in_df.dtypes[in_df.dtypes == np.dtype("O")]
empty_df[obj_dtypes.index] = "O"
else:
if in_df.dtype == np.dtype("O"):
empty_df = pd.Series(
"O", index=pd.RangeIndex(2), name=in_df.name, dtype=np.dtype("O")
)
else:
empty_df = build_series(in_df, size=1, name=in_df.name)
new_kw = self.groupby_params
new_kw.update(kwargs)
if new_kw.get("level"):
new_kw["level"] = 0
if isinstance(new_kw["by"], list):
new_by = []
for v in new_kw["by"]:
if isinstance(v, (Base, Entity)):
new_by.append(build_series(v, size=1, name=v.name))
else:
new_by.append(v)
new_kw["by"] = new_by
return empty_df.groupby(**new_kw)
|
def build_mock_groupby(self, **kwargs):
in_df = self.inputs[0]
if self.is_dataframe_obj:
empty_df = build_df(in_df, size=2)
obj_dtypes = in_df.dtypes[in_df.dtypes == np.dtype("O")]
empty_df[obj_dtypes.index] = "O"
else:
if in_df.dtype == np.dtype("O"):
empty_df = pd.Series(
"O", index=pd.RangeIndex(2), name=in_df.name, dtype=np.dtype("O")
)
else:
empty_df = build_series(in_df, size=2, name=in_df.name)
new_kw = self.groupby_params
new_kw.update(kwargs)
if new_kw.get("level"):
new_kw["level"] = 0
if isinstance(new_kw["by"], list):
new_by = []
for v in new_kw["by"]:
if isinstance(v, (Base, Entity)):
new_by.append(build_series(v, size=2, name=v.name))
else:
new_by.append(v)
new_kw["by"] = new_by
return empty_df.groupby(**new_kw)
|
https://github.com/mars-project/mars/issues/1717
|
In [4]: import mars.dataframe as md
In [5]: md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-90507c117e4f> in <module>
----> 1 md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
~/Workspace/mars/mars/dataframe/base/map.py in map_(series, arg, na_action, dtype)
155 def map_(series, arg, na_action=None, dtype=None):
156 op = DataFrameMap(arg=arg, na_action=na_action)
--> 157 return op(series, dtype=dtype)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/dataframe/base/map.py in __call__(self, series, dtype)
93
94 if dtype is None:
---> 95 raise ValueError('cannot infer dtype, '
96 'it needs to be specified manually for `map`')
97 else:
ValueError: cannot infer dtype, it needs to be specified manually for `map`
|
ValueError
|
def __call__(self, df):
if self.col_names is not None:
# if col_names is a list, return a DataFrame, else return a Series
if isinstance(self._col_names, list):
dtypes = df.dtypes[self._col_names]
columns = parse_index(pd.Index(self._col_names), store_data=True)
return self.new_dataframe(
[df],
shape=(df.shape[0], len(self._col_names)),
dtypes=dtypes,
index_value=df.index_value,
columns_value=columns,
)
else:
dtype = df.dtypes[self._col_names]
return self.new_series(
[df],
shape=(df.shape[0],),
dtype=dtype,
index_value=df.index_value,
name=self._col_names,
)
else:
if isinstance(self.mask, (SERIES_TYPE, DATAFRAME_TYPE)):
index_value = parse_index(
pd.Index(
[], dtype=df.index_value.to_pandas().dtype, name=df.index_value.name
),
df,
self._mask,
)
return self.new_dataframe(
[df, self._mask],
shape=(np.nan, df.shape[1]),
dtypes=df.dtypes,
index_value=index_value,
columns_value=df.columns_value,
)
else:
index_value = parse_index(
pd.Index(
[], dtype=df.index_value.to_pandas().dtype, name=df.index_value.name
),
df,
self._mask,
)
return self.new_dataframe(
[df],
shape=(np.nan, df.shape[1]),
dtypes=df.dtypes,
index_value=index_value,
columns_value=df.columns_value,
)
|
def __call__(self, df):
if self.col_names is not None:
# if col_names is a list, return a DataFrame, else return a Series
if isinstance(self._col_names, list):
dtypes = df.dtypes[self._col_names]
columns = parse_index(pd.Index(self._col_names), store_data=True)
return self.new_dataframe(
[df],
shape=(df.shape[0], len(self._col_names)),
dtypes=dtypes,
index_value=df.index_value,
columns_value=columns,
)
else:
dtype = df.dtypes[self._col_names]
return self.new_series(
[df],
shape=(df.shape[0],),
dtype=dtype,
index_value=df.index_value,
name=self._col_names,
)
else:
if isinstance(self.mask, (SERIES_TYPE, DATAFRAME_TYPE)):
index_value = parse_index(
pd.Index([], dtype=df.index_value.to_pandas().dtype), df, self._mask
)
return self.new_dataframe(
[df, self._mask],
shape=(np.nan, df.shape[1]),
dtypes=df.dtypes,
index_value=index_value,
columns_value=df.columns_value,
)
else:
index_value = parse_index(
pd.Index([], dtype=df.index_value.to_pandas().dtype), df, self._mask
)
return self.new_dataframe(
[df],
shape=(np.nan, df.shape[1]),
dtypes=df.dtypes,
index_value=index_value,
columns_value=df.columns_value,
)
|
https://github.com/mars-project/mars/issues/1717
|
In [4]: import mars.dataframe as md
In [5]: md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-90507c117e4f> in <module>
----> 1 md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
~/Workspace/mars/mars/dataframe/base/map.py in map_(series, arg, na_action, dtype)
155 def map_(series, arg, na_action=None, dtype=None):
156 op = DataFrameMap(arg=arg, na_action=na_action)
--> 157 return op(series, dtype=dtype)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/dataframe/base/map.py in __call__(self, series, dtype)
93
94 if dtype is None:
---> 95 raise ValueError('cannot infer dtype, '
96 'it needs to be specified manually for `map`')
97 else:
ValueError: cannot infer dtype, it needs to be specified manually for `map`
|
ValueError
|
def set_index(df, keys, drop=True, append=False, inplace=False, verify_integrity=False):
op = DataFrameSetIndex(
keys=keys,
drop=drop,
append=append,
verify_integrity=verify_integrity,
output_types=[OutputType.dataframe],
)
result = op(df)
if not inplace:
return result
else:
df.data = result.data
|
def set_index(df, keys, drop=True, append=False, verify_integrity=False, **kw):
op = DataFrameSetIndex(
keys=keys,
drop=drop,
append=append,
verify_integrity=verify_integrity,
output_types=[OutputType.dataframe],
**kw,
)
return op(df)
|
https://github.com/mars-project/mars/issues/1717
|
In [4]: import mars.dataframe as md
In [5]: md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-90507c117e4f> in <module>
----> 1 md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
~/Workspace/mars/mars/dataframe/base/map.py in map_(series, arg, na_action, dtype)
155 def map_(series, arg, na_action=None, dtype=None):
156 op = DataFrameMap(arg=arg, na_action=na_action)
--> 157 return op(series, dtype=dtype)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/dataframe/base/map.py in __call__(self, series, dtype)
93
94 if dtype is None:
---> 95 raise ValueError('cannot infer dtype, '
96 'it needs to be specified manually for `map`')
97 else:
ValueError: cannot infer dtype, it needs to be specified manually for `map`
|
ValueError
|
def parse_index(index_value, *args, store_data=False, key=None):
from .core import IndexValue
def _extract_property(index, tp, ret_data):
kw = {
"_min_val": _get_index_min(index),
"_max_val": _get_index_max(index),
"_min_val_close": True,
"_max_val_close": True,
"_key": key or _tokenize_index(index, *args),
}
if ret_data:
kw["_data"] = index.values
for field in tp._FIELDS:
if field in kw or field == "_data":
continue
val = getattr(index, field.lstrip("_"), None)
if val is not None:
kw[field] = val
return kw
def _tokenize_index(index, *token_objects):
if not index.empty:
return tokenize(index)
else:
return tokenize(index, *token_objects)
def _get_index_min(index):
try:
return index.min()
except ValueError:
if isinstance(index, pd.IntervalIndex):
return None
raise
except TypeError:
return None
def _get_index_max(index):
try:
return index.max()
except ValueError:
if isinstance(index, pd.IntervalIndex):
return None
raise
except TypeError:
return None
def _serialize_index(index):
tp = getattr(IndexValue, type(index).__name__)
properties = _extract_property(index, tp, store_data)
properties["_name"] = index.name
return tp(**properties)
def _serialize_range_index(index):
if is_pd_range_empty(index):
properties = {
"_is_monotonic_increasing": True,
"_is_monotonic_decreasing": False,
"_is_unique": True,
"_min_val": _get_index_min(index),
"_max_val": _get_index_max(index),
"_min_val_close": True,
"_max_val_close": False,
"_key": key or _tokenize_index(index, *args),
"_name": index.name,
"_dtype": index.dtype,
}
else:
properties = _extract_property(index, IndexValue.RangeIndex, False)
return IndexValue.RangeIndex(
_slice=slice(
_get_range_index_start(index),
_get_range_index_stop(index),
_get_range_index_step(index),
),
**properties,
)
def _serialize_multi_index(index):
kw = _extract_property(index, IndexValue.MultiIndex, store_data)
kw["_sortorder"] = index.sortorder
kw["_dtypes"] = [lev.dtype for lev in index.levels]
return IndexValue.MultiIndex(**kw)
if index_value is None:
return IndexValue(
_index_value=IndexValue.Index(
_is_monotonic_increasing=False,
_is_monotonic_decreasing=False,
_is_unique=False,
_min_val=None,
_max_val=None,
_min_val_close=True,
_max_val_close=True,
_key=key or tokenize(*args),
)
)
if isinstance(index_value, pd.RangeIndex):
return IndexValue(_index_value=_serialize_range_index(index_value))
elif isinstance(index_value, pd.MultiIndex):
return IndexValue(_index_value=_serialize_multi_index(index_value))
else:
return IndexValue(_index_value=_serialize_index(index_value))
|
def parse_index(index_value, *args, store_data=False, key=None):
from .core import IndexValue
def _extract_property(index, tp, ret_data):
kw = {
"_min_val": _get_index_min(index),
"_max_val": _get_index_max(index),
"_min_val_close": True,
"_max_val_close": True,
"_key": key or _tokenize_index(index, *args),
}
if ret_data:
kw["_data"] = index.values
for field in tp._FIELDS:
if field in kw or field == "_data":
continue
val = getattr(index, field.lstrip("_"), None)
if val is not None:
kw[field] = val
return kw
def _tokenize_index(index, *token_objects):
if not index.empty:
return tokenize(index)
else:
return tokenize(index, *token_objects)
def _get_index_min(index):
try:
return index.min()
except ValueError:
if isinstance(index, pd.IntervalIndex):
return None
raise
except TypeError:
return None
def _get_index_max(index):
try:
return index.max()
except ValueError:
if isinstance(index, pd.IntervalIndex):
return None
raise
except TypeError:
return None
def _serialize_index(index):
tp = getattr(IndexValue, type(index).__name__)
properties = _extract_property(index, tp, store_data)
return tp(**properties)
def _serialize_range_index(index):
if is_pd_range_empty(index):
properties = {
"_is_monotonic_increasing": True,
"_is_monotonic_decreasing": False,
"_is_unique": True,
"_min_val": _get_index_min(index),
"_max_val": _get_index_max(index),
"_min_val_close": True,
"_max_val_close": False,
"_key": key or _tokenize_index(index, *args),
"_name": index.name,
"_dtype": index.dtype,
}
else:
properties = _extract_property(index, IndexValue.RangeIndex, False)
return IndexValue.RangeIndex(
_slice=slice(
_get_range_index_start(index),
_get_range_index_stop(index),
_get_range_index_step(index),
),
**properties,
)
def _serialize_multi_index(index):
kw = _extract_property(index, IndexValue.MultiIndex, store_data)
kw["_sortorder"] = index.sortorder
kw["_dtypes"] = [lev.dtype for lev in index.levels]
return IndexValue.MultiIndex(**kw)
if index_value is None:
return IndexValue(
_index_value=IndexValue.Index(
_is_monotonic_increasing=False,
_is_monotonic_decreasing=False,
_is_unique=False,
_min_val=None,
_max_val=None,
_min_val_close=True,
_max_val_close=True,
_key=key or tokenize(*args),
)
)
if isinstance(index_value, pd.RangeIndex):
return IndexValue(_index_value=_serialize_range_index(index_value))
elif isinstance(index_value, pd.MultiIndex):
return IndexValue(_index_value=_serialize_multi_index(index_value))
else:
return IndexValue(_index_value=_serialize_index(index_value))
|
https://github.com/mars-project/mars/issues/1717
|
In [4]: import mars.dataframe as md
In [5]: md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-90507c117e4f> in <module>
----> 1 md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
~/Workspace/mars/mars/dataframe/base/map.py in map_(series, arg, na_action, dtype)
155 def map_(series, arg, na_action=None, dtype=None):
156 op = DataFrameMap(arg=arg, na_action=na_action)
--> 157 return op(series, dtype=dtype)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/dataframe/base/map.py in __call__(self, series, dtype)
93
94 if dtype is None:
---> 95 raise ValueError('cannot infer dtype, '
96 'it needs to be specified manually for `map`')
97 else:
ValueError: cannot infer dtype, it needs to be specified manually for `map`
|
ValueError
|
def _serialize_index(index):
tp = getattr(IndexValue, type(index).__name__)
properties = _extract_property(index, tp, store_data)
properties["_name"] = index.name
return tp(**properties)
|
def _serialize_index(index):
tp = getattr(IndexValue, type(index).__name__)
properties = _extract_property(index, tp, store_data)
return tp(**properties)
|
https://github.com/mars-project/mars/issues/1717
|
In [4]: import mars.dataframe as md
In [5]: md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-90507c117e4f> in <module>
----> 1 md.Series(['1-1', '2-2']).map(lambda x: x.split('-')[0]).execute()
~/Workspace/mars/mars/dataframe/base/map.py in map_(series, arg, na_action, dtype)
155 def map_(series, arg, na_action=None, dtype=None):
156 op = DataFrameMap(arg=arg, na_action=na_action)
--> 157 return op(series, dtype=dtype)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
449 def _inner(*args, **kwargs):
450 with self:
--> 451 return func(*args, **kwargs)
452
453 return _inner
~/Workspace/mars/mars/dataframe/base/map.py in __call__(self, series, dtype)
93
94 if dtype is None:
---> 95 raise ValueError('cannot infer dtype, '
96 'it needs to be specified manually for `map`')
97 else:
ValueError: cannot infer dtype, it needs to be specified manually for `map`
|
ValueError
|
def _install():
from ..core import DATAFRAME_TYPE, SERIES_TYPE, INDEX_TYPE
from .standardize_range_index import ChunkStandardizeRangeIndex
from .string_ import _string_method_to_handlers
from .datetimes import _datetime_method_to_handlers
from .accessor import StringAccessor, DatetimeAccessor, CachedAccessor
for t in DATAFRAME_TYPE:
setattr(t, "to_gpu", to_gpu)
setattr(t, "to_cpu", to_cpu)
setattr(t, "rechunk", rechunk)
setattr(t, "describe", describe)
setattr(t, "apply", df_apply)
setattr(t, "transform", df_transform)
setattr(t, "fillna", fillna)
setattr(t, "ffill", ffill)
setattr(t, "bfill", bfill)
setattr(t, "isin", df_isin)
setattr(t, "isna", isna)
setattr(t, "isnull", isnull)
setattr(t, "notna", notna)
setattr(t, "notnull", notnull)
setattr(t, "dropna", df_dropna)
setattr(t, "shift", shift)
setattr(t, "tshift", tshift)
setattr(t, "diff", df_diff)
setattr(t, "astype", astype)
setattr(t, "drop", df_drop)
setattr(t, "pop", df_pop)
setattr(
t, "__delitem__", lambda df, items: df_drop(df, items, axis=1, inplace=True)
)
setattr(t, "drop_duplicates", df_drop_duplicates)
setattr(t, "melt", melt)
setattr(t, "memory_usage", df_memory_usage)
setattr(t, "select_dtypes", select_dtypes)
setattr(t, "map_chunk", map_chunk)
setattr(t, "rebalance", rebalance)
setattr(t, "stack", stack)
setattr(t, "explode", df_explode)
for t in SERIES_TYPE:
setattr(t, "to_gpu", to_gpu)
setattr(t, "to_cpu", to_cpu)
setattr(t, "rechunk", rechunk)
setattr(t, "map", map_)
setattr(t, "describe", describe)
setattr(t, "apply", series_apply)
setattr(t, "transform", series_transform)
setattr(t, "fillna", fillna)
setattr(t, "ffill", ffill)
setattr(t, "bfill", bfill)
setattr(t, "isin", series_isin)
setattr(t, "isna", isna)
setattr(t, "isnull", isnull)
setattr(t, "notna", notna)
setattr(t, "notnull", notnull)
setattr(t, "dropna", series_dropna)
setattr(t, "shift", shift)
setattr(t, "tshift", tshift)
setattr(t, "diff", series_diff)
setattr(t, "value_counts", value_counts)
setattr(t, "astype", astype)
setattr(t, "drop", series_drop)
setattr(t, "drop_duplicates", series_drop_duplicates)
setattr(t, "memory_usage", series_memory_usage)
setattr(t, "map_chunk", map_chunk)
setattr(t, "rebalance", rebalance)
setattr(t, "explode", series_explode)
for t in INDEX_TYPE:
setattr(t, "rechunk", rechunk)
setattr(t, "drop", index_drop)
setattr(t, "drop_duplicates", index_drop_duplicates)
setattr(t, "memory_usage", index_memory_usage)
for method in _string_method_to_handlers:
if not hasattr(StringAccessor, method):
StringAccessor._register(method)
for method in _datetime_method_to_handlers:
if not hasattr(DatetimeAccessor, method):
DatetimeAccessor._register(method)
for series in SERIES_TYPE:
series.str = CachedAccessor("str", StringAccessor)
series.dt = CachedAccessor("dt", DatetimeAccessor)
|
def _install():
from ..core import DATAFRAME_TYPE, SERIES_TYPE, INDEX_TYPE
from .standardize_range_index import ChunkStandardizeRangeIndex
from .string_ import _string_method_to_handlers
from .datetimes import _datetime_method_to_handlers
from .accessor import StringAccessor, DatetimeAccessor, CachedAccessor
for t in DATAFRAME_TYPE:
setattr(t, "to_gpu", to_gpu)
setattr(t, "to_cpu", to_cpu)
setattr(t, "rechunk", rechunk)
setattr(t, "describe", describe)
setattr(t, "apply", df_apply)
setattr(t, "transform", df_transform)
setattr(t, "fillna", fillna)
setattr(t, "ffill", ffill)
setattr(t, "bfill", bfill)
setattr(t, "isin", df_isin)
setattr(t, "isna", isna)
setattr(t, "isnull", isnull)
setattr(t, "notna", notna)
setattr(t, "notnull", notnull)
setattr(t, "dropna", df_dropna)
setattr(t, "shift", shift)
setattr(t, "tshift", tshift)
setattr(t, "diff", df_diff)
setattr(t, "astype", astype)
setattr(t, "drop", df_drop)
setattr(t, "pop", df_pop)
setattr(
t, "__delitem__", lambda df, items: df_drop(df, items, axis=1, inplace=True)
)
setattr(t, "drop_duplicates", df_drop_duplicates)
setattr(t, "melt", melt)
setattr(t, "memory_usage", df_memory_usage)
setattr(t, "select_dtypes", select_dtypes)
setattr(t, "map_chunk", map_chunk)
setattr(t, "rebalance", rebalance)
setattr(t, "stack", stack)
for t in SERIES_TYPE:
setattr(t, "to_gpu", to_gpu)
setattr(t, "to_cpu", to_cpu)
setattr(t, "rechunk", rechunk)
setattr(t, "map", map_)
setattr(t, "describe", describe)
setattr(t, "apply", series_apply)
setattr(t, "transform", series_transform)
setattr(t, "fillna", fillna)
setattr(t, "ffill", ffill)
setattr(t, "bfill", bfill)
setattr(t, "isin", series_isin)
setattr(t, "isna", isna)
setattr(t, "isnull", isnull)
setattr(t, "notna", notna)
setattr(t, "notnull", notnull)
setattr(t, "dropna", series_dropna)
setattr(t, "shift", shift)
setattr(t, "tshift", tshift)
setattr(t, "diff", series_diff)
setattr(t, "value_counts", value_counts)
setattr(t, "astype", astype)
setattr(t, "drop", series_drop)
setattr(t, "drop_duplicates", series_drop_duplicates)
setattr(t, "memory_usage", series_memory_usage)
setattr(t, "map_chunk", map_chunk)
setattr(t, "rebalance", rebalance)
for t in INDEX_TYPE:
setattr(t, "rechunk", rechunk)
setattr(t, "drop", index_drop)
setattr(t, "drop_duplicates", index_drop_duplicates)
setattr(t, "memory_usage", index_memory_usage)
for method in _string_method_to_handlers:
if not hasattr(StringAccessor, method):
StringAccessor._register(method)
for method in _datetime_method_to_handlers:
if not hasattr(DatetimeAccessor, method):
DatetimeAccessor._register(method)
for series in SERIES_TYPE:
series.str = CachedAccessor("str", StringAccessor)
series.dt = CachedAccessor("dt", DatetimeAccessor)
|
https://github.com/mars-project/mars/issues/1704
|
In [4]: df.sort_values(by='col1').execute()
Out[4]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/miniconda3/lib/python3.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392 if cls is not object \
393 and callable(cls.__dict__.get('__repr__')):
--> 394 return _repr_pprint(obj, self, cycle)
395
396 return _default_pprint(obj, self, cycle)
~/miniconda3/lib/python3.7/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
698 """A pprint that just redirects to the normal repr function."""
699 # Find newlines and replace them with p.break_()
--> 700 output = repr(obj)
701 lines = output.splitlines()
702 with p.group():
~/Documents/mars_dev/mars/mars/core.py in __repr__(self)
129
130 def __repr__(self):
--> 131 return self._data.__repr__()
132
133 def _check_data(self, data):
~/Documents/mars_dev/mars/mars/dataframe/core.py in __repr__(self)
1084
1085 def __repr__(self):
-> 1086 return self._to_str(representation=True)
1087
1088 def _repr_html_(self):
~/Documents/mars_dev/mars/mars/dataframe/core.py in _to_str(self, representation)
1057 else:
1058 corner_data = fetch_corner_data(
-> 1059 self, session=self._executed_sessions[-1])
1060
1061 buf = StringIO()
~/Documents/mars_dev/mars/mars/dataframe/utils.py in fetch_corner_data(df_or_series, session)
895 head_data, tail_data = \
896 ExecutableTuple([head, tail]).fetch(session=session)
--> 897 return pd.concat([head_data, tail_data], axis='index')
898
899
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
279 verify_integrity=verify_integrity,
280 copy=copy,
--> 281 sort=sort,
282 )
283
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
358
359 # consolidate
--> 360 obj._consolidate(inplace=True)
361 ndims.add(obj.ndim)
362
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _consolidate(self, inplace)
5363 inplace = validate_bool_kwarg(inplace, "inplace")
5364 if inplace:
-> 5365 self._consolidate_inplace()
5366 else:
5367 f = lambda: self._data.consolidate()
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _consolidate_inplace(self)
5345 self._data = self._data.consolidate()
5346
-> 5347 self._protect_consolidate(f)
5348
5349 def _consolidate(self, inplace: bool_t = False):
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _protect_consolidate(self, f)
5333 cache
5334 """
-> 5335 blocks_before = len(self._data.blocks)
5336 result = f()
5337 if len(self._data.blocks) != blocks_before:
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5268 or name in self._accessors
5269 ):
-> 5270 return object.__getattribute__(self, name)
5271 else:
5272 if self._info_axis._can_hold_identifiers_and_holds_name(name):
AttributeError: 'DataFrame' object has no attribute '_data'
|
AttributeError
|
def load(file):
header = read_file_header(file)
file = open_decompression_file(file, header.compress)
try:
buf = file.read()
finally:
if header.compress != CompressType.NONE:
file.close()
if header.type == SerialType.ARROW:
return deserialize(memoryview(buf))
else:
return _patch_pandas_mgr(pickle.loads(buf)) # nosec
|
def load(file):
header = read_file_header(file)
file = open_decompression_file(file, header.compress)
try:
buf = file.read()
finally:
if header.compress != CompressType.NONE:
file.close()
if header.type == SerialType.ARROW:
return pyarrow.deserialize(memoryview(buf), mars_serialize_context())
else:
return pickle.loads(buf)
|
https://github.com/mars-project/mars/issues/1704
|
In [4]: df.sort_values(by='col1').execute()
Out[4]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/miniconda3/lib/python3.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392 if cls is not object \
393 and callable(cls.__dict__.get('__repr__')):
--> 394 return _repr_pprint(obj, self, cycle)
395
396 return _default_pprint(obj, self, cycle)
~/miniconda3/lib/python3.7/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
698 """A pprint that just redirects to the normal repr function."""
699 # Find newlines and replace them with p.break_()
--> 700 output = repr(obj)
701 lines = output.splitlines()
702 with p.group():
~/Documents/mars_dev/mars/mars/core.py in __repr__(self)
129
130 def __repr__(self):
--> 131 return self._data.__repr__()
132
133 def _check_data(self, data):
~/Documents/mars_dev/mars/mars/dataframe/core.py in __repr__(self)
1084
1085 def __repr__(self):
-> 1086 return self._to_str(representation=True)
1087
1088 def _repr_html_(self):
~/Documents/mars_dev/mars/mars/dataframe/core.py in _to_str(self, representation)
1057 else:
1058 corner_data = fetch_corner_data(
-> 1059 self, session=self._executed_sessions[-1])
1060
1061 buf = StringIO()
~/Documents/mars_dev/mars/mars/dataframe/utils.py in fetch_corner_data(df_or_series, session)
895 head_data, tail_data = \
896 ExecutableTuple([head, tail]).fetch(session=session)
--> 897 return pd.concat([head_data, tail_data], axis='index')
898
899
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
279 verify_integrity=verify_integrity,
280 copy=copy,
--> 281 sort=sort,
282 )
283
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
358
359 # consolidate
--> 360 obj._consolidate(inplace=True)
361 ndims.add(obj.ndim)
362
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _consolidate(self, inplace)
5363 inplace = validate_bool_kwarg(inplace, "inplace")
5364 if inplace:
-> 5365 self._consolidate_inplace()
5366 else:
5367 f = lambda: self._data.consolidate()
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _consolidate_inplace(self)
5345 self._data = self._data.consolidate()
5346
-> 5347 self._protect_consolidate(f)
5348
5349 def _consolidate(self, inplace: bool_t = False):
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _protect_consolidate(self, f)
5333 cache
5334 """
-> 5335 blocks_before = len(self._data.blocks)
5336 result = f()
5337 if len(self._data.blocks) != blocks_before:
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5268 or name in self._accessors
5269 ):
-> 5270 return object.__getattribute__(self, name)
5271 else:
5272 if self._info_axis._can_hold_identifiers_and_holds_name(name):
AttributeError: 'DataFrame' object has no attribute '_data'
|
AttributeError
|
def loads(buf):
mv = memoryview(buf)
header = read_file_header(mv)
compress = header.compress
if compress == CompressType.NONE:
data = buf[HEADER_LENGTH:]
else:
data = decompressors[compress](mv[HEADER_LENGTH:])
if header.type == SerialType.ARROW:
try:
return deserialize(memoryview(data))
except pyarrow.lib.ArrowInvalid: # pragma: no cover
# reconstruct value from buffers of arrow components
data_view = memoryview(data)
meta_block_size = np.frombuffer(data_view[0:4], dtype="int32").item()
meta = pickle.loads(data_view[4 : 4 + meta_block_size]) # nosec
buffer_sizes = meta.pop("buffer_sizes")
bounds = np.cumsum([4 + meta_block_size] + buffer_sizes)
meta["data"] = [
pyarrow.py_buffer(data_view[bounds[idx] : bounds[idx + 1]])
for idx in range(len(buffer_sizes))
]
return _patch_pandas_mgr(
pyarrow.deserialize_components(meta, mars_serialize_context())
)
else:
return _patch_pandas_mgr(pickle.loads(data)) # nosec
|
def loads(buf):
mv = memoryview(buf)
header = read_file_header(mv)
compress = header.compress
if compress == CompressType.NONE:
data = buf[HEADER_LENGTH:]
else:
data = decompressors[compress](mv[HEADER_LENGTH:])
if header.type == SerialType.ARROW:
try:
return deserialize(memoryview(data))
except pyarrow.lib.ArrowInvalid: # pragma: no cover
# reconstruct value from buffers of arrow components
data_view = memoryview(data)
meta_block_size = np.frombuffer(data_view[0:4], dtype="int32").item()
meta = pickle.loads(data_view[4 : 4 + meta_block_size]) # nosec
buffer_sizes = meta.pop("buffer_sizes")
bounds = np.cumsum([4 + meta_block_size] + buffer_sizes)
meta["data"] = [
pyarrow.py_buffer(data_view[bounds[idx] : bounds[idx + 1]])
for idx in range(len(buffer_sizes))
]
return pyarrow.deserialize_components(meta, mars_serialize_context())
else:
return pickle.loads(data)
|
https://github.com/mars-project/mars/issues/1704
|
In [4]: df.sort_values(by='col1').execute()
Out[4]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/miniconda3/lib/python3.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392 if cls is not object \
393 and callable(cls.__dict__.get('__repr__')):
--> 394 return _repr_pprint(obj, self, cycle)
395
396 return _default_pprint(obj, self, cycle)
~/miniconda3/lib/python3.7/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
698 """A pprint that just redirects to the normal repr function."""
699 # Find newlines and replace them with p.break_()
--> 700 output = repr(obj)
701 lines = output.splitlines()
702 with p.group():
~/Documents/mars_dev/mars/mars/core.py in __repr__(self)
129
130 def __repr__(self):
--> 131 return self._data.__repr__()
132
133 def _check_data(self, data):
~/Documents/mars_dev/mars/mars/dataframe/core.py in __repr__(self)
1084
1085 def __repr__(self):
-> 1086 return self._to_str(representation=True)
1087
1088 def _repr_html_(self):
~/Documents/mars_dev/mars/mars/dataframe/core.py in _to_str(self, representation)
1057 else:
1058 corner_data = fetch_corner_data(
-> 1059 self, session=self._executed_sessions[-1])
1060
1061 buf = StringIO()
~/Documents/mars_dev/mars/mars/dataframe/utils.py in fetch_corner_data(df_or_series, session)
895 head_data, tail_data = \
896 ExecutableTuple([head, tail]).fetch(session=session)
--> 897 return pd.concat([head_data, tail_data], axis='index')
898
899
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
279 verify_integrity=verify_integrity,
280 copy=copy,
--> 281 sort=sort,
282 )
283
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
358
359 # consolidate
--> 360 obj._consolidate(inplace=True)
361 ndims.add(obj.ndim)
362
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _consolidate(self, inplace)
5363 inplace = validate_bool_kwarg(inplace, "inplace")
5364 if inplace:
-> 5365 self._consolidate_inplace()
5366 else:
5367 f = lambda: self._data.consolidate()
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _consolidate_inplace(self)
5345 self._data = self._data.consolidate()
5346
-> 5347 self._protect_consolidate(f)
5348
5349 def _consolidate(self, inplace: bool_t = False):
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in _protect_consolidate(self, f)
5333 cache
5334 """
-> 5335 blocks_before = len(self._data.blocks)
5336 result = f()
5337 if len(self._data.blocks) != blocks_before:
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5268 or name in self._accessors
5269 ):
-> 5270 return object.__getattribute__(self, name)
5271 else:
5272 if self._info_axis._can_hold_identifiers_and_holds_name(name):
AttributeError: 'DataFrame' object has no attribute '_data'
|
AttributeError
|
def __init__(self, meta_store=None):
self.meta_store = meta_store or RemoteMetaStore.remote()
|
def __init__(self):
self._store = dict()
|
https://github.com/mars-project/mars/issues/1711
|
2020-11-17 16:48:29,349 WARNING worker.py:1157 -- Traceback (most recent call last):
File "/home/admin/.local/lib/python3.6/site-packages/ray/function_manager.py", line 445, in _load_actor_class_from_local
actor_class = getattr(module, class_name)
AttributeError: module 'mars.ray.core' has no attribute 'RemoteMetaStore'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "python/ray/_raylet.pyx", line 563, in ray._raylet.task_execution_handler
File "python/ray/_raylet.pyx", line 567, in ray._raylet.task_execution_handler
File "python/ray/_raylet.pyx", line 364, in ray._raylet.execute_task
File "/home/admin/.local/lib/python3.6/site-packages/ray/function_manager.py", line 394, in load_actor_class
job_id, actor_creation_function_descriptor)
File "/home/admin/.local/lib/python3.6/site-packages/ray/function_manager.py", line 454, in _load_actor_class_from_local
class_name))
RuntimeError: Actor RemoteMetaStore failed to be imported from local code.
An unexpected internal error occurred while the worker was executing a task.
