after_merge stringlengths 28 79.6k | before_merge stringlengths 20 79.6k | url stringlengths 38 71 | full_traceback stringlengths 43 922k | traceback_type stringclasses 555
values |
|---|---|---|---|---|
def create_pool(self, *args, **kwargs):
self._service = SchedulerService(disable_failover=self.args.disable_failover)
self.n_process = int(self.args.nproc or resource.cpu_count())
kwargs["distributor"] = MarsDistributor(self.n_process, "s:h1:")
return super().create_pool(*args, **kwargs)
| def create_pool(self, *args, **kwargs):
self._service = SchedulerService()
self.n_process = int(self.args.nproc or resource.cpu_count())
kwargs["distributor"] = MarsDistributor(self.n_process, "s:h1:")
return super().create_pool(*args, **kwargs)
| https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def __init__(self, **kwargs):
self._cluster_info_ref = None
self._session_manager_ref = None
self._assigner_ref = None
self._resource_ref = None
self._chunk_meta_ref = None
self._kv_store_ref = None
self._node_info_ref = None
self._result_receiver_ref = None
options.scheduler.enable... | def __init__(self):
self._cluster_info_ref = None
self._session_manager_ref = None
self._assigner_ref = None
self._resource_ref = None
self._chunk_meta_ref = None
self._kv_store_ref = None
self._node_info_ref = None
self._result_receiver_ref = None
| https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def config_args(self, parser):
super().config_args(parser)
parser.add_argument("--cpu-procs", help="number of processes used for cpu")
parser.add_argument(
"--cuda-device", help="CUDA device to use, if not specified, will use CPU only"
)
parser.add_argument("--net-procs", help="number of pro... | def config_args(self, parser):
super().config_args(parser)
parser.add_argument("--cpu-procs", help="number of processes used for cpu")
parser.add_argument(
"--cuda-device", help="CUDA device to use, if not specified, will use CPU only"
)
parser.add_argument("--net-procs", help="number of pro... | https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def parse_args(self, parser, argv, environ=None):
args = super().parse_args(parser, argv)
environ = environ or os.environ
args.plasma_dir = args.plasma_dir or environ.get("MARS_PLASMA_DIRS")
args.spill_dir = args.spill_dir or environ.get("MARS_SPILL_DIRS")
args.cache_mem = args.cache_mem or environ... | def parse_args(self, parser, argv, environ=None):
args = super().parse_args(parser, argv)
args.plasma_dir = args.plasma_dir or os.environ.get("MARS_PLASMA_DIRS")
args.spill_dir = args.spill_dir or os.environ.get("MARS_SPILL_DIRS")
args.cache_mem = args.cache_mem or os.environ.get("MARS_CACHE_MEM_SIZE")
... | https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def create_pool(self, *args, **kwargs):
# here we create necessary actors on worker
# and distribute them over processes
cuda_devices = [self.args.cuda_device] if self.args.cuda_device else None
self._service = WorkerService(
advertise_addr=self.args.advertise,
n_cpu_process=self.args.c... | def create_pool(self, *args, **kwargs):
# here we create