|
AttributeError
|
def __init__(self, pure_depends=None, axis=None, output_types=None, **kwargs):
super().__init__(
_pure_depends=pure_depends, _axis=axis, _output_types=output_types, **kwargs
)
|
def __init__(self, prepare_inputs=None, axis=None, output_types=None, **kwargs):
super().__init__(
_prepare_inputs=prepare_inputs, _axis=axis, _output_types=output_types, **kwargs
)
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def standardize_range_index(chunks, axis=0):
from .base.standardize_range_index import ChunkStandardizeRangeIndex
row_chunks = dict(
(k, next(v)) for k, v in itertools.groupby(chunks, key=lambda x: x.index[axis])
)
row_chunks = [row_chunks[i] for i in range(len(row_chunks))]
out_chunks = []
for c in chunks:
inputs = row_chunks[: c.index[axis]] + [c]
op = ChunkStandardizeRangeIndex(
pure_depends=[True] * (len(inputs) - 1) + [False],
axis=axis,
output_types=c.op.output_types,
)
out_chunks.append(op.new_chunk(inputs, **c.params.copy()))
return out_chunks
|
def standardize_range_index(chunks, axis=0):
from .base.standardize_range_index import ChunkStandardizeRangeIndex
row_chunks = dict(
(k, next(v)) for k, v in itertools.groupby(chunks, key=lambda x: x.index[axis])
)
row_chunks = [row_chunks[i] for i in range(len(row_chunks))]
out_chunks = []
for c in chunks:
inputs = row_chunks[: c.index[axis]] + [c]
op = ChunkStandardizeRangeIndex(
prepare_inputs=[False] * (len(inputs) - 1) + [True],
axis=axis,
output_types=c.op.output_types,
)
out_chunks.append(op.new_chunk(inputs, **c.params.copy()))
return out_chunks
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def estimate_size(cls, ctx, op):
exec_size = 0
outputs = op.outputs
pure_dep_keys = set(
inp.key for inp, is_dep in zip(op.inputs or (), op.pure_depends or ()) if is_dep
)
if all(
not c.is_sparse() and hasattr(c, "nbytes") and not np.isnan(c.nbytes)
for c in outputs
):
for out in outputs:
ctx[out.key] = (out.nbytes, out.nbytes)
all_overhead = 0
for inp in op.inputs or ():
if inp.key in pure_dep_keys:
continue
try:
if isinstance(inp.op, FetchShuffle):
keys_and_shapes = inp.extra_params.get("_shapes", dict()).items()
else:
keys_and_shapes = [(inp.key, getattr(inp, "shape", None))]
# execution size of a specific data chunk may be
# larger than stored type due to objects
for key, shape in keys_and_shapes:
overhead = calc_object_overhead(inp, shape)
all_overhead += overhead
exec_size += ctx[key][0] + overhead
except KeyError:
if not op.sparse:
inp_size = calc_data_size(inp)
if not np.isnan(inp_size):
exec_size += inp_size
exec_size = int(exec_size)
total_out_size = 0
chunk_sizes = dict()
for out in outputs:
try:
if not out.is_sparse():
chunk_size = calc_data_size(out) + all_overhead // len(outputs)
else:
chunk_size = exec_size
if np.isnan(chunk_size):
raise TypeError
chunk_sizes[out.key] = chunk_size
total_out_size += chunk_size
except (AttributeError, TypeError, ValueError):
pass
exec_size = max(exec_size, total_out_size)
for out in outputs:
if out.key in ctx:
continue
if out.key in chunk_sizes:
store_size = chunk_sizes[out.key]
else:
store_size = max(
exec_size // len(outputs), total_out_size // max(len(chunk_sizes), 1)
)
try:
if out.is_sparse():
max_sparse_size = (
out.nbytes
+ np.dtype(np.int64).itemsize * np.prod(out.shape) * out.ndim
)
else:
max_sparse_size = np.nan
except TypeError: # pragma: no cover
max_sparse_size = np.nan
if not np.isnan(max_sparse_size):
store_size = min(store_size, max_sparse_size)
ctx[out.key] = (store_size, exec_size // len(outputs))
|
def estimate_size(cls, ctx, op):
exec_size = 0
outputs = op.outputs
if all(
not c.is_sparse() and hasattr(c, "nbytes") and not np.isnan(c.nbytes)
for c in outputs
):
for out in outputs:
ctx[out.key] = (out.nbytes, out.nbytes)
all_overhead = 0
for inp in op.inputs or ():
try:
if isinstance(inp.op, FetchShuffle):
keys_and_shapes = inp.extra_params.get("_shapes", dict()).items()
else:
keys_and_shapes = [(inp.key, getattr(inp, "shape", None))]
# execution size of a specific data chunk may be
# larger than stored type due to objects
for key, shape in keys_and_shapes:
overhead = calc_object_overhead(inp, shape)
all_overhead += overhead
exec_size += ctx[key][0] + overhead
except KeyError:
if not op.sparse:
inp_size = calc_data_size(inp)
if not np.isnan(inp_size):
exec_size += inp_size
exec_size = int(exec_size)
total_out_size = 0
chunk_sizes = dict()
for out in outputs:
try:
if not out.is_sparse():
chunk_size = calc_data_size(out) + all_overhead // len(outputs)
else:
chunk_size = exec_size
if np.isnan(chunk_size):
raise TypeError
chunk_sizes[out.key] = chunk_size
total_out_size += chunk_size
except (AttributeError, TypeError, ValueError):
pass
exec_size = max(exec_size, total_out_size)
for out in outputs:
if out.key in ctx:
continue
if out.key in chunk_sizes:
store_size = chunk_sizes[out.key]
else:
store_size = max(
exec_size // len(outputs), total_out_size // max(len(chunk_sizes), 1)
)
try:
if out.is_sparse():
max_sparse_size = (
out.nbytes
+ np.dtype(np.int64).itemsize * np.prod(out.shape) * out.ndim
)
else:
max_sparse_size = np.nan
except TypeError: # pragma: no cover
max_sparse_size = np.nan
if not np.isnan(max_sparse_size):
store_size = min(store_size, max_sparse_size)
ctx[out.key] = (store_size, exec_size // len(outputs))
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def _allocate_resource(
self, session_id, op_key, op_info, target_worker=None, reject_workers=None
):
"""
Allocate resource for single operand
:param session_id: session id
:param op_key: operand key
:param op_info: operand info dict
:param target_worker: worker to allocate, can be None
:param reject_workers: workers denied to assign to
"""
if target_worker not in self._worker_metrics:
target_worker = None
reject_workers = reject_workers or set()
op_io_meta = op_info.get("io_meta", {})
try:
input_metas = op_io_meta["input_data_metas"]
except KeyError:
input_metas = self._get_chunks_meta(
session_id, op_io_meta.get("input_chunks", {})
)
missing_keys = [k for k, m in input_metas.items() if m is None]
if missing_keys:
raise DependencyMissing(
f"Dependencies {missing_keys!r} missing for operand {op_key}"
)
if target_worker is None:
input_sizes = dict((k, v.chunk_size) for k, v in input_metas.items())
who_has = dict((k, meta.workers) for k, meta in input_metas.items())
candidate_workers = self._get_eps_by_worker_locality(who_has, input_sizes)
else:
candidate_workers = [target_worker]
candidate_workers = [w for w in candidate_workers if w not in reject_workers]
if not candidate_workers:
return None, []
# todo make more detailed allocation plans
calc_device = op_info.get("calc_device", "cpu")
try:
mem_usage = self._mem_usage_cache[op_key]
except KeyError:
pure_dep_keys = set(op_io_meta.get("pure_dep_chunk_keys", ()))
mem_usage = self._mem_usage_cache[op_key] = sum(
v.chunk_size for k, v in input_metas.items() if k not in pure_dep_keys
)
if calc_device == "cpu":
alloc_dict = dict(cpu=options.scheduler.default_cpu_usage, mem_quota=mem_usage)
elif calc_device == "cuda":
alloc_dict = dict(
cuda=options.scheduler.default_cuda_usage, mem_quota=mem_usage
)
else: # pragma: no cover
raise NotImplementedError(f"Calc device {calc_device} not supported")
last_assign = self._session_last_assigns.get(session_id, time.time())
timeout_on_fail = time.time() - last_assign > options.scheduler.assign_timeout
rejects = []
for worker_ep in candidate_workers:
if self._resource_ref.allocate_resource(
session_id, op_key, worker_ep, alloc_dict, log_fail=timeout_on_fail
):
logger.debug(
"Operand %s(%s) allocated to run in %s",
op_key,
op_info["op_name"],
worker_ep,
)
self._mem_usage_cache.pop(op_key, None)
self.get_actor_ref(
BaseOperandActor.gen_uid(session_id, op_key)
).submit_to_worker(worker_ep, input_metas, _tell=True, _wait=False)
return worker_ep, rejects
else:
rejects.append(worker_ep)
if timeout_on_fail:
running_ops = sum(
len(metrics.get("progress", dict()).get(str(session_id), dict()))
for metrics in self._worker_metrics.values()
)
if running_ops == 0:
raise TimeoutError(f"Assign resources to operand {op_key} timed out")
else:
self._session_last_assigns[session_id] = time.time()
return None, rejects
|
def _allocate_resource(
self, session_id, op_key, op_info, target_worker=None, reject_workers=None
):
"""
Allocate resource for single operand
:param session_id: session id
:param op_key: operand key
:param op_info: operand info dict
:param target_worker: worker to allocate, can be None
:param reject_workers: workers denied to assign to
"""
if target_worker not in self._worker_metrics:
target_worker = None
reject_workers = reject_workers or set()
op_io_meta = op_info.get("io_meta", {})
try:
input_metas = op_io_meta["input_data_metas"]
except KeyError:
input_metas = self._get_chunks_meta(
session_id, op_io_meta.get("input_chunks", {})
)
missing_keys = [k for k, m in input_metas.items() if m is None]
if missing_keys:
raise DependencyMissing(
f"Dependencies {missing_keys!r} missing for operand {op_key}"
)
if target_worker is None:
input_sizes = dict((k, v.chunk_size) for k, v in input_metas.items())
who_has = dict((k, meta.workers) for k, meta in input_metas.items())
candidate_workers = self._get_eps_by_worker_locality(who_has, input_sizes)
else:
candidate_workers = [target_worker]
candidate_workers = [w for w in candidate_workers if w not in reject_workers]
if not candidate_workers:
return None, []
# todo make more detailed allocation plans
calc_device = op_info.get("calc_device", "cpu")
try:
mem_usage = self._mem_usage_cache[op_key]
except KeyError:
mem_usage = self._mem_usage_cache[op_key] = sum(
v.chunk_size for v in input_metas.values()
)
if calc_device == "cpu":
alloc_dict = dict(cpu=options.scheduler.default_cpu_usage, mem_quota=mem_usage)
elif calc_device == "cuda":
alloc_dict = dict(
cuda=options.scheduler.default_cuda_usage, mem_quota=mem_usage
)
else: # pragma: no cover
raise NotImplementedError(f"Calc device {calc_device} not supported")
last_assign = self._session_last_assigns.get(session_id, time.time())
timeout_on_fail = time.time() - last_assign > options.scheduler.assign_timeout
rejects = []
for worker_ep in candidate_workers:
if self._resource_ref.allocate_resource(
session_id, op_key, worker_ep, alloc_dict, log_fail=timeout_on_fail
):
logger.debug(
"Operand %s(%s) allocated to run in %s",
op_key,
op_info["op_name"],
worker_ep,
)
self._mem_usage_cache.pop(op_key, None)
self.get_actor_ref(
BaseOperandActor.gen_uid(session_id, op_key)
).submit_to_worker(worker_ep, input_metas, _tell=True, _wait=False)
return worker_ep, rejects
else:
rejects.append(worker_ep)
if timeout_on_fail:
running_ops = sum(
len(metrics.get("progress", dict()).get(str(session_id), dict()))
for metrics in self._worker_metrics.values()
)
if running_ops == 0:
raise TimeoutError(f"Assign resources to operand {op_key} timed out")
else:
self._session_last_assigns[session_id] = time.time()
return None, rejects
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def _collect_operand_io_meta(graph, chunks):
# collect operand i/o information
predecessor_keys = set()
successor_keys = set()
input_chunk_keys = set()
shared_input_chunk_keys = set()
pure_dep_chunk_keys = set()
no_prepare_chunk_keys = set()
chunk_keys = set()
shuffle_keys = dict()
predecessors_to_successors = dict()
for c in chunks:
# handling predecessor args
for pn in graph.iter_predecessors(c):
if not isinstance(pn.op, Fetch):
predecessor_keys.add(pn.op.key)
input_chunk_keys.add(pn.key)
if graph.count_successors(pn) > 1:
shared_input_chunk_keys.add(pn.key)
for inp, prep in zip(c.op.inputs or (), c.op.prepare_inputs):
if not prep and inp.key in input_chunk_keys:
no_prepare_chunk_keys.add(inp.key)
for inp, is_dep in zip(c.op.inputs or (), c.op.pure_depends):
if is_dep and inp.key in input_chunk_keys:
pure_dep_chunk_keys.add(inp.key)
# handling successor args
for sn in graph.iter_successors(c):
successor_keys.add(sn.op.key)
if isinstance(c.op, ShuffleProxy):
for sn in graph.iter_successors(c):
shuffle_keys[sn.op.key] = get_chunk_shuffle_key(sn)
if isinstance(c.op, SuccessorsExclusive):
for sn in graph.iter_successors(c):
predecessors_to_successors[sn.inputs[0].op.key] = sn.op.key
chunk_keys.update(co.key for co in c.op.outputs)
io_meta = dict(
predecessors=list(predecessor_keys),
successors=list(successor_keys),
input_chunks=list(input_chunk_keys),
no_prepare_chunk_keys=list(no_prepare_chunk_keys),
pure_dep_chunk_keys=list(pure_dep_chunk_keys),
shared_input_chunks=list(shared_input_chunk_keys),
chunks=list(chunk_keys),
)
if shuffle_keys:
io_meta["shuffle_keys"] = [shuffle_keys.get(k) for k in io_meta["successors"]]
if predecessors_to_successors:
io_meta["predecessors_to_successors"] = predecessors_to_successors
return io_meta
|
def _collect_operand_io_meta(graph, chunks):
# collect operand i/o information
predecessor_keys = set()
successor_keys = set()
input_chunk_keys = set()
shared_input_chunk_keys = set()
no_prepare_chunk_keys = set()
chunk_keys = set()
shuffle_keys = dict()
predecessors_to_successors = dict()
for c in chunks:
# handling predecessor args
for pn in graph.iter_predecessors(c):
if not isinstance(pn.op, Fetch):
predecessor_keys.add(pn.op.key)
input_chunk_keys.add(pn.key)
if graph.count_successors(pn) > 1:
shared_input_chunk_keys.add(pn.key)
for inp, prep in zip(c.op.inputs or (), c.op.prepare_inputs):
if not prep and inp.key in input_chunk_keys:
no_prepare_chunk_keys.add(inp.key)
# handling successor args
for sn in graph.iter_successors(c):
successor_keys.add(sn.op.key)
if isinstance(c.op, ShuffleProxy):
for sn in graph.iter_successors(c):
shuffle_keys[sn.op.key] = get_chunk_shuffle_key(sn)
if isinstance(c.op, SuccessorsExclusive):
for sn in graph.iter_successors(c):
predecessors_to_successors[sn.inputs[0].op.key] = sn.op.key
chunk_keys.update(co.key for co in c.op.outputs)
io_meta = dict(
predecessors=list(predecessor_keys),
successors=list(successor_keys),
input_chunks=list(input_chunk_keys),
no_prepare_chunk_keys=list(no_prepare_chunk_keys),
shared_input_chunks=list(shared_input_chunk_keys),
chunks=list(chunk_keys),
)
if shuffle_keys:
io_meta["shuffle_keys"] = [shuffle_keys.get(k) for k in io_meta["successors"]]
if predecessors_to_successors:
io_meta["predecessors_to_successors"] = predecessors_to_successors
return io_meta
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def _get_keys_to_fetch(graph):
from ..operands import Fetch, FetchShuffle
fetch_keys = set()
exclude_fetch_keys = set()
for chunk in graph:
if isinstance(chunk.op, Fetch):
fetch_keys.add(chunk.op.to_fetch_key or chunk.key)
elif isinstance(chunk.op, FetchShuffle):
shuffle_key = get_chunk_shuffle_key(graph.successors(chunk)[0])
for k in chunk.op.to_fetch_keys:
fetch_keys.add((k, shuffle_key))
else:
for inp, prepare_inp, is_dep in zip(
chunk.inputs, chunk.op.prepare_inputs, chunk.op.pure_depends
):
if not prepare_inp or is_dep:
exclude_fetch_keys.add(inp.key)
return list(fetch_keys - exclude_fetch_keys)
|
def _get_keys_to_fetch(graph):
from ..operands import Fetch, FetchShuffle
fetch_keys = set()
exclude_fetch_keys = set()
for chunk in graph:
if isinstance(chunk.op, Fetch):
fetch_keys.add(chunk.op.to_fetch_key or chunk.key)
elif isinstance(chunk.op, FetchShuffle):
shuffle_key = get_chunk_shuffle_key(graph.successors(chunk)[0])
for k in chunk.op.to_fetch_keys:
fetch_keys.add((k, shuffle_key))
else:
for inp, prepare_inp in zip(chunk.inputs, chunk.op.prepare_inputs):
if not prepare_inp:
exclude_fetch_keys.add(inp.key)
return list(fetch_keys - exclude_fetch_keys)
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def _calc_results(self, session_id, graph_key, graph, context_dict, chunk_targets):
_, op_name = concat_operand_keys(graph, "_")
logger.debug("Start calculating operand %s in %s.", graph_key, self.uid)
start_time = time.time()
for chunk in graph:
for inp, prepare_inp, is_dep in zip(
chunk.inputs, chunk.op.prepare_inputs, chunk.op.pure_depends
):
if not prepare_inp or is_dep:
context_dict[inp.key] = None
local_context_dict = DistributedDictContext(
self.get_scheduler(self.default_uid()),
session_id,
actor_ctx=self.ctx,
address=self.address,
n_cpu=self._get_n_cpu(),
is_distributed=True,
resource_ref=self._resource_ref,
)
local_context_dict["_actor_cls"] = type(self)
local_context_dict["_actor_uid"] = self.uid
local_context_dict["_op_key"] = graph_key
local_context_dict.update(context_dict)
context_dict.clear()
if self._execution_ref:
self._execution_ref.deallocate_scheduler_resource(
session_id, graph_key, delay=0.5, _tell=True, _wait=False
)
# start actual execution
executor = Executor(storage=local_context_dict)
with EventContext(
self._events_ref,
EventCategory.PROCEDURE,
EventLevel.NORMAL,
self._calc_event_type,
self.uid,
):
self._execution_pool.submit(
executor.execute_graph, graph, chunk_targets, retval=False
).result()
end_time = time.time()
# collect results
result_keys = []
result_values, result_sizes, result_shapes = [], [], []
collected_chunk_keys = set()
for k in list(local_context_dict.keys()):
v = local_context_dict[k]
if isinstance(k, tuple):
k = tuple(to_str(i) for i in k)
else:
k = to_str(k)
chunk_key = get_chunk_key(k)
if chunk_key in chunk_targets:
result_keys.append(k)
if self._calc_intermediate_device in self._calc_dest_devices:
result_values.append(v)
result_sizes.append(calc_data_size(v))
else:
result_values.append(dataserializer.serialize(v))
result_sizes.append(result_values[-1].total_bytes)
result_shapes.append(getattr(v, "shape", None))
collected_chunk_keys.add(chunk_key)
local_context_dict.pop(k)
# check if all targets generated
if any(k not in collected_chunk_keys for k in chunk_targets):
raise KeyError([k for k in chunk_targets if k not in collected_chunk_keys])
# adjust sizes in allocation
apply_allocs = defaultdict(lambda: 0)
for k, size in zip(result_keys, result_sizes):
apply_allocs[get_chunk_key(k)] += size
apply_alloc_quota_keys, apply_alloc_sizes = [], []
for k, v in apply_allocs.items():
apply_alloc_quota_keys.append(
build_quota_key(session_id, k, owner=self.proc_id)
)
apply_alloc_sizes.append(v)
self._mem_quota_ref.alter_allocations(
apply_alloc_quota_keys, apply_alloc_sizes, _tell=True, _wait=False
)
self._mem_quota_ref.hold_quotas(apply_alloc_quota_keys, _tell=True)
if self._status_ref:
self._status_ref.update_mean_stats(
"calc_speed." + op_name,
sum(apply_alloc_sizes) * 1.0 / (end_time - start_time),
_tell=True,
_wait=False,
)
return self.storage_client.put_objects(
session_id,
result_keys,
result_values,
[self._calc_intermediate_device],
sizes=result_sizes,
shapes=result_shapes,
).then(lambda *_: result_keys)
|
def _calc_results(self, session_id, graph_key, graph, context_dict, chunk_targets):
_, op_name = concat_operand_keys(graph, "_")
logger.debug("Start calculating operand %s in %s.", graph_key, self.uid)
start_time = time.time()
for chunk in graph:
for inp, prepare_inp in zip(chunk.inputs, chunk.op.prepare_inputs):
if not prepare_inp:
context_dict[inp.key] = None
local_context_dict = DistributedDictContext(
self.get_scheduler(self.default_uid()),
session_id,
actor_ctx=self.ctx,
address=self.address,
n_cpu=self._get_n_cpu(),
is_distributed=True,
resource_ref=self._resource_ref,
)
local_context_dict["_actor_cls"] = type(self)
local_context_dict["_actor_uid"] = self.uid
local_context_dict["_op_key"] = graph_key
local_context_dict.update(context_dict)
context_dict.clear()
if self._execution_ref:
self._execution_ref.deallocate_scheduler_resource(
session_id, graph_key, delay=0.5, _tell=True, _wait=False
)
# start actual execution
executor = Executor(storage=local_context_dict)
with EventContext(
self._events_ref,
EventCategory.PROCEDURE,
EventLevel.NORMAL,
self._calc_event_type,
self.uid,
):
self._execution_pool.submit(
executor.execute_graph, graph, chunk_targets, retval=False
).result()
end_time = time.time()
# collect results
result_keys = []
result_values, result_sizes, result_shapes = [], [], []
collected_chunk_keys = set()
for k in list(local_context_dict.keys()):
v = local_context_dict[k]
if isinstance(k, tuple):
k = tuple(to_str(i) for i in k)
else:
k = to_str(k)
chunk_key = get_chunk_key(k)
if chunk_key in chunk_targets:
result_keys.append(k)
if self._calc_intermediate_device in self._calc_dest_devices:
result_values.append(v)
result_sizes.append(calc_data_size(v))
else:
result_values.append(dataserializer.serialize(v))
result_sizes.append(result_values[-1].total_bytes)
result_shapes.append(getattr(v, "shape", None))
collected_chunk_keys.add(chunk_key)
local_context_dict.pop(k)
# check if all targets generated
if any(k not in collected_chunk_keys for k in chunk_targets):
raise KeyError([k for k in chunk_targets if k not in collected_chunk_keys])
# adjust sizes in allocation
apply_allocs = defaultdict(lambda: 0)
for k, size in zip(result_keys, result_sizes):
apply_allocs[get_chunk_key(k)] += size
apply_alloc_quota_keys, apply_alloc_sizes = [], []
for k, v in apply_allocs.items():
apply_alloc_quota_keys.append(
build_quota_key(session_id, k, owner=self.proc_id)
)
apply_alloc_sizes.append(v)
self._mem_quota_ref.alter_allocations(
apply_alloc_quota_keys, apply_alloc_sizes, _tell=True, _wait=False
)
self._mem_quota_ref.hold_quotas(apply_alloc_quota_keys, _tell=True)
if self._status_ref:
self._status_ref.update_mean_stats(
"calc_speed." + op_name,
sum(apply_alloc_sizes) * 1.0 / (end_time - start_time),
_tell=True,
_wait=False,
)
return self.storage_client.put_objects(
session_id,
result_keys,
result_values,
[self._calc_intermediate_device],
sizes=result_sizes,
shapes=result_shapes,
).then(lambda *_: result_keys)
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def __init__(
self,
graph_serialized,
state,
chunk_targets=None,
data_targets=None,
io_meta=None,
data_metas=None,
mem_request=None,
shared_input_chunks=None,
pinned_keys=None,
mem_overhead_keys=None,
est_finish_time=None,
calc_actor_uid=None,
send_addresses=None,
retry_delay=None,
finish_callbacks=None,
stop_requested=False,
calc_device=None,
preferred_data_device=None,
resource_released=False,
no_prepare_chunk_keys=None,
pure_dep_chunk_keys=None,
):
self.graph_serialized = graph_serialized
graph = self.graph = deserialize_graph(graph_serialized)
self._state = state
self.state_time = time.time()
self.data_targets = data_targets or []
self.chunk_targets = chunk_targets or []
self.io_meta = io_meta or dict()
self.data_metas = data_metas or dict()
self.shared_input_chunks = shared_input_chunks or set()
self.mem_request = mem_request or dict()
self.pinned_keys = set(pinned_keys or [])
self.mem_overhead_keys = set(mem_overhead_keys or [])
self.est_finish_time = est_finish_time or time.time()
self.calc_actor_uid = calc_actor_uid
self.send_addresses = send_addresses
self.retry_delay = retry_delay or 0
self.retry_pending = False
self.finish_callbacks = finish_callbacks or []
self.stop_requested = stop_requested or False
self.calc_device = calc_device
self.preferred_data_device = preferred_data_device
self.resource_released = resource_released
self.no_prepare_chunk_keys = no_prepare_chunk_keys or set()
self.pure_dep_chunk_keys = pure_dep_chunk_keys or set()
_, self.op_string = concat_operand_keys(graph)
|
def __init__(
self,
graph_serialized,
state,
chunk_targets=None,
data_targets=None,
io_meta=None,
data_metas=None,
mem_request=None,
shared_input_chunks=None,
pinned_keys=None,
mem_overhead_keys=None,
est_finish_time=None,
calc_actor_uid=None,
send_addresses=None,
retry_delay=None,
finish_callbacks=None,
stop_requested=False,
calc_device=None,
preferred_data_device=None,
resource_released=False,
no_prepare_chunk_keys=None,
):
self.graph_serialized = graph_serialized
graph = self.graph = deserialize_graph(graph_serialized)
self._state = state
self.state_time = time.time()
self.data_targets = data_targets or []
self.chunk_targets = chunk_targets or []
self.io_meta = io_meta or dict()
self.data_metas = data_metas or dict()
self.shared_input_chunks = shared_input_chunks or set()
self.mem_request = mem_request or dict()
self.pinned_keys = set(pinned_keys or [])
self.mem_overhead_keys = set(mem_overhead_keys or [])
self.est_finish_time = est_finish_time or time.time()
self.calc_actor_uid = calc_actor_uid
self.send_addresses = send_addresses
self.retry_delay = retry_delay or 0
self.retry_pending = False
self.finish_callbacks = finish_callbacks or []
self.stop_requested = stop_requested or False
self.calc_device = calc_device
self.preferred_data_device = preferred_data_device
self.resource_released = resource_released
self.no_prepare_chunk_keys = no_prepare_chunk_keys or set()
_, self.op_string = concat_operand_keys(graph)
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def _prepare_quota_request(self, session_id, graph_key):
"""
Calculate quota request for an execution graph
:param session_id: session id
:param graph_key: key of the execution graph
:return: allocation dict
"""
try:
graph_record = self._graph_records[(session_id, graph_key)]
except KeyError:
return None
graph = graph_record.graph
storage_client = self.storage_client
input_data_sizes = dict(
(k, v.chunk_size) for k, v in graph_record.data_metas.items()
)
alloc_mem_batch = dict()
alloc_cache_batch = dict()
input_chunk_keys = dict()
if self._status_ref:
self.estimate_graph_finish_time(session_id, graph_key)
if (
graph_record.preferred_data_device == DataStorageDevice.SHARED_MEMORY
or graph_record.preferred_data_device == DataStorageDevice.VINEYARD
): # pragma: no cover
memory_estimations = self._estimate_calc_memory(session_id, graph_key)
else:
memory_estimations = dict()
graph_record.mem_overhead_keys = set()
# collect potential allocation sizes
for chunk in graph:
op = chunk.op
overhead_keys_and_shapes = []
if isinstance(op, Fetch):
if (
chunk.key in graph_record.no_prepare_chunk_keys
or chunk.key in graph_record.pure_dep_chunk_keys
):
continue
# use actual size as potential allocation size
input_chunk_keys[chunk.key] = input_data_sizes.get(
chunk.key
) or calc_data_size(chunk)
overhead_keys_and_shapes = [(chunk.key, getattr(chunk, "shape", None))]
elif isinstance(op, FetchShuffle):
shuffle_key = get_chunk_shuffle_key(graph.successors(chunk)[0])
overhead_keys_and_shapes = chunk.extra_params.get("_shapes", dict()).items()
for k in op.to_fetch_keys:
part_key = (k, shuffle_key)
try:
input_chunk_keys[part_key] = input_data_sizes[part_key]
except KeyError:
pass
elif chunk.key in graph_record.chunk_targets:
# use estimated size as potential allocation size
estimation_data = memory_estimations.get(chunk.key)
if not estimation_data:
continue
quota_key = build_quota_key(session_id, chunk.key, owner=graph_key)
cache_batch, alloc_mem_batch[quota_key] = estimation_data
if not isinstance(chunk.key, tuple):
alloc_cache_batch[chunk.key] = cache_batch
for key, shape in overhead_keys_and_shapes:
overhead = calc_object_overhead(chunk, shape)
if overhead:
graph_record.mem_overhead_keys.add(key)
quota_key = build_quota_key(session_id, key, owner=graph_key)
alloc_mem_batch[quota_key] = overhead
keys_to_pin = list(input_chunk_keys.keys())
graph_record.pinned_keys = set()
self._pin_shared_data_keys(session_id, graph_key, keys_to_pin)
for k, v in input_chunk_keys.items():
quota_key = build_quota_key(session_id, k, owner=graph_key)
if quota_key not in alloc_mem_batch:
if k in graph_record.pinned_keys or k in graph_record.shared_input_chunks:
continue
alloc_mem_batch[quota_key] = alloc_mem_batch.get(quota_key, 0) + v
if alloc_cache_batch:
storage_client.spill_size(
sum(alloc_cache_batch.values()), [graph_record.preferred_data_device]
)
graph_record.mem_request = alloc_mem_batch or dict()
return alloc_mem_batch
|
def _prepare_quota_request(self, session_id, graph_key):
"""
Calculate quota request for an execution graph
:param session_id: session id
:param graph_key: key of the execution graph
:return: allocation dict
"""
try:
graph_record = self._graph_records[(session_id, graph_key)]
except KeyError:
return None
graph = graph_record.graph
storage_client = self.storage_client
input_data_sizes = dict(
(k, v.chunk_size) for k, v in graph_record.data_metas.items()
)
alloc_mem_batch = dict()
alloc_cache_batch = dict()
input_chunk_keys = dict()
if self._status_ref:
self.estimate_graph_finish_time(session_id, graph_key)
if (
graph_record.preferred_data_device == DataStorageDevice.SHARED_MEMORY
or graph_record.preferred_data_device == DataStorageDevice.VINEYARD
): # pragma: no cover
memory_estimations = self._estimate_calc_memory(session_id, graph_key)
else:
memory_estimations = dict()
graph_record.mem_overhead_keys = set()
# collect potential allocation sizes
for chunk in graph:
op = chunk.op
overhead_keys_and_shapes = []
if isinstance(op, Fetch):
if chunk.key in graph_record.no_prepare_chunk_keys:
continue
# use actual size as potential allocation size
input_chunk_keys[chunk.key] = input_data_sizes.get(
chunk.key
) or calc_data_size(chunk)
overhead_keys_and_shapes = [(chunk.key, getattr(chunk, "shape", None))]
elif isinstance(op, FetchShuffle):
shuffle_key = get_chunk_shuffle_key(graph.successors(chunk)[0])
overhead_keys_and_shapes = chunk.extra_params.get("_shapes", dict()).items()
for k in op.to_fetch_keys:
part_key = (k, shuffle_key)
try:
input_chunk_keys[part_key] = input_data_sizes[part_key]
except KeyError:
pass
elif chunk.key in graph_record.chunk_targets:
# use estimated size as potential allocation size
estimation_data = memory_estimations.get(chunk.key)
if not estimation_data:
continue
quota_key = build_quota_key(session_id, chunk.key, owner=graph_key)
cache_batch, alloc_mem_batch[quota_key] = estimation_data
if not isinstance(chunk.key, tuple):
alloc_cache_batch[chunk.key] = cache_batch
for key, shape in overhead_keys_and_shapes:
overhead = calc_object_overhead(chunk, shape)
if overhead:
graph_record.mem_overhead_keys.add(key)
quota_key = build_quota_key(session_id, key, owner=graph_key)
alloc_mem_batch[quota_key] = overhead
keys_to_pin = list(input_chunk_keys.keys())
graph_record.pinned_keys = set()
self._pin_shared_data_keys(session_id, graph_key, keys_to_pin)
for k, v in input_chunk_keys.items():
quota_key = build_quota_key(session_id, k, owner=graph_key)
if quota_key not in alloc_mem_batch:
if k in graph_record.pinned_keys or k in graph_record.shared_input_chunks:
continue
alloc_mem_batch[quota_key] = alloc_mem_batch.get(quota_key, 0) + v
if alloc_cache_batch:
storage_client.spill_size(
sum(alloc_cache_batch.values()), [graph_record.preferred_data_device]
)
graph_record.mem_request = alloc_mem_batch or dict()
return alloc_mem_batch
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def execute_graph(
self,
session_id,
graph_key,
graph_ser,
io_meta,
data_metas,
calc_device=None,
send_addresses=None,
callback=None,
):
"""
Submit graph to the worker and control the execution
:param session_id: session id
:param graph_key: graph key
:param graph_ser: serialized executable graph
:param io_meta: io meta of the chunk
:param data_metas: data meta of each input chunk, as a dict
:param calc_device: device for calculation, can be 'gpu' or 'cpu'
:param send_addresses: targets to send results after execution
:param callback: promise callback
"""
session_graph_key = (session_id, graph_key)
callback = callback or []
if not isinstance(callback, list):
callback = [callback]
try:
all_callbacks = self._graph_records[session_graph_key].finish_callbacks or []
self._graph_records[session_graph_key].finish_callbacks.extend(callback)
if not self._graph_records[session_graph_key].retry_pending:
self._graph_records[session_graph_key].finish_callbacks = (
all_callbacks + callback
)
return
except KeyError:
all_callbacks = []
all_callbacks.extend(callback)
calc_device = calc_device or "cpu"
if calc_device == "cpu": # pragma: no cover
if options.vineyard.socket:
preferred_data_device = DataStorageDevice.VINEYARD
else:
preferred_data_device = DataStorageDevice.SHARED_MEMORY
else:
preferred_data_device = DataStorageDevice.CUDA
# todo change this when handling multiple devices
if preferred_data_device == DataStorageDevice.CUDA:
slot = self._dispatch_ref.get_slots(calc_device)[0]
proc_id = self.ctx.distributor.distribute(slot)
preferred_data_device = (proc_id, preferred_data_device)
graph_record = self._graph_records[(session_id, graph_key)] = GraphExecutionRecord(
graph_ser,
ExecutionState.ALLOCATING,
io_meta=io_meta,
data_metas=data_metas,
chunk_targets=io_meta["chunks"],
data_targets=list(io_meta.get("data_targets") or io_meta["chunks"]),
shared_input_chunks=set(io_meta.get("shared_input_chunks", [])),
send_addresses=send_addresses,
finish_callbacks=all_callbacks,
calc_device=calc_device,
preferred_data_device=preferred_data_device,
no_prepare_chunk_keys=io_meta.get("no_prepare_chunk_keys") or set(),
pure_dep_chunk_keys=io_meta.get("pure_dep_chunk_keys") or set(),
)
_, long_op_string = concat_operand_keys(graph_record.graph, decompose=True)
if long_op_string != graph_record.op_string:
long_op_string = graph_record.op_string + ":" + long_op_string
logger.debug(
"Worker graph %s(%s) targeting at %r accepted.",
graph_key,
long_op_string,
graph_record.chunk_targets,
)
self._update_state(session_id, graph_key, ExecutionState.ALLOCATING)
try:
del self._result_cache[session_graph_key]
except KeyError:
pass
@log_unhandled
def _handle_success(*_):
self._invoke_finish_callbacks(session_id, graph_key)
@log_unhandled
def _handle_rejection(*exc):
# some error occurred...
logger.debug("Entering _handle_rejection() for graph %s", graph_key)
self._dump_execution_states()
if graph_record.stop_requested:
graph_record.stop_requested = False
if not isinstance(exc[1], ExecutionInterrupted):
exc = build_exc_info(ExecutionInterrupted)
if isinstance(exc[1], ExecutionInterrupted):
logger.warning("Execution of graph %s interrupted.", graph_key)
else:
logger.exception(
"Unexpected error occurred in executing graph %s",
graph_key,
exc_info=exc,
)
self._result_cache[(session_id, graph_key)] = GraphResultRecord(
*exc, succeeded=False
)
self._invoke_finish_callbacks(session_id, graph_key)
# collect target data already computed
attrs = self.storage_client.get_data_attrs(session_id, graph_record.data_targets)
save_attrs = dict((k, v) for k, v in zip(graph_record.data_targets, attrs) if v)
# when all target data are computed, report success directly
if all(k in save_attrs for k in graph_record.data_targets):
logger.debug(
"All predecessors of graph %s already computed, call finish directly.",
graph_key,
)
sizes = dict((k, v.size) for k, v in save_attrs.items())
shapes = dict((k, v.shape) for k, v in save_attrs.items())
self._result_cache[(session_id, graph_key)] = GraphResultRecord(sizes, shapes)
_handle_success()
else:
try:
quota_request = self._prepare_quota_request(session_id, graph_key)
except PinDataKeyFailed:
logger.debug("Failed to pin chunk for graph %s", graph_key)
# cannot pin input chunks: retry later
retry_delay = graph_record.retry_delay + 0.5 + random.random()
graph_record.retry_delay = min(1 + graph_record.retry_delay, 30)
graph_record.retry_pending = True
self.ref().execute_graph(
session_id,
graph_key,
graph_record.graph_serialized,
graph_record.io_meta,
graph_record.data_metas,
calc_device=calc_device,
send_addresses=send_addresses,
_tell=True,
_delay=retry_delay,
)
return
promise.finished().then(
lambda *_: self._mem_quota_ref.request_batch_quota(
quota_request, _promise=True
)
if quota_request
else None
).then(lambda *_: self._prepare_graph_inputs(session_id, graph_key)).then(
lambda *_: self._dispatch_ref.acquire_free_slot(calc_device, _promise=True)
).then(lambda uid: self._send_calc_request(session_id, graph_key, uid)).then(
lambda saved_keys: self._store_results(session_id, graph_key, saved_keys)
).then(_handle_success, _handle_rejection)
|
def execute_graph(
self,
session_id,
graph_key,
graph_ser,
io_meta,
data_metas,
calc_device=None,
send_addresses=None,
callback=None,
):
"""
Submit graph to the worker and control the execution
:param session_id: session id
:param graph_key: graph key
:param graph_ser: serialized executable graph
:param io_meta: io meta of the chunk
:param data_metas: data meta of each input chunk, as a dict
:param calc_device: device for calculation, can be 'gpu' or 'cpu'
:param send_addresses: targets to send results after execution
:param callback: promise callback
"""
session_graph_key = (session_id, graph_key)
callback = callback or []
if not isinstance(callback, list):
callback = [callback]
try:
all_callbacks = self._graph_records[session_graph_key].finish_callbacks or []
self._graph_records[session_graph_key].finish_callbacks.extend(callback)
if not self._graph_records[session_graph_key].retry_pending:
self._graph_records[session_graph_key].finish_callbacks = (
all_callbacks + callback
)
return
except KeyError:
all_callbacks = []
all_callbacks.extend(callback)
calc_device = calc_device or "cpu"
if calc_device == "cpu": # pragma: no cover
if options.vineyard.socket:
preferred_data_device = DataStorageDevice.VINEYARD
else:
preferred_data_device = DataStorageDevice.SHARED_MEMORY
else:
preferred_data_device = DataStorageDevice.CUDA
# todo change this when handling multiple devices
if preferred_data_device == DataStorageDevice.CUDA:
slot = self._dispatch_ref.get_slots(calc_device)[0]
proc_id = self.ctx.distributor.distribute(slot)
preferred_data_device = (proc_id, preferred_data_device)
graph_record = self._graph_records[(session_id, graph_key)] = GraphExecutionRecord(
graph_ser,
ExecutionState.ALLOCATING,
io_meta=io_meta,
data_metas=data_metas,
chunk_targets=io_meta["chunks"],
data_targets=list(io_meta.get("data_targets") or io_meta["chunks"]),
shared_input_chunks=set(io_meta.get("shared_input_chunks", [])),
send_addresses=send_addresses,
finish_callbacks=all_callbacks,
calc_device=calc_device,
preferred_data_device=preferred_data_device,
no_prepare_chunk_keys=io_meta.get("no_prepare_chunk_keys") or set(),
)
_, long_op_string = concat_operand_keys(graph_record.graph, decompose=True)
if long_op_string != graph_record.op_string:
long_op_string = graph_record.op_string + ":" + long_op_string
logger.debug(
"Worker graph %s(%s) targeting at %r accepted.",
graph_key,
long_op_string,
graph_record.chunk_targets,
)
self._update_state(session_id, graph_key, ExecutionState.ALLOCATING)
try:
del self._result_cache[session_graph_key]
except KeyError:
pass
@log_unhandled
def _handle_success(*_):
self._invoke_finish_callbacks(session_id, graph_key)
@log_unhandled
def _handle_rejection(*exc):
# some error occurred...
logger.debug("Entering _handle_rejection() for graph %s", graph_key)
self._dump_execution_states()
if graph_record.stop_requested:
graph_record.stop_requested = False
if not isinstance(exc[1], ExecutionInterrupted):
exc = build_exc_info(ExecutionInterrupted)
if isinstance(exc[1], ExecutionInterrupted):
logger.warning("Execution of graph %s interrupted.", graph_key)
else:
logger.exception(
"Unexpected error occurred in executing graph %s",
graph_key,
exc_info=exc,
)
self._result_cache[(session_id, graph_key)] = GraphResultRecord(
*exc, succeeded=False
)
self._invoke_finish_callbacks(session_id, graph_key)
# collect target data already computed
attrs = self.storage_client.get_data_attrs(session_id, graph_record.data_targets)
save_attrs = dict((k, v) for k, v in zip(graph_record.data_targets, attrs) if v)
# when all target data are computed, report success directly
if all(k in save_attrs for k in graph_record.data_targets):
logger.debug(
"All predecessors of graph %s already computed, call finish directly.",
graph_key,
)
sizes = dict((k, v.size) for k, v in save_attrs.items())
shapes = dict((k, v.shape) for k, v in save_attrs.items())
self._result_cache[(session_id, graph_key)] = GraphResultRecord(sizes, shapes)
_handle_success()
else:
try:
quota_request = self._prepare_quota_request(session_id, graph_key)
except PinDataKeyFailed:
logger.debug("Failed to pin chunk for graph %s", graph_key)
# cannot pin input chunks: retry later
retry_delay = graph_record.retry_delay + 0.5 + random.random()
graph_record.retry_delay = min(1 + graph_record.retry_delay, 30)
graph_record.retry_pending = True
self.ref().execute_graph(
session_id,
graph_key,
graph_record.graph_serialized,
graph_record.io_meta,
graph_record.data_metas,
calc_device=calc_device,
send_addresses=send_addresses,
_tell=True,
_delay=retry_delay,
)
return
promise.finished().then(
lambda *_: self._mem_quota_ref.request_batch_quota(
quota_request, _promise=True
)
if quota_request
else None
).then(lambda *_: self._prepare_graph_inputs(session_id, graph_key)).then(
lambda *_: self._dispatch_ref.acquire_free_slot(calc_device, _promise=True)
).then(lambda uid: self._send_calc_request(session_id, graph_key, uid)).then(
lambda saved_keys: self._store_results(session_id, graph_key, saved_keys)
).then(_handle_success, _handle_rejection)
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def _prepare_graph_inputs(self, session_id, graph_key):
"""
Load input data from spilled storage and other workers
:param session_id: session id
:param graph_key: key of the execution graph
"""
storage_client = self.storage_client
graph_record = self._graph_records[(session_id, graph_key)]
graph = graph_record.graph
input_metas = graph_record.io_meta.get("input_data_metas", dict())
if graph_record.stop_requested:
raise ExecutionInterrupted
logger.debug("Start preparing input data for graph %s", graph_key)
self._update_state(session_id, graph_key, ExecutionState.PREPARING_INPUTS)
prepare_promises = []
input_keys = set()
shuffle_keys = set()
for chunk in graph:
op = chunk.op
if isinstance(op, Fetch):
if (
chunk.key in graph_record.no_prepare_chunk_keys
or chunk.key in graph_record.pure_dep_chunk_keys
):
continue
input_keys.add(op.to_fetch_key or chunk.key)
elif isinstance(op, FetchShuffle):
shuffle_key = get_chunk_shuffle_key(graph.successors(chunk)[0])
for input_key in op.to_fetch_keys:
part_key = (input_key, shuffle_key)
input_keys.add(part_key)
shuffle_keys.add(part_key)
local_keys = graph_record.pinned_keys & set(input_keys)
non_local_keys = [k for k in input_keys if k not in local_keys]
non_local_locations = storage_client.get_data_locations(session_id, non_local_keys)
copy_keys = set(k for k, loc in zip(non_local_keys, non_local_locations) if loc)
remote_keys = [k for k in non_local_keys if k not in copy_keys]
# handle local keys
self._release_shared_store_quotas(session_id, graph_key, local_keys)
# handle move keys
prepare_promises.extend(self._prepare_copy_keys(session_id, graph_key, copy_keys))
# handle remote keys
prepare_promises.extend(
self._prepare_remote_keys(session_id, graph_key, remote_keys, input_metas)
)
logger.debug(
"Graph key %s: Targets %r, loaded keys %r, copy keys %s, remote keys %r",
graph_key,
graph_record.chunk_targets,
local_keys,
copy_keys,
remote_keys,
)
p = promise.all_(prepare_promises).then(
lambda *_: logger.debug("Data preparation for graph %s finished", graph_key)
)
return p
|
def _prepare_graph_inputs(self, session_id, graph_key):
"""
Load input data from spilled storage and other workers
:param session_id: session id
:param graph_key: key of the execution graph
"""
storage_client = self.storage_client
graph_record = self._graph_records[(session_id, graph_key)]
graph = graph_record.graph
input_metas = graph_record.io_meta.get("input_data_metas", dict())
if graph_record.stop_requested:
raise ExecutionInterrupted
logger.debug("Start preparing input data for graph %s", graph_key)
self._update_state(session_id, graph_key, ExecutionState.PREPARING_INPUTS)
prepare_promises = []
input_keys = set()
shuffle_keys = set()
for chunk in graph:
op = chunk.op
if isinstance(op, Fetch):
if chunk.key in graph_record.no_prepare_chunk_keys:
continue
input_keys.add(op.to_fetch_key or chunk.key)
elif isinstance(op, FetchShuffle):
shuffle_key = get_chunk_shuffle_key(graph.successors(chunk)[0])
for input_key in op.to_fetch_keys:
part_key = (input_key, shuffle_key)
input_keys.add(part_key)
shuffle_keys.add(part_key)
local_keys = graph_record.pinned_keys & set(input_keys)
non_local_keys = [k for k in input_keys if k not in local_keys]
non_local_locations = storage_client.get_data_locations(session_id, non_local_keys)
copy_keys = set(k for k, loc in zip(non_local_keys, non_local_locations) if loc)
remote_keys = [k for k in non_local_keys if k not in copy_keys]
# handle local keys
self._release_shared_store_quotas(session_id, graph_key, local_keys)
# handle move keys
prepare_promises.extend(self._prepare_copy_keys(session_id, graph_key, copy_keys))
# handle remote keys
prepare_promises.extend(
self._prepare_remote_keys(session_id, graph_key, remote_keys, input_metas)
)
logger.debug(
"Graph key %s: Targets %r, loaded keys %r, copy keys %s, remote keys %r",
graph_key,
graph_record.chunk_targets,
local_keys,
copy_keys,
remote_keys,
)
p = promise.all_(prepare_promises).then(
lambda *_: logger.debug("Data preparation for graph %s finished", graph_key)
)
return p
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def collect_status(self):
"""
Collect worker status and write to kvstore
"""
meta_dict = dict()
try:
if not self._upload_status:
return
cpu_percent = resource.cpu_percent()
disk_io = resource.disk_io_usage()
net_io = resource.net_io_usage()
if cpu_percent is None or disk_io is None or net_io is None:
return
hw_metrics = dict()
hw_metrics["cpu"] = max(0.0, resource.cpu_count() - cpu_percent / 100.0)
hw_metrics["cpu_used"] = cpu_percent / 100.0
hw_metrics["cpu_total"] = resource.cpu_count()
cuda_info = resource.cuda_info() if self._with_gpu else None
if cuda_info:
hw_metrics["cuda"] = cuda_info.gpu_count
hw_metrics["cuda_total"] = cuda_info.gpu_count
hw_metrics["disk_read"] = disk_io[0]
hw_metrics["disk_write"] = disk_io[1]
hw_metrics["net_receive"] = net_io[0]
hw_metrics["net_send"] = net_io[1]
iowait = resource.iowait()
if iowait is not None:
hw_metrics["iowait"] = iowait
mem_stats = resource.virtual_memory()
hw_metrics["memory"] = int(mem_stats.available)
hw_metrics["memory_used"] = int(mem_stats.used)
hw_metrics["memory_total"] = int(mem_stats.total)
cache_allocations = self._status_ref.get_cache_allocations()
cache_total = cache_allocations.get("total", 0)
hw_metrics["cached_total"] = int(cache_total)
hw_metrics["cached_hold"] = int(cache_allocations.get("hold", 0))
mem_quota_allocations = self._status_ref.get_mem_quota_allocations()
mem_quota_total = mem_quota_allocations.get("total", 0)
mem_quota_allocated = mem_quota_allocations.get("allocated", 0)
if not mem_quota_allocations:
hw_metrics["mem_quota"] = hw_metrics["memory"]
hw_metrics["mem_quota_used"] = hw_metrics["memory_used"]
hw_metrics["mem_quota_total"] = hw_metrics["memory_total"]
hw_metrics["mem_quota_hold"] = 0
else:
hw_metrics["mem_quota"] = (
int(mem_quota_total - mem_quota_allocated) or hw_metrics["memory"]
)
hw_metrics["mem_quota_used"] = int(mem_quota_allocated)
hw_metrics["mem_quota_total"] = int(mem_quota_total)
hw_metrics["mem_quota_hold"] = int(mem_quota_allocations.get("hold", 0))
if options.worker.spill_directory:
if isinstance(options.worker.spill_directory, str):
spill_dirs = options.worker.spill_directory.split(":")
else:
spill_dirs = options.worker.spill_directory
if spill_dirs and "disk_stats" not in hw_metrics:
hw_metrics["disk_stats"] = dict()
disk_stats = hw_metrics["disk_stats"]
agg_disk_used = agg_disk_total = 0.0
agg_inode_used = agg_inode_total = 0
for spill_dir in spill_dirs:
if not os.path.exists(spill_dir):
continue
if spill_dir not in disk_stats:
disk_stats[spill_dir] = dict()
disk_usage = resource.disk_usage(spill_dir)
disk_stats[spill_dir]["disk_total"] = disk_usage.total
agg_disk_total += disk_usage.total
disk_stats[spill_dir]["disk_used"] = disk_usage.used
agg_disk_used += disk_usage.used
vfs_stat = os.statvfs(spill_dir)
disk_stats[spill_dir]["inode_total"] = vfs_stat.f_files
agg_inode_total += vfs_stat.f_files
disk_stats[spill_dir]["inode_used"] = (
vfs_stat.f_files - vfs_stat.f_favail
)
agg_inode_used += vfs_stat.f_files - vfs_stat.f_favail
hw_metrics["disk_used"] = agg_disk_used
hw_metrics["disk_total"] = agg_disk_total
hw_metrics["inode_used"] = agg_inode_used
hw_metrics["inode_total"] = agg_inode_total
cuda_card_stats = resource.cuda_card_stats() if self._with_gpu else None
if cuda_card_stats:
hw_metrics["cuda_stats"] = [
dict(
product_name=stat.product_name,
gpu_usage=stat.gpu_usage,
temperature=stat.temperature,
fb_memory_total=stat.fb_mem_info.total,
fb_memory_used=stat.fb_mem_info.used,
)
for stat in cuda_card_stats
]
meta_dict = dict()
meta_dict["hardware"] = hw_metrics
meta_dict["update_time"] = time.time()
meta_dict["stats"] = dict()
meta_dict["slots"] = dict()
status_data = self._status_ref.get_stats()
for k, v in status_data.items():
meta_dict["stats"][k] = v
slots_data = self._status_ref.get_slots()
for k, v in slots_data.items():
meta_dict["slots"][k] = v
meta_dict["progress"] = self._status_ref.get_progress()
meta_dict["details"] = gather_node_info()
if options.vineyard.socket: # pragma: no cover
import vineyard
client = vineyard.connect(options.vineyard.socket)
meta_dict["vineyard"] = {"instance_id": client.instance_id}
self._resource_ref.set_worker_meta(self._endpoint, meta_dict)
except Exception as ex:
logger.error(
"Failed to save status: %s. repr(meta_dict)=%r", str(ex), meta_dict
)
finally:
self.ref().collect_status(_tell=True, _delay=1)
|
def collect_status(self):
"""
Collect worker status and write to kvstore
"""
meta_dict = dict()
try:
if not self._upload_status:
return
cpu_percent = resource.cpu_percent()
disk_io = resource.disk_io_usage()
net_io = resource.net_io_usage()
if cpu_percent is None or disk_io is None or net_io is None:
return
hw_metrics = dict()
hw_metrics["cpu"] = max(0.0, resource.cpu_count() - cpu_percent / 100.0)
hw_metrics["cpu_used"] = cpu_percent / 100.0
hw_metrics["cpu_total"] = resource.cpu_count()
cuda_info = resource.cuda_info() if self._with_gpu else None
if cuda_info:
hw_metrics["cuda"] = cuda_info.gpu_count
hw_metrics["cuda_total"] = cuda_info.gpu_count
hw_metrics["disk_read"] = disk_io[0]
hw_metrics["disk_write"] = disk_io[1]
hw_metrics["net_receive"] = net_io[0]
hw_metrics["net_send"] = net_io[1]
iowait = resource.iowait()
if iowait is not None:
hw_metrics["iowait"] = iowait
mem_stats = resource.virtual_memory()
hw_metrics["memory"] = int(mem_stats.available)
hw_metrics["memory_used"] = int(mem_stats.used)
hw_metrics["memory_total"] = int(mem_stats.total)
cache_allocations = self._status_ref.get_cache_allocations()
cache_total = cache_allocations.get("total", 0)
hw_metrics["cached_total"] = int(cache_total)
hw_metrics["cached_hold"] = int(cache_allocations.get("hold", 0))
mem_quota_allocations = self._status_ref.get_mem_quota_allocations()
mem_quota_total = mem_quota_allocations.get("total", 0)
mem_quota_allocated = mem_quota_allocations.get("allocated", 0)
if not mem_quota_allocations:
hw_metrics["mem_quota"] = hw_metrics["memory"]
hw_metrics["mem_quota_used"] = hw_metrics["memory_used"]
hw_metrics["mem_quota_total"] = hw_metrics["memory_total"]
hw_metrics["mem_quota_hold"] = 0
else:
hw_metrics["mem_quota"] = (
int(mem_quota_total - mem_quota_allocated) or hw_metrics["memory"]
)
hw_metrics["mem_quota_used"] = int(mem_quota_allocated)
hw_metrics["mem_quota_total"] = int(mem_quota_total)
hw_metrics["mem_quota_hold"] = int(mem_quota_allocations.get("hold", 0))
if options.worker.spill_directory:
if isinstance(options.worker.spill_directory, str):
spill_dirs = options.worker.spill_directory.split(":")
else:
spill_dirs = options.worker.spill_directory
if spill_dirs and "disk_stats" not in hw_metrics:
hw_metrics["disk_stats"] = dict()
disk_stats = hw_metrics["disk_stats"]
agg_disk_used = 0.0
agg_disk_total = 0.0
for spill_dir in spill_dirs:
if not os.path.exists(spill_dir):
continue
if spill_dir not in disk_stats:
disk_stats[spill_dir] = dict()
disk_usage = resource.disk_usage(spill_dir)
disk_stats[spill_dir]["disk_total"] = disk_usage.total
agg_disk_total += disk_usage.total
disk_stats[spill_dir]["disk_used"] = disk_usage.used
agg_disk_used += disk_usage.used
hw_metrics["disk_used"] = agg_disk_used
hw_metrics["disk_total"] = agg_disk_total
cuda_card_stats = resource.cuda_card_stats() if self._with_gpu else None
if cuda_card_stats:
hw_metrics["cuda_stats"] = [
dict(
product_name=stat.product_name,
gpu_usage=stat.gpu_usage,
temperature=stat.temperature,
fb_memory_total=stat.fb_mem_info.total,
fb_memory_used=stat.fb_mem_info.used,
)
for stat in cuda_card_stats
]
meta_dict = dict()
meta_dict["hardware"] = hw_metrics
meta_dict["update_time"] = time.time()
meta_dict["stats"] = dict()
meta_dict["slots"] = dict()
status_data = self._status_ref.get_stats()
for k, v in status_data.items():
meta_dict["stats"][k] = v
slots_data = self._status_ref.get_slots()
for k, v in slots_data.items():
meta_dict["slots"][k] = v
meta_dict["progress"] = self._status_ref.get_progress()
meta_dict["details"] = gather_node_info()
if options.vineyard.socket: # pragma: no cover
import vineyard
client = vineyard.connect(options.vineyard.socket)
meta_dict["vineyard"] = {"instance_id": client.instance_id}
self._resource_ref.set_worker_meta(self._endpoint, meta_dict)
except Exception as ex:
logger.error(
"Failed to save status: %s. repr(meta_dict)=%r", str(ex), meta_dict
)
finally:
self.ref().collect_status(_tell=True, _delay=1)
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def put_objects_by_keys(self, session_id, data_keys, shapes=None, pin_token=None):
sizes = []
for data_key in data_keys:
buf = None
try:
buf = self._shared_store.get_buffer(session_id, data_key)
size = len(buf)
self._internal_put_object(session_id, data_key, buf, size)
finally:
del buf
sizes.append(size)
if pin_token:
self.pin_data_keys(session_id, data_keys, pin_token)
self._finish_put_objects(session_id, data_keys)
self.storage_client.register_data(
session_id, data_keys, (0, self._storage_device), sizes, shapes=shapes
)
|
def put_objects_by_keys(self, session_id, data_keys, shapes=None, pin_token=None):
sizes = []
for data_key in data_keys:
buf = None
try:
buf = self._shared_store.get_buffer(session_id, data_key)
size = len(buf)
self._internal_put_object(session_id, data_key, buf, size)
finally:
del buf
sizes.append(size)
if pin_token:
self.pin_data_keys(session_id, data_keys, pin_token)
self.storage_client.register_data(
session_id, data_keys, (0, self._storage_device), sizes, shapes=shapes
)
|
https://github.com/mars-project/mars/issues/1672
|
2020-11-02 16:51:59,275 mars.scheduler.operands.common 143 ERROR Attempt 1: Unexpected error ValueError occurred in executing operand 05f71b4ed53f21cea47398b40c0ec61d in 33.19.117.174:21137
Traceback (most recent call last):
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/promise.py", line 378, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 111, in request_batch_quota
make_first=all_allocated, process_quota=process_quota)
File "/home/admin/work/turing_dev-pymars-0.6.0a3.zip/mars/worker/quota.py", line 158, in _request_quota
raise ValueError(f'Cannot allocate quota size {delta} '
ValueError: Cannot allocate quota size 22002064004 larger than total capacity 21259621171.
|
ValueError
|
def execute(cls, ctx, op):
def _base_concat(chunk, inputs):
# auto generated concat when executing a DataFrame, Series or Index
if chunk.op.output_types[0] == OutputType.dataframe:
return _auto_concat_dataframe_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.series:
return _auto_concat_series_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.index:
return _auto_concat_index_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.categorical:
return _auto_concat_categorical_chunks(chunk, inputs)
else: # pragma: no cover
raise TypeError(
"Only DataFrameChunk, SeriesChunk, IndexChunk, "
"and CategoricalChunk can be automatically concatenated"
)
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = (
pd
if isinstance(inputs[0], (pd.DataFrame, pd.Series)) or cudf is None
else cudf
)
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
max_rows = max(inp.index[0] for inp in chunk.inputs)
min_rows = min(inp.index[0] for inp in chunk.inputs)
n_rows = max_rows - min_rows + 1
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) or cudf is None else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
def _auto_concat_index_chunks(chunk, inputs):
if len(inputs) == 1:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
concat_df = xdf.DataFrame(index=inputs[0])
else:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
concat_df = xdf.concat(empty_dfs, axis=0)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat_df.sort_index(inplace=True)
return concat_df.index
def _auto_concat_categorical_chunks(_, inputs):
if len(inputs) == 1: # pragma: no cover
return inputs[0]
else:
# convert categorical into array
arrays = [np.asarray(inp) for inp in inputs]
array = np.concatenate(arrays)
return pd.Categorical(
array, categories=inputs[0].categories, ordered=inputs[0].ordered
)
chunk = op.outputs[0]
inputs = [ctx[input.key] for input in op.inputs]
if isinstance(inputs[0], tuple):
ctx[chunk.key] = tuple(
_base_concat(chunk, [input[i] for input in inputs])
for i in range(len(inputs[0]))
)
else:
ctx[chunk.key] = _base_concat(chunk, inputs)
|
def execute(cls, ctx, op):
def _base_concat(chunk, inputs):
# auto generated concat when executing a DataFrame, Series or Index
if chunk.op.output_types[0] == OutputType.dataframe:
return _auto_concat_dataframe_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.series:
return _auto_concat_series_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.index:
return _auto_concat_index_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.categorical:
return _auto_concat_categorical_chunks(chunk, inputs)
else: # pragma: no cover
raise TypeError(
"Only DataFrameChunk, SeriesChunk, IndexChunk, "
"and CategoricalChunk can be automatically concatenated"
)
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = (
pd
if isinstance(inputs[0], (pd.DataFrame, pd.Series)) or cudf is None
else cudf
)
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
max_rows = max(inp.index[0] for inp in chunk.inputs)
min_rows = min(inp.index[0] for inp in chunk.inputs)
n_rows = max_rows - min_rows + 1
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if all(np.isscalar(inp) for inp in inputs):
return pd.Series(inputs)
else:
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) or cudf is None else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
def _auto_concat_index_chunks(chunk, inputs):
if len(inputs) == 1:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
concat_df = xdf.DataFrame(index=inputs[0])
else:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
concat_df = xdf.concat(empty_dfs, axis=0)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat_df.sort_index(inplace=True)
return concat_df.index
def _auto_concat_categorical_chunks(_, inputs):
if len(inputs) == 1: # pragma: no cover
return inputs[0]
else:
# convert categorical into array
arrays = [np.asarray(inp) for inp in inputs]
array = np.concatenate(arrays)
return pd.Categorical(
array, categories=inputs[0].categories, ordered=inputs[0].ordered
)
chunk = op.outputs[0]
inputs = [ctx[input.key] for input in op.inputs]
if isinstance(inputs[0], tuple):
ctx[chunk.key] = tuple(
_base_concat(chunk, [input[i] for input in inputs])
for i in range(len(inputs[0]))
)
else:
ctx[chunk.key] = _base_concat(chunk, inputs)
|
https://github.com/mars-project/mars/issues/1682
|
In [1]: import mars.dataframe as md
In [2]: from datetime import datetime
In [3]: s = md.Series([datetime.now(), datetime.now(), datetime.now()], chunk_si
...: ze=2)
In [4]: s.max().execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8891e4a12063> in <module>
----> 1 s.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
638
639 if wait:
--> 640 return run()
641 else:
642 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
634
635 def run():
--> 636 self.data.execute(session, **kw)
637 return self
638
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
374
375 if wait:
--> 376 return run()
377 else:
378 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
369 def run():
370 # no more fetch, thus just fire run
--> 371 session.run(self, **kw)
372 # return Tileable or ExecutableTuple itself
373 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/dataframe/merge/concat.py in execute(cls, ctx, op)
290 for i in range(len(inputs[0])))
291 else:
--> 292 ctx[chunk.key] = _base_concat(chunk, inputs)
293
294 @classmethod
~/Workspace/mars/mars/dataframe/merge/concat.py in _base_concat(chunk, inputs)
193 return _auto_concat_dataframe_chunks(chunk, inputs)
194 elif chunk.op.output_types[0] == OutputType.series:
--> 195 return _auto_concat_series_chunks(chunk, inputs)
196 elif chunk.op.output_types[0] == OutputType.index:
197 return _auto_concat_index_chunks(chunk, inputs)
~/Workspace/mars/mars/dataframe/merge/concat.py in _auto_concat_series_chunks(chunk, inputs)
256 concat = xdf.concat(inputs, axis=chunk.op.axis)
257 else:
--> 258 concat = xdf.concat(inputs)
259 if getattr(chunk.index_value, 'should_be_monotonic', False):
260 concat.sort_index(inplace=True)
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 verify_integrity=verify_integrity,
283 copy=copy,
--> 284 sort=sort,
285 )
286
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid
|
TypeError
|
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) or cudf is None else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
|
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if all(np.isscalar(inp) for inp in inputs):
return pd.Series(inputs)
else:
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) or cudf is None else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
|
https://github.com/mars-project/mars/issues/1682
|
In [1]: import mars.dataframe as md
In [2]: from datetime import datetime
In [3]: s = md.Series([datetime.now(), datetime.now(), datetime.now()], chunk_si
...: ze=2)
In [4]: s.max().execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8891e4a12063> in <module>
----> 1 s.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
638
639 if wait:
--> 640 return run()
641 else:
642 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
634
635 def run():
--> 636 self.data.execute(session, **kw)
637 return self
638
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
374
375 if wait:
--> 376 return run()
377 else:
378 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
369 def run():
370 # no more fetch, thus just fire run
--> 371 session.run(self, **kw)
372 # return Tileable or ExecutableTuple itself
373 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/dataframe/merge/concat.py in execute(cls, ctx, op)
290 for i in range(len(inputs[0])))
291 else:
--> 292 ctx[chunk.key] = _base_concat(chunk, inputs)
293
294 @classmethod
~/Workspace/mars/mars/dataframe/merge/concat.py in _base_concat(chunk, inputs)
193 return _auto_concat_dataframe_chunks(chunk, inputs)
194 elif chunk.op.output_types[0] == OutputType.series:
--> 195 return _auto_concat_series_chunks(chunk, inputs)
196 elif chunk.op.output_types[0] == OutputType.index:
197 return _auto_concat_index_chunks(chunk, inputs)
~/Workspace/mars/mars/dataframe/merge/concat.py in _auto_concat_series_chunks(chunk, inputs)
256 concat = xdf.concat(inputs, axis=chunk.op.axis)
257 else:
--> 258 concat = xdf.concat(inputs)
259 if getattr(chunk.index_value, 'should_be_monotonic', False):
260 concat.sort_index(inplace=True)
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 verify_integrity=verify_integrity,
283 copy=copy,
--> 284 sort=sort,
285 )
286
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid
|
TypeError
|
def _execute_map_with_count(cls, ctx, op, reduction_func=None):
# Execution with specified `min_count` in the map stage
xdf = cudf if op.gpu else pd
in_data = ctx[op.inputs[0].key]
if isinstance(in_data, pd.Series):
count = in_data.count()
else:
count = in_data.count(axis=op.axis, numeric_only=op.numeric_only)
r = cls._execute_reduction(in_data, op, reduction_func=reduction_func)
if isinstance(in_data, xdf.Series):
if op.output_types[0] == OutputType.series:
r = xdf.Series([r])
count = xdf.Series([count])
ctx[op.outputs[0].key] = (r, count)
else:
# For dataframe, will keep dimensions for intermediate results.
ctx[op.outputs[0].key] = (
(xdf.DataFrame(r), xdf.DataFrame(count))
if op.axis == 1
else (xdf.DataFrame(r).transpose(), xdf.DataFrame(count).transpose())
)
|
def _execute_map_with_count(cls, ctx, op, reduction_func=None):
# Execution with specified `min_count` in the map stage
xdf = cudf if op.gpu else pd
in_data = ctx[op.inputs[0].key]
if isinstance(in_data, pd.Series):
count = in_data.count()
else:
count = in_data.count(axis=op.axis, numeric_only=op.numeric_only)
r = cls._execute_reduction(in_data, op, reduction_func=reduction_func)
if isinstance(in_data, xdf.Series):
ctx[op.outputs[0].key] = (r, count)
else:
# For dataframe, will keep dimensions for intermediate results.
ctx[op.outputs[0].key] = (
(xdf.DataFrame(r), xdf.DataFrame(count))
if op.axis == 1
else (xdf.DataFrame(r).transpose(), xdf.DataFrame(count).transpose())
)
|
https://github.com/mars-project/mars/issues/1682
|
In [1]: import mars.dataframe as md
In [2]: from datetime import datetime
In [3]: s = md.Series([datetime.now(), datetime.now(), datetime.now()], chunk_si
...: ze=2)
In [4]: s.max().execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8891e4a12063> in <module>
----> 1 s.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
638
639 if wait:
--> 640 return run()
641 else:
642 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
634
635 def run():
--> 636 self.data.execute(session, **kw)
637 return self
638
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
374
375 if wait:
--> 376 return run()
377 else:
378 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
369 def run():
370 # no more fetch, thus just fire run
--> 371 session.run(self, **kw)
372 # return Tileable or ExecutableTuple itself
373 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/dataframe/merge/concat.py in execute(cls, ctx, op)
290 for i in range(len(inputs[0])))
291 else:
--> 292 ctx[chunk.key] = _base_concat(chunk, inputs)
293
294 @classmethod
~/Workspace/mars/mars/dataframe/merge/concat.py in _base_concat(chunk, inputs)
193 return _auto_concat_dataframe_chunks(chunk, inputs)
194 elif chunk.op.output_types[0] == OutputType.series:
--> 195 return _auto_concat_series_chunks(chunk, inputs)
196 elif chunk.op.output_types[0] == OutputType.index:
197 return _auto_concat_index_chunks(chunk, inputs)
~/Workspace/mars/mars/dataframe/merge/concat.py in _auto_concat_series_chunks(chunk, inputs)
256 concat = xdf.concat(inputs, axis=chunk.op.axis)
257 else:
--> 258 concat = xdf.concat(inputs)
259 if getattr(chunk.index_value, 'should_be_monotonic', False):
260 concat.sort_index(inplace=True)
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 verify_integrity=verify_integrity,
283 copy=copy,
--> 284 sort=sort,
285 )
286
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid
|
TypeError
|
def _execute_combine_with_count(cls, ctx, op, reduction_func=None):
# Execution with specified `min_count` in the combine stage
xdf = cudf if op.gpu else pd
in_data, concat_count = ctx[op.inputs[0].key]
count = concat_count.sum(axis=op.axis)
r = cls._execute_reduction(in_data, op, reduction_func=reduction_func)
if isinstance(in_data, xdf.Series):
if op.output_types[0] == OutputType.series:
r = xdf.Series([r])
count = xdf.Series([count])
ctx[op.outputs[0].key] = (r, count)
else:
# For dataframe, will keep dimensions for intermediate results.
ctx[op.outputs[0].key] = (
(xdf.DataFrame(r), xdf.DataFrame(count))
if op.axis == 1
else (xdf.DataFrame(r).transpose(), xdf.DataFrame(count).transpose())
)
|
def _execute_combine_with_count(cls, ctx, op, reduction_func=None):
# Execution with specified `min_count` in the combine stage
xdf = cudf if op.gpu else pd
in_data, concat_count = ctx[op.inputs[0].key]
count = concat_count.sum(axis=op.axis)
r = cls._execute_reduction(in_data, op, reduction_func=reduction_func)
if isinstance(in_data, xdf.Series):
ctx[op.outputs[0].key] = (r, count)
else:
# For dataframe, will keep dimensions for intermediate results.
ctx[op.outputs[0].key] = (
(xdf.DataFrame(r), xdf.DataFrame(count))
if op.axis == 1
else (xdf.DataFrame(r).transpose(), xdf.DataFrame(count).transpose())
)
|
https://github.com/mars-project/mars/issues/1682
|
In [1]: import mars.dataframe as md
In [2]: from datetime import datetime
In [3]: s = md.Series([datetime.now(), datetime.now(), datetime.now()], chunk_si
...: ze=2)
In [4]: s.max().execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8891e4a12063> in <module>
----> 1 s.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
638
639 if wait:
--> 640 return run()
641 else:
642 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
634
635 def run():
--> 636 self.data.execute(session, **kw)
637 return self
638
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
374
375 if wait:
--> 376 return run()
377 else:
378 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
369 def run():
370 # no more fetch, thus just fire run
--> 371 session.run(self, **kw)
372 # return Tileable or ExecutableTuple itself
373 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/dataframe/merge/concat.py in execute(cls, ctx, op)
290 for i in range(len(inputs[0])))
291 else:
--> 292 ctx[chunk.key] = _base_concat(chunk, inputs)
293
294 @classmethod
~/Workspace/mars/mars/dataframe/merge/concat.py in _base_concat(chunk, inputs)
193 return _auto_concat_dataframe_chunks(chunk, inputs)
194 elif chunk.op.output_types[0] == OutputType.series:
--> 195 return _auto_concat_series_chunks(chunk, inputs)
196 elif chunk.op.output_types[0] == OutputType.index:
197 return _auto_concat_index_chunks(chunk, inputs)
~/Workspace/mars/mars/dataframe/merge/concat.py in _auto_concat_series_chunks(chunk, inputs)
256 concat = xdf.concat(inputs, axis=chunk.op.axis)
257 else:
--> 258 concat = xdf.concat(inputs)
259 if getattr(chunk.index_value, 'should_be_monotonic', False):
260 concat.sort_index(inplace=True)
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 verify_integrity=verify_integrity,
283 copy=copy,
--> 284 sort=sort,
285 )
286
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid
|
TypeError
|
def _execute_without_count(cls, ctx, op, reduction_func=None):
# Execution for normal reduction operands.
# For dataframe, will keep dimensions for intermediate results.
xdf = cudf if op.gpu else pd
in_data = ctx[op.inputs[0].key]
r = cls._execute_reduction(
in_data, op, min_count=op.min_count, reduction_func=reduction_func
)
if isinstance(in_data, xdf.Series) or op.output_types[0] == OutputType.series:
if op.output_types[0] == OutputType.series and not isinstance(r, xdf.Series):
r = xdf.Series([r])
ctx[op.outputs[0].key] = r
else:
if op.axis == 0:
if op.gpu:
df = xdf.DataFrame(r).transpose()
df.columns = r.index.to_arrow().to_pylist()
else:
# cannot just do xdf.DataFrame(r).T
# cuz the dtype will be object since pandas 1.0
df = xdf.DataFrame(OrderedDict((d, [v]) for d, v in r.iteritems()))
else:
df = xdf.DataFrame(r)
ctx[op.outputs[0].key] = df
|
def _execute_without_count(cls, ctx, op, reduction_func=None):
# Execution for normal reduction operands.
# For dataframe, will keep dimensions for intermediate results.
xdf = cudf if op.gpu else pd
in_data = ctx[op.inputs[0].key]
r = cls._execute_reduction(
in_data, op, min_count=op.min_count, reduction_func=reduction_func
)
if isinstance(in_data, xdf.Series) or op.output_types[0] == OutputType.series:
ctx[op.outputs[0].key] = r
else:
if op.axis == 0:
if op.gpu:
df = xdf.DataFrame(r).transpose()
df.columns = r.index.to_arrow().to_pylist()
else:
# cannot just do xdf.DataFrame(r).T
# cuz the dtype will be object since pandas 1.0
df = xdf.DataFrame(OrderedDict((d, [v]) for d, v in r.iteritems()))
else:
df = xdf.DataFrame(r)
ctx[op.outputs[0].key] = df
|
https://github.com/mars-project/mars/issues/1682
|
In [1]: import mars.dataframe as md
In [2]: from datetime import datetime
In [3]: s = md.Series([datetime.now(), datetime.now(), datetime.now()], chunk_si
...: ze=2)
In [4]: s.max().execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8891e4a12063> in <module>
----> 1 s.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
638
639 if wait:
--> 640 return run()
641 else:
642 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
634
635 def run():
--> 636 self.data.execute(session, **kw)
637 return self
638
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
374
375 if wait:
--> 376 return run()
377 else:
378 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
369 def run():
370 # no more fetch, thus just fire run
--> 371 session.run(self, **kw)
372 # return Tileable or ExecutableTuple itself
373 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/dataframe/merge/concat.py in execute(cls, ctx, op)
290 for i in range(len(inputs[0])))
291 else:
--> 292 ctx[chunk.key] = _base_concat(chunk, inputs)
293
294 @classmethod
~/Workspace/mars/mars/dataframe/merge/concat.py in _base_concat(chunk, inputs)
193 return _auto_concat_dataframe_chunks(chunk, inputs)
194 elif chunk.op.output_types[0] == OutputType.series:
--> 195 return _auto_concat_series_chunks(chunk, inputs)
196 elif chunk.op.output_types[0] == OutputType.index:
197 return _auto_concat_index_chunks(chunk, inputs)
~/Workspace/mars/mars/dataframe/merge/concat.py in _auto_concat_series_chunks(chunk, inputs)
256 concat = xdf.concat(inputs, axis=chunk.op.axis)
257 else:
--> 258 concat = xdf.concat(inputs)
259 if getattr(chunk.index_value, 'should_be_monotonic', False):
260 concat.sort_index(inplace=True)
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 verify_integrity=verify_integrity,
283 copy=copy,
--> 284 sort=sort,
285 )
286
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid
|
TypeError
|
def _execute_combine(cls, ctx, op):
xdf = cudf if op.gpu else pd
in_data = ctx[op.inputs[0].key]
count_sum = in_data.sum(axis=op.axis)
if isinstance(in_data, xdf.Series):
if op.output_types[0] == OutputType.series and not isinstance(
count_sum, xdf.Series
):
count_sum = xdf.Series([count_sum])
ctx[op.outputs[0].key] = count_sum
else:
ctx[op.outputs[0].key] = (
xdf.DataFrame(count_sum)
if op.axis == 1
else xdf.DataFrame(count_sum).transpose()
)
|
def _execute_combine(cls, ctx, op):
xdf = cudf if op.gpu else pd
in_data = ctx[op.inputs[0].key]
count_sum = in_data.sum(axis=op.axis)
if isinstance(in_data, xdf.Series):
ctx[op.outputs[0].key] = count_sum
else:
ctx[op.outputs[0].key] = (
xdf.DataFrame(count_sum)
if op.axis == 1
else xdf.DataFrame(count_sum).transpose()
)
|
https://github.com/mars-project/mars/issues/1682
|
In [1]: import mars.dataframe as md
In [2]: from datetime import datetime
In [3]: s = md.Series([datetime.now(), datetime.now(), datetime.now()], chunk_si
...: ze=2)
In [4]: s.max().execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8891e4a12063> in <module>
----> 1 s.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
638
639 if wait:
--> 640 return run()
641 else:
642 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
634
635 def run():
--> 636 self.data.execute(session, **kw)
637 return self
638
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
374
375 if wait:
--> 376 return run()
377 else:
378 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
369 def run():
370 # no more fetch, thus just fire run
--> 371 session.run(self, **kw)
372 # return Tileable or ExecutableTuple itself
373 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/dataframe/merge/concat.py in execute(cls, ctx, op)
290 for i in range(len(inputs[0])))
291 else:
--> 292 ctx[chunk.key] = _base_concat(chunk, inputs)
293
294 @classmethod
~/Workspace/mars/mars/dataframe/merge/concat.py in _base_concat(chunk, inputs)
193 return _auto_concat_dataframe_chunks(chunk, inputs)
194 elif chunk.op.output_types[0] == OutputType.series:
--> 195 return _auto_concat_series_chunks(chunk, inputs)
196 elif chunk.op.output_types[0] == OutputType.index:
197 return _auto_concat_index_chunks(chunk, inputs)
~/Workspace/mars/mars/dataframe/merge/concat.py in _auto_concat_series_chunks(chunk, inputs)
256 concat = xdf.concat(inputs, axis=chunk.op.axis)
257 else:
--> 258 concat = xdf.concat(inputs)
259 if getattr(chunk.index_value, 'should_be_monotonic', False):
260 concat.sort_index(inplace=True)
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 verify_integrity=verify_integrity,
283 copy=copy,
--> 284 sort=sort,
285 )
286
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid
|
TypeError
|
def _execute_map(cls, ctx, op):
xdf = cudf if op.gpu else pd
in_data = ctx[op.inputs[0].key]
if isinstance(in_data, pd.Series):
count = in_data.count()
else:
count = in_data.count(axis=op.axis, numeric_only=op.numeric_only)
r = cls._execute_reduction(in_data, op, reduction_func="sum")
avg = cls._keep_dim(r / count, op)
kwargs = dict(axis=op.axis, skipna=op.skipna)
if op.numeric_only:
in_data = in_data[avg.columns]
avg = avg if np.isscalar(avg) else np.array(avg)
var_square = ((in_data.subtract(avg)) ** 2).sum(**kwargs)
if isinstance(in_data, xdf.Series):
if op.output_types[0] == OutputType.series and not isinstance(r, xdf.Series):
r = xdf.Series([r])
count = xdf.Series([count])
var_square = xdf.Series([var_square])
ctx[op.outputs[0].key] = (r, count, var_square)
else:
ctx[op.outputs[0].key] = tuple(
cls._keep_dim(df, op) for df in [r, count, var_square]
)
|
def _execute_map(cls, ctx, op):
xdf = cudf if op.gpu else pd
in_data = ctx[op.inputs[0].key]
if isinstance(in_data, pd.Series):
count = in_data.count()
else:
count = in_data.count(axis=op.axis, numeric_only=op.numeric_only)
r = cls._execute_reduction(in_data, op, reduction_func="sum")
avg = cls._keep_dim(r / count, op)
kwargs = dict(axis=op.axis, skipna=op.skipna)
if op.numeric_only:
in_data = in_data[avg.columns]
avg = avg if np.isscalar(avg) else np.array(avg)
var_square = ((in_data.subtract(avg)) ** 2).sum(**kwargs)
if isinstance(in_data, xdf.Series):
ctx[op.outputs[0].key] = (r, count, var_square)
else:
ctx[op.outputs[0].key] = tuple(
cls._keep_dim(df, op) for df in [r, count, var_square]
)
|
https://github.com/mars-project/mars/issues/1682
|
In [1]: import mars.dataframe as md
In [2]: from datetime import datetime
In [3]: s = md.Series([datetime.now(), datetime.now(), datetime.now()], chunk_si
...: ze=2)
In [4]: s.max().execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8891e4a12063> in <module>
----> 1 s.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
638
639 if wait:
--> 640 return run()
641 else:
642 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
634
635 def run():
--> 636 self.data.execute(session, **kw)
637 return self
638
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
374
375 if wait:
--> 376 return run()
377 else:
378 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
369 def run():
370 # no more fetch, thus just fire run
--> 371 session.run(self, **kw)
372 # return Tileable or ExecutableTuple itself
373 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/dataframe/merge/concat.py in execute(cls, ctx, op)
290 for i in range(len(inputs[0])))
291 else:
--> 292 ctx[chunk.key] = _base_concat(chunk, inputs)
293
294 @classmethod
~/Workspace/mars/mars/dataframe/merge/concat.py in _base_concat(chunk, inputs)
193 return _auto_concat_dataframe_chunks(chunk, inputs)
194 elif chunk.op.output_types[0] == OutputType.series:
--> 195 return _auto_concat_series_chunks(chunk, inputs)
196 elif chunk.op.output_types[0] == OutputType.index:
197 return _auto_concat_index_chunks(chunk, inputs)
~/Workspace/mars/mars/dataframe/merge/concat.py in _auto_concat_series_chunks(chunk, inputs)
256 concat = xdf.concat(inputs, axis=chunk.op.axis)
257 else:
--> 258 concat = xdf.concat(inputs)
259 if getattr(chunk.index_value, 'should_be_monotonic', False):
260 concat.sort_index(inplace=True)
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 verify_integrity=verify_integrity,
283 copy=copy,
--> 284 sort=sort,
285 )
286
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid
|
TypeError
|
def _execute_combine(cls, ctx, op):
data, concat_count, var_square = ctx[op.inputs[0].key]
xdf = cudf if op.gpu else pd
count = concat_count.sum(axis=op.axis)
r = cls._execute_reduction(data, op, reduction_func="sum")
avg = cls._keep_dim(r / count, op)
avg_diff = data / concat_count - avg
kwargs = dict(axis=op.axis, skipna=op.skipna)
reduced_var_square = var_square.sum(**kwargs) + (concat_count * avg_diff**2).sum(
**kwargs
)
if isinstance(data, xdf.Series):
if op.output_types[0] == OutputType.series and not isinstance(r, xdf.Series):
r = xdf.Series([r])
count = xdf.Series([count])
reduced_var_square = xdf.Series([reduced_var_square])
ctx[op.outputs[0].key] = (r, count, reduced_var_square)
else:
ctx[op.outputs[0].key] = tuple(
cls._keep_dim(df, op) for df in [r, count, reduced_var_square]
)
|
def _execute_combine(cls, ctx, op):
data, concat_count, var_square = ctx[op.inputs[0].key]
xdf = cudf if op.gpu else pd
count = concat_count.sum(axis=op.axis)
r = cls._execute_reduction(data, op, reduction_func="sum")
avg = cls._keep_dim(r / count, op)
avg_diff = data / concat_count - avg
kwargs = dict(axis=op.axis, skipna=op.skipna)
reduced_var_square = var_square.sum(**kwargs) + (concat_count * avg_diff**2).sum(
**kwargs
)
if isinstance(data, xdf.Series):
ctx[op.outputs[0].key] = (r, count, reduced_var_square)
else:
ctx[op.outputs[0].key] = tuple(
cls._keep_dim(df, op) for df in [r, count, reduced_var_square]
)
|
https://github.com/mars-project/mars/issues/1682
|
In [1]: import mars.dataframe as md
In [2]: from datetime import datetime
In [3]: s = md.Series([datetime.now(), datetime.now(), datetime.now()], chunk_si
...: ze=2)
In [4]: s.max().execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8891e4a12063> in <module>
----> 1 s.max().execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
638
639 if wait:
--> 640 return run()
641 else:
642 thread_executor = ThreadPoolExecutor(1)
~/Workspace/mars/mars/core.py in run()
634
635 def run():
--> 636 self.data.execute(session, **kw)
637 return self
638
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
374
375 if wait:
--> 376 return run()
377 else:
378 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
369 def run():
370 # no more fetch, thus just fire run
--> 371 session.run(self, **kw)
372 # return Tileable or ExecutableTuple itself
373 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/dataframe/merge/concat.py in execute(cls, ctx, op)
290 for i in range(len(inputs[0])))
291 else:
--> 292 ctx[chunk.key] = _base_concat(chunk, inputs)
293
294 @classmethod
~/Workspace/mars/mars/dataframe/merge/concat.py in _base_concat(chunk, inputs)
193 return _auto_concat_dataframe_chunks(chunk, inputs)
194 elif chunk.op.output_types[0] == OutputType.series:
--> 195 return _auto_concat_series_chunks(chunk, inputs)
196 elif chunk.op.output_types[0] == OutputType.index:
197 return _auto_concat_index_chunks(chunk, inputs)
~/Workspace/mars/mars/dataframe/merge/concat.py in _auto_concat_series_chunks(chunk, inputs)
256 concat = xdf.concat(inputs, axis=chunk.op.axis)
257 else:
--> 258 concat = xdf.concat(inputs)
259 if getattr(chunk.index_value, 'should_be_monotonic', False):
260 concat.sort_index(inplace=True)
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 verify_integrity=verify_integrity,
283 copy=copy,
--> 284 sort=sort,
285 )
286
~/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
357 "only Series and DataFrame objs are valid"
358 )
--> 359 raise TypeError(msg)
360
361 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'; only Series and DataFrame objs are valid
|
TypeError
|
def _tile_with_tensor(cls, op):
out = op.outputs[0]
axis = op.axis
rhs_is_tensor = isinstance(op.rhs, TENSOR_TYPE)
tensor, other = (op.rhs, op.lhs) if rhs_is_tensor else (op.lhs, op.rhs)
if tensor.shape == other.shape:
tensor = tensor.rechunk(other.nsplits)._inplace_tile()
else:
# shape differs only when dataframe add 1-d tensor, we need rechunk on columns axis.
if op.axis in ["columns", 1] and other.ndim == 1:
# force axis == 0 if it's Series other than DataFrame
axis = 0
rechunk_size = (
other.nsplits[1] if axis == "columns" or axis == 1 else other.nsplits[0]
)
if tensor.ndim > 0:
tensor = tensor.rechunk((rechunk_size,))._inplace_tile()
cum_splits = [0] + np.cumsum(other.nsplits[axis]).tolist()
out_chunks = []
for out_index in itertools.product(*(map(range, other.chunk_shape))):
tensor_chunk = tensor.cix[out_index[: tensor.ndim]]
other_chunk = other.cix[out_index]
out_op = op.copy().reset_key()
inputs = (
[other_chunk, tensor_chunk]
if rhs_is_tensor
else [tensor_chunk, other_chunk]
)
if isinstance(other_chunk, DATAFRAME_CHUNK_TYPE):
start = cum_splits[out_index[axis]]
end = cum_splits[out_index[axis] + 1]
chunk_dtypes = out.dtypes.iloc[start:end]
out_chunk = out_op.new_chunk(
inputs,
shape=other_chunk.shape,
index=other_chunk.index,
dtypes=chunk_dtypes,
index_value=other_chunk.index_value,
columns_value=other.columns_value,
)
else:
out_chunk = out_op.new_chunk(
inputs,
shape=other_chunk.shape,
index=other_chunk.index,
dtype=out.dtype,
index_value=other_chunk.index_value,
name=other_chunk.name,
)
out_chunks.append(out_chunk)
new_op = op.copy()
if isinstance(other, SERIES_TYPE):
return new_op.new_seriess(
op.inputs,
other.shape,
nsplits=other.nsplits,
dtype=out.dtype,
index_value=other.index_value,
chunks=out_chunks,
)
else:
return new_op.new_dataframes(
op.inputs,
other.shape,
nsplits=other.nsplits,
dtypes=out.dtypes,
index_value=other.index_value,
columns_value=other.columns_value,
chunks=out_chunks,
)
|
def _tile_with_tensor(cls, op):
rhs_is_tensor = isinstance(op.rhs, TENSOR_TYPE)
tensor, other = (op.rhs, op.lhs) if rhs_is_tensor else (op.lhs, op.rhs)
if tensor.shape == other.shape:
tensor = tensor.rechunk(other.nsplits)._inplace_tile()
else:
# shape differs only when dataframe add 1-d tensor, we need rechunk on columns axis.
rechunk_size = (
other.nsplits[1]
if op.axis == "columns" or op.axis == 1
else other.nsplits[0]
)
if tensor.ndim > 0:
tensor = tensor.rechunk((rechunk_size,))._inplace_tile()
out_chunks = []
for out_index in itertools.product(*(map(range, other.chunk_shape))):
tensor_chunk = tensor.cix[out_index[: tensor.ndim]]
other_chunk = other.cix[out_index]
out_op = op.copy().reset_key()
inputs = (
[other_chunk, tensor_chunk]
if rhs_is_tensor
else [tensor_chunk, other_chunk]
)
if isinstance(other_chunk, DATAFRAME_CHUNK_TYPE):
out_chunk = out_op.new_chunk(
inputs,
shape=other_chunk.shape,
index=other_chunk.index,
dtypes=other_chunk.dtypes,
index_value=other_chunk.index_value,
columns_value=other.columns_value,
)
else:
out_chunk = out_op.new_chunk(
inputs,
shape=other_chunk.shape,
index=other_chunk.index,
dtype=other_chunk.dtype,
index_value=other_chunk.index_value,
name=other_chunk.name,
)
out_chunks.append(out_chunk)
new_op = op.copy()
out = op.outputs[0]
if isinstance(other, SERIES_TYPE):
return new_op.new_seriess(
op.inputs,
other.shape,
nsplits=other.nsplits,
dtype=out.dtype,
index_value=other.index_value,
chunks=out_chunks,
)
else:
return new_op.new_dataframes(
op.inputs,
other.shape,
nsplits=other.nsplits,
dtypes=out.dtypes,
index_value=other.index_value,
columns_value=other.columns_value,
chunks=out_chunks,
)
|
https://github.com/mars-project/mars/issues/1674
|
In [9]: df = md.DataFrame({'a': [1, 2, 3], 'b': [1.1, 2.2, 3.3],
...: 'c': [datetime(2020, 1, 1), datetime.now(), datetime(2000, 3, 3, 11, 22, 23)]})
In[10]: df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
Traceback (most recent call last):
File "/Users/qinxuye/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-10-d9c9c3710287>", line 1, in <module>
df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 640, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 636, in run
self.data.execute(session, **kw)
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 376, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 371, in run
session.run(self, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 500, in run
result = self._sess.run(*tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 108, in run
res = self._executor.execute_tileables(tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/executor.py", line 861, in execute_tileables
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 201, in _tile
tds[0]._inplace_tile()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 165, in _inplace_tile
return handler.inplace_tile(self)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 136, in inplace_tile
dispatched = self.dispatch(to_tile.op)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 261, in tile
return cls._tile_with_tensor(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 216, in _tile_with_tensor
rechunk_size = other.nsplits[1] if op.axis == 'columns' or op.axis == 1 else other.nsplits[0]
IndexError: tuple index out of range
|
IndexError
|
def execute(cls, ctx, op):
if len(op.inputs) == 2:
df, other = ctx[op.inputs[0].key], ctx[op.inputs[1].key]
if isinstance(op.inputs[0], SERIES_CHUNK_TYPE) and isinstance(
op.inputs[1], DATAFRAME_CHUNK_TYPE
):
df, other = other, df
func_name = getattr(cls, "_rfunc_name")
else:
func_name = getattr(cls, "_func_name")
elif pd.api.types.is_scalar(op.lhs) or isinstance(op.lhs, np.ndarray):
df = ctx[op.rhs.key]
other = op.lhs
func_name = getattr(cls, "_rfunc_name")
else:
df = ctx[op.lhs.key]
other = op.rhs
func_name = getattr(cls, "_func_name")
if df.ndim == 2:
kw = dict({"axis": op.axis})
else:
kw = dict()
if op.fill_value is not None:
# comparison function like eq does not have `fill_value`
kw["fill_value"] = op.fill_value
if op.level is not None:
# logical function like and may don't have `level` (for Series type)
kw["level"] = op.level
if hasattr(other, "ndim") and other.ndim == 0:
other = other.item()
ctx[op.outputs[0].key] = getattr(df, func_name)(other, **kw)
|
def execute(cls, ctx, op):
if len(op.inputs) == 2:
df, other = ctx[op.inputs[0].key], ctx[op.inputs[1].key]
if isinstance(op.inputs[0], SERIES_CHUNK_TYPE) and isinstance(
op.inputs[1], DATAFRAME_CHUNK_TYPE
):
df, other = other, df
func_name = getattr(cls, "_rfunc_name")
else:
func_name = getattr(cls, "_func_name")
elif pd.api.types.is_scalar(op.lhs) or isinstance(op.lhs, np.ndarray):
df = ctx[op.rhs.key]
other = op.lhs
func_name = getattr(cls, "_rfunc_name")
else:
df = ctx[op.lhs.key]
other = op.rhs
func_name = getattr(cls, "_func_name")
if df.ndim == 2:
kw = dict({"axis": op.axis})
else:
kw = dict()
if op.fill_value is not None:
# comparison function like eq does not have `fill_value`
kw["fill_value"] = op.fill_value
if op.level is not None:
# logical function like and may don't have `level` (for Series type)
kw["level"] = op.level
ctx[op.outputs[0].key] = getattr(df, func_name)(other, **kw)
|
https://github.com/mars-project/mars/issues/1674
|
In [9]: df = md.DataFrame({'a': [1, 2, 3], 'b': [1.1, 2.2, 3.3],
...: 'c': [datetime(2020, 1, 1), datetime.now(), datetime(2000, 3, 3, 11, 22, 23)]})
In[10]: df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
Traceback (most recent call last):
File "/Users/qinxuye/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-10-d9c9c3710287>", line 1, in <module>
df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 640, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 636, in run
self.data.execute(session, **kw)
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 376, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 371, in run
session.run(self, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 500, in run
result = self._sess.run(*tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 108, in run
res = self._executor.execute_tileables(tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/executor.py", line 861, in execute_tileables
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 201, in _tile
tds[0]._inplace_tile()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 165, in _inplace_tile
return handler.inplace_tile(self)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 136, in inplace_tile
dispatched = self.dispatch(to_tile.op)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 261, in tile
return cls._tile_with_tensor(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 216, in _tile_with_tensor
rechunk_size = other.nsplits[1] if op.axis == 'columns' or op.axis == 1 else other.nsplits[0]
IndexError: tuple index out of range
|
IndexError
|
def _calc_properties(cls, x1, x2=None, axis="columns"):
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and (
x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
):
if x2 is None:
dtypes = x1.dtypes
elif pd.api.types.is_scalar(x2):
dtypes = cls._operator(build_empty_df(x1.dtypes), x2).dtypes
elif x1.dtypes is not None and isinstance(x2, TENSOR_TYPE):
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
else:
dtypes = x1.dtypes
return {
"shape": x1.shape,
"dtypes": dtypes,
"columns_value": x1.columns_value,
"index_value": x1.index_value,
}
if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and (
x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
):
x2_dtype = x2.dtype if hasattr(x2, "dtype") else type(x2)
dtype = infer_dtype(x1.dtype, np.dtype(x2_dtype), cls._operator)
ret = {"shape": x1.shape, "dtype": dtype, "index_value": x1.index_value}
if pd.api.types.is_scalar(x2) or (hasattr(x2, "ndim") and x2.ndim == 0):
ret["name"] = x1.name
return ret
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
x2, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)
):
index_shape, column_shape, dtypes, columns, index = (
np.nan,
np.nan,
None,
None,
None,
)
if (
x1.columns_value is not None
and x2.columns_value is not None
and x1.columns_value.key == x2.columns_value.key
):
dtypes = pd.Series(
[
infer_dtype(dt1, dt2, cls._operator)
for dt1, dt2 in zip(x1.dtypes, x2.dtypes)
],
index=x1.dtypes.index,
)
columns = copy.copy(x1.columns_value)
columns.value.should_be_monotonic = False
column_shape = len(dtypes)
elif x1.dtypes is not None and x2.dtypes is not None:
dtypes = infer_dtypes(x1.dtypes, x2.dtypes, cls._operator)
columns = parse_index(dtypes.index, store_data=True)
columns.value.should_be_monotonic = True
column_shape = len(dtypes)
if x1.index_value is not None and x2.index_value is not None:
if x1.index_value.key == x2.index_value.key:
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
if index.key == x1.index_value.key == x2.index_value.key and (
not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
):
index_shape = (
x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
)
return {
"shape": (index_shape, column_shape),
"dtypes": dtypes,
"columns_value": columns,
"index_value": index,
}
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
):
if axis == "columns" or axis == 1:
index_shape = x1.shape[0]
index = x1.index_value
column_shape, dtypes, columns = np.nan, None, None
if x1.columns_value is not None and x1.index_value is not None:
if x1.columns_value.key == x2.index_value.key:
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
columns = copy.copy(x1.columns_value)
columns.value.should_be_monotonic = False
column_shape = len(dtypes)
else: # pragma: no cover
dtypes = x1.dtypes # FIXME
columns = infer_index_value(x1.columns_value, x2.index_value)
columns.value.should_be_monotonic = True
column_shape = np.nan
else:
assert axis == "index" or axis == 0
column_shape = x1.shape[1]
columns = x1.columns_value
dtypes = x1.dtypes
index_shape, index = np.nan, None
if x1.index_value is not None and x1.index_value is not None:
if x1.index_value.key == x2.index_value.key:
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
if x1.dtypes is not None:
dtypes = pd.Series(
[
infer_dtype(dt, x2.dtype, cls._operator)
for dt in x1.dtypes
],
index=x1.dtypes.index,
)
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
index_shape = np.nan
return {
"shape": (index_shape, column_shape),
"dtypes": dtypes,
"columns_value": columns,
"index_value": index,
}
if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and isinstance(
x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
):
index_shape, dtype, index = np.nan, None, None
dtype = infer_dtype(x1.dtype, x2.dtype, cls._operator)
if x1.index_value is not None and x2.index_value is not None:
if x1.index_value.key == x2.index_value.key:
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
if index.key == x1.index_value.key == x2.index_value.key and (
not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
):
index_shape = (
x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
)
ret = {"shape": (index_shape,), "dtype": dtype, "index_value": index}
if x1.name == x2.name:
ret["name"] = x1.name
return ret
raise NotImplementedError("Unknown combination of parameters")
|
def _calc_properties(cls, x1, x2=None, axis="columns"):
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and (
x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
):
if x2 is None:
dtypes = x1.dtypes
elif pd.api.types.is_scalar(x2):
dtypes = cls._operator(build_empty_df(x1.dtypes), x2).dtypes
elif x1.dtypes is not None and isinstance(x2, TENSOR_TYPE):
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
else:
dtypes = x1.dtypes
return {
"shape": x1.shape,
"dtypes": dtypes,
"columns_value": x1.columns_value,
"index_value": x1.index_value,
}
if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and (
x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
):
x2_dtype = x2.dtype if hasattr(x2, "dtype") else type(x2)
dtype = infer_dtype(x1.dtype, np.dtype(x2_dtype), cls._operator)
return {"shape": x1.shape, "dtype": dtype, "index_value": x1.index_value}
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
x2, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)
):
index_shape, column_shape, dtypes, columns, index = (
np.nan,
np.nan,
None,
None,
None,
)
if (
x1.columns_value is not None
and x2.columns_value is not None
and x1.columns_value.key == x2.columns_value.key
):
dtypes = pd.Series(
[
infer_dtype(dt1, dt2, cls._operator)
for dt1, dt2 in zip(x1.dtypes, x2.dtypes)
],
index=x1.dtypes.index,
)
columns = copy.copy(x1.columns_value)
columns.value.should_be_monotonic = False
column_shape = len(dtypes)
elif x1.dtypes is not None and x2.dtypes is not None:
dtypes = infer_dtypes(x1.dtypes, x2.dtypes, cls._operator)
columns = parse_index(dtypes.index, store_data=True)
columns.value.should_be_monotonic = True
column_shape = len(dtypes)
if x1.index_value is not None and x2.index_value is not None:
if x1.index_value.key == x2.index_value.key:
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
if index.key == x1.index_value.key == x2.index_value.key and (
not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
):
index_shape = (
x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
)
return {
"shape": (index_shape, column_shape),
"dtypes": dtypes,
"columns_value": columns,
"index_value": index,
}
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
):
if axis == "columns" or axis == 1:
index_shape = x1.shape[0]
index = x1.index_value
column_shape, dtypes, columns = np.nan, None, None
if x1.columns_value is not None and x1.index_value is not None:
if x1.columns_value.key == x2.index_value.key:
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
columns = copy.copy(x1.columns_value)
columns.value.should_be_monotonic = False
column_shape = len(dtypes)
else: # pragma: no cover
dtypes = x1.dtypes # FIXME
columns = infer_index_value(x1.columns_value, x2.index_value)
columns.value.should_be_monotonic = True
column_shape = np.nan
else:
assert axis == "index" or axis == 0
column_shape = x1.shape[1]
columns = x1.columns_value
dtypes = x1.dtypes
index_shape, index = np.nan, None
if x1.index_value is not None and x1.index_value is not None:
if x1.index_value.key == x2.index_value.key:
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
if x1.dtypes is not None:
dtypes = pd.Series(
[
infer_dtype(dt, x2.dtype, cls._operator)
for dt in x1.dtypes
],
index=x1.dtypes.index,
)
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
index_shape = np.nan
return {
"shape": (index_shape, column_shape),
"dtypes": dtypes,
"columns_value": columns,
"index_value": index,
}
if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and isinstance(
x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
):
index_shape, dtype, index = np.nan, None, None
dtype = infer_dtype(x1.dtype, x2.dtype, cls._operator)
if x1.index_value is not None and x2.index_value is not None:
if x1.index_value.key == x2.index_value.key:
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
if index.key == x1.index_value.key == x2.index_value.key and (
not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
):
index_shape = (
x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
)
return {"shape": (index_shape,), "dtype": dtype, "index_value": index}
raise NotImplementedError("Unknown combination of parameters")
|
https://github.com/mars-project/mars/issues/1674
|
In [9]: df = md.DataFrame({'a': [1, 2, 3], 'b': [1.1, 2.2, 3.3],
...: 'c': [datetime(2020, 1, 1), datetime.now(), datetime(2000, 3, 3, 11, 22, 23)]})
In[10]: df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
Traceback (most recent call last):
File "/Users/qinxuye/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-10-d9c9c3710287>", line 1, in <module>
df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 640, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 636, in run
self.data.execute(session, **kw)
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 376, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 371, in run
session.run(self, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 500, in run
result = self._sess.run(*tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 108, in run
res = self._executor.execute_tileables(tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/executor.py", line 861, in execute_tileables
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 201, in _tile
tds[0]._inplace_tile()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 165, in _inplace_tile
return handler.inplace_tile(self)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 136, in inplace_tile
dispatched = self.dispatch(to_tile.op)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 261, in tile
return cls._tile_with_tensor(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 216, in _tile_with_tensor
rechunk_size = other.nsplits[1] if op.axis == 'columns' or op.axis == 1 else other.nsplits[0]
IndexError: tuple index out of range
|
IndexError
|
def execute(cls, ctx, op):
def _base_concat(chunk, inputs):
# auto generated concat when executing a DataFrame, Series or Index
if chunk.op.output_types[0] == OutputType.dataframe:
return _auto_concat_dataframe_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.series:
return _auto_concat_series_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.index:
return _auto_concat_index_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.categorical:
return _auto_concat_categorical_chunks(chunk, inputs)
else: # pragma: no cover
raise TypeError(
"Only DataFrameChunk, SeriesChunk, IndexChunk, "
"and CategoricalChunk can be automatically concatenated"
)
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = (
pd
if isinstance(inputs[0], (pd.DataFrame, pd.Series)) or cudf is None
else cudf
)
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
max_rows = max(inp.index[0] for inp in chunk.inputs)
min_rows = min(inp.index[0] for inp in chunk.inputs)
n_rows = max_rows - min_rows + 1
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if all(np.isscalar(inp) for inp in inputs):
return pd.Series(inputs)
else:
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) or cudf is None else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
def _auto_concat_index_chunks(chunk, inputs):
if len(inputs) == 1:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
concat_df = xdf.DataFrame(index=inputs[0])
else:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
concat_df = xdf.concat(empty_dfs, axis=0)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat_df.sort_index(inplace=True)
return concat_df.index
def _auto_concat_categorical_chunks(_, inputs):
if len(inputs) == 1: # pragma: no cover
return inputs[0]
else:
# convert categorical into array
arrays = [np.asarray(inp) for inp in inputs]
array = np.concatenate(arrays)
return pd.Categorical(
array, categories=inputs[0].categories, ordered=inputs[0].ordered
)
chunk = op.outputs[0]
inputs = [ctx[input.key] for input in op.inputs]
if isinstance(inputs[0], tuple):
ctx[chunk.key] = tuple(
_base_concat(chunk, [input[i] for input in inputs])
for i in range(len(inputs[0]))
)
else:
ctx[chunk.key] = _base_concat(chunk, inputs)
|
def execute(cls, ctx, op):
def _base_concat(chunk, inputs):
# auto generated concat when executing a DataFrame, Series or Index
if chunk.op.output_types[0] == OutputType.dataframe:
return _auto_concat_dataframe_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.series:
return _auto_concat_series_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.index:
return _auto_concat_index_chunks(chunk, inputs)
elif chunk.op.output_types[0] == OutputType.categorical:
return _auto_concat_categorical_chunks(chunk, inputs)
else: # pragma: no cover
raise TypeError(
"Only DataFrameChunk, SeriesChunk, IndexChunk, "
"and CategoricalChunk can be automatically concatenated"
)
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = pd if isinstance(inputs[0], (pd.DataFrame, pd.Series)) else cudf
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
max_rows = max(inp.index[0] for inp in chunk.inputs)
min_rows = min(inp.index[0] for inp in chunk.inputs)
n_rows = max_rows - min_rows + 1
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if all(np.isscalar(inp) for inp in inputs):
return pd.Series(inputs)
else:
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
def _auto_concat_index_chunks(chunk, inputs):
if len(inputs) == 1:
xdf = pd if isinstance(inputs[0], pd.Index) else cudf
concat_df = xdf.DataFrame(index=inputs[0])
else:
xdf = pd if isinstance(inputs[0], pd.Index) else cudf
empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
concat_df = xdf.concat(empty_dfs, axis=0)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat_df.sort_index(inplace=True)
return concat_df.index
def _auto_concat_categorical_chunks(_, inputs):
if len(inputs) == 1: # pragma: no cover
return inputs[0]
else:
# convert categorical into array
arrays = [np.asarray(inp) for inp in inputs]
array = np.concatenate(arrays)
return pd.Categorical(
array, categories=inputs[0].categories, ordered=inputs[0].ordered
)
chunk = op.outputs[0]
inputs = [ctx[input.key] for input in op.inputs]
if isinstance(inputs[0], tuple):
ctx[chunk.key] = tuple(
_base_concat(chunk, [input[i] for input in inputs])
for i in range(len(inputs[0]))
)
else:
ctx[chunk.key] = _base_concat(chunk, inputs)
|
https://github.com/mars-project/mars/issues/1674
|
In [9]: df = md.DataFrame({'a': [1, 2, 3], 'b': [1.1, 2.2, 3.3],
...: 'c': [datetime(2020, 1, 1), datetime.now(), datetime(2000, 3, 3, 11, 22, 23)]})
In[10]: df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
Traceback (most recent call last):
File "/Users/qinxuye/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-10-d9c9c3710287>", line 1, in <module>
df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 640, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 636, in run
self.data.execute(session, **kw)
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 376, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 371, in run
session.run(self, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 500, in run
result = self._sess.run(*tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 108, in run
res = self._executor.execute_tileables(tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/executor.py", line 861, in execute_tileables
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 201, in _tile
tds[0]._inplace_tile()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 165, in _inplace_tile
return handler.inplace_tile(self)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 136, in inplace_tile
dispatched = self.dispatch(to_tile.op)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 261, in tile
return cls._tile_with_tensor(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 216, in _tile_with_tensor
rechunk_size = other.nsplits[1] if op.axis == 'columns' or op.axis == 1 else other.nsplits[0]
IndexError: tuple index out of range
|
IndexError
|
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = (
pd if isinstance(inputs[0], (pd.DataFrame, pd.Series)) or cudf is None else cudf
)
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
max_rows = max(inp.index[0] for inp in chunk.inputs)
min_rows = min(inp.index[0] for inp in chunk.inputs)
n_rows = max_rows - min_rows + 1
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
|
def _auto_concat_dataframe_chunks(chunk, inputs):
xdf = pd if isinstance(inputs[0], (pd.DataFrame, pd.Series)) else cudf
if chunk.op.axis is not None:
return xdf.concat(inputs, axis=op.axis)
# auto generated concat when executing a DataFrame
if len(inputs) == 1:
ret = inputs[0]
else:
max_rows = max(inp.index[0] for inp in chunk.inputs)
min_rows = min(inp.index[0] for inp in chunk.inputs)
n_rows = max_rows - min_rows + 1
n_cols = int(len(inputs) // n_rows)
assert n_rows * n_cols == len(inputs)
concats = []
for i in range(n_rows):
if n_cols == 1:
concats.append(inputs[i])
else:
concat = xdf.concat(
[inputs[i * n_cols + j] for j in range(n_cols)], axis=1
)
concats.append(concat)
if xdf is pd:
# The `sort=False` is to suppress a `FutureWarning` of pandas,
# when the index or column of chunks to concatenate is not aligned,
# which may happens for certain ops.
#
# See also Note [Columns of Left Join] in test_merge_execution.py.
ret = xdf.concat(concats, sort=False)
else:
ret = xdf.concat(concats)
# cuDF will lost index name when concat two seriess.
ret.index.name = concats[0].index.name
if getattr(chunk.index_value, "should_be_monotonic", False):
ret.sort_index(inplace=True)
if getattr(chunk.columns_value, "should_be_monotonic", False):
ret.sort_index(axis=1, inplace=True)
return ret
|
https://github.com/mars-project/mars/issues/1674
|
In [9]: df = md.DataFrame({'a': [1, 2, 3], 'b': [1.1, 2.2, 3.3],
...: 'c': [datetime(2020, 1, 1), datetime.now(), datetime(2000, 3, 3, 11, 22, 23)]})
In[10]: df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
Traceback (most recent call last):
File "/Users/qinxuye/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-10-d9c9c3710287>", line 1, in <module>
df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 640, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 636, in run
self.data.execute(session, **kw)
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 376, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 371, in run
session.run(self, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 500, in run
result = self._sess.run(*tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 108, in run
res = self._executor.execute_tileables(tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/executor.py", line 861, in execute_tileables
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 201, in _tile
tds[0]._inplace_tile()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 165, in _inplace_tile
return handler.inplace_tile(self)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 136, in inplace_tile
dispatched = self.dispatch(to_tile.op)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 261, in tile
return cls._tile_with_tensor(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 216, in _tile_with_tensor
rechunk_size = other.nsplits[1] if op.axis == 'columns' or op.axis == 1 else other.nsplits[0]
IndexError: tuple index out of range
|
IndexError
|
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if all(np.isscalar(inp) for inp in inputs):
return pd.Series(inputs)
else:
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) or cudf is None else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
|
def _auto_concat_series_chunks(chunk, inputs):
# auto generated concat when executing a Series
if all(np.isscalar(inp) for inp in inputs):
return pd.Series(inputs)
else:
if len(inputs) == 1:
concat = inputs[0]
else:
xdf = pd if isinstance(inputs[0], pd.Series) else cudf
if chunk.op.axis is not None:
concat = xdf.concat(inputs, axis=chunk.op.axis)
else:
concat = xdf.concat(inputs)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat.sort_index(inplace=True)
return concat
|
https://github.com/mars-project/mars/issues/1674
|
In [9]: df = md.DataFrame({'a': [1, 2, 3], 'b': [1.1, 2.2, 3.3],
...: 'c': [datetime(2020, 1, 1), datetime.now(), datetime(2000, 3, 3, 11, 22, 23)]})
In[10]: df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
Traceback (most recent call last):
File "/Users/qinxuye/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-10-d9c9c3710287>", line 1, in <module>
df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 640, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 636, in run
self.data.execute(session, **kw)
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 376, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 371, in run
session.run(self, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 500, in run
result = self._sess.run(*tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 108, in run
res = self._executor.execute_tileables(tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/executor.py", line 861, in execute_tileables
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 201, in _tile
tds[0]._inplace_tile()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 165, in _inplace_tile
return handler.inplace_tile(self)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 136, in inplace_tile
dispatched = self.dispatch(to_tile.op)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 261, in tile
return cls._tile_with_tensor(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 216, in _tile_with_tensor
rechunk_size = other.nsplits[1] if op.axis == 'columns' or op.axis == 1 else other.nsplits[0]
IndexError: tuple index out of range
|
IndexError
|
def _auto_concat_index_chunks(chunk, inputs):
if len(inputs) == 1:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
concat_df = xdf.DataFrame(index=inputs[0])
else:
xdf = pd if isinstance(inputs[0], pd.Index) or cudf is None else cudf
empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
concat_df = xdf.concat(empty_dfs, axis=0)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat_df.sort_index(inplace=True)
return concat_df.index
|
def _auto_concat_index_chunks(chunk, inputs):
if len(inputs) == 1:
xdf = pd if isinstance(inputs[0], pd.Index) else cudf
concat_df = xdf.DataFrame(index=inputs[0])
else:
xdf = pd if isinstance(inputs[0], pd.Index) else cudf
empty_dfs = [xdf.DataFrame(index=inp) for inp in inputs]
concat_df = xdf.concat(empty_dfs, axis=0)
if getattr(chunk.index_value, "should_be_monotonic", False):
concat_df.sort_index(inplace=True)
return concat_df.index
|
https://github.com/mars-project/mars/issues/1674
|
In [9]: df = md.DataFrame({'a': [1, 2, 3], 'b': [1.1, 2.2, 3.3],
...: 'c': [datetime(2020, 1, 1), datetime.now(), datetime(2000, 3, 3, 11, 22, 23)]})
In[10]: df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
Traceback (most recent call last):
File "/Users/qinxuye/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-10-d9c9c3710287>", line 1, in <module>
df[(df['c'] > md.to_datetime('2020-08-01')) & (df['c'] < md.to_datetime('2020-11-01'))].head().execute()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 640, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 636, in run
self.data.execute(session, **kw)
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 376, in execute
return run()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 371, in run
session.run(self, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 500, in run
result = self._sess.run(*tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/session.py", line 108, in run
res = self._executor.execute_tileables(tileables, **kw)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/executor.py", line 861, in execute_tileables
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 201, in _tile
tds[0]._inplace_tile()
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 165, in _inplace_tile
return handler.inplace_tile(self)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 136, in inplace_tile
dispatched = self.dispatch(to_tile.op)
File "/Users/qinxuye/Workspace/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/qinxuye/Workspace/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 261, in tile
return cls._tile_with_tensor(op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/arithmetic/core.py", line 216, in _tile_with_tensor
rechunk_size = other.nsplits[1] if op.axis == 'columns' or op.axis == 1 else other.nsplits[0]
IndexError: tuple index out of range
|
IndexError
|
def get_output_types(*objs, unknown_as=None):
output_types = []
for obj in objs:
if obj is None:
continue
elif isinstance(obj, (FuseChunk, FuseChunkData)):
obj = obj.chunk
try:
output_types.append(_get_output_type_by_cls(type(obj)))
except TypeError:
if unknown_as is not None:
output_types.append(unknown_as)
else: # pragma: no cover
raise
return output_types
|
def get_output_types(*objs, unknown_as=None):
output_types = []
for obj in objs:
if obj is None:
continue
for tp in OutputType.__members__.values():
try:
tileable_types = _OUTPUT_TYPE_TO_TILEABLE_TYPES[tp]
chunk_types = _OUTPUT_TYPE_TO_CHUNK_TYPES[tp]
if isinstance(obj, (tileable_types, chunk_types)):
output_types.append(tp)
break
except KeyError:
continue
else:
if unknown_as is not None:
output_types.append(unknown_as)
else: # pragma: no cover
raise TypeError("Output can only be tensor, dataframe or series")
return output_types
|
https://github.com/mars-project/mars/issues/1664
|
TypeError Traceback (most recent call last)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~\AppData\Roaming\Python\Python37\site-packages\mars\graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
643 exc_info = sys.exc_info()
--> 644 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
645 f'value {value}, reason: {exc_info[1]}') \
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Failed to set field `extra_params` for Chunk <op=DataFrameFetch, key=08ec6fd4af751d9f5dcec87ea3a6dde3> with value {'order': <TensorOrder.C_ORDER: 'C'>, 'dtype': dtype('<U'), '_i': 0}, reason: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
ExecutionFailed Traceback (most recent call last)
<ipython-input-41-4dd810a40cfc> in <module>
----> 1 fsr_st.to_csv("fsr_st03.csv",encoding="utf8").execute()
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~\AppData\Roaming\Python\Python37\site-packages\mars\session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in run(self, *tileables, **kw)
212 timeout_val = min(check_interval, timeout - time_elapsed) if timeout > 0 else check_interval
213 try:
--> 214 if self._check_response_finished(graph_url, timeout_val):
215 break
216 except KeyboardInterrupt:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in _check_response_finished(self, graph_url, timeout)
172 exc_info = pickle.loads(base64.b64decode(resp_json['exc_info']))
173 exc = exc_info[1].with_traceback(exc_info[2])
--> 174 raise ExecutionFailed('Graph execution failed.') from exc
175 else:
176 raise ExecutionFailed('Graph execution failed with unknown reason.')
ExecutionFailed: 'Graph execution failed.'
|
TypeError
|
def get_fetch_op_cls(self, obj):
output_types = get_output_types(obj, unknown_as=OutputType.object)
fetch_cls, fetch_shuffle_cls = get_fetch_class(output_types[0])
if isinstance(self, ShuffleProxy):
cls = fetch_shuffle_cls
else:
cls = fetch_cls
def _inner(**kw):
return cls(output_types=output_types, **kw)
return _inner
|
def get_fetch_op_cls(self, obj):
raise NotImplementedError
|
https://github.com/mars-project/mars/issues/1664
|
TypeError Traceback (most recent call last)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~\AppData\Roaming\Python\Python37\site-packages\mars\graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
643 exc_info = sys.exc_info()
--> 644 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
645 f'value {value}, reason: {exc_info[1]}') \
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Failed to set field `extra_params` for Chunk <op=DataFrameFetch, key=08ec6fd4af751d9f5dcec87ea3a6dde3> with value {'order': <TensorOrder.C_ORDER: 'C'>, 'dtype': dtype('<U'), '_i': 0}, reason: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
ExecutionFailed Traceback (most recent call last)
<ipython-input-41-4dd810a40cfc> in <module>
----> 1 fsr_st.to_csv("fsr_st03.csv",encoding="utf8").execute()
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~\AppData\Roaming\Python\Python37\site-packages\mars\session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in run(self, *tileables, **kw)
212 timeout_val = min(check_interval, timeout - time_elapsed) if timeout > 0 else check_interval
213 try:
--> 214 if self._check_response_finished(graph_url, timeout_val):
215 break
216 except KeyboardInterrupt:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in _check_response_finished(self, graph_url, timeout)
172 exc_info = pickle.loads(base64.b64decode(resp_json['exc_info']))
173 exc = exc_info[1].with_traceback(exc_info[2])
--> 174 raise ExecutionFailed('Graph execution failed.') from exc
175 else:
176 raise ExecutionFailed('Graph execution failed with unknown reason.')
ExecutionFailed: 'Graph execution failed.'
|
TypeError
|
def __init__(self, to_fetch_key=None, **kw):
kw.pop("output_types", None)
kw.pop("_output_types", None)
super().__init__(_to_fetch_key=to_fetch_key, **kw)
|
def __init__(self, to_fetch_key=None, **kw):
super().__init__(_to_fetch_key=to_fetch_key, **kw)
|
https://github.com/mars-project/mars/issues/1664
|
TypeError Traceback (most recent call last)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~\AppData\Roaming\Python\Python37\site-packages\mars\graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
643 exc_info = sys.exc_info()
--> 644 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
645 f'value {value}, reason: {exc_info[1]}') \
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Failed to set field `extra_params` for Chunk <op=DataFrameFetch, key=08ec6fd4af751d9f5dcec87ea3a6dde3> with value {'order': <TensorOrder.C_ORDER: 'C'>, 'dtype': dtype('<U'), '_i': 0}, reason: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
ExecutionFailed Traceback (most recent call last)
<ipython-input-41-4dd810a40cfc> in <module>
----> 1 fsr_st.to_csv("fsr_st03.csv",encoding="utf8").execute()
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~\AppData\Roaming\Python\Python37\site-packages\mars\session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in run(self, *tileables, **kw)
212 timeout_val = min(check_interval, timeout - time_elapsed) if timeout > 0 else check_interval
213 try:
--> 214 if self._check_response_finished(graph_url, timeout_val):
215 break
216 except KeyboardInterrupt:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in _check_response_finished(self, graph_url, timeout)
172 exc_info = pickle.loads(base64.b64decode(resp_json['exc_info']))
173 exc = exc_info[1].with_traceback(exc_info[2])
--> 174 raise ExecutionFailed('Graph execution failed.') from exc
175 else:
176 raise ExecutionFailed('Graph execution failed with unknown reason.')
ExecutionFailed: 'Graph execution failed.'
|
TypeError
|
def get_fetch_op_cls(self, obj):
output_types = get_output_types(obj, unknown_as=OutputType.object)
fetch_cls, fetch_shuffle_cls = get_fetch_class(output_types[0])
if isinstance(self, ShuffleProxy):
cls = fetch_shuffle_cls
else:
cls = fetch_cls
def _inner(**kw):
return cls(output_types=output_types, **kw)
return _inner
|
def get_fetch_op_cls(self, obj):
return ObjectFetch
|
https://github.com/mars-project/mars/issues/1664
|
TypeError Traceback (most recent call last)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~\AppData\Roaming\Python\Python37\site-packages\mars\graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
643 exc_info = sys.exc_info()
--> 644 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
645 f'value {value}, reason: {exc_info[1]}') \
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Failed to set field `extra_params` for Chunk <op=DataFrameFetch, key=08ec6fd4af751d9f5dcec87ea3a6dde3> with value {'order': <TensorOrder.C_ORDER: 'C'>, 'dtype': dtype('<U'), '_i': 0}, reason: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
ExecutionFailed Traceback (most recent call last)
<ipython-input-41-4dd810a40cfc> in <module>
----> 1 fsr_st.to_csv("fsr_st03.csv",encoding="utf8").execute()
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~\AppData\Roaming\Python\Python37\site-packages\mars\session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in run(self, *tileables, **kw)
212 timeout_val = min(check_interval, timeout - time_elapsed) if timeout > 0 else check_interval
213 try:
--> 214 if self._check_response_finished(graph_url, timeout_val):
215 break
216 except KeyboardInterrupt:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in _check_response_finished(self, graph_url, timeout)
172 exc_info = pickle.loads(base64.b64decode(resp_json['exc_info']))
173 exc = exc_info[1].with_traceback(exc_info[2])
--> 174 raise ExecutionFailed('Graph execution failed.') from exc
175 else:
176 raise ExecutionFailed('Graph execution failed with unknown reason.')
ExecutionFailed: 'Graph execution failed.'
|
TypeError
|
def __init__(self, dtype=None, to_fetch_key=None, sparse=False, **kw):
kw.pop("output_types", None)
kw.pop("_output_types", None)
super().__init__(_dtype=dtype, _to_fetch_key=to_fetch_key, _sparse=sparse, **kw)
|
def __init__(self, dtype=None, to_fetch_key=None, sparse=False, **kw):
super().__init__(_dtype=dtype, _to_fetch_key=to_fetch_key, _sparse=sparse, **kw)
|
https://github.com/mars-project/mars/issues/1664
|
TypeError Traceback (most recent call last)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~\AppData\Roaming\Python\Python37\site-packages\mars\graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
643 exc_info = sys.exc_info()
--> 644 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
645 f'value {value}, reason: {exc_info[1]}') \
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Failed to set field `extra_params` for Chunk <op=DataFrameFetch, key=08ec6fd4af751d9f5dcec87ea3a6dde3> with value {'order': <TensorOrder.C_ORDER: 'C'>, 'dtype': dtype('<U'), '_i': 0}, reason: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
ExecutionFailed Traceback (most recent call last)
<ipython-input-41-4dd810a40cfc> in <module>
----> 1 fsr_st.to_csv("fsr_st03.csv",encoding="utf8").execute()
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~\AppData\Roaming\Python\Python37\site-packages\mars\session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in run(self, *tileables, **kw)
212 timeout_val = min(check_interval, timeout - time_elapsed) if timeout > 0 else check_interval
213 try:
--> 214 if self._check_response_finished(graph_url, timeout_val):
215 break
216 except KeyboardInterrupt:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in _check_response_finished(self, graph_url, timeout)
172 exc_info = pickle.loads(base64.b64decode(resp_json['exc_info']))
173 exc = exc_info[1].with_traceback(exc_info[2])
--> 174 raise ExecutionFailed('Graph execution failed.') from exc
175 else:
176 raise ExecutionFailed('Graph execution failed with unknown reason.')
ExecutionFailed: 'Graph execution failed.'
|
TypeError
|
def __init__(self, dtype=None, to_fetch_keys=None, to_fetch_idxes=None, **kw):
kw.pop("output_types", None)
kw.pop("_output_types", None)
super().__init__(
_dtype=dtype, _to_fetch_keys=to_fetch_keys, _to_fetch_idxes=to_fetch_idxes, **kw
)
|
def __init__(self, dtype=None, to_fetch_keys=None, to_fetch_idxes=None, **kw):
super().__init__(
_dtype=dtype, _to_fetch_keys=to_fetch_keys, _to_fetch_idxes=to_fetch_idxes, **kw
)
|
https://github.com/mars-project/mars/issues/1664
|
TypeError Traceback (most recent call last)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~\AppData\Roaming\Python\Python37\site-packages\mars\graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Provider.serialize_model()
797 cpdef serialize_model(self, model_instance, obj=None):
--> 798 if obj is None:
799 obj = model_instance.cls(self)()
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
643 exc_info = sys.exc_info()
--> 644 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
645 f'value {value}, reason: {exc_info[1]}') \
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
640 try:
--> 641 self._set_value(value, field_obj, field.type, weak_ref=field.weak_ref)
642 except TypeError:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
485 # dict type
--> 486 self._set_dict(<dict>value, obj, tp, weak_ref=weak_ref)
487 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_dict()
407 value_obj = obj.dict.values.value.add()
--> 408 self._set_value(v, value_obj, tp=tp.value_type if tp is not None else tp)
409
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
557 if tp is None:
--> 558 cls._set_untyped_value(value, obj, weak_ref=weak_ref)
559 else:
~\AppData\Roaming\Python\Python37\site-packages\mars\serialize\pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_untyped_value()
553 else:
--> 554 raise TypeError(f'Unknown type to serialize: {type(value)}')
555
TypeError: Failed to set field `extra_params` for Chunk <op=DataFrameFetch, key=08ec6fd4af751d9f5dcec87ea3a6dde3> with value {'order': <TensorOrder.C_ORDER: 'C'>, 'dtype': dtype('<U'), '_i': 0}, reason: Unknown type to serialize: <enum 'TensorOrder'>
The above exception was the direct cause of the following exception:
ExecutionFailed Traceback (most recent call last)
<ipython-input-41-4dd810a40cfc> in <module>
----> 1 fsr_st.to_csv("fsr_st03.csv",encoding="utf8").execute()
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~\AppData\Roaming\Python\Python37\site-packages\mars\core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~\AppData\Roaming\Python\Python37\site-packages\mars\session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in run(self, *tileables, **kw)
212 timeout_val = min(check_interval, timeout - time_elapsed) if timeout > 0 else check_interval
213 try:
--> 214 if self._check_response_finished(graph_url, timeout_val):
215 break
216 except KeyboardInterrupt:
~\AppData\Roaming\Python\Python37\site-packages\mars\web\session.py in _check_response_finished(self, graph_url, timeout)
172 exc_info = pickle.loads(base64.b64decode(resp_json['exc_info']))
173 exc = exc_info[1].with_traceback(exc_info[2])
--> 174 raise ExecutionFailed('Graph execution failed.') from exc
175 else:
176 raise ExecutionFailed('Graph execution failed with unknown reason.')
ExecutionFailed: 'Graph execution failed.'
|
TypeError
|
def _tile_dataframe(cls, op):
from ..indexing.iloc import DataFrameIlocGetItem
out_df = op.outputs[0]
inputs = op.inputs
check_chunks_unknown_shape(inputs, TilesError)
normalized_nsplits = (
{1: inputs[0].nsplits[1]} if op.axis == 0 else {0: inputs[0].nsplits[0]}
)
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
out_chunks = []
nsplits = []
cum_index = 0
for df in inputs:
for c in df.chunks:
if op.axis == 0:
index = (c.index[0] + cum_index, c.index[1])
else:
index = (c.index[0], c.index[1] + cum_index)
iloc_op = DataFrameIlocGetItem(indexes=(slice(None),) * 2)
out_chunks.append(
iloc_op.new_chunk(
[c],
shape=c.shape,
index=index,
dtypes=c.dtypes,
index_value=c.index_value,
columns_value=c.columns_value,
)
)
nsplits.extend(df.nsplits[op.axis])
cum_index += len(df.nsplits[op.axis])
out_nsplits = (
(tuple(nsplits), inputs[0].nsplits[1])
if op.axis == 0
else (inputs[0].nsplits[0], tuple(nsplits))
)
if op.ignore_index:
out_chunks = standardize_range_index(out_chunks)
new_op = op.copy()
return new_op.new_dataframes(
op.inputs,
out_df.shape,
nsplits=out_nsplits,
chunks=out_chunks,
dtypes=out_df.dtypes,
index_value=out_df.index_value,
columns_value=out_df.columns_value,
)
|
def _tile_dataframe(cls, op):
from ..indexing.iloc import DataFrameIlocGetItem
out_df = op.outputs[0]
inputs = op.inputs
normalized_nsplits = (
{1: inputs[0].nsplits[1]} if op.axis == 0 else {0: inputs[0].nsplits[0]}
)
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
out_chunks = []
nsplits = []
cum_index = 0
for df in inputs:
for c in df.chunks:
if op.axis == 0:
index = (c.index[0] + cum_index, c.index[1])
else:
index = (c.index[0], c.index[1] + cum_index)
iloc_op = DataFrameIlocGetItem(indexes=(slice(None),) * 2)
out_chunks.append(
iloc_op.new_chunk(
[c],
shape=c.shape,
index=index,
dtypes=c.dtypes,
index_value=c.index_value,
columns_value=c.columns_value,
)
)
nsplits.extend(df.nsplits[op.axis])
cum_index += len(df.nsplits[op.axis])
out_nsplits = (
(tuple(nsplits), inputs[0].nsplits[1])
if op.axis == 0
else (inputs[0].nsplits[0], tuple(nsplits))
)
if op.ignore_index:
out_chunks = standardize_range_index(out_chunks)
new_op = op.copy()
return new_op.new_dataframes(
op.inputs,
out_df.shape,
nsplits=out_nsplits,
chunks=out_chunks,
dtypes=out_df.dtypes,
index_value=out_df.index_value,
columns_value=out_df.columns_value,
)
|
https://github.com/mars-project/mars/issues/1654
|
Traceback (most recent call last):
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 382, in execute_graph
self._execute_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 648, in prepare_graph
self._target_tileable_datas + fetch_tileables, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 182, in tile
return cls._tile_dataframe(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in _tile_dataframe
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in <listcomp>
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/base/rechunk.py", line 96, in rechunk
chunk_size = get_nsplits(a, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/rechunk/core.py", line 38, in get_nsplits
return decide_chunk_sizes(tileable.shape, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 551, in decide_chunk_sizes
return normalize_chunk_sizes(shape, tuple(chunk_size[j] for j in range(len(shape))))
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 66, in normalize_chunk_sizes
raise ValueError('chunks shape should be of the same length, '
ValueError: chunks shape should be of the same length, got shape: 100000, chunks: (nan, nan, nan, nan, nan, nan)
|
ValueError
|
def _tile_series(cls, op):
from ..indexing.iloc import SeriesIlocGetItem
out = op.outputs[0]
inputs = op.inputs
out_chunks = []
if op.axis == 1:
check_chunks_unknown_shape(inputs, TilesError)
inputs = [item.rechunk(op.inputs[0].nsplits)._inplace_tile() for item in inputs]
cum_index = 0
nsplits = []
for series in inputs:
for c in series.chunks:
if op.axis == 0:
index = (c.index[0] + cum_index,)
shape = c.shape
else:
index = (c.index[0], cum_index)
shape = (c.shape[0], 1)
iloc_op = SeriesIlocGetItem(indexes=(slice(None),))
out_chunks.append(
iloc_op.new_chunk(
[c],
shape=shape,
index=index,
index_value=c.index_value,
dtype=c.dtype,
name=c.name,
)
)
if op.axis == 0:
nsplits.extend(series.nsplits[0])
cum_index += len(series.nsplits[op.axis])
else:
nsplits.append(1)
cum_index += 1
if op.ignore_index:
out_chunks = standardize_range_index(out_chunks)
new_op = op.copy()
if op.axis == 0:
nsplits = (tuple(nsplits),)
return new_op.new_seriess(
op.inputs,
out.shape,
nsplits=nsplits,
chunks=out_chunks,
dtype=out.dtype,
index_value=out.index_value,
name=out.name,
)
else:
nsplits = (inputs[0].nsplits[0], tuple(nsplits))
return new_op.new_dataframes(
op.inputs,
out.shape,
nsplits=nsplits,
chunks=out_chunks,
dtypes=out.dtypes,
index_value=out.index_value,
columns_value=out.columns_value,
)
|
def _tile_series(cls, op):
from ..indexing.iloc import SeriesIlocGetItem
out = op.outputs[0]
inputs = op.inputs
out_chunks = []
if op.axis == 1:
inputs = [item.rechunk(op.inputs[0].nsplits)._inplace_tile() for item in inputs]
cum_index = 0
nsplits = []
for series in inputs:
for c in series.chunks:
if op.axis == 0:
index = (c.index[0] + cum_index,)
shape = c.shape
else:
index = (c.index[0], cum_index)
shape = (c.shape[0], 1)
iloc_op = SeriesIlocGetItem(indexes=(slice(None),))
out_chunks.append(
iloc_op.new_chunk(
[c],
shape=shape,
index=index,
index_value=c.index_value,
dtype=c.dtype,
name=c.name,
)
)
if op.axis == 0:
nsplits.extend(series.nsplits[0])
cum_index += len(series.nsplits[op.axis])
else:
nsplits.append(1)
cum_index += 1
if op.ignore_index:
out_chunks = standardize_range_index(out_chunks)
new_op = op.copy()
if op.axis == 0:
nsplits = (tuple(nsplits),)
return new_op.new_seriess(
op.inputs,
out.shape,
nsplits=nsplits,
chunks=out_chunks,
dtype=out.dtype,
index_value=out.index_value,
name=out.name,
)
else:
nsplits = (inputs[0].nsplits[0], tuple(nsplits))
return new_op.new_dataframes(
op.inputs,
out.shape,
nsplits=nsplits,
chunks=out_chunks,
dtypes=out.dtypes,
index_value=out.index_value,
columns_value=out.columns_value,
)
|
https://github.com/mars-project/mars/issues/1654
|
Traceback (most recent call last):
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 382, in execute_graph
self._execute_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 648, in prepare_graph
self._target_tileable_datas + fetch_tileables, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 182, in tile
return cls._tile_dataframe(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in _tile_dataframe
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in <listcomp>
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/base/rechunk.py", line 96, in rechunk
chunk_size = get_nsplits(a, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/rechunk/core.py", line 38, in get_nsplits
return decide_chunk_sizes(tileable.shape, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 551, in decide_chunk_sizes
return normalize_chunk_sizes(shape, tuple(chunk_size[j] for j in range(len(shape))))
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 66, in normalize_chunk_sizes
raise ValueError('chunks shape should be of the same length, '
ValueError: chunks shape should be of the same length, got shape: 100000, chunks: (nan, nan, nan, nan, nan, nan)
|
ValueError
|
def watch_workers(self):
from kubernetes import client, config
cls = type(self)
if os.environ.get("KUBE_API_ADDRESS"): # pragma: no cover
k8s_config = client.Configuration()
k8s_config.host = os.environ["KUBE_API_ADDRESS"]
else:
k8s_config = config.load_incluster_config()
watcher = self.watcher_cls(k8s_config, os.environ["MARS_K8S_POD_NAMESPACE"])
for workers in watcher.watch_workers(): # pragma: no branch
if not cls._watcher_running: # pragma: no cover
break
if self._resource_ref is None:
self.set_schedulers(self._cluster_info_ref.get_schedulers())
self._resource_ref = self.get_actor_ref(ResourceActor.default_uid())
if self._resource_ref: # pragma: no branch
self._resource_ref.mark_workers_alive(workers)
|
def watch_workers(self):
from kubernetes import client, config
cls = type(self)
worker_set = set()
workers_from_resource = set()
if os.environ.get("KUBE_API_ADDRESS"): # pragma: no cover
k8s_config = client.Configuration()
k8s_config.host = os.environ["KUBE_API_ADDRESS"]
else:
k8s_config = config.load_incluster_config()
watcher = self.watcher_cls(k8s_config, os.environ["MARS_K8S_POD_NAMESPACE"])
for workers in watcher.watch_workers(): # pragma: no branch
if not cls._watcher_running: # pragma: no cover
break
if self._resource_ref is None:
self.set_schedulers(self._cluster_info_ref.get_schedulers())
self._resource_ref = self.get_actor_ref(ResourceActor.default_uid())
if self._resource_ref is not None: # pragma: no branch
workers_from_resource = set(self._resource_ref.get_worker_endpoints())
removed = (worker_set - set(workers)) or (
worker_set - set(workers_from_resource)
)
if removed:
logger.debug("Remove of workers %r detected by kubernetes.", removed)
if self._resource_ref: # pragma: no branch
self._resource_ref.detach_dead_workers(
list(removed), _tell=True, _wait=False
)
worker_set = set(workers)
|
https://github.com/mars-project/mars/issues/1654
|
Traceback (most recent call last):
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 382, in execute_graph
self._execute_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 648, in prepare_graph
self._target_tileable_datas + fetch_tileables, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 182, in tile
return cls._tile_dataframe(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in _tile_dataframe
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in <listcomp>
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/base/rechunk.py", line 96, in rechunk
chunk_size = get_nsplits(a, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/rechunk/core.py", line 38, in get_nsplits
return decide_chunk_sizes(tileable.shape, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 551, in decide_chunk_sizes
return normalize_chunk_sizes(shape, tuple(chunk_size[j] for j in range(len(shape))))
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 66, in normalize_chunk_sizes
raise ValueError('chunks shape should be of the same length, '
ValueError: chunks shape should be of the same length, got shape: 100000, chunks: (nan, nan, nan, nan, nan, nan)
|
ValueError
|
def tile(cls, op):
tensor = op.tensor
pk = op.pk
out = op.outputs[0]
index_path = op.index_path
ctx = get_context()
fs = None
if index_path is not None:
fs = get_fs(index_path, op.storage_options)
# check index_path for distributed
if getattr(ctx, "running_mode", None) == RunningMode.distributed:
if index_path is not None:
if isinstance(fs, LocalFileSystem):
raise ValueError(
"`index_path` cannot be local file dir "
"for distributed index building"
)
if index_path is not None:
# check if the index path is empty
try:
files = [f for f in fs.ls(index_path) if "proxima_" in f]
if files:
raise ValueError(
f"Directory {index_path} contains built proxima index, "
f"clean them to perform new index building"
)
except FileNotFoundError:
# if not exist, create directory
fs.mkdir(index_path)
# make sure all inputs have known chunk sizes
check_chunks_unknown_shape(op.inputs, TilesError)
nsplit = decide_unify_split(tensor.nsplits[0], pk.nsplits[0])
if op.topk is not None:
nsplit = cls._get_atleast_topk_nsplit(nsplit, op.topk)
if tensor.chunk_shape[1] > 1:
tensor = tensor.rechunk({0: nsplit, 1: tensor.shape[1]})._inplace_tile()
else:
tensor = tensor.rechunk({0: nsplit})._inplace_tile()
pk = pk.rechunk({0: nsplit})._inplace_tile()
out_chunks = []
for chunk, pk_col_chunk in zip(tensor.chunks, pk.chunks):
chunk_op = op.copy().reset_key()
chunk_op._stage = OperandStage.map
out_chunk = chunk_op.new_chunk([chunk, pk_col_chunk], index=pk_col_chunk.index)
out_chunks.append(out_chunk)
logger.warning(f"index chunks count: {len(out_chunks)} ")
params = out.params
params["chunks"] = out_chunks
params["nsplits"] = ((1,) * len(out_chunks),)
new_op = op.copy()
return new_op.new_tileables(op.inputs, kws=[params])
|
def tile(cls, op):
tensor = op.tensor
pk = op.pk
out = op.outputs[0]
index_path = op.index_path
ctx = get_context()
# check index_path for distributed
if getattr(ctx, "running_mode", None) == RunningMode.distributed:
if index_path is not None:
fs = get_fs(index_path, op.storage_options)
if isinstance(fs, LocalFileSystem):
raise ValueError(
"`index_path` cannot be local file dir for distributed index building"
)
# make sure all inputs have known chunk sizes
check_chunks_unknown_shape(op.inputs, TilesError)
nsplit = decide_unify_split(tensor.nsplits[0], pk.nsplits[0])
if op.topk is not None:
nsplit = cls._get_atleast_topk_nsplit(nsplit, op.topk)
if tensor.chunk_shape[1] > 1:
tensor = tensor.rechunk({0: nsplit, 1: tensor.shape[1]})._inplace_tile()
else:
tensor = tensor.rechunk({0: nsplit})._inplace_tile()
pk = pk.rechunk({0: nsplit})._inplace_tile()
out_chunks = []
for chunk, pk_col_chunk in zip(tensor.chunks, pk.chunks):
chunk_op = op.copy().reset_key()
chunk_op._stage = OperandStage.map
out_chunk = chunk_op.new_chunk([chunk, pk_col_chunk], index=pk_col_chunk.index)
out_chunks.append(out_chunk)
logger.warning(f"index chunks count: {len(out_chunks)} ")
params = out.params
params["chunks"] = out_chunks
params["nsplits"] = ((1,) * len(out_chunks),)
new_op = op.copy()
return new_op.new_tileables(op.inputs, kws=[params])
|
https://github.com/mars-project/mars/issues/1654
|
Traceback (most recent call last):
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 382, in execute_graph
self._execute_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 648, in prepare_graph
self._target_tileable_datas + fetch_tileables, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 182, in tile
return cls._tile_dataframe(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in _tile_dataframe
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in <listcomp>
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/base/rechunk.py", line 96, in rechunk
chunk_size = get_nsplits(a, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/rechunk/core.py", line 38, in get_nsplits
return decide_chunk_sizes(tileable.shape, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 551, in decide_chunk_sizes
return normalize_chunk_sizes(shape, tuple(chunk_size[j] for j in range(len(shape))))
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 66, in normalize_chunk_sizes
raise ValueError('chunks shape should be of the same length, '
ValueError: chunks shape should be of the same length, got shape: 100000, chunks: (nan, nan, nan, nan, nan, nan)
|
ValueError
|
def _execute_map(cls, ctx, op: "ProximaBuilder"):
inp = ctx[op.tensor.key]
out = op.outputs[0]
pks = ctx[op.pk.key]
proxima_type = get_proxima_type(inp.dtype)
# holder
holder = proxima.IndexHolder(type=proxima_type, dimension=op.dimension)
for pk, record in zip(pks, inp):
pk = pk.item() if hasattr(pk, "item") else pk
holder.emplace(pk, record.copy())
# converter
meta = proxima.IndexMeta(
proxima_type, dimension=op.dimension, measure_name=op.distance_metric
)
if op.index_converter is not None:
converter = proxima.IndexConverter(
name=op.index_converter, meta=meta, params=op.index_converter_params
)
converter.train_and_transform(holder)
holder = converter.result()
meta = converter.meta()
# builder && dumper
builder = proxima.IndexBuilder(
name=op.index_builder, meta=meta, params=op.index_builder_params
)
builder = builder.train_and_build(holder)
path = tempfile.mkstemp(prefix="proxima-", suffix=".index")[1]
dumper = proxima.IndexDumper(name="FileDumper", path=path)
builder.dump(dumper)
dumper.close()
if op.index_path is None:
ctx[out.key] = path
else:
# write to external file system
fs = get_fs(op.index_path, op.storage_options)
filename = f"proxima_{out.index[0]}_index"
out_path = f"{op.index_path.rstrip('/')}/{filename}"
with fs.open(out_path, "wb") as out_f:
with open(path, "rb") as in_f:
# 32M
chunk_bytes = 32 * 1024**2
while True:
data = in_f.read(chunk_bytes)
if data:
out_f.write(data)
else:
break
ctx[out.key] = filename
|
def _execute_map(cls, ctx, op: "ProximaBuilder"):
inp = ctx[op.tensor.key]
out = op.outputs[0]
pks = ctx[op.pk.key]
proxima_type = get_proxima_type(inp.dtype)
# holder
holder = proxima.IndexHolder(type=proxima_type, dimension=op.dimension)
for pk, record in zip(pks, inp):
pk = pk.item() if hasattr(pk, "item") else pk
holder.emplace(pk, record.copy())
# converter
meta = proxima.IndexMeta(
proxima_type, dimension=op.dimension, measure_name=op.distance_metric
)
if op.index_converter is not None:
converter = proxima.IndexConverter(
name=op.index_converter, meta=meta, params=op.index_converter_params
)
converter.train_and_transform(holder)
holder = converter.result()
meta = converter.meta()
# builder && dumper
builder = proxima.IndexBuilder(
name=op.index_builder, meta=meta, params=op.index_builder_params
)
builder = builder.train_and_build(holder)
path = tempfile.mkstemp(prefix="proxima-", suffix=".index")[1]
dumper = proxima.IndexDumper(name="FileDumper", path=path)
builder.dump(dumper)
dumper.close()
if op.index_path is None:
ctx[out.key] = path
else:
# write to external file system
fs = get_fs(op.index_path, op.storage_options)
filename = f"proxima-{out.index[0]}.index"
out_path = f"{op.index_path.rstrip('/')}/{filename}"
with fs.open(out_path, "wb") as out_f:
with open(path, "rb") as in_f:
# 32M
chunk_bytes = 32 * 1024**2
while True:
data = in_f.read(chunk_bytes)
if data:
out_f.write(data)
else:
break
ctx[out.key] = filename
|
https://github.com/mars-project/mars/issues/1654
|
Traceback (most recent call last):
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 382, in execute_graph
self._execute_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 648, in prepare_graph
self._target_tileable_datas + fetch_tileables, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 182, in tile
return cls._tile_dataframe(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in _tile_dataframe
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in <listcomp>
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/base/rechunk.py", line 96, in rechunk
chunk_size = get_nsplits(a, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/rechunk/core.py", line 38, in get_nsplits
return decide_chunk_sizes(tileable.shape, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 551, in decide_chunk_sizes
return normalize_chunk_sizes(shape, tuple(chunk_size[j] for j in range(len(shape))))
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 66, in normalize_chunk_sizes
raise ValueError('chunks shape should be of the same length, '
ValueError: chunks shape should be of the same length, got shape: 100000, chunks: (nan, nan, nan, nan, nan, nan)
|
ValueError
|
def build_index(
tensor,
pk,
dimension=None,
index_path=None,
need_shuffle=False,
distance_metric="SquaredEuclidean",
index_builder="SsgBuilder",
index_builder_params=None,
index_converter=None,
index_converter_params=None,
topk=None,
storage_options=None,
run=True,
session=None,
run_kwargs=None,
):
tensor = validate_tensor(tensor)
if tensor.dtype not in available_numpy_dtypes:
raise ValueError(
f"Dtype to build index should be one of {available_numpy_dtypes}, "
f"got {tensor.dtype}"
)
if dimension is None:
dimension = tensor.shape[1]
if index_builder_params is None:
index_builder_params = {}
if index_converter_params is None:
index_converter_params = {}
if need_shuffle:
tensor = mt.random.permutation(tensor)
if not isinstance(pk, (Base, Entity)):
pk = mt.tensor(pk)
op = ProximaBuilder(
tensor=tensor,
pk=pk,
distance_metric=distance_metric,
index_path=index_path,
dimension=dimension,
index_builder=index_builder,
index_builder_params=index_builder_params,
index_converter=index_converter,
index_converter_params=index_converter_params,
topk=topk,
storage_options=storage_options,
)
result = op(tensor, pk)
if run:
return result.execute(session=session, **(run_kwargs or dict()))
else:
return result
|
def build_index(
tensor,
pk,
dimension=None,
index_path=None,
need_shuffle=False,
distance_metric="SquaredEuclidean",
index_builder="SsgBuilder",
index_builder_params=None,
index_converter=None,
index_converter_params=None,
topk=None,
storage_options=None,
run=True,
session=None,
run_kwargs=None,
):
tensor = validate_tensor(tensor)
if dimension is None:
dimension = tensor.shape[1]
if index_builder_params is None:
index_builder_params = {}
if index_converter_params is None:
index_converter_params = {}
if need_shuffle:
tensor = mt.random.permutation(tensor)
if not isinstance(pk, (Base, Entity)):
pk = mt.tensor(pk)
op = ProximaBuilder(
tensor=tensor,
pk=pk,
distance_metric=distance_metric,
index_path=index_path,
dimension=dimension,
index_builder=index_builder,
index_builder_params=index_builder_params,
index_converter=index_converter,
index_converter_params=index_converter_params,
topk=topk,
storage_options=storage_options,
)
result = op(tensor, pk)
if run:
return result.execute(session=session, **(run_kwargs or dict()))
else:
return result
|
https://github.com/mars-project/mars/issues/1654
|
Traceback (most recent call last):
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 382, in execute_graph
self._execute_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 648, in prepare_graph
self._target_tileable_datas + fetch_tileables, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 182, in tile
return cls._tile_dataframe(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in _tile_dataframe
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in <listcomp>
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/base/rechunk.py", line 96, in rechunk
chunk_size = get_nsplits(a, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/rechunk/core.py", line 38, in get_nsplits
return decide_chunk_sizes(tileable.shape, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 551, in decide_chunk_sizes
return normalize_chunk_sizes(shape, tuple(chunk_size[j] for j in range(len(shape))))
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 66, in normalize_chunk_sizes
raise ValueError('chunks shape should be of the same length, '
ValueError: chunks shape should be of the same length, got shape: 100000, chunks: (nan, nan, nan, nan, nan, nan)
|
ValueError
|
def tile(cls, op: "ProximaSearcher"):
tensor = op.tensor
index = op.index
topk = op.topk
outs = op.outputs
# make sure all inputs have known chunk sizes
check_chunks_unknown_shape(op.inputs, TilesError)
if tensor.chunk_shape[1] > 1:
tensor = tensor.rechunk({1: tensor.shape[1]})._inplace_tile()
logger.warning(f"query chunks count: {len(tensor.chunks)} ")
if hasattr(index, "op"):
built_indexes = index.chunks
else:
# index path
fs: FileSystem = get_fs(index, op.storage_options)
built_indexes = [
f for f in fs.ls(index) if f.rsplit("/", 1)[-1].startswith("proxima_")
]
if hasattr(index, "op"):
ctx = get_context()
index_chunks_workers = [
m.workers[0] if m.workers else None
for m in ctx.get_chunk_metas([c.key for c in index.chunks])
]
else:
index_chunks_workers = [None] * len(built_indexes)
out_chunks = [], []
for tensor_chunk in tensor.chunks:
pk_chunks, distance_chunks = [], []
for j, chunk_index, worker in zip(
itertools.count(), built_indexes, index_chunks_workers
):
chunk_op = op.copy().reset_key()
chunk_op._stage = OperandStage.map
if hasattr(chunk_index, "op"):
chunk_op._expect_worker = worker
chunk_op._index = chunk_index
chunk_kws = [
{
"index": (tensor_chunk.index[0], j),
"dtype": outs[0].dtype,
"shape": (tensor_chunk.shape[0], topk),
"order": TensorOrder.C_ORDER,
},
{
"index": (tensor_chunk.index[0], j),
"dtype": outs[1].dtype,
"shape": (tensor_chunk.shape[0], topk),
"order": TensorOrder.C_ORDER,
},
]
chunk_inputs = [tensor_chunk]
if hasattr(chunk_index, "op"):
chunk_inputs.append(chunk_index)
pk_chunk, distance_chunk = chunk_op.new_chunks(chunk_inputs, kws=chunk_kws)
pk_chunks.append(pk_chunk)
distance_chunks.append(distance_chunk)
if len(pk_chunks) == 1:
out_chunks[0].append(pk_chunks[0])
out_chunks[1].append(distance_chunks[0])
continue
shape = (tensor_chunk.shape[0], topk * len(pk_chunks))
pk_merge_op = TensorConcatenate(axis=1)
pk_merge_chunk = pk_merge_op.new_chunk(
pk_chunks,
index=(pk_chunks[0].index[0], 0),
shape=shape,
dtype=pk_chunks[0].dtype,
order=pk_chunks[0].order,
)
distance_merge_op = TensorConcatenate(axis=1)
distance_merge_chunk = distance_merge_op.new_chunk(
distance_chunks,
index=(distance_chunks[0].index[0], 0),
shape=shape,
dtype=distance_chunks[0].dtype,
order=distance_chunks[0].order,
)
agg_op = ProximaSearcher(
stage=OperandStage.agg, topk=op.topk, distance_metric=op.distance_metric
)
agg_chunk_kws = [
{
"index": pk_merge_chunk.index,
"dtype": outs[0].dtype,
"shape": (tensor_chunk.shape[0], topk),
"order": outs[0].order,
},
{
"index": pk_merge_chunk.index,
"dtype": outs[1].dtype,
"shape": (tensor_chunk.shape[0], topk),
"order": outs[1].order,
},
]
pk_result_chunk, distance_result_chunk = agg_op.new_chunks(
[pk_merge_chunk, distance_merge_chunk], kws=agg_chunk_kws
)
out_chunks[0].append(pk_result_chunk)
out_chunks[1].append(distance_result_chunk)
logger.warning(f"query out_chunks count: {len(out_chunks)} ")
kws = []
pk_params = outs[0].params
pk_params["chunks"] = out_chunks[0]
pk_params["nsplits"] = (tensor.nsplits[0], (topk,))
kws.append(pk_params)
distance_params = outs[1].params
distance_params["chunks"] = out_chunks[1]
distance_params["nsplits"] = (tensor.nsplits[0], (topk,))
kws.append(distance_params)
new_op = op.copy()
return new_op.new_tileables(op.inputs, kws=kws)
|
def tile(cls, op: "ProximaSearcher"):
tensor = op.tensor
index = op.index
topk = op.topk
outs = op.outputs
# make sure all inputs have known chunk sizes
check_chunks_unknown_shape(op.inputs, TilesError)
if tensor.chunk_shape[1] > 1:
tensor = tensor.rechunk({1: tensor.shape[1]})._inplace_tile()
logger.warning(f"query chunks count: {len(tensor.chunks)} ")
if hasattr(index, "op"):
built_indexes = index.chunks
else:
# index path
fs: FileSystem = get_fs(index, op.storage_options)
built_indexes = [
f for f in fs.ls(index) if f.rsplit("/", 1)[-1].startswith("proxima-")
]
if hasattr(index, "op"):
ctx = get_context()
index_chunks_workers = [
m.workers[0] if m.workers else None
for m in ctx.get_chunk_metas([c.key for c in index.chunks])
]
else:
index_chunks_workers = [None] * len(built_indexes)
out_chunks = [], []
for tensor_chunk in tensor.chunks:
pk_chunks, distance_chunks = [], []
for j, chunk_index, worker in zip(
itertools.count(), built_indexes, index_chunks_workers
):
chunk_op = op.copy().reset_key()
chunk_op._stage = OperandStage.map
if hasattr(chunk_index, "op"):
chunk_op._expect_worker = worker
chunk_op._index = chunk_index
chunk_kws = [
{
"index": (tensor_chunk.index[0], j),
"dtype": outs[0].dtype,
"shape": (tensor_chunk.shape[0], topk),
"order": TensorOrder.C_ORDER,
},
{
"index": (tensor_chunk.index[0], j),
"dtype": outs[1].dtype,
"shape": (tensor_chunk.shape[0], topk),
"order": TensorOrder.C_ORDER,
},
]
chunk_inputs = [tensor_chunk]
if hasattr(chunk_index, "op"):
chunk_inputs.append(chunk_index)
pk_chunk, distance_chunk = chunk_op.new_chunks(chunk_inputs, kws=chunk_kws)
pk_chunks.append(pk_chunk)
distance_chunks.append(distance_chunk)
if len(pk_chunks) == 1:
out_chunks[0].append(pk_chunks[0])
out_chunks[1].append(distance_chunks[0])
continue
shape = (tensor_chunk.shape[0], topk * len(pk_chunks))
pk_merge_op = TensorConcatenate(axis=1)
pk_merge_chunk = pk_merge_op.new_chunk(
pk_chunks,
index=(pk_chunks[0].index[0], 0),
shape=shape,
dtype=pk_chunks[0].dtype,
order=pk_chunks[0].order,
)
distance_merge_op = TensorConcatenate(axis=1)
distance_merge_chunk = distance_merge_op.new_chunk(
distance_chunks,
index=(distance_chunks[0].index[0], 0),
shape=shape,
dtype=distance_chunks[0].dtype,
order=distance_chunks[0].order,
)
agg_op = ProximaSearcher(
stage=OperandStage.agg, topk=op.topk, distance_metric=op.distance_metric
)
agg_chunk_kws = [
{
"index": pk_merge_chunk.index,
"dtype": outs[0].dtype,
"shape": (tensor_chunk.shape[0], topk),
"order": outs[0].order,
},
{
"index": pk_merge_chunk.index,
"dtype": outs[1].dtype,
"shape": (tensor_chunk.shape[0], topk),
"order": outs[1].order,
},
]
pk_result_chunk, distance_result_chunk = agg_op.new_chunks(
[pk_merge_chunk, distance_merge_chunk], kws=agg_chunk_kws
)
out_chunks[0].append(pk_result_chunk)
out_chunks[1].append(distance_result_chunk)
logger.warning(f"query out_chunks count: {len(out_chunks)} ")
kws = []
pk_params = outs[0].params
pk_params["chunks"] = out_chunks[0]
pk_params["nsplits"] = (tensor.nsplits[0], (topk,))
kws.append(pk_params)
distance_params = outs[1].params
distance_params["chunks"] = out_chunks[1]
distance_params["nsplits"] = (tensor.nsplits[0], (topk,))
kws.append(distance_params)
new_op = op.copy()
return new_op.new_tileables(op.inputs, kws=kws)
|
https://github.com/mars-project/mars/issues/1654
|
Traceback (most recent call last):
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 382, in execute_graph
self._execute_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 410, in _execute_graph
self.prepare_graph(compose=compose)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 648, in prepare_graph
self._target_tileable_datas + fetch_tileables, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 348, in build
tileables, tileable_graph=tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 203, in _tile
tds = on_tile(tileable_data.op.outputs, tds)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/scheduler/graph.py", line 630, in on_tile
return self.context.wraps(handler.dispatch)(first.op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/context.py", line 72, in h
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 182, in tile
return cls._tile_dataframe(op)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in _tile_dataframe
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/merge/concat.py", line 94, in <listcomp>
inputs = [item.rechunk(normalized_nsplits)._inplace_tile() for item in inputs]
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/dataframe/base/rechunk.py", line 96, in rechunk
chunk_size = get_nsplits(a, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/rechunk/core.py", line 38, in get_nsplits
return decide_chunk_sizes(tileable.shape, chunk_size, itemsize)
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 551, in decide_chunk_sizes
return normalize_chunk_sizes(shape, tuple(chunk_size[j] for j in range(len(shape))))
File "/home/admin/work/tip_dev-pymars-0.6.0a3.zip/mars/tensor/utils.py", line 66, in normalize_chunk_sizes
raise ValueError('chunks shape should be of the same length, '
ValueError: chunks shape should be of the same length, got shape: 100000, chunks: (nan, nan, nan, nan, nan, nan)
|
ValueError
|
def execute_sort_values(data, op, inplace=None, by=None):
if inplace is None:
inplace = op.inplace
# ignore_index is new in Pandas version 1.0.0.
ignore_index = getattr(op, "ignore_index", False)
if isinstance(data, (pd.DataFrame, pd.Series)):
kwargs = dict(
axis=op.axis,
ascending=op.ascending,
ignore_index=ignore_index,
na_position=op.na_position,
kind=op.kind,
)
if isinstance(data, pd.DataFrame):
kwargs["by"] = by if by is not None else op.by
if inplace:
kwargs["inplace"] = True
try:
data.sort_values(**kwargs)
except TypeError: # pragma: no cover
kwargs.pop("ignore_index", None)
data.sort_values(**kwargs)
return data
else:
try:
return data.sort_values(**kwargs)
except TypeError: # pragma: no cover
kwargs.pop("ignore_index", None)
return data.sort_values(**kwargs)
else: # pragma: no cover
# cudf doesn't support axis and kind
if isinstance(data, cudf.DataFrame):
return data.sort_values(
op.by, ascending=op.ascending, na_position=op.na_position
)
else:
return data.sort_values(ascending=op.ascending, na_position=op.na_position)
|
def execute_sort_values(data, op, inplace=None):
if inplace is None:
inplace = op.inplace
# ignore_index is new in Pandas version 1.0.0.
ignore_index = getattr(op, "ignore_index", False)
if isinstance(data, (pd.DataFrame, pd.Series)):
kwargs = dict(
axis=op.axis,
ascending=op.ascending,
ignore_index=ignore_index,
na_position=op.na_position,
kind=op.kind,
)
if isinstance(data, pd.DataFrame):
kwargs["by"] = op.by
if inplace:
kwargs["inplace"] = True
try:
data.sort_values(**kwargs)
except TypeError: # pragma: no cover
kwargs.pop("ignore_index", None)
data.sort_values(**kwargs)
return data
else:
try:
return data.sort_values(**kwargs)
except TypeError: # pragma: no cover
kwargs.pop("ignore_index", None)
return data.sort_values(**kwargs)
else: # pragma: no cover
# cudf doesn't support axis and kind
if isinstance(data, cudf.DataFrame):
return data.sort_values(
op.by, ascending=op.ascending, na_position=op.na_position
)
else:
return data.sort_values(ascending=op.ascending, na_position=op.na_position)
|
https://github.com/mars-project/mars/issues/1641
|
2020-10-19 19:46:44,463 Unexpected exception occurred in BaseCalcActor._calc_results. graph_key=cfd4b1a2cc914a2b30aa228eda1e7ea8
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/worker/calc.py", line 201, in _calc_results
self._execution_pool.submit(executor.execute_graph, graph,
File "src/gevent/event.py", line 383, in gevent._gevent_cevent.AsyncResult.result
File "src/gevent/event.py", line 305, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 335, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 323, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 303, in gevent._gevent_cevent.AsyncResult._raise_exception
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/gevent/_compat.py", line 65, in reraise
raise value.with_traceback(tb)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/gevent/threadpool.py", line 142, in __run_task
thread_result.set(func(*args, **kwargs))
File "mars/actors/pool/gevent_pool.pyx", line 127, in mars.actors.pool.gevent_pool.GeventThreadPool._wrap_watch.inner
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/sort/psrs.py", line 357, in execute
ctx[op.outputs[-1].key] = res[by].iloc[slc]
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 2908, in __getitem__
indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1254, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1304, in _validate_read_indexer
raise KeyError(f"{not_found} not in index")
KeyError: "['__PSRS_TMP_DISTINCT_COL'] not in index"
|
KeyError
|
def execute(cls, ctx, op):
a = ctx[op.inputs[0].key]
if op.sort_type == "sort_values":
ctx[op.outputs[0].key] = res = execute_sort_values(a, op)
else:
ctx[op.outputs[0].key] = res = execute_sort_index(a, op)
by = op.by
add_distinct_col = (
bool(int(os.environ.get("PSRS_DISTINCT_COL", "0")))
or getattr(ctx, "running_mode", None) == RunningMode.distributed
)
if (
add_distinct_col
and isinstance(a, pd.DataFrame)
and op.sort_type == "sort_values"
):
# when running under distributed mode, we introduce an extra column
# to make sure pivots are distinct
chunk_idx = op.inputs[0].index[0]
distinct_col = (
_PSRS_DISTINCT_COL
if a.columns.nlevels == 1
else (_PSRS_DISTINCT_COL,) + ("",) * (a.columns.nlevels - 1)
)
res[distinct_col] = np.arange(
chunk_idx << 32, (chunk_idx << 32) + len(a), dtype=np.int64
)
by = list(by) + [distinct_col]
n = op.n_partition
if a.shape[op.axis] < n:
num = n // a.shape[op.axis] + 1
res = execute_sort_values(pd.concat([res] * num), op, by=by)
w = int(res.shape[op.axis] // n)
slc = (slice(None),) * op.axis + (slice(0, n * w, w),)
if op.sort_type == "sort_values":
# do regular sample
if op.by is not None:
ctx[op.outputs[-1].key] = res[by].iloc[slc]
else:
ctx[op.outputs[-1].key] = res.iloc[slc]
else:
# do regular sample
ctx[op.outputs[-1].key] = res.iloc[slc]
|
def execute(cls, ctx, op):
a = ctx[op.inputs[0].key]
if op.sort_type == "sort_values":
ctx[op.outputs[0].key] = res = execute_sort_values(a, op)
else:
ctx[op.outputs[0].key] = res = execute_sort_index(a, op)
by = op.by
if (
getattr(ctx, "running_mode", None) == RunningMode.distributed
and isinstance(a, pd.DataFrame)
and op.sort_type == "sort_values"
):
# when running under distributed mode, we introduce an extra column
# to make sure pivots are distinct
chunk_idx = op.inputs[0].index[0]
distinct_col = (
_PSRS_DISTINCT_COL
if a.columns.nlevels == 1
else (_PSRS_DISTINCT_COL,) + ("",) * (a.columns.nlevels - 1)
)
res[distinct_col] = np.arange(chunk_idx << 32, (chunk_idx << 32) + len(a))
by = list(by) + [distinct_col]
n = op.n_partition
if a.shape[op.axis] < n:
num = n // a.shape[op.axis] + 1
res = execute_sort_values(pd.concat([a] * num), op)
w = int(res.shape[op.axis] // n)
slc = (slice(None),) * op.axis + (slice(0, n * w, w),)
if op.sort_type == "sort_values":
# do regular sample
if op.by is not None:
ctx[op.outputs[-1].key] = res[by].iloc[slc]
else:
ctx[op.outputs[-1].key] = res.iloc[slc]
else:
# do regular sample
ctx[op.outputs[-1].key] = res.iloc[slc]
|
https://github.com/mars-project/mars/issues/1641
|
2020-10-19 19:46:44,463 Unexpected exception occurred in BaseCalcActor._calc_results. graph_key=cfd4b1a2cc914a2b30aa228eda1e7ea8
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/worker/calc.py", line 201, in _calc_results
self._execution_pool.submit(executor.execute_graph, graph,
File "src/gevent/event.py", line 383, in gevent._gevent_cevent.AsyncResult.result
File "src/gevent/event.py", line 305, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 335, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 323, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 303, in gevent._gevent_cevent.AsyncResult._raise_exception
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/gevent/_compat.py", line 65, in reraise
raise value.with_traceback(tb)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/gevent/threadpool.py", line 142, in __run_task
thread_result.set(func(*args, **kwargs))
File "mars/actors/pool/gevent_pool.pyx", line 127, in mars.actors.pool.gevent_pool.GeventThreadPool._wrap_watch.inner
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/sort/psrs.py", line 357, in execute
ctx[op.outputs[-1].key] = res[by].iloc[slc]
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 2908, in __getitem__
indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1254, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1304, in _validate_read_indexer
raise KeyError(f"{not_found} not in index")
KeyError: "['__PSRS_TMP_DISTINCT_COL'] not in index"
|
KeyError
|
def _execute_dataframe_map(cls, ctx, op):
a, pivots = [ctx[c.key] for c in op.inputs]
out = op.outputs[0]
if isinstance(a, pd.DataFrame):
# use numpy.searchsorted to find split positions.
by = op.by
distinct_col = (
_PSRS_DISTINCT_COL
if a.columns.nlevels == 1
else (_PSRS_DISTINCT_COL,) + ("",) * (a.columns.nlevels - 1)
)
if distinct_col in a.columns:
by += [distinct_col]
records = a[by].to_records(index=False)
p_records = pivots.to_records(index=False)
if op.ascending:
poses = records.searchsorted(p_records, side="right")
else:
poses = len(records) - records[::-1].searchsorted(p_records, side="right")
del records, p_records
poses = (None,) + tuple(poses) + (None,)
for i in range(op.n_partition):
values = a.iloc[poses[i] : poses[i + 1]]
ctx[(out.key, str(i))] = values
else: # pragma: no cover
# for cudf, find split positions in loops.
if op.ascending:
pivots.append(a.iloc[-1][op.by])
for i in range(op.n_partition):
selected = a
for label in op.by:
selected = selected.loc[a[label] <= pivots.iloc[i][label]]
ctx[(out.key, str(i))] = selected
else:
pivots.append(a.iloc[-1][op.by])
for i in range(op.n_partition):
selected = a
for label in op.by:
selected = selected.loc[a[label] >= pivots.iloc[i][label]]
ctx[(out.key, str(i))] = selected
|
def _execute_dataframe_map(cls, ctx, op):
a, pivots = [ctx[c.key] for c in op.inputs]
out = op.outputs[0]
if isinstance(a, pd.DataFrame):
# use numpy.searchsorted to find split positions.
by = op.by
distinct_col = (
_PSRS_DISTINCT_COL
if a.columns.nlevels == 1
else (_PSRS_DISTINCT_COL,) + ("",) * (a.columns.nlevels - 1)
)
if _PSRS_DISTINCT_COL in a.columns:
by += [distinct_col]
records = a[by].to_records(index=False)
p_records = pivots.to_records(index=False)
if op.ascending:
poses = records.searchsorted(p_records, side="right")
else:
poses = len(records) - records[::-1].searchsorted(p_records, side="right")
del records, p_records
poses = (None,) + tuple(poses) + (None,)
for i in range(op.n_partition):
values = a.iloc[poses[i] : poses[i + 1]]
ctx[(out.key, str(i))] = values
else: # pragma: no cover
# for cudf, find split positions in loops.
if op.ascending:
pivots.append(a.iloc[-1][op.by])
for i in range(op.n_partition):
selected = a
for label in op.by:
selected = selected.loc[a[label] <= pivots.iloc[i][label]]
ctx[(out.key, str(i))] = selected
else:
pivots.append(a.iloc[-1][op.by])
for i in range(op.n_partition):
selected = a
for label in op.by:
selected = selected.loc[a[label] >= pivots.iloc[i][label]]
ctx[(out.key, str(i))] = selected
|
https://github.com/mars-project/mars/issues/1641
|
2020-10-19 19:46:44,463 Unexpected exception occurred in BaseCalcActor._calc_results. graph_key=cfd4b1a2cc914a2b30aa228eda1e7ea8
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/worker/calc.py", line 201, in _calc_results
self._execution_pool.submit(executor.execute_graph, graph,
File "src/gevent/event.py", line 383, in gevent._gevent_cevent.AsyncResult.result
File "src/gevent/event.py", line 305, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 335, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 323, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 303, in gevent._gevent_cevent.AsyncResult._raise_exception
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/gevent/_compat.py", line 65, in reraise
raise value.with_traceback(tb)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/gevent/threadpool.py", line 142, in __run_task
thread_result.set(func(*args, **kwargs))
File "mars/actors/pool/gevent_pool.pyx", line 127, in mars.actors.pool.gevent_pool.GeventThreadPool._wrap_watch.inner
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/sort/psrs.py", line 357, in execute
ctx[op.outputs[-1].key] = res[by].iloc[slc]
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 2908, in __getitem__
indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1254, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1304, in _validate_read_indexer
raise KeyError(f"{not_found} not in index")
KeyError: "['__PSRS_TMP_DISTINCT_COL'] not in index"
|
KeyError
|
def loads(buf):
mv = memoryview(buf)
header = read_file_header(mv)
compress = header.compress
if compress == CompressType.NONE:
data = buf[HEADER_LENGTH:]
else:
data = decompressors[compress](mv[HEADER_LENGTH:])
if header.type == SerialType.ARROW:
try:
return deserialize(memoryview(data))
except pyarrow.lib.ArrowInvalid: # pragma: no cover
# reconstruct value from buffers of arrow components
data_view = memoryview(data)
meta_block_size = np.frombuffer(data_view[0:4], dtype="int32").item()
meta = pickle.loads(data_view[4 : 4 + meta_block_size]) # nosec
buffer_sizes = meta.pop("buffer_sizes")
bounds = np.cumsum([4 + meta_block_size] + buffer_sizes)
meta["data"] = [
pyarrow.py_buffer(data_view[bounds[idx] : bounds[idx + 1]])
for idx in range(len(buffer_sizes))
]
return pyarrow.deserialize_components(meta, mars_serialize_context())
else:
return pickle.loads(data)
|
def loads(buf):
mv = memoryview(buf)
header = read_file_header(mv)
compress = header.compress
if compress == CompressType.NONE:
data = buf[HEADER_LENGTH:]
else:
data = decompressors[compress](mv[HEADER_LENGTH:])
if header.type == SerialType.ARROW:
try:
return pyarrow.deserialize(memoryview(data), mars_serialize_context())
except pyarrow.lib.ArrowInvalid: # pragma: no cover
# reconstruct value from buffers of arrow components
data_view = memoryview(data)
meta_block_size = np.frombuffer(data_view[0:4], dtype="int32").item()
meta = pickle.loads(data_view[4 : 4 + meta_block_size]) # nosec
buffer_sizes = meta.pop("buffer_sizes")
bounds = np.cumsum([4 + meta_block_size] + buffer_sizes)
meta["data"] = [
pyarrow.py_buffer(data_view[bounds[idx] : bounds[idx + 1]])
for idx in range(len(buffer_sizes))
]
return pyarrow.deserialize_components(meta, mars_serialize_context())
else:
return pickle.loads(data)
|
https://github.com/mars-project/mars/issues/1641
|
2020-10-19 19:46:44,463 Unexpected exception occurred in BaseCalcActor._calc_results. graph_key=cfd4b1a2cc914a2b30aa228eda1e7ea8
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/worker/calc.py", line 201, in _calc_results
self._execution_pool.submit(executor.execute_graph, graph,
File "src/gevent/event.py", line 383, in gevent._gevent_cevent.AsyncResult.result
File "src/gevent/event.py", line 305, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 335, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 323, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 303, in gevent._gevent_cevent.AsyncResult._raise_exception
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/gevent/_compat.py", line 65, in reraise
raise value.with_traceback(tb)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/gevent/threadpool.py", line 142, in __run_task
thread_result.set(func(*args, **kwargs))
File "mars/actors/pool/gevent_pool.pyx", line 127, in mars.actors.pool.gevent_pool.GeventThreadPool._wrap_watch.inner
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun.swj/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/sort/psrs.py", line 357, in execute
ctx[op.outputs[-1].key] = res[by].iloc[slc]
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 2908, in __getitem__
indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1254, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1304, in _validate_read_indexer
raise KeyError(f"{not_found} not in index")
KeyError: "['__PSRS_TMP_DISTINCT_COL'] not in index"
|
KeyError
|
def _execute_map(cls, ctx, op):
(data,), device_id, xp = as_same_device(
[ctx[op.inputs[0].key]], device=op.device, ret_extra=True
)
index = ctx[op.inputs[1].key] if len(op.inputs) == 2 else None
with device(device_id):
data = xp.ascontiguousarray(data)
if index is not None:
# fetch the trained index
trained_index = _load_index(ctx, op, index, device_id)
return_index_type = _get_index_type(op.return_index_type, ctx)
if return_index_type == "object":
# clone a new one,
# because faiss does not ensure thread-safe for operations that change index
# https://github.com/facebookresearch/faiss/wiki/Threads-and-asynchronous-calls#thread-safety
trained_index = faiss.clone_index(trained_index)
else:
trained_index = faiss.index_factory(
data.shape[1], op.faiss_index, op.faiss_metric_type
)
if op.same_distribution:
# no need to train, just create index
pass
else:
# distribution no the same, train on each chunk
trained_index.train(data)
if device_id >= 0: # pragma: no cover
trained_index = _index_to_gpu(trained_index, device_id)
if op.metric == "cosine":
# faiss does not support cosine distances directly,
# data needs to be normalize before adding to index,
# refer to:
# https://github.com/facebookresearch/faiss/wiki/FAQ#how-can-i-index-vectors-for-cosine-distance
faiss.normalize_L2(data)
# add data into index
if device_id >= 0: # pragma: no cover
# gpu
trained_index.add_c(data.shape[0], _swig_ptr_from_cupy_float32_array(data))
else:
trained_index.add(data)
ctx[op.outputs[0].key] = _store_index(ctx, op, trained_index, device_id)
|
def _execute_map(cls, ctx, op):
(data,), device_id, _ = as_same_device(
[ctx[op.inputs[0].key]], device=op.device, ret_extra=True
)
index = ctx[op.inputs[1].key] if len(op.inputs) == 2 else None
with device(device_id):
if index is not None:
# fetch the trained index
trained_index = _load_index(ctx, op, index, device_id)
return_index_type = _get_index_type(op.return_index_type, ctx)
if return_index_type == "object":
# clone a new one,
# because faiss does not ensure thread-safe for operations that change index
# https://github.com/facebookresearch/faiss/wiki/Threads-and-asynchronous-calls#thread-safety
trained_index = faiss.clone_index(trained_index)
else:
trained_index = faiss.index_factory(
data.shape[1], op.faiss_index, op.faiss_metric_type
)
if op.same_distribution:
# no need to train, just create index
pass
else:
# distribution no the same, train on each chunk
trained_index.train(data)
if device_id >= 0: # pragma: no cover
trained_index = _index_to_gpu(trained_index, device_id)
if op.metric == "cosine":
# faiss does not support cosine distances directly,
# data needs to be normalize before adding to index,
# refer to:
# https://github.com/facebookresearch/faiss/wiki/FAQ#how-can-i-index-vectors-for-cosine-distance
faiss.normalize_L2(data)
# add data into index
if device_id >= 0: # pragma: no cover
# gpu
trained_index.add_c(data.shape[0], _swig_ptr_from_cupy_float32_array(data))
else:
trained_index.add(data)
ctx[op.outputs[0].key] = _store_index(ctx, op, trained_index, device_id)
|
https://github.com/mars-project/mars/issues/1629
|
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
648 try:
--> 649 self._set_tuple(value, field_obj, tp=field.type, weak_ref=field.weak_ref)
650 except TypeError:
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_tuple()
364 it_obj = Value()
--> 365 self._set_value(val, it_obj, tp=tp.type if tp is not None else tp)
366 res.append(it_obj)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
443 value_field = PRIMITIVE_TYPE_TO_VALUE_FIELD[tp]
--> 444 setattr(obj, value_field, value)
445 elif tp is ValueType.slice:
TypeError: 10.0 has type float, but expected one of: int, long
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~/Workspace/mars/mars/graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Provider.serialize_model()
797 for name, field in model_instance._FIELDS.items():
--> 798 field.serialize(self, model_instance, obj)
799
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Provider.serialize_model()
797 for name, field in model_instance._FIELDS.items():
--> 798 field.serialize(self, model_instance, obj)
799
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
651 exc_info = sys.exc_info()
--> 652 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
653 f'value {value}, reason: {exc_info[1]}') \
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
648 try:
--> 649 self._set_tuple(value, field_obj, tp=field.type, weak_ref=field.weak_ref)
650 except TypeError:
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_tuple()
364 it_obj = Value()
--> 365 self._set_value(val, it_obj, tp=tp.type if tp is not None else tp)
366 res.append(it_obj)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
443 value_field = PRIMITIVE_TYPE_TO_VALUE_FIELD[tp]
--> 444 setattr(obj, value_field, value)
445 elif tp is ValueType.slice:
TypeError: Failed to set field `shape` for Chunk <op=TensorTranspose, key=6fd87469bf886695950bdfb3164a7ce7> with value (10.0, -1), reason: 10.0 has type float, but expected one of: int, long
|
TypeError
|
def __call__(self, a):
shape = tuple(s if np.isnan(s) else int(s) for s in _reorder(a.shape, self._axes))
if self._axes == list(reversed(range(a.ndim))):
# order reversed
tensor_order = reverse_order(a.order)
else:
tensor_order = TensorOrder.C_ORDER
return self.new_tensor([a], shape, order=tensor_order)
|
def __call__(self, a):
shape = _reorder(a.shape, self._axes)
if self._axes == list(reversed(range(a.ndim))):
# order reversed
tensor_order = reverse_order(a.order)
else:
tensor_order = TensorOrder.C_ORDER
return self.new_tensor([a], shape, order=tensor_order)
|
https://github.com/mars-project/mars/issues/1629
|
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
648 try:
--> 649 self._set_tuple(value, field_obj, tp=field.type, weak_ref=field.weak_ref)
650 except TypeError:
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_tuple()
364 it_obj = Value()
--> 365 self._set_value(val, it_obj, tp=tp.type if tp is not None else tp)
366 res.append(it_obj)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
443 value_field = PRIMITIVE_TYPE_TO_VALUE_FIELD[tp]
--> 444 setattr(obj, value_field, value)
445 elif tp is ValueType.slice:
TypeError: 10.0 has type float, but expected one of: int, long
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~/Workspace/mars/mars/graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Provider.serialize_model()
797 for name, field in model_instance._FIELDS.items():
--> 798 field.serialize(self, model_instance, obj)
799
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Provider.serialize_model()
797 for name, field in model_instance._FIELDS.items():
--> 798 field.serialize(self, model_instance, obj)
799
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
651 exc_info = sys.exc_info()
--> 652 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
653 f'value {value}, reason: {exc_info[1]}') \
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
648 try:
--> 649 self._set_tuple(value, field_obj, tp=field.type, weak_ref=field.weak_ref)
650 except TypeError:
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_tuple()
364 it_obj = Value()
--> 365 self._set_value(val, it_obj, tp=tp.type if tp is not None else tp)
366 res.append(it_obj)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
443 value_field = PRIMITIVE_TYPE_TO_VALUE_FIELD[tp]
--> 444 setattr(obj, value_field, value)
445 elif tp is ValueType.slice:
TypeError: Failed to set field `shape` for Chunk <op=TensorTranspose, key=6fd87469bf886695950bdfb3164a7ce7> with value (10.0, -1), reason: 10.0 has type float, but expected one of: int, long
|
TypeError
|
def tile(cls, op):
tensor = op.outputs[0]
out_chunks = []
for c in op.inputs[0].chunks:
chunk_op = op.copy().reset_key()
chunk_shape = tuple(
s if np.isnan(s) else int(s) for s in _reorder(c.shape, op.axes)
)
chunk_idx = _reorder(c.index, op.axes)
out_chunk = chunk_op.new_chunk(
[c], shape=chunk_shape, index=chunk_idx, order=tensor.order
)
out_chunks.append(out_chunk)
new_op = op.copy()
nsplits = _reorder(op.inputs[0].nsplits, op.axes)
return new_op.new_tensors(
op.inputs,
op.outputs[0].shape,
order=tensor.order,
chunks=out_chunks,
nsplits=nsplits,
)
|
def tile(cls, op):
tensor = op.outputs[0]
out_chunks = []
for c in op.inputs[0].chunks:
chunk_op = op.copy().reset_key()
chunk_shape = _reorder(c.shape, op.axes)
chunk_idx = _reorder(c.index, op.axes)
out_chunk = chunk_op.new_chunk(
[c], shape=chunk_shape, index=chunk_idx, order=tensor.order
)
out_chunks.append(out_chunk)
new_op = op.copy()
nsplits = _reorder(op.inputs[0].nsplits, op.axes)
return new_op.new_tensors(
op.inputs,
op.outputs[0].shape,
order=tensor.order,
chunks=out_chunks,
nsplits=nsplits,
)
|
https://github.com/mars-project/mars/issues/1629
|
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
648 try:
--> 649 self._set_tuple(value, field_obj, tp=field.type, weak_ref=field.weak_ref)
650 except TypeError:
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_tuple()
364 it_obj = Value()
--> 365 self._set_value(val, it_obj, tp=tp.type if tp is not None else tp)
366 res.append(it_obj)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
443 value_field = PRIMITIVE_TYPE_TO_VALUE_FIELD[tp]
--> 444 setattr(obj, value_field, value)
445 elif tp is ValueType.slice:
TypeError: 10.0 has type float, but expected one of: int, long
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in _execute_graph()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in create_operand_actors()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in _wrapped()
/home/admin/work/public-mars-0.5.1.zip/mars/scheduler/graph.py in get_executable_operand_dag()
/home/admin/work/public-mars-0.5.1.zip/mars/utils.py in serialize_graph()
~/Workspace/mars/mars/graph.pyx in mars.graph.DirectedGraph.to_pb()
420 return graph
--> 421
422 def to_pb(self, pb_obj=None, data_serial_type=None, pickle_protocol=None):
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.to_pb()
686 pickle_protocol=pickle_protocol)
--> 687 return self.serialize(provider, obj=obj)
688
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Provider.serialize_model()
797 for name, field in model_instance._FIELDS.items():
--> 798 field.serialize(self, model_instance, obj)
799
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
630 if val is not None:
--> 631 self._serial_reference_value(tag, field.type.type.model, val, it_obj)
632 elif isinstance(it_obj, Value):
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._serial_reference_value()
572 field_obj = value.cls(self)()
--> 573 value.serialize(self, obj=field_obj)
574 value_pb.type_id = value.__serializable_index__
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Serializable.serialize()
669 def serialize(self, Provider provider, obj=None):
--> 670 return provider.serialize_model(self, obj=obj)
671
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Provider.serialize_model()
797 for name, field in model_instance._FIELDS.items():
--> 798 field.serialize(self, model_instance, obj)
799
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
154
--> 155 cpdef serialize(self, Provider provider, model_instance, obj):
156 return provider.serialize_field(self, model_instance, obj)
~/Workspace/mars/mars/serialize/core.pyx in mars.serialize.core.Field.serialize()
155 cpdef serialize(self, Provider provider, model_instance, obj):
--> 156 return provider.serialize_field(self, model_instance, obj)
157
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
651 exc_info = sys.exc_info()
--> 652 raise TypeError(f'Failed to set field `{tag}` for {model_instance} with '
653 f'value {value}, reason: {exc_info[1]}') \
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider.serialize_field()
648 try:
--> 649 self._set_tuple(value, field_obj, tp=field.type, weak_ref=field.weak_ref)
650 except TypeError:
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_tuple()
364 it_obj = Value()
--> 365 self._set_value(val, it_obj, tp=tp.type if tp is not None else tp)
366 res.append(it_obj)
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_value()
559 else:
--> 560 cls._set_typed_value(value, obj, tp, weak_ref=weak_ref)
561
~/Workspace/mars/mars/serialize/pbserializer.pyx in mars.serialize.pbserializer.ProtobufSerializeProvider._set_typed_value()
443 value_field = PRIMITIVE_TYPE_TO_VALUE_FIELD[tp]
--> 444 setattr(obj, value_field, value)
445 elif tp is ValueType.slice:
TypeError: Failed to set field `shape` for Chunk <op=TensorTranspose, key=6fd87469bf886695950bdfb3164a7ce7> with value (10.0, -1), reason: 10.0 has type float, but expected one of: int, long
|
TypeError
|
def _pandas_read_csv(cls, f, op):
csv_kwargs = op.extra_params.copy()
out_df = op.outputs[0]
start, end = _find_chunk_start_end(f, op.offset, op.size)
f.seek(start)
b = FixedSizeFileObject(f, end - start)
if hasattr(out_df, "dtypes"):
dtypes = out_df.dtypes
else:
# Output will be a Series in some optimize rules.
dtypes = pd.Series([out_df.dtype], index=[out_df.name])
if end == start:
# the last chunk may be empty
df = build_empty_df(dtypes)
if op.keep_usecols_order and not isinstance(op.usecols, list):
# convert to Series, if usecols is a scalar
df = df[op.usecols]
else:
if start == 0:
# The first chunk contains header
# As we specify names and dtype, we need to skip header rows
csv_kwargs["skiprows"] = 1 if op.header == "infer" else op.header
if op.usecols:
usecols = op.usecols if isinstance(op.usecols, list) else [op.usecols]
else:
usecols = op.usecols
if contain_arrow_dtype(dtypes):
# when keep_default_na is True which is default,
# will replace null value with np.nan,
# which will cause failure when converting to arrow string array
csv_kwargs["keep_default_na"] = False
csv_kwargs["dtype"] = cls._select_arrow_dtype(dtypes)
df = pd.read_csv(
b,
sep=op.sep,
names=op.names,
index_col=op.index_col,
usecols=usecols,
nrows=op.nrows,
**csv_kwargs,
)
if op.keep_usecols_order:
df = df[op.usecols]
return df
|
def _pandas_read_csv(cls, f, op):
csv_kwargs = op.extra_params.copy()
out_df = op.outputs[0]
start, end = _find_chunk_start_end(f, op.offset, op.size)
f.seek(start)
b = FixedSizeFileObject(f, end - start)
if hasattr(out_df, "dtypes"):
dtypes = out_df.dtypes
else:
# Output will be a Series in some optimize rules.
dtypes = pd.Series([out_df.dtype], index=[out_df.name])
if end == start:
# the last chunk may be empty
df = build_empty_df(dtypes)
if op.keep_usecols_order and not isinstance(op.usecols, list):
# convert to Series, if usecols is a scalar
df = df[op.usecols]
else:
if start == 0:
# The first chunk contains header
# As we specify names and dtype, we need to skip header rows
csv_kwargs["skiprows"] = 1 if op.header == "infer" else op.header
if op.usecols:
usecols = op.usecols if isinstance(op.usecols, list) else [op.usecols]
else:
usecols = op.usecols
if contain_arrow_dtype(dtypes):
# when keep_default_na is True which is default,
# will replace null value with np.nan,
# which will cause failure when converting to arrow string array
csv_kwargs["keep_default_na"] = False
df = pd.read_csv(
b,
sep=op.sep,
names=op.names,
index_col=op.index_col,
usecols=usecols,
dtype=dtypes.to_dict(),
nrows=op.nrows,
**csv_kwargs,
)
if op.keep_usecols_order:
df = df[op.usecols]
return df
|
https://github.com/mars-project/mars/issues/1604
|
In [9]: df = pd.DataFrame({
...: 'col1': np.random.randint(0, 100, (100000,)),
...: 'col2': np.random.choice(['a', 'b', 'c'], (100000,)),
...: 'col3': np.arange(100000)
...: })
...: df.iloc[-100:, :] = pd.NA
In [10]: df.to_csv('test.csv', index=False)
In [11]: md.read_csv('test.csv').execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-560f5a720bd9> in <module>
----> 1 md.read_csv('test.csv').execute()
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~/Documents/mars_dev/mars/mars/core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~/Documents/mars_dev/mars/mars/core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
105 # set number of running cores
106 self.context.set_ncores(kw['n_parallel'])
--> 107 res = self._executor.execute_tileables(tileables, **kw)
108 return res
109
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
876 n_parallel=n_parallel or n_thread,
877 print_progress=print_progress, mock=mock,
--> 878 chunk_result=chunk_result)
879
880 # update shape of tileable and its chunks whatever it's successful or not
~/Documents/mars_dev/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
688 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
689 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 690 res = graph_execution.execute(retval)
691 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
692 if mock:
~/Documents/mars_dev/mars/mars/executor.py in execute(self, retval)
569 # wait until all the futures completed
570 for future in executed_futures:
--> 571 future.result()
572
573 if retval:
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in _execute_operand(self, op)
441 # so we pass the first operand's first output to Executor.handle
442 first_op = ops[0]
--> 443 Executor.handle(first_op, results, self._mock)
444
445 # update maximal memory usage during execution
~/Documents/mars_dev/mars/mars/executor.py in handle(cls, op, results, mock)
639 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
640 try:
--> 641 return runner(results, op)
642 except UFuncTypeError as e:
643 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in execute(cls, ctx, op)
321 df = df[op.usecols]
322 else:
--> 323 df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
324
325 ctx[out_df.key] = df
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in _pandas_read_csv(cls, f, op)
272 csv_kwargs['keep_default_na'] = False
273 df = pd.read_csv(b, sep=op.sep, names=op.names, index_col=op.index_col, usecols=usecols,
--> 274 dtype=dtypes.to_dict(), nrows=op.nrows, **csv_kwargs)
275 if op.keep_usecols_order:
276 df = df[op.usecols]
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype()
ValueError: Integer column has NA values in column 0
|
ValueError
|
def _cudf_read_csv(cls, op): # pragma: no cover
if op.usecols:
usecols = op.usecols if isinstance(op.usecols, list) else [op.usecols]
else:
usecols = op.usecols
csv_kwargs = op.extra_params
if op.offset == 0:
df = cudf.read_csv(
op.path,
byte_range=(op.offset, op.size),
sep=op.sep,
usecols=usecols,
**csv_kwargs,
)
else:
df = cudf.read_csv(
op.path,
byte_range=(op.offset, op.size),
sep=op.sep,
names=op.names,
usecols=usecols,
nrows=op.nrows,
**csv_kwargs,
)
if op.keep_usecols_order:
df = df[op.usecols]
return df
|
def _cudf_read_csv(cls, op): # pragma: no cover
if op.usecols:
usecols = op.usecols if isinstance(op.usecols, list) else [op.usecols]
else:
usecols = op.usecols
csv_kwargs = op.extra_params
if op.offset == 0:
df = cudf.read_csv(
op.path,
byte_range=(op.offset, op.size),
sep=op.sep,
usecols=usecols,
**csv_kwargs,
)
else:
df = cudf.read_csv(
op.path,
byte_range=(op.offset, op.size),
sep=op.sep,
names=op.names,
usecols=usecols,
dtype=cls._validate_dtypes(op.outputs[0].dtypes, op.gpu),
nrows=op.nrows,
**csv_kwargs,
)
if op.keep_usecols_order:
df = df[op.usecols]
return df
|
https://github.com/mars-project/mars/issues/1604
|
In [9]: df = pd.DataFrame({
...: 'col1': np.random.randint(0, 100, (100000,)),
...: 'col2': np.random.choice(['a', 'b', 'c'], (100000,)),
...: 'col3': np.arange(100000)
...: })
...: df.iloc[-100:, :] = pd.NA
In [10]: df.to_csv('test.csv', index=False)
In [11]: md.read_csv('test.csv').execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-560f5a720bd9> in <module>
----> 1 md.read_csv('test.csv').execute()
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~/Documents/mars_dev/mars/mars/core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~/Documents/mars_dev/mars/mars/core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
105 # set number of running cores
106 self.context.set_ncores(kw['n_parallel'])
--> 107 res = self._executor.execute_tileables(tileables, **kw)
108 return res
109
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
876 n_parallel=n_parallel or n_thread,
877 print_progress=print_progress, mock=mock,
--> 878 chunk_result=chunk_result)
879
880 # update shape of tileable and its chunks whatever it's successful or not
~/Documents/mars_dev/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
688 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
689 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 690 res = graph_execution.execute(retval)
691 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
692 if mock:
~/Documents/mars_dev/mars/mars/executor.py in execute(self, retval)
569 # wait until all the futures completed
570 for future in executed_futures:
--> 571 future.result()
572
573 if retval:
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in _execute_operand(self, op)
441 # so we pass the first operand's first output to Executor.handle
442 first_op = ops[0]
--> 443 Executor.handle(first_op, results, self._mock)
444
445 # update maximal memory usage during execution
~/Documents/mars_dev/mars/mars/executor.py in handle(cls, op, results, mock)
639 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
640 try:
--> 641 return runner(results, op)
642 except UFuncTypeError as e:
643 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in execute(cls, ctx, op)
321 df = df[op.usecols]
322 else:
--> 323 df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
324
325 ctx[out_df.key] = df
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in _pandas_read_csv(cls, f, op)
272 csv_kwargs['keep_default_na'] = False
273 df = pd.read_csv(b, sep=op.sep, names=op.names, index_col=op.index_col, usecols=usecols,
--> 274 dtype=dtypes.to_dict(), nrows=op.nrows, **csv_kwargs)
275 if op.keep_usecols_order:
276 df = df[op.usecols]
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype()
ValueError: Integer column has NA values in column 0
|
ValueError
|
def execute(cls, ctx, op):
xdf = cudf if op.gpu else pd
out_df = op.outputs[0]
csv_kwargs = op.extra_params.copy()
with open_file(
op.path, compression=op.compression, storage_options=op.storage_options
) as f:
if op.compression is not None:
# As we specify names and dtype, we need to skip header rows
csv_kwargs["skiprows"] = 1 if op.header == "infer" else op.header
dtypes = op.outputs[0].dtypes
if contain_arrow_dtype(dtypes):
# when keep_default_na is True which is default,
# will replace null value with np.nan,
# which will cause failure when converting to arrow string array
csv_kwargs["keep_default_na"] = False
csv_kwargs["dtype"] = cls._select_arrow_dtype(dtypes)
df = xdf.read_csv(
f,
sep=op.sep,
names=op.names,
index_col=op.index_col,
usecols=op.usecols,
nrows=op.nrows,
**csv_kwargs,
)
if op.keep_usecols_order:
df = df[op.usecols]
else:
df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
ctx[out_df.key] = df
|
def execute(cls, ctx, op):
xdf = cudf if op.gpu else pd
out_df = op.outputs[0]
csv_kwargs = op.extra_params.copy()
with open_file(
op.path, compression=op.compression, storage_options=op.storage_options
) as f:
if op.compression is not None:
# As we specify names and dtype, we need to skip header rows
csv_kwargs["skiprows"] = 1 if op.header == "infer" else op.header
dtypes = cls._validate_dtypes(op.outputs[0].dtypes, op.gpu)
if contain_arrow_dtype(dtypes.values()):
# when keep_default_na is True which is default,
# will replace null value with np.nan,
# which will cause failure when converting to arrow string array
csv_kwargs["keep_default_na"] = False
df = xdf.read_csv(
f,
sep=op.sep,
names=op.names,
index_col=op.index_col,
usecols=op.usecols,
dtype=dtypes,
nrows=op.nrows,
**csv_kwargs,
)
if op.keep_usecols_order:
df = df[op.usecols]
else:
df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
ctx[out_df.key] = df
|
https://github.com/mars-project/mars/issues/1604
|
In [9]: df = pd.DataFrame({
...: 'col1': np.random.randint(0, 100, (100000,)),
...: 'col2': np.random.choice(['a', 'b', 'c'], (100000,)),
...: 'col3': np.arange(100000)
...: })
...: df.iloc[-100:, :] = pd.NA
In [10]: df.to_csv('test.csv', index=False)
In [11]: md.read_csv('test.csv').execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-560f5a720bd9> in <module>
----> 1 md.read_csv('test.csv').execute()
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~/Documents/mars_dev/mars/mars/core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~/Documents/mars_dev/mars/mars/core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
105 # set number of running cores
106 self.context.set_ncores(kw['n_parallel'])
--> 107 res = self._executor.execute_tileables(tileables, **kw)
108 return res
109
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
876 n_parallel=n_parallel or n_thread,
877 print_progress=print_progress, mock=mock,
--> 878 chunk_result=chunk_result)
879
880 # update shape of tileable and its chunks whatever it's successful or not
~/Documents/mars_dev/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
688 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
689 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 690 res = graph_execution.execute(retval)
691 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
692 if mock:
~/Documents/mars_dev/mars/mars/executor.py in execute(self, retval)
569 # wait until all the futures completed
570 for future in executed_futures:
--> 571 future.result()
572
573 if retval:
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in _execute_operand(self, op)
441 # so we pass the first operand's first output to Executor.handle
442 first_op = ops[0]
--> 443 Executor.handle(first_op, results, self._mock)
444
445 # update maximal memory usage during execution
~/Documents/mars_dev/mars/mars/executor.py in handle(cls, op, results, mock)
639 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
640 try:
--> 641 return runner(results, op)
642 except UFuncTypeError as e:
643 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in execute(cls, ctx, op)
321 df = df[op.usecols]
322 else:
--> 323 df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
324
325 ctx[out_df.key] = df
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in _pandas_read_csv(cls, f, op)
272 csv_kwargs['keep_default_na'] = False
273 df = pd.read_csv(b, sep=op.sep, names=op.names, index_col=op.index_col, usecols=usecols,
--> 274 dtype=dtypes.to_dict(), nrows=op.nrows, **csv_kwargs)
275 if op.keep_usecols_order:
276 df = df[op.usecols]
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype()
ValueError: Integer column has NA values in column 0
|
ValueError
|
def agg(groupby, func, method="auto", *args, **kwargs):
"""
Aggregate using one or more operations on grouped data.
Parameters
----------
groupby : Mars Groupby
Groupby data.
func : str or list-like
Aggregation functions.
method : {'auto', 'shuffle', 'tree'}, default 'auto'
'tree' method provide a better performance, 'shuffle' is recommended
if aggregated result is very large, 'auto' will use 'shuffle' method
in distributed mode and use 'tree' in local mode.
Returns
-------
Series or DataFrame
Aggregated result.
"""
# When perform a computation on the grouped data, we won't shuffle
# the data in the stage of groupby and do shuffle after aggregation.
if not isinstance(groupby, GROUPBY_TYPE):
raise TypeError(f"Input should be type of groupby, not {type(groupby)}")
if method not in ["shuffle", "tree", "auto"]:
raise ValueError(
f"Method {method} is not available, please specify 'tree' or 'shuffle"
)
if not _check_if_func_available(func):
return groupby.transform(func, *args, _call_agg=True, **kwargs)
agg_op = DataFrameGroupByAgg(
func=func,
method=method,
raw_func=func,
groupby_params=groupby.op.groupby_params,
)
return agg_op(groupby)
|
def agg(groupby, func, method="auto", *args, **kwargs):
"""
Aggregate using one or more operations on grouped data.
:param groupby: Groupby data.
:param func: Aggregation functions.
:param method: 'shuffle' or 'tree', 'tree' method provide a better performance, 'shuffle' is recommended
if aggregated result is very large, 'auto' will use 'shuffle' method in distributed mode and use 'tree'
in local mode.
:return: Aggregated result.
"""
# When perform a computation on the grouped data, we won't shuffle
# the data in the stage of groupby and do shuffle after aggregation.
if not isinstance(groupby, GROUPBY_TYPE):
raise TypeError(f"Input should be type of groupby, not {type(groupby)}")
if method not in ["shuffle", "tree", "auto"]:
raise ValueError(
f"Method {method} is not available, please specify 'tree' or 'shuffle"
)
if not _check_if_func_available(func):
return groupby.transform(func, *args, _call_agg=True, **kwargs)
agg_op = DataFrameGroupByAgg(
func=func,
method=method,
raw_func=func,
groupby_params=groupby.op.groupby_params,
)
return agg_op(groupby)
|
https://github.com/mars-project/mars/issues/1604
|
In [9]: df = pd.DataFrame({
...: 'col1': np.random.randint(0, 100, (100000,)),
...: 'col2': np.random.choice(['a', 'b', 'c'], (100000,)),
...: 'col3': np.arange(100000)
...: })
...: df.iloc[-100:, :] = pd.NA
In [10]: df.to_csv('test.csv', index=False)
In [11]: md.read_csv('test.csv').execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-560f5a720bd9> in <module>
----> 1 md.read_csv('test.csv').execute()
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~/Documents/mars_dev/mars/mars/core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~/Documents/mars_dev/mars/mars/core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
105 # set number of running cores
106 self.context.set_ncores(kw['n_parallel'])
--> 107 res = self._executor.execute_tileables(tileables, **kw)
108 return res
109
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
876 n_parallel=n_parallel or n_thread,
877 print_progress=print_progress, mock=mock,
--> 878 chunk_result=chunk_result)
879
880 # update shape of tileable and its chunks whatever it's successful or not
~/Documents/mars_dev/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
688 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
689 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 690 res = graph_execution.execute(retval)
691 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
692 if mock:
~/Documents/mars_dev/mars/mars/executor.py in execute(self, retval)
569 # wait until all the futures completed
570 for future in executed_futures:
--> 571 future.result()
572
573 if retval:
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in _execute_operand(self, op)
441 # so we pass the first operand's first output to Executor.handle
442 first_op = ops[0]
--> 443 Executor.handle(first_op, results, self._mock)
444
445 # update maximal memory usage during execution
~/Documents/mars_dev/mars/mars/executor.py in handle(cls, op, results, mock)
639 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
640 try:
--> 641 return runner(results, op)
642 except UFuncTypeError as e:
643 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in execute(cls, ctx, op)
321 df = df[op.usecols]
322 else:
--> 323 df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
324
325 ctx[out_df.key] = df
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in _pandas_read_csv(cls, f, op)
272 csv_kwargs['keep_default_na'] = False
273 df = pd.read_csv(b, sep=op.sep, names=op.names, index_col=op.index_col, usecols=usecols,
--> 274 dtype=dtypes.to_dict(), nrows=op.nrows, **csv_kwargs)
275 if op.keep_usecols_order:
276 df = df[op.usecols]
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype()
ValueError: Integer column has NA values in column 0
|
ValueError
|
def dataframe_sort_values(
df,
by,
axis=0,
ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
ignore_index=False,
parallel_kind="PSRS",
psrs_kinds=None,
):
"""
Sort by the values along either axis.
Parameters
----------
df : Mars DataFrame
Input dataframe.
by : str
Name or list of names to sort by.
axis : %(axes_single_arg)s, default 0
Axis to be sorted.
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
Puts NaNs at the beginning if `first`; `last` puts NaNs at the
end.
ignore_index : bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
parallel_kind : {'PSRS'}, default 'PSRS'
Parallel sorting algorithm, for the details, refer to:
http://csweb.cs.wfu.edu/bigiron/LittleFE-PSRS/build/html/PSRSalgorithm.html
Returns
-------
sorted_obj : DataFrame or None
DataFrame with sorted values if inplace=False, None otherwise.
Examples
--------
>>> import mars.dataframe as md
>>> df = md.DataFrame({
... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df.execute()
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3
Sort by col1
>>> df.sort_values(by=['col1']).execute()
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2']).execute()
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False).execute()
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first').execute()
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
"""
if na_position not in ["last", "first"]: # pragma: no cover
raise TypeError(f"invalid na_position: {na_position}")
axis = validate_axis(axis, df)
if axis != 0:
raise NotImplementedError("Only support sort on axis 0")
psrs_kinds = _validate_sort_psrs_kinds(psrs_kinds)
by = by if isinstance(by, (list, tuple)) else [by]
op = DataFrameSortValues(
by=by,
axis=axis,
ascending=ascending,
inplace=inplace,
kind=kind,
na_position=na_position,
ignore_index=ignore_index,
parallel_kind=parallel_kind,
psrs_kinds=psrs_kinds,
output_types=[OutputType.dataframe],
)
sorted_df = op(df)
if inplace:
df.data = sorted_df.data
else:
return sorted_df
|
def dataframe_sort_values(
df,
by,
axis=0,
ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
ignore_index=False,
parallel_kind="PSRS",
psrs_kinds=None,
):
"""
Sort by the values along either axis.
:param df: input DataFrame.
:param by: Name or list of names to sort by.
:param axis: Axis to be sorted.
:param ascending: Sort ascending vs. descending. Specify list for multiple sort orders.
If this is a list of bools, must match the length of the by.
:param inplace: If True, perform operation in-place.
:param kind: Choice of sorting algorithm. See also ndarray.np.sort for more information.
mergesort is the only stable algorithm. For DataFrames, this option is only applied
when sorting on a single column or label.
:param na_position: Puts NaNs at the beginning if first; last puts NaNs at the end.
:param ignore_index: If True, the resulting axis will be labeled 0, 1, …, n - 1.
:param parallel_kind: {'PSRS'}, optional. Parallel sorting algorithm, for the details, refer to:
http://csweb.cs.wfu.edu/bigiron/LittleFE-PSRS/build/html/PSRSalgorithm.html
:param psrs_kinds: Sorting algorithms during PSRS algorithm.
:return: sorted dataframe.
Examples
--------
>>> import mars.dataframe as md
>>> raw = pd.DataFrame({
... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df = md.DataFrame(raw)
>>> df.execute()
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3
Sort by col1
>>> df.sort_values(by=['col1']).execute()
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2']).execute()
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False).execute()
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4
"""
if na_position not in ["last", "first"]: # pragma: no cover
raise TypeError(f"invalid na_position: {na_position}")
axis = validate_axis(axis, df)
if axis != 0:
raise NotImplementedError("Only support sort on axis 0")
psrs_kinds = _validate_sort_psrs_kinds(psrs_kinds)
by = by if isinstance(by, (list, tuple)) else [by]
op = DataFrameSortValues(
by=by,
axis=axis,
ascending=ascending,
inplace=inplace,
kind=kind,
na_position=na_position,
ignore_index=ignore_index,
parallel_kind=parallel_kind,
psrs_kinds=psrs_kinds,
output_types=[OutputType.dataframe],
)
sorted_df = op(df)
if inplace:
df.data = sorted_df.data
else:
return sorted_df
|
https://github.com/mars-project/mars/issues/1604
|
In [9]: df = pd.DataFrame({
...: 'col1': np.random.randint(0, 100, (100000,)),
...: 'col2': np.random.choice(['a', 'b', 'c'], (100000,)),
...: 'col3': np.arange(100000)
...: })
...: df.iloc[-100:, :] = pd.NA
In [10]: df.to_csv('test.csv', index=False)
In [11]: md.read_csv('test.csv').execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-560f5a720bd9> in <module>
----> 1 md.read_csv('test.csv').execute()
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~/Documents/mars_dev/mars/mars/core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~/Documents/mars_dev/mars/mars/core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
105 # set number of running cores
106 self.context.set_ncores(kw['n_parallel'])
--> 107 res = self._executor.execute_tileables(tileables, **kw)
108 return res
109
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
876 n_parallel=n_parallel or n_thread,
877 print_progress=print_progress, mock=mock,
--> 878 chunk_result=chunk_result)
879
880 # update shape of tileable and its chunks whatever it's successful or not
~/Documents/mars_dev/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
688 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
689 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 690 res = graph_execution.execute(retval)
691 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
692 if mock:
~/Documents/mars_dev/mars/mars/executor.py in execute(self, retval)
569 # wait until all the futures completed
570 for future in executed_futures:
--> 571 future.result()
572
573 if retval:
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in _execute_operand(self, op)
441 # so we pass the first operand's first output to Executor.handle
442 first_op = ops[0]
--> 443 Executor.handle(first_op, results, self._mock)
444
445 # update maximal memory usage during execution
~/Documents/mars_dev/mars/mars/executor.py in handle(cls, op, results, mock)
639 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
640 try:
--> 641 return runner(results, op)
642 except UFuncTypeError as e:
643 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in execute(cls, ctx, op)
321 df = df[op.usecols]
322 else:
--> 323 df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
324
325 ctx[out_df.key] = df
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in _pandas_read_csv(cls, f, op)
272 csv_kwargs['keep_default_na'] = False
273 df = pd.read_csv(b, sep=op.sep, names=op.names, index_col=op.index_col, usecols=usecols,
--> 274 dtype=dtypes.to_dict(), nrows=op.nrows, **csv_kwargs)
275 if op.keep_usecols_order:
276 df = df[op.usecols]
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype()
ValueError: Integer column has NA values in column 0
|
ValueError
|
def einsum(
subscripts, *operands, dtype=None, order="K", casting="safe", optimize=False
):
"""
Evaluates the Einstein summation convention on the operands.
Using the Einstein summation convention, many common multi-dimensional,
linear algebraic array operations can be represented in a simple fashion.
In *implicit* mode `einsum` computes these values.
In *explicit* mode, `einsum` provides further flexibility to compute
other array operations that might not be considered classical Einstein
summation operations, by disabling, or forcing summation over specified
subscript labels.
See the notes and examples for clarification.
Parameters
----------
subscripts : str
Specifies the subscripts for summation as comma separated list of
subscript labels. An implicit (classical Einstein summation)
calculation is performed unless the explicit indicator '->' is
included as well as subscript labels of the precise output form.
operands : list of array_like
These are the arrays for the operation.
dtype : {data-type, None}, optional
If provided, forces the calculation to use the data type specified.
Note that you may have to also give a more liberal `casting`
parameter to allow the conversions. Default is None.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the output. 'C' means it should
be C contiguous. 'F' means it should be Fortran contiguous,
'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
'K' means it should be as close to the layout as the inputs as
is possible, including arbitrarily permuted axes.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Setting this to
'unsafe' is not recommended, as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Default is 'safe'.
optimize : {False, True, 'greedy', 'optimal'}, optional
Controls if intermediate optimization should occur. No optimization
will occur if False and True will default to the 'greedy' algorithm.
Also accepts an explicit contraction list from the ``np.einsum_path``
function. See ``np.einsum_path`` for more details. Defaults to False.
Returns
-------
output : Mars.tensor
The calculation based on the Einstein summation convention.
The Einstein summation convention can be used to compute
many multi-dimensional, linear algebraic array operations. `einsum`
provides a succinct way of representing these.
A non-exhaustive list of these operations,
which can be computed by `einsum`, is shown below along with examples:
* Trace of an array, :py:func:`numpy.trace`.
* Return a diagonal, :py:func:`numpy.diag`.
* Array axis summations, :py:func:`numpy.sum`.
* Transpositions and permutations, :py:func:`numpy.transpose`.
* Matrix multiplication and dot product, :py:func:`numpy.matmul` :py:func:`numpy.dot`.
* Vector inner and outer products, :py:func:`numpy.inner` :py:func:`numpy.outer`.
* Broadcasting, element-wise and scalar multiplication, :py:func:`numpy.multiply`.
* Tensor contractions, :py:func:`numpy.tensordot`.
* Chained array operations, in efficient calculation order, :py:func:`numpy.einsum_path`.
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Whenever a label is repeated it is summed, so ``mt.einsum('i,i', a, b)``
is equivalent to :py:func:`mt.inner(a,b) <mars.tensor.inner>`. If a label
appears only once, it is not summed, so ``mt.einsum('i', a)`` produces a
view of ``a`` with no changes. A further example ``mt.einsum('ij,jk', a, b)``
describes traditional matrix multiplication and is equivalent to
:py:func:`mt.matmul(a,b) <mars.tensor.matmul>`.
In *implicit mode*, the chosen subscripts are important
since the axes of the output are reordered alphabetically. This
means that ``mt.einsum('ij', a)`` doesn't affect a 2D array, while
``mt.einsum('ji', a)`` takes its transpose. Additionally,
``mt.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
``mt.einsum('ij,jh', a, b)`` returns the transpose of the
multiplication since subscript 'h' precedes subscript 'i'.
In *explicit mode* the output can be directly controlled by
specifying output subscript labels. This requires the
identifier '->' as well as the list of output subscript labels.
This feature increases the flexibility of the function since
summing can be disabled or forced when required. The call
``mt.einsum('i->', a)`` is like :py:func:`mt.sum(a, axis=-1) <mars.tensor.sum>`,
and ``mt.einsum('ii->i', a)`` is like :py:func:`mt.diag(a) <mars.tensor.diag>`.
The difference is that `einsum` does not allow broadcasting by default.
Additionally ``mt.einsum('ij,jh->ih', a, b)`` directly specifies the
order of the output subscript labels and therefore returns matrix
multiplication, unlike the example above in implicit mode.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like ``mt.einsum('...ii->...i', a)``.
To take the trace along the first and last axes,
you can do ``mt.einsum('i...i', a)``, or to do a matrix-matrix
product with the left-most indices instead of rightmost, one can do
``mt.einsum('ij...,jk...->ik...', a, b)``.
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as ``mt.einsum('ii->i', a)``
produces a view (changed in version 1.10.0).
`einsum` also provides an alternative way to provide the subscripts
and operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``.
If the output shape is not provided in this format `einsum` will be
calculated in implicit mode, otherwise it will be performed explicitly.
The examples below have corresponding `einsum` calls with the two
parameter methods.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.arange(25).reshape(5,5)
>>> b = mt.arange(5)
>>> c = mt.arange(6).reshape(2,3)
Trace of a matrix:
>>> mt.einsum('ii', a).execute()
60
>>> mt.einsum(a, [0,0]).execute()
60
Extract the diagonal (requires explicit form):
>>> mt.einsum('ii->i', a).execute()
array([ 0, 6, 12, 18, 24])
>>> mt.einsum(a, [0,0], [0]).execute()
array([ 0, 6, 12, 18, 24])
>>> mt.diag(a).execute()
array([ 0, 6, 12, 18, 24])
Sum over an axis (requires explicit form):
>>> mt.einsum('ij->i', a).execute()
array([ 10, 35, 60, 85, 110])
>>> mt.einsum(a, [0,1], [0]).execute()
array([ 10, 35, 60, 85, 110])
>>> mt.sum(a, axis=1).execute()
array([ 10, 35, 60, 85, 110])
For higher dimensional arrays summing a single axis can be done with ellipsis:
>>> mt.einsum('...j->...', a).execute()
array([ 10, 35, 60, 85, 110])
>>> mt.einsum(a, [Ellipsis,1], [Ellipsis]).execute()
array([ 10, 35, 60, 85, 110])
Compute a matrix transpose, or reorder any number of axes:
>>> mt.einsum('ji', c).execute()
array([[0, 3],
[1, 4],
[2, 5]])
>>> mt.einsum('ij->ji', c).execute()
array([[0, 3],
[1, 4],
[2, 5]])
>>> mt.einsum(c, [1,0]).execute()
array([[0, 3],
[1, 4],
[2, 5]])
>>> mt.transpose(c).execute()
array([[0, 3],
[1, 4],
[2, 5]])
Vector inner products:
>>> mt.einsum('i,i', b, b).execute()
30
>>> mt.einsum(b, [0], b, [0]).execute()
30
>>> mt.inner(b,b).execute()
30
Matrix vector multiplication:
>>> mt.einsum('ij,j', a, b).execute()
array([ 30, 80, 130, 180, 230])
>>> mt.einsum(a, [0,1], b, [1]).execute()
array([ 30, 80, 130, 180, 230])
>>> mt.dot(a, b).execute()
array([ 30, 80, 130, 180, 230])
>>> mt.einsum('...j,j', a, b).execute()
array([ 30, 80, 130, 180, 230])
Broadcasting and scalar multiplication:
>>> mt.einsum('..., ...', 3, c).execute()
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> mt.einsum(',ij', 3, c).execute()
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> mt.einsum(3, [Ellipsis], c, [Ellipsis]).execute()
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> mt.multiply(3, c).execute()
array([[ 0, 3, 6],
[ 9, 12, 15]])
Vector outer product:
>>> mt.einsum('i,j', mt.arange(2)+1, b).execute()
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> mt.einsum(mt.arange(2)+1, [0], b, [1]).execute()
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> mt.outer(mt.arange(2)+1, b).execute()
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
Tensor contraction:
>>> a = mt.arange(60.).reshape(3,4,5)
>>> b = mt.arange(24.).reshape(4,3,2)
>>> mt.einsum('ijk,jil->kl', a, b).execute()
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> mt.einsum(a, [0,1,2], b, [1,0,3], [2,3]).execute()
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> mt.tensordot(a,b, axes=([1,0],[0,1])).execute()
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
Writeable returned arrays (since version 1.10.0):
>>> a = mt.zeros((3, 3))
>>> mt.einsum('ii->i', a)[:] = 1
>>> a.execute()
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
Example of ellipsis use:
>>> a = mt.arange(6).reshape((3,2))
>>> b = mt.arange(12).reshape((4,3))
>>> mt.einsum('ki,jk->ij', a, b).execute()
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> mt.einsum('ki,...k->i...', a, b).execute()
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> mt.einsum('k...,jk', a, b).execute()
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
Chained array operations. For more complicated contractions, speed ups
might be achieved by repeatedly computing a 'greedy' path or pre-computing the
'optimal' path and repeatedly applying it, using an
`einsum_path` insertion (since version 1.12.0). Performance improvements can be
particularly significant with larger arrays:
>>> a = mt.ones(64).reshape(2,4,8)
Basic `einsum`: ~1520ms (benchmarked on 3.1GHz Intel i5.)
>>> for iteration in range(500):
... _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
Sub-optimal `einsum` (due to repeated path calculation time): ~330ms
>>> for iteration in range(500):
... _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')
Greedy `einsum` (faster optimal path approximation): ~160ms
>>> for iteration in range(500):
... _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='greedy')
Optimal `einsum` (best usage pattern in some use cases): ~110ms
>>> path = mt.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')[0]
>>> for iteration in range(500):
... _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path)
"""
all_inputs = [subscripts] + list(operands)
inputs, outputs, operands = parse_einsum_input(all_inputs)
subscripts = "->".join((inputs, outputs))
axes_shape = dict()
for axes, op in zip(inputs.split(","), operands):
for ax, s in zip(axes, op.shape):
axes_shape[ax] = s
if optimize:
optimize, _ = einsum_path(*all_inputs, optimize=optimize)
shape = tuple(axes_shape[ax] for ax in outputs)
op = TensorEinsum(
subscripts=subscripts,
optimize=optimize,
dtype=dtype or operands[0].dtype,
order=order,
casting=casting,
)
return op(operands, shape)
|
def einsum(
subscripts, *operands, dtype=None, order="K", casting="safe", optimize=False
):
"""
Evaluates the Einstein summation convention on the operands.
Using the Einstein summation convention, many common multi-dimensional,
linear algebraic array operations can be represented in a simple fashion.
In *implicit* mode `einsum` computes these values.
In *explicit* mode, `einsum` provides further flexibility to compute
other array operations that might not be considered classical Einstein
summation operations, by disabling, or forcing summation over specified
subscript labels.
See the notes and examples for clarification.
:param subscripts: Specifies the subscripts for summation as comma separated list of subscript labels.
An implicit (classical Einstein summation) calculation is performed unless the explicit indicator ‘->’ is
included as well as subscript labels of the precise output form.
:param operands: These are the arrays for the operation.
:param dtype: If provided, forces the calculation to use the data type specified.
Note that you may have to also give a more liberal casting parameter to allow the conversions.
Default is None.
:param order: Controls the memory layout of the output.
:param casting: Controls what kind of data casting may occur. Setting this to ‘unsafe’ is not recommended,
as it can adversely affect accumulations.
:param optimize: Controls if intermediate optimization should occur.
:return: The calculation based on the Einstein summation convention.
The Einstein summation convention can be used to compute
many multi-dimensional, linear algebraic array operations. `einsum`
provides a succinct way of representing these.
A non-exhaustive list of these operations,
which can be computed by `einsum`, is shown below along with examples:
* Trace of an array, :py:func:`numpy.trace`.
* Return a diagonal, :py:func:`numpy.diag`.
* Array axis summations, :py:func:`numpy.sum`.
* Transpositions and permutations, :py:func:`numpy.transpose`.
* Matrix multiplication and dot product, :py:func:`numpy.matmul` :py:func:`numpy.dot`.
* Vector inner and outer products, :py:func:`numpy.inner` :py:func:`numpy.outer`.
* Broadcasting, element-wise and scalar multiplication, :py:func:`numpy.multiply`.
* Tensor contractions, :py:func:`numpy.tensordot`.
* Chained array operations, in efficient calculation order, :py:func:`numpy.einsum_path`.
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Whenever a label is repeated it is summed, so ``mt.einsum('i,i', a, b)``
is equivalent to :py:func:`mt.inner(a,b) <mars.tensor.inner>`. If a label
appears only once, it is not summed, so ``mt.einsum('i', a)`` produces a
view of ``a`` with no changes. A further example ``mt.einsum('ij,jk', a, b)``
describes traditional matrix multiplication and is equivalent to
:py:func:`mt.matmul(a,b) <mars.tensor.matmul>`.
In *implicit mode*, the chosen subscripts are important
since the axes of the output are reordered alphabetically. This
means that ``mt.einsum('ij', a)`` doesn't affect a 2D array, while
``mt.einsum('ji', a)`` takes its transpose. Additionally,
``mt.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
``mt.einsum('ij,jh', a, b)`` returns the transpose of the
multiplication since subscript 'h' precedes subscript 'i'.
In *explicit mode* the output can be directly controlled by
specifying output subscript labels. This requires the
identifier '->' as well as the list of output subscript labels.
This feature increases the flexibility of the function since
summing can be disabled or forced when required. The call
``mt.einsum('i->', a)`` is like :py:func:`mt.sum(a, axis=-1) <mars.tensor.sum>`,
and ``mt.einsum('ii->i', a)`` is like :py:func:`mt.diag(a) <mars.tensor.diag>`.
The difference is that `einsum` does not allow broadcasting by default.
Additionally ``mt.einsum('ij,jh->ih', a, b)`` directly specifies the
order of the output subscript labels and therefore returns matrix
multiplication, unlike the example above in implicit mode.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like ``mt.einsum('...ii->...i', a)``.
To take the trace along the first and last axes,
you can do ``mt.einsum('i...i', a)``, or to do a matrix-matrix
product with the left-most indices instead of rightmost, one can do
``mt.einsum('ij...,jk...->ik...', a, b)``.
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as ``mt.einsum('ii->i', a)``
produces a view (changed in version 1.10.0).
`einsum` also provides an alternative way to provide the subscripts
and operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``.
If the output shape is not provided in this format `einsum` will be
calculated in implicit mode, otherwise it will be performed explicitly.
The examples below have corresponding `einsum` calls with the two
parameter methods.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.arange(25).reshape(5,5)
>>> b = mt.arange(5)
>>> c = mt.arange(6).reshape(2,3)
Trace of a matrix:
>>> mt.einsum('ii', a).execute()
60
>>> mt.einsum(a, [0,0]).execute()
60
Extract the diagonal (requires explicit form):
>>> mt.einsum('ii->i', a).execute()
array([ 0, 6, 12, 18, 24])
>>> mt.einsum(a, [0,0], [0]).execute()
array([ 0, 6, 12, 18, 24])
>>> mt.diag(a).execute()
array([ 0, 6, 12, 18, 24])
Sum over an axis (requires explicit form):
>>> mt.einsum('ij->i', a).execute()
array([ 10, 35, 60, 85, 110])
>>> mt.einsum(a, [0,1], [0]).execute()
array([ 10, 35, 60, 85, 110])
>>> mt.sum(a, axis=1).execute()
array([ 10, 35, 60, 85, 110])
For higher dimensional arrays summing a single axis can be done with ellipsis:
>>> mt.einsum('...j->...', a).execute()
array([ 10, 35, 60, 85, 110])
>>> mt.einsum(a, [Ellipsis,1], [Ellipsis]).execute()
array([ 10, 35, 60, 85, 110])
Compute a matrix transpose, or reorder any number of axes:
>>> mt.einsum('ji', c).execute()
array([[0, 3],
[1, 4],
[2, 5]])
>>> mt.einsum('ij->ji', c).execute()
array([[0, 3],
[1, 4],
[2, 5]])
>>> mt.einsum(c, [1,0]).execute()
array([[0, 3],
[1, 4],
[2, 5]])
>>> mt.transpose(c).execute()
array([[0, 3],
[1, 4],
[2, 5]])
Vector inner products:
>>> mt.einsum('i,i', b, b).execute()
30
>>> mt.einsum(b, [0], b, [0]).execute()
30
>>> mt.inner(b,b).execute()
30
Matrix vector multiplication:
>>> mt.einsum('ij,j', a, b).execute()
array([ 30, 80, 130, 180, 230])
>>> mt.einsum(a, [0,1], b, [1]).execute()
array([ 30, 80, 130, 180, 230])
>>> mt.dot(a, b).execute()
array([ 30, 80, 130, 180, 230])
>>> mt.einsum('...j,j', a, b).execute()
array([ 30, 80, 130, 180, 230])
Broadcasting and scalar multiplication:
>>> mt.einsum('..., ...', 3, c).execute()
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> mt.einsum(',ij', 3, c).execute()
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> mt.einsum(3, [Ellipsis], c, [Ellipsis]).execute()
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> mt.multiply(3, c).execute()
array([[ 0, 3, 6],
[ 9, 12, 15]])
Vector outer product:
>>> mt.einsum('i,j', mt.arange(2)+1, b).execute()
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> mt.einsum(mt.arange(2)+1, [0], b, [1]).execute()
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> mt.outer(mt.arange(2)+1, b).execute()
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
Tensor contraction:
>>> a = mt.arange(60.).reshape(3,4,5)
>>> b = mt.arange(24.).reshape(4,3,2)
>>> mt.einsum('ijk,jil->kl', a, b).execute()
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> mt.einsum(a, [0,1,2], b, [1,0,3], [2,3]).execute()
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> mt.tensordot(a,b, axes=([1,0],[0,1])).execute()
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
Writeable returned arrays (since version 1.10.0):
>>> a = mt.zeros((3, 3))
>>> mt.einsum('ii->i', a)[:] = 1
>>> a.execute()
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
Example of ellipsis use:
>>> a = mt.arange(6).reshape((3,2))
>>> b = mt.arange(12).reshape((4,3))
>>> mt.einsum('ki,jk->ij', a, b).execute()
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> mt.einsum('ki,...k->i...', a, b).execute()
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> mt.einsum('k...,jk', a, b).execute()
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
Chained array operations. For more complicated contractions, speed ups
might be achieved by repeatedly computing a 'greedy' path or pre-computing the
'optimal' path and repeatedly applying it, using an
`einsum_path` insertion (since version 1.12.0). Performance improvements can be
particularly significant with larger arrays:
>>> a = mt.ones(64).reshape(2,4,8)
Basic `einsum`: ~1520ms (benchmarked on 3.1GHz Intel i5.)
>>> for iteration in range(500):
... _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
Sub-optimal `einsum` (due to repeated path calculation time): ~330ms
>>> for iteration in range(500):
... _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')
Greedy `einsum` (faster optimal path approximation): ~160ms
>>> for iteration in range(500):
... _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='greedy')
Optimal `einsum` (best usage pattern in some use cases): ~110ms
>>> path = mt.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')[0]
>>> for iteration in range(500):
... _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path)
"""
all_inputs = [subscripts] + list(operands)
inputs, outputs, operands = parse_einsum_input(all_inputs)
subscripts = "->".join((inputs, outputs))
axes_shape = dict()
for axes, op in zip(inputs.split(","), operands):
for ax, s in zip(axes, op.shape):
axes_shape[ax] = s
if optimize:
optimize, _ = einsum_path(*all_inputs, optimize=optimize)
shape = tuple(axes_shape[ax] for ax in outputs)
op = TensorEinsum(
subscripts=subscripts,
optimize=optimize,
dtype=dtype or operands[0].dtype,
order=order,
casting=casting,
)
return op(operands, shape)
|
https://github.com/mars-project/mars/issues/1604
|
In [9]: df = pd.DataFrame({
...: 'col1': np.random.randint(0, 100, (100000,)),
...: 'col2': np.random.choice(['a', 'b', 'c'], (100000,)),
...: 'col3': np.arange(100000)
...: })
...: df.iloc[-100:, :] = pd.NA
In [10]: df.to_csv('test.csv', index=False)
In [11]: md.read_csv('test.csv').execute()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-560f5a720bd9> in <module>
----> 1 md.read_csv('test.csv').execute()
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
626
627 if wait:
--> 628 return run()
629 else:
630 thread_executor = ThreadPoolExecutor(1)
~/Documents/mars_dev/mars/mars/core.py in run()
622
623 def run():
--> 624 self.data.execute(session, **kw)
625 return self
626
~/Documents/mars_dev/mars/mars/core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~/Documents/mars_dev/mars/mars/core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
476 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
477 for t in tileables)
--> 478 result = self._sess.run(*tileables, **kw)
479
480 for t in tileables:
~/Documents/mars_dev/mars/mars/session.py in run(self, *tileables, **kw)
105 # set number of running cores
106 self.context.set_ncores(kw['n_parallel'])
--> 107 res = self._executor.execute_tileables(tileables, **kw)
108 return res
109
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
876 n_parallel=n_parallel or n_thread,
877 print_progress=print_progress, mock=mock,
--> 878 chunk_result=chunk_result)
879
880 # update shape of tileable and its chunks whatever it's successful or not
~/Documents/mars_dev/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
688 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
689 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 690 res = graph_execution.execute(retval)
691 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
692 if mock:
~/Documents/mars_dev/mars/mars/executor.py in execute(self, retval)
569 # wait until all the futures completed
570 for future in executed_futures:
--> 571 future.result()
572
573 if retval:
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/envs/py3.7.2/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Documents/mars_dev/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Documents/mars_dev/mars/mars/executor.py in _execute_operand(self, op)
441 # so we pass the first operand's first output to Executor.handle
442 first_op = ops[0]
--> 443 Executor.handle(first_op, results, self._mock)
444
445 # update maximal memory usage during execution
~/Documents/mars_dev/mars/mars/executor.py in handle(cls, op, results, mock)
639 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
640 try:
--> 641 return runner(results, op)
642 except UFuncTypeError as e:
643 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in execute(cls, ctx, op)
321 df = df[op.usecols]
322 else:
--> 323 df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
324
325 ctx[out_df.key] = df
~/Documents/mars_dev/mars/mars/dataframe/datasource/read_csv.py in _pandas_read_csv(cls, f, op)
272 csv_kwargs['keep_default_na'] = False
273 df = pd.read_csv(b, sep=op.sep, names=op.names, index_col=op.index_col, usecols=usecols,
--> 274 dtype=dtypes.to_dict(), nrows=op.nrows, **csv_kwargs)
275 if op.keep_usecols_order:
276 df = df[op.usecols]
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
~/miniconda3/envs/py3.7.2/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype()
ValueError: Integer column has NA values in column 0
|
ValueError
|
def _wrap_train_tuple(cls, data, label, sample_weight=None, init_score=None):
data = cls._convert_tileable(data)
label = cls._convert_tileable(label)
sample_weight = cls._convert_tileable(sample_weight)
init_score = cls._convert_tileable(init_score)
return TrainTuple(data, label, sample_weight, init_score)
|
def _wrap_train_tuple(data, label, sample_weight=None, init_score=None):
return TrainTuple(data, label, sample_weight, init_score)
|
https://github.com/mars-project/mars/issues/1605
|
In [1]: from mars.learn.contrib import lightgbm as lgb
/Users/qinxuye/miniconda3/envs/mars3.6/lib/python3.6/site-packages/lightgbm/__init__.py:48: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.
This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.
Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.
You can install the OpenMP library by the following command: ``brew install libomp``.
"You can install the OpenMP library by the following command: ``brew install libomp``.", UserWarning)
In [2]: lg_reg = lgb.LGBMRegressor(colsample_bytree=0.3, learning_rate=0.1,
...: max_depth=5, reg_alpha=10, n_estimators=10)
In [3]: from sklearn.datasets import make_classification
In [4]: x, y = make_classification()
In [6]: lg_reg.fit(x, y)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-649d7fa4c388> in <module>
----> 1 lg_reg.fit(x, y)
~/Workspace/mars/mars/learn/contrib/lightgbm/regressor.py in fit(self, X, y, sample_weight, init_score, eval_set, eval_sample_weight, eval_init_score, session, run_kwargs, **kwargs)
30 eval_sets=self._wrap_eval_tuples(eval_set, eval_sample_weight, eval_init_score),
31 model_type=LGBMModelType.REGRESSOR,
---> 32 session=session, run_kwargs=run_kwargs, **kwargs)
33
34 self.set_params(**model.get_params())
~/Workspace/mars/mars/learn/contrib/lightgbm/train.py in train(params, train_set, eval_sets, **kwargs)
323 base_port = kwargs.pop('base_port', None)
324
--> 325 aligns = align_data_set(train_set)
326 for eval_set in eval_sets:
327 aligns += align_data_set(eval_set)
~/Workspace/mars/mars/learn/contrib/lightgbm/align.py in align_data_set(dataset)
104
105 def align_data_set(dataset):
--> 106 out_types = get_output_types(dataset.data, dataset.label, dataset.sample_weight, dataset.init_score)
107 op = LGBMAlign(data=dataset.data, label=dataset.label, sample_weight=dataset.sample_weight,
108 init_score=dataset.init_score, output_types=out_types)
~/Workspace/mars/mars/core.py in get_output_types(unknown_as, *objs)
891 output_types.append(unknown_as)
892 else: # pragma: no cover
--> 893 raise TypeError('Output can only be tensor, dataframe or series')
894 return output_types
TypeError: Output can only be tensor, dataframe or series
|
TypeError
|
def predict(self, X, **kw):
session = kw.pop("session", None)
run_kwargs = kw.pop("run_kwargs", None)
X = self._convert_tileable(X)
return predict(self, X, session=session, run_kwargs=run_kwargs, **kw)
|
def predict(self, X, **kw):
session = kw.pop("session", None)
run_kwargs = kw.pop("run_kwargs", None)
return predict(self, X, session=session, run_kwargs=run_kwargs, **kw)
|
https://github.com/mars-project/mars/issues/1605
|
In [1]: from mars.learn.contrib import lightgbm as lgb
/Users/qinxuye/miniconda3/envs/mars3.6/lib/python3.6/site-packages/lightgbm/__init__.py:48: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.
This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.
Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.
You can install the OpenMP library by the following command: ``brew install libomp``.
"You can install the OpenMP library by the following command: ``brew install libomp``.", UserWarning)
In [2]: lg_reg = lgb.LGBMRegressor(colsample_bytree=0.3, learning_rate=0.1,
...: max_depth=5, reg_alpha=10, n_estimators=10)
In [3]: from sklearn.datasets import make_classification
In [4]: x, y = make_classification()
In [6]: lg_reg.fit(x, y)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-649d7fa4c388> in <module>
----> 1 lg_reg.fit(x, y)
~/Workspace/mars/mars/learn/contrib/lightgbm/regressor.py in fit(self, X, y, sample_weight, init_score, eval_set, eval_sample_weight, eval_init_score, session, run_kwargs, **kwargs)
30 eval_sets=self._wrap_eval_tuples(eval_set, eval_sample_weight, eval_init_score),
31 model_type=LGBMModelType.REGRESSOR,
---> 32 session=session, run_kwargs=run_kwargs, **kwargs)
33
34 self.set_params(**model.get_params())
~/Workspace/mars/mars/learn/contrib/lightgbm/train.py in train(params, train_set, eval_sets, **kwargs)
323 base_port = kwargs.pop('base_port', None)
324
--> 325 aligns = align_data_set(train_set)
326 for eval_set in eval_sets:
327 aligns += align_data_set(eval_set)
~/Workspace/mars/mars/learn/contrib/lightgbm/align.py in align_data_set(dataset)
104
105 def align_data_set(dataset):
--> 106 out_types = get_output_types(dataset.data, dataset.label, dataset.sample_weight, dataset.init_score)
107 op = LGBMAlign(data=dataset.data, label=dataset.label, sample_weight=dataset.sample_weight,
108 init_score=dataset.init_score, output_types=out_types)
~/Workspace/mars/mars/core.py in get_output_types(unknown_as, *objs)
891 output_types.append(unknown_as)
892 else: # pragma: no cover
--> 893 raise TypeError('Output can only be tensor, dataframe or series')
894 return output_types
TypeError: Output can only be tensor, dataframe or series
|
TypeError
|
def kill_process_tree(pid, include_parent=True):
try:
import psutil
except ImportError: # pragma: no cover
return
try:
proc = psutil.Process(pid)
except psutil.NoSuchProcess:
return
plasma_sock_dir = None
try:
children = proc.children(recursive=True)
except psutil.NoSuchProcess: # pragma: no cover
return
if include_parent:
children.append(proc)
for p in children:
try:
if "plasma" in p.name():
plasma_sock_dir = next(
(
conn.laddr
for conn in p.connections("unix")
if "plasma" in conn.laddr
),
None,
)
p.kill()
except psutil.NoSuchProcess: # pragma: no cover
pass
if plasma_sock_dir:
shutil.rmtree(plasma_sock_dir, ignore_errors=True)
|
def kill_process_tree(pid, include_parent=True):
try:
import psutil
except ImportError: # pragma: no cover
return
try:
proc = psutil.Process(pid)
except psutil.NoSuchProcess:
return
plasma_sock_dir = None
children = proc.children(recursive=True)
if include_parent:
children.append(proc)
for p in children:
try:
if "plasma" in p.name():
plasma_sock_dir = next(
(
conn.laddr
for conn in p.connections("unix")
if "plasma" in conn.laddr
),
None,
)
p.kill()
except psutil.NoSuchProcess: # pragma: no cover
pass
if plasma_sock_dir:
shutil.rmtree(plasma_sock_dir, ignore_errors=True)
|
https://github.com/mars-project/mars/issues/1605
|
In [1]: from mars.learn.contrib import lightgbm as lgb
/Users/qinxuye/miniconda3/envs/mars3.6/lib/python3.6/site-packages/lightgbm/__init__.py:48: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.
This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.
Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.
You can install the OpenMP library by the following command: ``brew install libomp``.
"You can install the OpenMP library by the following command: ``brew install libomp``.", UserWarning)
In [2]: lg_reg = lgb.LGBMRegressor(colsample_bytree=0.3, learning_rate=0.1,
...: max_depth=5, reg_alpha=10, n_estimators=10)
In [3]: from sklearn.datasets import make_classification
In [4]: x, y = make_classification()
In [6]: lg_reg.fit(x, y)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-649d7fa4c388> in <module>
----> 1 lg_reg.fit(x, y)
~/Workspace/mars/mars/learn/contrib/lightgbm/regressor.py in fit(self, X, y, sample_weight, init_score, eval_set, eval_sample_weight, eval_init_score, session, run_kwargs, **kwargs)
30 eval_sets=self._wrap_eval_tuples(eval_set, eval_sample_weight, eval_init_score),
31 model_type=LGBMModelType.REGRESSOR,
---> 32 session=session, run_kwargs=run_kwargs, **kwargs)
33
34 self.set_params(**model.get_params())
~/Workspace/mars/mars/learn/contrib/lightgbm/train.py in train(params, train_set, eval_sets, **kwargs)
323 base_port = kwargs.pop('base_port', None)
324
--> 325 aligns = align_data_set(train_set)
326 for eval_set in eval_sets:
327 aligns += align_data_set(eval_set)
~/Workspace/mars/mars/learn/contrib/lightgbm/align.py in align_data_set(dataset)
104
105 def align_data_set(dataset):
--> 106 out_types = get_output_types(dataset.data, dataset.label, dataset.sample_weight, dataset.init_score)
107 op = LGBMAlign(data=dataset.data, label=dataset.label, sample_weight=dataset.sample_weight,
108 init_score=dataset.init_score, output_types=out_types)
~/Workspace/mars/mars/core.py in get_output_types(unknown_as, *objs)
891 output_types.append(unknown_as)
892 else: # pragma: no cover
--> 893 raise TypeError('Output can only be tensor, dataframe or series')
894 return output_types
TypeError: Output can only be tensor, dataframe or series
|
TypeError
|
def post_create(self):
from ..dispatcher import DispatchActor
from ..status import StatusActor
super().post_create()
self.register_actors_down_handler()
self._dispatch_ref = self.promise_ref(DispatchActor.default_uid())
parse_num, is_percent = parse_readable_size(options.worker.min_spill_size)
self._min_spill_size = int(
self._size_limit * parse_num if is_percent else parse_num
)
parse_num, is_percent = parse_readable_size(options.worker.max_spill_size)
self._max_spill_size = int(
self._size_limit * parse_num if is_percent else parse_num
)
status_ref = self.ctx.actor_ref(StatusActor.default_uid())
self._status_ref = status_ref if self.ctx.has_actor(status_ref) else None
self._storage_handler = self.storage_client.get_storage_handler(
self._storage_device.build_location(self.proc_id)
)
self.ref().update_cache_status(_tell=True)
|
def post_create(self):
from ..dispatcher import DispatchActor
from ..status import StatusActor
super().post_create()
self.register_actors_down_handler()
self._dispatch_ref = self.promise_ref(DispatchActor.default_uid())
parse_num, is_percent = parse_readable_size(options.worker.min_spill_size)
self._min_spill_size = int(
self._size_limit * parse_num if is_percent else parse_num
)
parse_num, is_percent = parse_readable_size(options.worker.max_spill_size)
self._max_spill_size = int(
self._size_limit * parse_num if is_percent else parse_num
)
status_ref = self.ctx.actor_ref(StatusActor.default_uid())
self._status_ref = status_ref if self.ctx.has_actor(status_ref) else None
self._storage_handler = self.storage_client.get_storage_handler(
self._storage_device.build_location(self.proc_id)
)
|
https://github.com/mars-project/mars/issues/1605
|
In [1]: from mars.learn.contrib import lightgbm as lgb
/Users/qinxuye/miniconda3/envs/mars3.6/lib/python3.6/site-packages/lightgbm/__init__.py:48: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.
This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.
Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.
You can install the OpenMP library by the following command: ``brew install libomp``.
"You can install the OpenMP library by the following command: ``brew install libomp``.", UserWarning)
In [2]: lg_reg = lgb.LGBMRegressor(colsample_bytree=0.3, learning_rate=0.1,
...: max_depth=5, reg_alpha=10, n_estimators=10)
In [3]: from sklearn.datasets import make_classification
In [4]: x, y = make_classification()
In [6]: lg_reg.fit(x, y)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-649d7fa4c388> in <module>
----> 1 lg_reg.fit(x, y)
~/Workspace/mars/mars/learn/contrib/lightgbm/regressor.py in fit(self, X, y, sample_weight, init_score, eval_set, eval_sample_weight, eval_init_score, session, run_kwargs, **kwargs)
30 eval_sets=self._wrap_eval_tuples(eval_set, eval_sample_weight, eval_init_score),
31 model_type=LGBMModelType.REGRESSOR,
---> 32 session=session, run_kwargs=run_kwargs, **kwargs)
33
34 self.set_params(**model.get_params())
~/Workspace/mars/mars/learn/contrib/lightgbm/train.py in train(params, train_set, eval_sets, **kwargs)
323 base_port = kwargs.pop('base_port', None)
324
--> 325 aligns = align_data_set(train_set)
326 for eval_set in eval_sets:
327 aligns += align_data_set(eval_set)
~/Workspace/mars/mars/learn/contrib/lightgbm/align.py in align_data_set(dataset)
104
105 def align_data_set(dataset):
--> 106 out_types = get_output_types(dataset.data, dataset.label, dataset.sample_weight, dataset.init_score)
107 op = LGBMAlign(data=dataset.data, label=dataset.label, sample_weight=dataset.sample_weight,
108 init_score=dataset.init_score, output_types=out_types)
~/Workspace/mars/mars/core.py in get_output_types(unknown_as, *objs)
891 output_types.append(unknown_as)
892 else: # pragma: no cover
--> 893 raise TypeError('Output can only be tensor, dataframe or series')
894 return output_types
TypeError: Output can only be tensor, dataframe or series
|
TypeError
|
def _tile_chunks(cls, op, in_tensor, faiss_index, n_sample):
"""
If the distribution on each chunk is the same,
refer to:
https://github.com/facebookresearch/faiss/wiki/FAQ#how-can-i-distribute-index-building-on-several-machines
1. train an IndexIVF* on a representative sample of the data, store it.
2. for each node, load the trained index, add the local data to it, store the resulting populated index
3. on a central node, load all the populated indexes and merge them.
"""
faiss_index_ = faiss.index_factory(
in_tensor.shape[1], faiss_index, op.faiss_metric_type
)
# Training on sample data when two conditions meet
# 1. the index type requires for training, e.g. Flat does not require
# 2. distributions of chunks are the same, in not,
# train separately on each chunk data
need_sample_train = not faiss_index_.is_trained and op.same_distribution
need_merge_index = (
hasattr(faiss_index_, "merge_from") if need_sample_train else False
)
train_chunk = None
if need_sample_train:
# sample data to train
rs = RandomState(op.seed)
sampled_index = rs.choice(
in_tensor.shape[0], size=n_sample, replace=False, chunk_size=n_sample
)
sample_tensor = recursive_tile(in_tensor[sampled_index])
assert len(sample_tensor.chunks) == 1
sample_chunk = sample_tensor.chunks[0]
train_op = FaissTrainSampledIndex(
faiss_index=faiss_index,
metric=op.metric,
return_index_type=op.return_index_type,
)
train_chunk = train_op.new_chunk([sample_chunk])
elif op.gpu: # pragma: no cover
# if not need train, and on gpu, just merge data together to train
in_tensor = in_tensor.rechunk(in_tensor.shape)._inplace_tile()
# build index for each input chunk
build_index_chunks = []
for i, chunk in enumerate(in_tensor.chunks):
build_index_op = op.copy().reset_key()
build_index_op._stage = OperandStage.map
build_index_op._faiss_index = faiss_index
if train_chunk is not None:
build_index_chunk = build_index_op.new_chunk(
[chunk, train_chunk], index=(i,)
)
else:
build_index_chunk = build_index_op.new_chunk([chunk], index=(i,))
build_index_chunks.append(build_index_chunk)
out_chunks = []
if need_merge_index:
assert op.n_sample is not None
# merge all indices into one, do only when trained on sample data
out_chunk_op = op.copy().reset_key()
out_chunk_op._faiss_index = faiss_index
out_chunk_op._stage = OperandStage.agg
out_chunk = out_chunk_op.new_chunk(build_index_chunks, index=(0,))
out_chunks.append(out_chunk)
else:
out_chunks.extend(build_index_chunks)
new_op = op.copy()
return new_op.new_tileables(
op.inputs, chunks=out_chunks, nsplits=((len(out_chunks),),)
)
|
def _tile_chunks(cls, op, in_tensor, faiss_index, n_sample):
"""
If the distribution on each chunk is the same,
refer to:
https://github.com/facebookresearch/faiss/wiki/FAQ#how-can-i-distribute-index-building-on-several-machines
1. train an IndexIVF* on a representative sample of the data, store it.
2. for each node, load the trained index, add the local data to it, store the resulting populated index
3. on a central node, load all the populated indexes and merge them.
"""
faiss_index_ = faiss.index_factory(
in_tensor.shape[1], faiss_index, op.faiss_metric_type
)
# Training on sample data when two conditions meet
# 1. the index type requires for training, e.g. Flat does not require
# 2. distributions of chunks are the same, in not,
# train separately on each chunk data
need_sample_train = not faiss_index_.is_trained and op.same_distribution
train_chunk = None
if need_sample_train:
# sample data to train
rs = RandomState(op.seed)
sampled_index = rs.choice(
in_tensor.shape[0], size=n_sample, replace=False, chunk_size=n_sample
)
sample_tensor = recursive_tile(in_tensor[sampled_index])
assert len(sample_tensor.chunks) == 1
sample_chunk = sample_tensor.chunks[0]
train_op = FaissTrainSampledIndex(
faiss_index=faiss_index,
metric=op.metric,
return_index_type=op.return_index_type,
)
train_chunk = train_op.new_chunk([sample_chunk])
elif op.gpu: # pragma: no cover
# if not need train, and on gpu, just merge data together to train
in_tensor = in_tensor.rechunk(in_tensor.shape)._inplace_tile()
# build index for each input chunk
build_index_chunks = []
for i, chunk in enumerate(in_tensor.chunks):
build_index_op = op.copy().reset_key()
build_index_op._stage = OperandStage.map
build_index_op._faiss_index = faiss_index
if train_chunk is not None:
build_index_chunk = build_index_op.new_chunk(
[chunk, train_chunk], index=(i,)
)
else:
build_index_chunk = build_index_op.new_chunk([chunk], index=(i,))
build_index_chunks.append(build_index_chunk)
out_chunks = []
if need_sample_train:
assert op.n_sample is not None
# merge all indices into one, do only when trained on sample data
out_chunk_op = op.copy().reset_key()
out_chunk_op._faiss_index = faiss_index
out_chunk_op._stage = OperandStage.agg
out_chunk = out_chunk_op.new_chunk(build_index_chunks, index=(0,))
out_chunks.append(out_chunk)
else:
out_chunks.extend(build_index_chunks)
new_op = op.copy()
return new_op.new_tileables(
op.inputs, chunks=out_chunks, nsplits=((len(out_chunks),),)
)
|
https://github.com/mars-project/mars/issues/1608
|
In [1]: from sklearn.datasets import make_classification
In [2]: x, y = make_classification()
In [3]: import mars.tensor as mt
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
In [4]: x = mt.tensor(x, chunk_size=20)
In [5]: x.shape
Out[5]: (100, 20)
In [6]: y = mt.tensor(y, chunk_size=20)
In [7]: y.shape
Out[7]: (100,)
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
In [8]: from mars.learn.neighbors._faiss import build_faiss_index
In [39]: index = build_faiss_index(x, index_name='PCAR6,IVF8_HNSW32,SQ8', n_samp
...: le=10)
In [40]: index.execute()
WARNING clustering 10 points to 8 centroids: please provide at least 312 training points
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-40-bd4069985a62> in <module>
----> 1 index.execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
433 raise CancelledError()
434 elif self._state == FINISHED:
--> 435 return self.__get_result()
436 else:
437 raise TimeoutError()
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/learn/neighbors/_faiss.py in execute(cls, ctx, op)
340 cls._execute_map(ctx, op)
341 elif op.stage == OperandStage.agg:
--> 342 cls._execute_agg(ctx, op)
343 else:
344 assert op.stage is None
~/Workspace/mars/mars/learn/neighbors/_faiss.py in _execute_agg(cls, ctx, op)
327 index = _load_index(ctx, op, index, device_id)
328 indexes.append(index)
--> 329 assert hasattr(index, 'merge_from')
330 if merged_index is None:
331 merged_index = index
AssertionError:
|
AssertionError
|
def _execute_one_chunk(cls, ctx, op):
(inp,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
with device(device_id):
inp = inp.astype(np.float32, copy=False)
# create index
index = faiss.index_factory(inp.shape[1], op.faiss_index, op.faiss_metric_type)
# GPU
if device_id >= 0: # pragma: no cover
index = _index_to_gpu(index, device_id)
# train index
if not index.is_trained:
assert op.n_sample is not None
sample_indices = xp.random.choice(
inp.shape[0], size=op.n_sample, replace=False
)
sampled = inp[sample_indices]
index.train(sampled)
if op.metric == "cosine":
# faiss does not support cosine distances directly,
# data needs to be normalize before adding to index,
# refer to:
# https://github.com/facebookresearch/faiss/wiki/FAQ#how-can-i-index-vectors-for-cosine-distance
faiss.normalize_L2(inp)
# add vectors to index
if device_id >= 0: # pragma: no cover
# gpu
index.add_c(inp.shape[0], _swig_ptr_from_cupy_float32_array(inp))
else:
index.add(inp)
ctx[op.outputs[0].key] = _store_index(ctx, op, index, device_id)
|
def _execute_one_chunk(cls, ctx, op):
(inp,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
with device(device_id):
# create index
index = faiss.index_factory(inp.shape[1], op.faiss_index, op.faiss_metric_type)
# GPU
if device_id >= 0: # pragma: no cover
index = _index_to_gpu(index, device_id)
# train index
if not index.is_trained:
assert op.n_sample is not None
sample_indices = xp.random.choice(
inp.shape[0], size=op.n_sample, replace=False
)
sampled = inp[sample_indices]
index.train(sampled)
if op.metric == "cosine":
# faiss does not support cosine distances directly,
# data needs to be normalize before adding to index,
# refer to:
# https://github.com/facebookresearch/faiss/wiki/FAQ#how-can-i-index-vectors-for-cosine-distance
faiss.normalize_L2(inp)
# add vectors to index
if device_id >= 0: # pragma: no cover
# gpu
inp = inp.astype(np.float32, copy=False)
index.add_c(inp.shape[0], _swig_ptr_from_cupy_float32_array(inp))
else:
index.add(inp)
ctx[op.outputs[0].key] = _store_index(ctx, op, index, device_id)
|
https://github.com/mars-project/mars/issues/1608
|
In [1]: from sklearn.datasets import make_classification
In [2]: x, y = make_classification()
In [3]: import mars.tensor as mt
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
In [4]: x = mt.tensor(x, chunk_size=20)
In [5]: x.shape
Out[5]: (100, 20)
In [6]: y = mt.tensor(y, chunk_size=20)
In [7]: y.shape
Out[7]: (100,)
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
/Users/qinxuye/miniconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
In [8]: from mars.learn.neighbors._faiss import build_faiss_index
In [39]: index = build_faiss_index(x, index_name='PCAR6,IVF8_HNSW32,SQ8', n_samp
...: le=10)
In [40]: index.execute()
WARNING clustering 10 points to 8 centroids: please provide at least 312 training points
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-40-bd4069985a62> in <module>
----> 1 index.execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
373
374 if wait:
--> 375 return run()
376 else:
377 # leverage ThreadPoolExecutor to submit task,
~/Workspace/mars/mars/core.py in run()
368 def run():
369 # no more fetch, thus just fire run
--> 370 session.run(self, **kw)
371 # return Tileable or ExecutableTuple itself
372 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
498 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
499 for t in tileables)
--> 500 result = self._sess.run(*tileables, **kw)
501
502 for t in tileables:
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
106 # set number of running cores
107 self.context.set_ncores(kw['n_parallel'])
--> 108 res = self._executor.execute_tileables(tileables, **kw)
109 return res
110
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
433 raise CancelledError()
434 elif self._state == FINISHED:
--> 435 return self.__get_result()
436 else:
437 raise TimeoutError()
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/executor.py in handle_op(self, *args, **kw)
376
377 def handle_op(self, *args, **kw):
--> 378 return Executor.handle(*args, **kw)
379
380 def _order_starts(self):
~/Workspace/mars/mars/executor.py in handle(cls, op, results, mock)
642 # The `UFuncTypeError` was introduced by numpy#12593 since v1.17.0.
643 try:
--> 644 return runner(results, op)
645 except UFuncTypeError as e:
646 raise TypeError(str(e)).with_traceback(sys.exc_info()[2]) from None
~/Workspace/mars/mars/learn/neighbors/_faiss.py in execute(cls, ctx, op)
340 cls._execute_map(ctx, op)
341 elif op.stage == OperandStage.agg:
--> 342 cls._execute_agg(ctx, op)
343 else:
344 assert op.stage is None
~/Workspace/mars/mars/learn/neighbors/_faiss.py in _execute_agg(cls, ctx, op)
327 index = _load_index(ctx, op, index, device_id)
328 indexes.append(index)
--> 329 assert hasattr(index, 'merge_from')
330 if merged_index is None:
331 merged_index = index
AssertionError:
|
AssertionError
|
def _make_indexable(iterable):
"""Ensure iterable supports indexing or convert to an indexable variant.
Convert sparse matrices to csr and other non-indexable iterable to arrays.
Let `None` and indexable objects (e.g. pandas dataframes) pass unchanged.
Parameters
----------
iterable : {list, dataframe, array, sparse} or None
Object to be converted to an indexable iterable.
"""
if issparse(iterable):
return mt.tensor(iterable)
elif hasattr(iterable, "iloc"):
if iterable.ndim == 1:
return md.Series(iterable)
else:
return md.DataFrame(iterable)
elif hasattr(iterable, "__getitem__"):
return mt.tensor(iterable)
elif iterable is None:
return iterable
return mt.tensor(iterable)
|
def _make_indexable(iterable):
"""Ensure iterable supports indexing or convert to an indexable variant.
Convert sparse matrices to csr and other non-indexable iterable to arrays.
Let `None` and indexable objects (e.g. pandas dataframes) pass unchanged.
Parameters
----------
iterable : {list, dataframe, array, sparse} or None
Object to be converted to an indexable iterable.
"""
if issparse(iterable):
return mt.tensor(iterable)
elif hasattr(iterable, "iloc"):
return md.DataFrame(iterable)
elif hasattr(iterable, "__getitem__"):
return mt.tensor(iterable)
elif iterable is None:
return iterable
return mt.tensor(iterable)
|
https://github.com/mars-project/mars/issues/1603
|
In [1]: import mars.dataframe as md
In [8]: X = df[['userId', 'rating']]
In [9]: y = df['movieId']
In [11]: train_test_split(X, y, train_size=0.7, random_state=0)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-94d1aec833af> in <module>
----> 1 train_test_split(X, y, train_size=0.7, random_state=0)
~/Workspace/mars/mars/learn/model_selection/_split.py in train_test_split(*arrays, **options)
112 raise TypeError(f"Invalid parameters passed: {options}")
113
--> 114 arrays = indexable(*arrays, session=session, run_kwargs=run_kwargs)
115
116 n_samples = _num_samples(arrays[0])
~/Workspace/mars/mars/learn/utils/validation.py in indexable(session, run_kwargs, *iterables)
139 List of objects to ensure sliceability.
140 """
--> 141 result = [_make_indexable(X) for X in iterables]
142 check_consistent_length(*result, session=session,
143 run_kwargs=run_kwargs)
~/Workspace/mars/mars/learn/utils/validation.py in <listcomp>(.0)
139 List of objects to ensure sliceability.
140 """
--> 141 result = [_make_indexable(X) for X in iterables]
142 check_consistent_length(*result, session=session,
143 run_kwargs=run_kwargs)
~/Workspace/mars/mars/learn/utils/validation.py in _make_indexable(iterable)
119 return mt.tensor(iterable)
120 elif hasattr(iterable, "iloc"):
--> 121 return md.DataFrame(iterable)
122 elif hasattr(iterable, "__getitem__"):
123 return mt.tensor(iterable)
~/Workspace/mars/mars/dataframe/initializer.py in __init__(self, data, index, columns, dtype, copy, chunk_size, gpu, sparse)
55 columns=columns, gpu=gpu, sparse=sparse)
56 else:
---> 57 pdf = pd.DataFrame(data, index=index, columns=columns, dtype=dtype, copy=copy)
58 df = from_pandas_df(pdf, chunk_size=chunk_size, gpu=gpu, sparse=sparse)
59 super().__init__(df.data)
~/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
527 else:
528 if index is None or columns is None:
--> 529 raise ValueError("DataFrame constructor not properly called!")
530
531 if not dtype:
ValueError: DataFrame constructor not properly called!
|
ValueError
|
def execute(cls, ctx, op: "LGBMTrain"):
if op.merge:
return super().execute(ctx, op)
from lightgbm.basic import _safe_call, _LIB
data_val = ctx[op.data.key]
label_val = ctx[op.label.key]
sample_weight_val = (
ctx[op.sample_weight.key] if op.sample_weight is not None else None
)
init_score_val = ctx[op.init_score.key] if op.init_score is not None else None
if op.eval_datas is None:
eval_set, eval_sample_weight, eval_init_score = None, None, None
else:
eval_set, eval_sample_weight, eval_init_score = [], [], []
for data, label in zip(op.eval_datas, op.eval_labels):
eval_set.append((ctx[data.key], ctx[label.key]))
for weight in op.eval_sample_weights:
eval_sample_weight.append(ctx[weight.key] if weight is not None else None)
for score in op.eval_init_scores:
eval_init_score.append(ctx[score.key] if score is not None else None)
eval_set = eval_set or None
eval_sample_weight = eval_sample_weight or None
eval_init_score = eval_init_score or None
params = op.params.copy()
# if model is trained, remove unsupported parameters
params.pop("out_dtype_", None)
if ctx.running_mode == RunningMode.distributed:
params["machines"] = ",".join(op.lgbm_endpoints)
params["time_out"] = op.timeout
params["num_machines"] = len(op.lgbm_endpoints)
params["local_listen_port"] = op.lgbm_port
if (op.tree_learner or "").lower() not in {"data", "feature", "voting"}:
logger.warning(
"Parameter tree_learner not set or set to incorrect value "
f'{op.tree_learner}, using "data" as default'
)
params["tree_learner"] = "data"
else:
params["tree_learner"] = op.tree_learner
try:
model_cls = get_model_cls_from_type(op.model_type)
model = model_cls(**params)
model.fit(
data_val,
label_val,
sample_weight=sample_weight_val,
init_score=init_score_val,
eval_set=eval_set,
eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score,
**op.kwds,
)
if (
op.model_type == LGBMModelType.RANKER
or op.model_type == LGBMModelType.REGRESSOR
):
model.set_params(out_dtype_=np.dtype("float"))
elif hasattr(label_val, "dtype"):
model.set_params(out_dtype_=label_val.dtype)
else:
model.set_params(out_dtype_=label_val.dtypes[0])
ctx[op.outputs[0].key] = pickle.dumps(model)
finally:
_safe_call(_LIB.LGBM_NetworkFree())
|
def execute(cls, ctx, op: "LGBMTrain"):
if op.merge:
return super().execute(ctx, op)
from lightgbm.basic import _safe_call, _LIB
data_val = ctx[op.data.key]
label_val = ctx[op.label.key]
sample_weight_val = (
ctx[op.sample_weight.key] if op.sample_weight is not None else None
)
init_score_val = ctx[op.init_score.key] if op.init_score is not None else None
if op.eval_datas is None:
eval_set, eval_sample_weight, eval_init_score = None, None, None
else:
eval_set, eval_sample_weight, eval_init_score = [], [], []
for data, label in zip(op.eval_datas, op.eval_labels):
eval_set.append((ctx[data.key], ctx[label.key]))
for weight in op.eval_sample_weights:
eval_sample_weight.append(ctx[weight.key] if weight is not None else None)
for score in op.eval_init_scores:
eval_init_score.append(ctx[score.key] if score is not None else None)
eval_set = eval_set or None
eval_sample_weight = eval_sample_weight or None
eval_init_score = eval_init_score or None
params = op.params.copy()
if ctx.running_mode == RunningMode.distributed:
params["machines"] = ",".join(op.lgbm_endpoints)
params["time_out"] = op.timeout
params["num_machines"] = len(op.lgbm_endpoints)
params["local_listen_port"] = op.lgbm_port
if (op.tree_learner or "").lower() not in {"data", "feature", "voting"}:
logger.warning(
"Parameter tree_learner not set or set to incorrect value "
f'{op.tree_learner}, using "data" as default'
)
params["tree_learner"] = "data"
else:
params["tree_learner"] = op.tree_learner
try:
model_cls = get_model_cls_from_type(op.model_type)
model = model_cls(**params)
model.fit(
data_val,
label_val,
sample_weight=sample_weight_val,
init_score=init_score_val,
eval_set=eval_set,
eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score,
**op.kwds,
)
if (
op.model_type == LGBMModelType.RANKER
or op.model_type == LGBMModelType.REGRESSOR
):
model.set_params(out_dtype_=np.dtype("float"))
elif hasattr(label_val, "dtype"):
model.set_params(out_dtype_=label_val.dtype)
else:
model.set_params(out_dtype_=label_val.dtypes[0])
ctx[op.outputs[0].key] = pickle.dumps(model)
finally:
_safe_call(_LIB.LGBM_NetworkFree())
|
https://github.com/mars-project/mars/issues/1597
|
Attempt 4: Unexpected error TypeError occurred in executing operand affdad0be8e3430b7b6088cd112ed634 in 10.xxx:8083
Traceback (most recent call last):
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/promise.py", line 100, in _wrapped
result = func(*args, **kwargs)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/worker/calc.py", line 299, in <lambda>
.then(lambda context_dict: _start_calc(context_dict)) \
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/worker/calc.py", line 273, in _start_calc
return self._calc_results(session_id, graph_key, graph, context_dict, chunk_targets)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/utils.py", line 365, in _wrapped
return func(*args, **kwargs)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/worker/calc.py", line 197, in _calc_results
chunk_targets, retval=False).result()
File "src/gevent/event.py", line 383, in gevent._gevent_cevent.AsyncResult.result
File "src/gevent/event.py", line 305, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 335, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 323, in gevent._gevent_cevent.AsyncResult.get
File "src/gevent/event.py", line 303, in gevent._gevent_cevent.AsyncResult._raise_exception
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/gevent/_compat.py", line 65, in reraise
raise value.with_traceback(tb)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/gevent/threadpool.py", line 142, in __run_task
thread_result.set(func(*args, **kwargs))
File "mars/actors/pool/gevent_pool.pyx", line 127, in mars.actors.pool.gevent_pool.GeventThreadPool._wrap_watch.inner
result = fn(*args, **kwargs)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/executor.py", line 690, in execute_graph
res = graph_execution.execute(retval)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/executor.py", line 571, in execute
future.result()
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/concurrent/futures/_base.py", line 435, in result
return self.__get_result()
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
raise self._exception
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/executor.py", line 443, in _execute_operand
Executor.handle(first_op, results, self._mock)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/executor.py", line 641, in handle
return runner(results, op)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/mars/learn/contrib/lightgbm/train.py", line 298, in execute
eval_init_score=eval_init_score, **op.kwds)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/lightgbm/sklearn.py", line 760, in fit
callbacks=callbacks, init_model=init_model)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/lightgbm/sklearn.py", line 600, in fit
callbacks=callbacks, init_model=init_model)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/lightgbm/engine.py", line 231, in train
booster = Booster(params=params, train_set=train_set)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/lightgbm/basic.py", line 1983, in __init__
train_set.construct()
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/lightgbm/basic.py", line 1325, in construct
categorical_feature=self.categorical_feature, params=self.params)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/lightgbm/basic.py", line 1102, in _lazy_init
params_str = param_dict_to_str(params)
File "/data/platform/anaconda3/envs/mars-dev/lib/python3.7/site-packages/lightgbm/basic.py", line 156, in param_dict_to_str
% (key, type(val).__name__))
TypeError: Unknown type of parameter:out_dtype_, got:dtype
|
TypeError
|
def _calc_properties(cls, x1, x2=None, axis="columns"):
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and (
x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
):
if x2 is None:
dtypes = x1.dtypes
elif pd.api.types.is_scalar(x2):
dtypes = cls._operator(build_empty_df(x1.dtypes), x2).dtypes
elif x1.dtypes is not None and isinstance(x2, TENSOR_TYPE):
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
else:
dtypes = x1.dtypes
return {
"shape": x1.shape,
"dtypes": dtypes,
"columns_value": x1.columns_value,
"index_value": x1.index_value,
}
if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and (
x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
):
x2_dtype = x2.dtype if hasattr(x2, "dtype") else type(x2)
dtype = infer_dtype(x1.dtype, np.dtype(x2_dtype), cls._operator)
return {"shape": x1.shape, "dtype": dtype, "index_value": x1.index_value}
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
x2, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)
):
index_shape, column_shape, dtypes, columns, index = (
np.nan,
np.nan,
None,
None,
None,
)
if (
x1.columns_value is not None
and x2.columns_value is not None
and x1.columns_value.key == x2.columns_value.key
):
dtypes = pd.Series(
[
infer_dtype(dt1, dt2, cls._operator)
for dt1, dt2 in zip(x1.dtypes, x2.dtypes)
],
index=x1.dtypes.index,
)
columns = copy.copy(x1.columns_value)
columns.value.should_be_monotonic = False
column_shape = len(dtypes)
elif x1.dtypes is not None and x2.dtypes is not None:
dtypes = infer_dtypes(x1.dtypes, x2.dtypes, cls._operator)
columns = parse_index(dtypes.index, store_data=True)
columns.value.should_be_monotonic = True
column_shape = len(dtypes)
if x1.index_value is not None and x2.index_value is not None:
if x1.index_value.key == x2.index_value.key:
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
if index.key == x1.index_value.key == x2.index_value.key and (
not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
):
index_shape = (
x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
)
return {
"shape": (index_shape, column_shape),
"dtypes": dtypes,
"columns_value": columns,
"index_value": index,
}
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
):
if axis == "columns" or axis == 1:
index_shape = x1.shape[0]
index = x1.index_value
column_shape, dtypes, columns = np.nan, None, None
if x1.columns_value is not None and x1.index_value is not None:
if x1.columns_value.key == x2.index_value.key:
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
columns = copy.copy(x1.columns_value)
columns.value.should_be_monotonic = False
column_shape = len(dtypes)
else: # pragma: no cover
dtypes = x1.dtypes # FIXME
columns = infer_index_value(x1.columns_value, x2.index_value)
columns.value.should_be_monotonic = True
column_shape = np.nan
else:
assert axis == "index" or axis == 0
column_shape = x1.shape[1]
columns = x1.columns_value
dtypes = x1.dtypes
index_shape, index = np.nan, None
if x1.index_value is not None and x1.index_value is not None:
if x1.index_value.key == x2.index_value.key:
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
if x1.dtypes is not None:
dtypes = pd.Series(
[
infer_dtype(dt, x2.dtype, cls._operator)
for dt in x1.dtypes
],
index=x1.dtypes.index,
)
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
index_shape = np.nan
return {
"shape": (index_shape, column_shape),
"dtypes": dtypes,
"columns_value": columns,
"index_value": index,
}
if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and isinstance(
x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
):
index_shape, dtype, index = np.nan, None, None
dtype = infer_dtype(x1.dtype, x2.dtype, cls._operator)
if x1.index_value is not None and x2.index_value is not None:
if x1.index_value.key == x2.index_value.key:
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
if index.key == x1.index_value.key == x2.index_value.key and (
not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
):
index_shape = (
x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
)
return {"shape": (index_shape,), "dtype": dtype, "index_value": index}
raise NotImplementedError("Unknown combination of parameters")
|
def _calc_properties(cls, x1, x2=None, axis="columns"):
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and (
x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
):
if x2 is None:
dtypes = x1.dtypes
elif pd.api.types.is_scalar(x2):
dtypes = infer_dtypes(
x1.dtypes, pd.Series(np.array(x2).dtype), cls._operator
)
elif x1.dtypes is not None and isinstance(x2, TENSOR_TYPE):
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
else:
dtypes = x1.dtypes
return {
"shape": x1.shape,
"dtypes": dtypes,
"columns_value": x1.columns_value,
"index_value": x1.index_value,
}
if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and (
x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
):
x2_dtype = x2.dtype if hasattr(x2, "dtype") else type(x2)
dtype = infer_dtype(x1.dtype, np.dtype(x2_dtype), cls._operator)
return {"shape": x1.shape, "dtype": dtype, "index_value": x1.index_value}
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
x2, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)
):
index_shape, column_shape, dtypes, columns, index = (
np.nan,
np.nan,
None,
None,
None,
)
if (
x1.columns_value is not None
and x2.columns_value is not None
and x1.columns_value.key == x2.columns_value.key
):
dtypes = pd.Series(
[
infer_dtype(dt1, dt2, cls._operator)
for dt1, dt2 in zip(x1.dtypes, x2.dtypes)
],
index=x1.dtypes.index,
)
columns = copy.copy(x1.columns_value)
columns.value.should_be_monotonic = False
column_shape = len(dtypes)
elif x1.dtypes is not None and x2.dtypes is not None:
dtypes = infer_dtypes(x1.dtypes, x2.dtypes, cls._operator)
columns = parse_index(dtypes.index, store_data=True)
columns.value.should_be_monotonic = True
column_shape = len(dtypes)
if x1.index_value is not None and x2.index_value is not None:
if x1.index_value.key == x2.index_value.key:
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
if index.key == x1.index_value.key == x2.index_value.key and (
not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
):
index_shape = (
x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
)
return {
"shape": (index_shape, column_shape),
"dtypes": dtypes,
"columns_value": columns,
"index_value": index,
}
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
):
if axis == "columns" or axis == 1:
index_shape = x1.shape[0]
index = x1.index_value
column_shape, dtypes, columns = np.nan, None, None
if x1.columns_value is not None and x1.index_value is not None:
if x1.columns_value.key == x2.index_value.key:
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
columns = copy.copy(x1.columns_value)
columns.value.should_be_monotonic = False
column_shape = len(dtypes)
else: # pragma: no cover
dtypes = x1.dtypes # FIXME
columns = infer_index_value(x1.columns_value, x2.index_value)
columns.value.should_be_monotonic = True
column_shape = np.nan
else:
assert axis == "index" or axis == 0
column_shape = x1.shape[1]
columns = x1.columns_value
dtypes = x1.dtypes
index_shape, index = np.nan, None
if x1.index_value is not None and x1.index_value is not None:
if x1.index_value.key == x2.index_value.key:
dtypes = pd.Series(
[infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
index=x1.dtypes.index,
)
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
if x1.dtypes is not None:
dtypes = pd.Series(
[
infer_dtype(dt, x2.dtype, cls._operator)
for dt in x1.dtypes
],
index=x1.dtypes.index,
)
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
index_shape = np.nan
return {
"shape": (index_shape, column_shape),
"dtypes": dtypes,
"columns_value": columns,
"index_value": index,
}
if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and isinstance(
x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
):
index_shape, dtype, index = np.nan, None, None
dtype = infer_dtype(x1.dtype, x2.dtype, cls._operator)
if x1.index_value is not None and x2.index_value is not None:
if x1.index_value.key == x2.index_value.key:
index = copy.copy(x1.index_value)
index.value.should_be_monotonic = False
index_shape = x1.shape[0]
else:
index = infer_index_value(x1.index_value, x2.index_value)
index.value.should_be_monotonic = True
if index.key == x1.index_value.key == x2.index_value.key and (
not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
):
index_shape = (
x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
)
return {"shape": (index_shape,), "dtype": dtype, "index_value": index}
raise NotImplementedError("Unknown combination of parameters")
|
https://github.com/mars-project/mars/issues/1590
|
import numpy as np
import pandas as pd
import mars.dataframe as md
rs = np.random.RandomState(0)
raw_df = rs.rand(20, 10)
raw_df = pd.DataFrame(np.where(raw_df > 0.4, raw_df, np.nan), columns=list('ABCDEFGHIJ'))
df = md.DataFrame(raw_df, chunk_size=6)
raw_df2 = rs.rand(20, 10)
raw_df2 = pd.DataFrame(np.where(raw_df2 > 0.4, raw_df2, np.nan), columns=list('ABCDEFGHIJ'))
df2 = md.DataFrame(raw_df2, chunk_size=4)
sumv = (df ** 2).mul(df2, axis=1, fill_value=0).sum(axis=0).execute()
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-16-f7addf55e6d4>", line 1, in <module>
sumv = (df ** 2).mul(df2, axis=1, fill_value=0).sum(axis=0).execute()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/__init__.py", line 118, in call_df_fill
return func(df, other, axis=axis, level=level, fill_value=fill_value)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/multiply.py", line 48, in mul
return op(df, other)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 499, in __call__
return self._call(x1, x2)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 479, in _call
kw = self._calc_properties(df1, df2, axis=self.axis)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 326, in _calc_properties
dtypes = pd.Series([infer_dtype(dt1, dt2, cls._operator) for dt1, dt2
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/series.py", line 313, in __init__
raise ValueError(
ValueError: Length of passed values is 10, index implies 11.
|
ValueError
|
def build_df(df_obj, fill_value=1, size=1):
empty_df = build_empty_df(df_obj.dtypes, index=df_obj.index_value.to_pandas()[:0])
dtypes = empty_df.dtypes
record = [_generate_value(dtype, fill_value) for dtype in dtypes]
if len(record) != 0: # columns is empty in some cases
if isinstance(empty_df.index, pd.MultiIndex):
index = tuple(
_generate_value(level.dtype, fill_value)
for level in empty_df.index.levels
)
empty_df = empty_df.reindex(
index=pd.MultiIndex.from_tuples([index], names=empty_df.index.names)
)
empty_df.iloc[0] = record
else:
index = _generate_value(empty_df.index.dtype, fill_value)
empty_df.loc[index] = record
empty_df = pd.concat([empty_df] * size)
# make sure dtypes correct for MultiIndex
for i, dtype in enumerate(dtypes.tolist()):
s = empty_df.iloc[:, i]
if not pd.api.types.is_dtype_equal(s.dtype, dtype):
empty_df.iloc[:, i] = s.astype(dtype)
return empty_df
|
def build_df(df_obj, fill_value=1, size=1):
empty_df = build_empty_df(df_obj.dtypes, index=df_obj.index_value.to_pandas()[:0])
dtypes = empty_df.dtypes
record = [_generate_value(dtype, fill_value) for dtype in dtypes]
if isinstance(empty_df.index, pd.MultiIndex):
index = tuple(
_generate_value(level.dtype, fill_value) for level in empty_df.index.levels
)
empty_df = empty_df.reindex(
index=pd.MultiIndex.from_tuples([index], names=empty_df.index.names)
)
empty_df.iloc[0] = record
else:
index = _generate_value(empty_df.index.dtype, fill_value)
empty_df.loc[index] = record
empty_df = pd.concat([empty_df] * size)
# make sure dtypes correct for MultiIndex
for i, dtype in enumerate(dtypes.tolist()):
s = empty_df.iloc[:, i]
if not pd.api.types.is_dtype_equal(s.dtype, dtype):
empty_df.iloc[:, i] = s.astype(dtype)
return empty_df
|
https://github.com/mars-project/mars/issues/1590
|
import numpy as np
import pandas as pd
import mars.dataframe as md
rs = np.random.RandomState(0)
raw_df = rs.rand(20, 10)
raw_df = pd.DataFrame(np.where(raw_df > 0.4, raw_df, np.nan), columns=list('ABCDEFGHIJ'))
df = md.DataFrame(raw_df, chunk_size=6)
raw_df2 = rs.rand(20, 10)
raw_df2 = pd.DataFrame(np.where(raw_df2 > 0.4, raw_df2, np.nan), columns=list('ABCDEFGHIJ'))
df2 = md.DataFrame(raw_df2, chunk_size=4)
sumv = (df ** 2).mul(df2, axis=1, fill_value=0).sum(axis=0).execute()
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-16-f7addf55e6d4>", line 1, in <module>
sumv = (df ** 2).mul(df2, axis=1, fill_value=0).sum(axis=0).execute()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/__init__.py", line 118, in call_df_fill
return func(df, other, axis=axis, level=level, fill_value=fill_value)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/multiply.py", line 48, in mul
return op(df, other)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 499, in __call__
return self._call(x1, x2)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 479, in _call
kw = self._calc_properties(df1, df2, axis=self.axis)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 326, in _calc_properties
dtypes = pd.Series([infer_dtype(dt1, dt2, cls._operator) for dt1, dt2
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/series.py", line 313, in __init__
raise ValueError(
ValueError: Length of passed values is 10, index implies 11.
|
ValueError
|
def fetch(self, *tileables, **kw):
ret_list = False
if len(tileables) == 1 and isinstance(tileables[0], (tuple, list)):
ret_list = True
tileables = tileables[0]
elif len(tileables) > 1:
ret_list = True
result = self._sess.fetch(*tileables, **kw)
ret = []
for r, t in zip(result, tileables):
if hasattr(t, "isscalar") and t.isscalar() and getattr(r, "size", None) == 1:
ret.append(r.item())
else:
ret.append(r)
if ret_list:
return ret
return ret[0]
|
def fetch(self, *tileables, **kw):
ret_list = False
if len(tileables) == 1 and isinstance(tileables[0], (tuple, list)):
ret_list = True
tileables = tileables[0]
elif len(tileables) > 1:
ret_list = True
result = self._sess.fetch(*tileables, **kw)
ret = []
for r, t in zip(result, tileables):
if hasattr(t, "isscalar") and t.isscalar() and hasattr(r, "item"):
ret.append(r.item())
else:
ret.append(r)
if ret_list:
return ret
return ret[0]
|
https://github.com/mars-project/mars/issues/1580
|
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/.local/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/.local/lib/python3.6/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392 if cls is not object \
393 and callable(cls.__dict__.get('__repr__')):
--> 394 return _repr_pprint(obj, self, cycle)
395
396 return _default_pprint(obj, self, cycle)
~/.local/lib/python3.6/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
682 """A pprint that just redirects to the normal repr function."""
683 # Find newlines and replace them with p.break_()
--> 684 output = repr(obj)
685 lines = output.splitlines()
686 with p.group():
~/.local/lib/python3.6/site-packages/mars/core.py in __repr__(self)
127
128 def __repr__(self):
--> 129 return self._data.__repr__()
130
131 def _check_data(self, data):
~/.local/lib/python3.6/site-packages/mars/tensor/core.py in __repr__(self)
177
178 def __repr__(self):
--> 179 return self._to_str(representation=True)
180
181 @property
~/.local/lib/python3.6/site-packages/mars/tensor/core.py in _to_str(self, representation)
165 threshold = print_options['threshold']
166
--> 167 corner_data = fetch_corner_data(self, session=self._executed_sessions[-1])
168 # if less than default threshold, just set it as default,
169 # if not, set to corner_data.size - 1 make sure ... exists in repr
~/.local/lib/python3.6/site-packages/mars/tensor/utils.py in fetch_corner_data(tensor, session)
824 return np.block(corners.tolist())
825 else:
--> 826 return tensor.fetch(session=session)
~/.local/lib/python3.6/site-packages/mars/core.py in fetch(self, session, **kw)
373 if session is None:
374 session = Session.default_or_local()
--> 375 return session.fetch(self, **kw)
376
377 def _attach_session(self, session):
~/.local/lib/python3.6/site-packages/mars/session.py in fetch(self, *tileables, **kw)
494 for r, t in zip(result, tileables):
495 if hasattr(t, 'isscalar') and t.isscalar() and hasattr(r, 'item'):
--> 496 ret.append(r.item())
497 else:
498 ret.append(r)
ValueError: can only convert an array of size 1 to a Python scalar
|
ValueError
|
def swapaxes(a, axis1, axis2):
"""
Interchange two axes of a tensor.
Parameters
----------
a : array_like
Input tensor.
axis1 : int
First axis.
axis2 : int
Second axis.
Returns
-------
a_swapped : Tensor
If `a` is a Tensor, then a view of `a` is
returned; otherwise a new tensor is created.
Examples
--------
>>> import mars.tensor as mt
>>> x = mt.array([[1,2,3]])
>>> mt.swapaxes(x,0,1).execute()
array([[1],
[2],
[3]])
>>> x = mt.array([[[0,1],[2,3]],[[4,5],[6,7]]])
>>> x.execute()
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> mt.swapaxes(x,0,2).execute()
array([[[0, 4],
[2, 6]],
[[1, 5],
[3, 7]]])
"""
a = astensor(a)
axis1 = validate_axis(a.ndim, axis1)
axis2 = validate_axis(a.ndim, axis2)
if axis1 == axis2:
return a
op = TensorSwapAxes(axis1, axis2, dtype=a.dtype, sparse=a.issparse())
return op(a)
|
def swapaxes(a, axis1, axis2):
"""
Interchange two axes of a tensor.
Parameters
----------
a : array_like
Input tensor.
axis1 : int
First axis.
axis2 : int
Second axis.
Returns
-------
a_swapped : Tensor
If `a` is a Tensor, then a view of `a` is
returned; otherwise a new tensor is created.
Examples
--------
>>> import mars.tensor as mt
>>> x = mt.array([[1,2,3]])
>>> mt.swapaxes(x,0,1).execute()
array([[1],
[2],
[3]])
>>> x = mt.array([[[0,1],[2,3]],[[4,5],[6,7]]])
>>> x.execute()
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> mt.swapaxes(x,0,2).execute()
array([[[0, 4],
[2, 6]],
[[1, 5],
[3, 7]]])
"""
axis1 = validate_axis(a.ndim, axis1)
axis2 = validate_axis(a.ndim, axis2)
if axis1 == axis2:
return a
op = TensorSwapAxes(axis1, axis2, dtype=a.dtype, sparse=a.issparse())
return op(a)
|
https://github.com/mars-project/mars/issues/1552
|
In [35]: p = np.random.rand(3,4,5)
In [36]: mt.swapaxes(p, 0, -1)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-36-016cb9916fdb> in <module>
----> 1 mt.swapaxes(p, 0, -1)
~/anaconda3/envs/pymars0.6/lib/python3.7/site-packages/mars/tensor/base/swapaxes.py in swapaxes(a, axis1, axis2)
150 return a
151
--> 152 op = TensorSwapAxes(axis1, axis2, dtype=a.dtype, sparse=a.issparse())
153 return op(a)
AttributeError: 'numpy.ndarray' object has no attribute 'issparse'
|
AttributeError
|
def yield_execution_pool(self):
actor_cls = self.get("_actor_cls")
actor_uid = self.get("_actor_uid")
op_key = self.get("_op_key")
if not actor_cls or not actor_uid: # pragma: no cover
return
from .actors import new_client
from .actors.errors import ActorAlreadyExist
from .worker.daemon import WorkerDaemonActor
client = new_client()
worker_addr = self.get_local_address()
if client.has_actor(
client.actor_ref(WorkerDaemonActor.default_uid(), address=worker_addr)
):
holder = client.actor_ref(WorkerDaemonActor.default_uid(), address=worker_addr)
else:
holder = client
while True:
try:
random_tail = "".join(
random.choice(string.ascii_letters + string.digits) for _ in range(5)
)
uid = f"w:0:mars-cpu-calc-backup-{os.getpid()}-{op_key}-{random_tail}"
uid = self._actor_ctx.distributor.make_same_process(uid, actor_uid)
ref = holder.create_actor(actor_cls, uid=uid, address=worker_addr)
break
except ActorAlreadyExist: # pragma: no cover
pass
return ref
|
def yield_execution_pool(self):
actor_cls = self.get("_actor_cls")
actor_uid = self.get("_actor_uid")
op_key = self.get("_op_key")
if not actor_cls or not actor_uid: # pragma: no cover
return
from .actors import new_client
from .worker.daemon import WorkerDaemonActor
client = new_client()
worker_addr = self.get_local_address()
if client.has_actor(
client.actor_ref(WorkerDaemonActor.default_uid(), address=worker_addr)
):
holder = client.actor_ref(WorkerDaemonActor.default_uid(), address=worker_addr)
else:
holder = client
uid = f"w:0:mars-cpu-calc-backup-{os.getpid()}-{op_key}-{random.randint(-1, 9999)}"
uid = self._actor_ctx.distributor.make_same_process(uid, actor_uid)
ref = holder.create_actor(actor_cls, uid=uid, address=worker_addr)
return ref
|
https://github.com/mars-project/mars/issues/1543
|
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/tests/test_groupby_execution.py", line 401, in testGroupByApply
pd.testing.assert_frame_equal(self.executor.execute_dataframe(applied, concat=True)[0],
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 686, in execute_tileable
result = super().execute_tileable(tileable, *args, **kwargs)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 720, in execute_tileable
ret = self.execute_graph(chunk_graph, result_keys, n_parallel=n_parallel or n_thread,
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 673, in execute_graph
return super().execute_graph(graph, keys, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 591, in _execute_operand
super()._execute_operand(op)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/apply.py", line 63, in execute
assert len(applied.index) == 1
AssertionError
|
AssertionError
|
def _call_dataframe(self, df, dtypes=None, index=None):
dtypes, index_value = self._infer_df_func_returns(df, dtypes, index)
if index_value is None:
index_value = parse_index(None, (df.key, df.index_value.key))
for arg, desc in zip((self.output_types, dtypes), ("output_types", "dtypes")):
if arg is None:
raise TypeError(
f"Cannot determine {desc} by calculating with enumerate data, "
"please specify it as arguments"
)
if index_value == "inherit":
index_value = df.index_value
if self._elementwise:
shape = df.shape
elif self.output_types[0] == OutputType.dataframe:
shape = [np.nan, np.nan]
shape[1 - self.axis] = df.shape[1 - self.axis]
shape = tuple(shape)
else:
shape = (df.shape[1 - self.axis],)
if self.output_types[0] == OutputType.dataframe:
if self.axis == 0:
return self.new_dataframe(
[df],
shape=shape,
dtypes=dtypes,
index_value=index_value,
columns_value=parse_index(dtypes.index),
)
else:
return self.new_dataframe(
[df],
shape=shape,
dtypes=dtypes,
index_value=df.index_value,
columns_value=parse_index(dtypes.index),
)
else:
return self.new_series([df], shape=shape, dtype=dtypes, index_value=index_value)
|
def _call_dataframe(self, df, dtypes=None, index=None):
dtypes, index_value = self._infer_df_func_returns(df, dtypes, index)
for arg, desc in zip(
(self.output_types, dtypes, index_value), ("output_types", "dtypes", "index")
):
if arg is None:
raise TypeError(
f"Cannot determine {desc} by calculating with enumerate data, "
"please specify it as arguments"
)
if index_value == "inherit":
index_value = df.index_value
if self._elementwise:
shape = df.shape
elif self.output_types[0] == OutputType.dataframe:
shape = [np.nan, np.nan]
shape[1 - self.axis] = df.shape[1 - self.axis]
shape = tuple(shape)
else:
shape = (df.shape[1 - self.axis],)
if self.output_types[0] == OutputType.dataframe:
if self.axis == 0:
return self.new_dataframe(
[df],
shape=shape,
dtypes=dtypes,
index_value=index_value,
columns_value=parse_index(dtypes.index),
)
else:
return self.new_dataframe(
[df],
shape=shape,
dtypes=dtypes,
index_value=df.index_value,
columns_value=parse_index(dtypes.index),
)
else:
return self.new_series([df], shape=shape, dtype=dtypes, index_value=index_value)
|
https://github.com/mars-project/mars/issues/1543
|
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/tests/test_groupby_execution.py", line 401, in testGroupByApply
pd.testing.assert_frame_equal(self.executor.execute_dataframe(applied, concat=True)[0],
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 686, in execute_tileable
result = super().execute_tileable(tileable, *args, **kwargs)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 720, in execute_tileable
ret = self.execute_graph(chunk_graph, result_keys, n_parallel=n_parallel or n_thread,
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 673, in execute_graph
return super().execute_graph(graph, keys, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 591, in _execute_operand
super()._execute_operand(op)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/apply.py", line 63, in execute
assert len(applied.index) == 1
AssertionError
|
AssertionError
|
def df_apply(
df,
func,
axis=0,
raw=False,
result_type=None,
args=(),
dtypes=None,
output_type=None,
index=None,
elementwise=None,
**kwds,
):
if isinstance(func, (list, dict)):
return df.aggregate(func)
output_types = kwds.pop("output_types", None)
object_type = kwds.pop("object_type", None)
output_types = validate_output_types(
output_type=output_type, output_types=output_types, object_type=object_type
)
output_type = output_types[0] if output_types else None
# calling member function
if isinstance(func, str):
func = getattr(df, func)
sig = inspect.getfullargspec(func)
if "axis" in sig.args:
kwds["axis"] = axis
return func(*args, **kwds)
op = ApplyOperand(
func=func,
axis=axis,
raw=raw,
result_type=result_type,
args=args,
kwds=kwds,
output_types=output_type,
elementwise=elementwise,
)
return op(df, dtypes=dtypes, index=index)
|
def df_apply(
df,
func,
axis=0,
raw=False,
result_type=None,
args=(),
dtypes=None,
output_type=None,
index=None,
elementwise=None,
**kwds,
):
if isinstance(func, (list, dict)):
return df.aggregate(func)
if isinstance(output_type, str):
output_type = getattr(OutputType, output_type.lower())
# calling member function
if isinstance(func, str):
func = getattr(df, func)
sig = inspect.getfullargspec(func)
if "axis" in sig.args:
kwds["axis"] = axis
return func(*args, **kwds)
op = ApplyOperand(
func=func,
axis=axis,
raw=raw,
result_type=result_type,
args=args,
kwds=kwds,
output_type=output_type,
elementwise=elementwise,
)
return op(df, dtypes=dtypes, index=index)
|
https://github.com/mars-project/mars/issues/1543
|
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/tests/test_groupby_execution.py", line 401, in testGroupByApply
pd.testing.assert_frame_equal(self.executor.execute_dataframe(applied, concat=True)[0],
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 686, in execute_tileable
result = super().execute_tileable(tileable, *args, **kwargs)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 720, in execute_tileable
ret = self.execute_graph(chunk_graph, result_keys, n_parallel=n_parallel or n_thread,
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 673, in execute_graph
return super().execute_graph(graph, keys, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 591, in _execute_operand
super()._execute_operand(op)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/apply.py", line 63, in execute
assert len(applied.index) == 1
AssertionError
|
AssertionError
|
def _infer_df_func_returns(self, in_groupby, in_df, dtypes, index):
index_value, output_type, new_dtypes = None, None, None
try:
if in_df.op.output_types[0] == OutputType.dataframe:
test_df = build_df(in_df, size=2)
else:
test_df = build_series(in_df, size=2, name=in_df.name)
selection = getattr(in_groupby.op, "selection", None)
if selection:
test_df = test_df[selection]
with np.errstate(all="ignore"):
infer_df = self.func(test_df, *self.args, **self.kwds)
# todo return proper index when sort=True is implemented
index_value = parse_index(None, in_df.key, self.func)
if infer_df is None:
output_type = get_output_types(in_df)[0]
index_value = parse_index(pd.Index([], dtype=np.object))
if output_type == OutputType.dataframe:
new_dtypes = pd.Series([], index=pd.Index([]))
else:
new_dtypes = (None, np.dtype("O"))
elif isinstance(infer_df, pd.DataFrame):
output_type = output_type or OutputType.dataframe
new_dtypes = new_dtypes or infer_df.dtypes
elif isinstance(infer_df, pd.Series):
output_type = output_type or OutputType.series
new_dtypes = new_dtypes or (infer_df.name, infer_df.dtype)
else:
output_type = OutputType.series
new_dtypes = (None, pd.Series(infer_df).dtype)
except: # noqa: E722 # nosec
pass
self.output_types = [output_type] if not self.output_types else self.output_types
dtypes = new_dtypes if dtypes is None else dtypes
index_value = index_value if index is None else parse_index(index)
return dtypes, index_value
|
def _infer_df_func_returns(self, in_groupby, in_df, dtypes, index):
index_value, output_type, new_dtypes = None, None, None
try:
if in_df.op.output_types[0] == OutputType.dataframe:
test_df = build_df(in_df, size=2)
else:
test_df = build_series(in_df, size=2, name=in_df.name)
selection = getattr(in_groupby.op, "selection", None)
if selection:
test_df = test_df[selection]
with np.errstate(all="ignore"):
infer_df = self.func(test_df, *self.args, **self.kwds)
# todo return proper index when sort=True is implemented
index_value = parse_index(None, in_df.key, self.func)
if isinstance(infer_df, pd.DataFrame):
output_type = output_type or OutputType.dataframe
new_dtypes = new_dtypes or infer_df.dtypes
elif isinstance(infer_df, pd.Series):
output_type = output_type or OutputType.series
new_dtypes = new_dtypes or (infer_df.name, infer_df.dtype)
else:
output_type = OutputType.series
new_dtypes = (None, pd.Series(infer_df).dtype)
except: # noqa: E722 # nosec
pass
self.output_types = [output_type] if not self.output_types else self.output_types
dtypes = new_dtypes if dtypes is None else dtypes
index_value = index_value if index is None else parse_index(index)
return dtypes, index_value
|
https://github.com/mars-project/mars/issues/1543
|
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/tests/test_groupby_execution.py", line 401, in testGroupByApply
pd.testing.assert_frame_equal(self.executor.execute_dataframe(applied, concat=True)[0],
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 686, in execute_tileable
result = super().execute_tileable(tileable, *args, **kwargs)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 720, in execute_tileable
ret = self.execute_graph(chunk_graph, result_keys, n_parallel=n_parallel or n_thread,
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 673, in execute_graph
return super().execute_graph(graph, keys, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 591, in _execute_operand
super()._execute_operand(op)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/apply.py", line 63, in execute
assert len(applied.index) == 1
AssertionError
|
AssertionError
|
def __call__(self, groupby, dtypes=None, index=None):
in_df = groupby
while in_df.op.output_types[0] not in (OutputType.dataframe, OutputType.series):
in_df = in_df.inputs[0]
dtypes, index_value = self._infer_df_func_returns(groupby, in_df, dtypes, index)
if index_value is None:
index_value = parse_index(None, (in_df.key, in_df.index_value.key))
for arg, desc in zip((self.output_types, dtypes), ("output_types", "dtypes")):
if arg is None:
raise TypeError(
f"Cannot determine {desc} by calculating with enumerate data, "
"please specify it as arguments"
)
if self.output_types[0] == OutputType.dataframe:
new_shape = (np.nan, len(dtypes))
return self.new_dataframe(
[groupby],
shape=new_shape,
dtypes=dtypes,
index_value=index_value,
columns_value=parse_index(dtypes.index, store_data=True),
)
else:
name, dtype = dtypes
new_shape = (np.nan,)
return self.new_series(
[groupby], name=name, shape=new_shape, dtype=dtype, index_value=index_value
)
|
def __call__(self, groupby, dtypes=None, index=None):
in_df = groupby
while in_df.op.output_types[0] not in (OutputType.dataframe, OutputType.series):
in_df = in_df.inputs[0]
dtypes, index_value = self._infer_df_func_returns(groupby, in_df, dtypes, index)
for arg, desc in zip(
(self.output_types, dtypes, index_value), ("output_types", "dtypes", "index")
):
if arg is None:
raise TypeError(
f"Cannot determine {desc} by calculating with enumerate data, "
"please specify it as arguments"
)
if self.output_types[0] == OutputType.dataframe:
new_shape = (np.nan, len(dtypes))
return self.new_dataframe(
[groupby],
shape=new_shape,
dtypes=dtypes,
index_value=index_value,
columns_value=parse_index(dtypes.index, store_data=True),
)
else:
name, dtype = dtypes
new_shape = (np.nan,)
return self.new_series(
[groupby], name=name, shape=new_shape, dtype=dtype, index_value=index_value
)
|
https://github.com/mars-project/mars/issues/1543
|
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/tests/test_groupby_execution.py", line 401, in testGroupByApply
pd.testing.assert_frame_equal(self.executor.execute_dataframe(applied, concat=True)[0],
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 686, in execute_tileable
result = super().execute_tileable(tileable, *args, **kwargs)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 720, in execute_tileable
ret = self.execute_graph(chunk_graph, result_keys, n_parallel=n_parallel or n_thread,
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 673, in execute_graph
return super().execute_graph(graph, keys, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 591, in _execute_operand
super()._execute_operand(op)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/apply.py", line 63, in execute
assert len(applied.index) == 1
AssertionError
|
AssertionError
|
def groupby_apply(
groupby, func, *args, dtypes=None, index=None, output_type=None, **kwargs
):
# todo this can be done with sort_index implemented
if not groupby.op.groupby_params.get("as_index", True):
raise NotImplementedError("apply when set_index == False is not supported")
output_types = kwargs.pop("output_types", None)
object_type = kwargs.pop("object_type", None)
output_types = validate_output_types(
output_types=output_types, output_type=output_type, object_type=object_type
)
op = GroupByApply(func=func, args=args, kwds=kwargs, output_types=output_types)
return op(groupby, dtypes=dtypes, index=index)
|
def groupby_apply(
groupby, func, *args, dtypes=None, index=None, output_types=None, **kwargs
):
# todo this can be done with sort_index implemented
if not groupby.op.groupby_params.get("as_index", True):
raise NotImplementedError("apply when set_index == False is not supported")
op = GroupByApply(func=func, args=args, kwds=kwargs, output_types=output_types)
return op(groupby, dtypes=dtypes, index=index)
|
https://github.com/mars-project/mars/issues/1543
|
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 60, in testPartExecutor
yield
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 676, in run
self._callTestMethod(testMethod)
File "/Users/wenjun/miniconda3/lib/python3.8/unittest/case.py", line 633, in _callTestMethod
method()
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/tests/test_groupby_execution.py", line 401, in testGroupByApply
pd.testing.assert_frame_equal(self.executor.execute_dataframe(applied, concat=True)[0],
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 686, in execute_tileable
result = super().execute_tileable(tileable, *args, **kwargs)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 720, in execute_tileable
ret = self.execute_graph(chunk_graph, result_keys, n_parallel=n_parallel or n_thread,
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 673, in execute_graph
return super().execute_graph(graph, keys, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 693, in execute_graph
res = graph_execution.execute(retval)
File "/Users/wenjun/Code/mars/mars/executor.py", line 574, in execute
future.result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/Users/wenjun/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/wenjun/Code/mars/mars/tests/core.py", line 591, in _execute_operand
super()._execute_operand(op)
File "/Users/wenjun/Code/mars/mars/utils.py", line 439, in _inner
return func(*args, **kwargs)
File "/Users/wenjun/Code/mars/mars/executor.py", line 446, in _execute_operand
self.handle_op(first_op, results, self._mock)
File "/Users/wenjun/Code/mars/mars/executor.py", line 378, in handle_op
return Executor.handle(*args, **kw)
File "/Users/wenjun/Code/mars/mars/executor.py", line 644, in handle
return runner(results, op)
File "/Users/wenjun/Code/mars/mars/dataframe/groupby/apply.py", line 63, in execute
assert len(applied.index) == 1
AssertionError
|
AssertionError
|
def to_pandas(self):
data = getattr(self, "_data", None)
sortorder = getattr(self, "_sortorder", None)
if data is None:
return pd.MultiIndex.from_arrays(
[np.array([], dtype=dtype) for dtype in self._dtypes],
sortorder=sortorder,
names=self._names,
)
return pd.MultiIndex.from_tuples(
[tuple(d) for d in data], sortorder=sortorder, names=self._names
)
|
def to_pandas(self):
data = getattr(self, "_data", None)
if data is None:
sortorder = getattr(self, "_sortorder", None)
return pd.MultiIndex.from_arrays(
[np.array([], dtype=dtype) for dtype in self._dtypes],
sortorder=sortorder,
names=self._names,
)
return pd.MultiIndex.from_tuples(
[tuple(d) for d in data], sortorder=self._sortorder, names=self._names
)
|
https://github.com/mars-project/mars/issues/1542
|
In [1]: from mars.session import new_session
In [2]: import mars.dataframe as md
In [3]: new_session(backend='ray').as_default()
2020-09-01 20:05:51,291 INFO resource_spec.py:231 -- Starting Ray with 5.08 GiB memory available for workers and up to 2.56 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).
2020-09-01 20:05:51,883 INFO services.py:1193 -- View the Ray dashboard at localhost:8265
Out[3]: <mars.session.Session at 0x7fc51364fb50>
In [4]: df = md.read_csv('Downloads/ratings.csv')
In [5]: df.groupby('userId').agg({'rating': ['min', 'max', 'mean', 'std']}).exec
...: ute()
---------------------------------------------------------------------------
RayTaskError(TypeError) Traceback (most recent call last)
<ipython-input-5-180cc92d1395> in <module>
----> 1 df.groupby('userId').agg({'rating': ['min', 'max', 'mean', 'std']}).execute()
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
576
577 def execute(self, session=None, **kw):
--> 578 self._data.execute(session, **kw)
579 return self
580
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
364
365 # no more fetch, thus just fire run
--> 366 session.run(self, **kw)
367 # return Tileable or ExecutableTuple itself
368 return self
~/Workspace/mars/mars/session.py in run(self, *tileables, **kw)
478 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t
479 for t in tileables)
--> 480 result = self._sess.run(*tileables, **kw)
481
482 for t in tileables:
~/Workspace/mars/mars/ray/core.py in run(self, *tileables, **kw)
188 if 'n_parallel' not in kw: # pragma: no cover
189 kw['n_parallel'] = ray.cluster_resources()['CPU']
--> 190 return self._executor.execute_tileables(tileables, **kw)
191
192 def __enter__(self):
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose, name)
879 n_parallel=n_parallel or n_thread,
880 print_progress=print_progress, mock=mock,
--> 881 chunk_result=chunk_result)
882
883 # update shape of tileable and its chunks whatever it's successful or not
~/Workspace/mars/mars/executor.py in execute_graph(self, graph, keys, n_parallel, print_progress, mock, no_intermediate, compose, retval, chunk_result)
691 print_progress=print_progress, mock=mock, mock_max_memory=self._mock_max_memory,
692 fetch_keys=fetch_keys, no_intermediate=no_intermediate)
--> 693 res = graph_execution.execute(retval)
694 self._mock_max_memory = max(self._mock_max_memory, graph_execution._mock_max_memory)
695 if mock:
~/Workspace/mars/mars/executor.py in execute(self, retval)
572 # wait until all the futures completed
573 for future in executed_futures:
--> 574 future.result()
575
576 if retval:
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/Workspace/mars/mars/utils.py in _inner(*args, **kwargs)
437 def _inner(*args, **kwargs):
438 with self:
--> 439 return func(*args, **kwargs)
440
441 return _inner
~/Workspace/mars/mars/executor.py in _execute_operand(self, op)
444 # so we pass the first operand's first output to Executor.handle
445 first_op = ops[0]
--> 446 self.handle_op(first_op, results, self._mock)
447
448 # update maximal memory usage during execution
~/Workspace/mars/mars/ray/core.py in handle_op(self, *args, **kw)
66 class GraphExecutionForRay(GraphExecution):
67 def handle_op(self, *args, **kw):
---> 68 return RayExecutor.handle(*args, **kw)
69
70
~/Workspace/mars/mars/ray/core.py in handle(cls, op, results, mock)
147
148 try:
--> 149 return ray.get(build_remote_funtion(runner).remote(results, op))
150 except NotImplementedError:
151 for op_cls in mapper.keys():
~/miniconda3/lib/python3.7/site-packages/ray/worker.py in get(object_refs, timeout)
1536 worker.core_worker.dump_object_store_memory_usage()
1537 if isinstance(value, RayTaskError):
-> 1538 raise value.as_instanceof_cause()
1539 else:
1540 raise value
RayTaskError(TypeError): ray::mars.ray.core.remote_runner() (pid=31351, ip=30.225.12.80)
File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task
File "/Users/qinxuye/Workspace/mars/mars/ray/core.py", line 144, in remote_runner
return func(results, op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/datasource/read_csv.py", line 322, in execute
df = cls._cudf_read_csv(op) if op.gpu else cls._pandas_read_csv(f, op)
File "/Users/qinxuye/Workspace/mars/mars/dataframe/datasource/read_csv.py", line 273, in _pandas_read_csv
dtype=dtypes.to_dict(), nrows=op.nrows, **csv_kwargs)
TypeError: parser_f() got an unexpected keyword argument 'outputs_ref'
|
TypeError
|
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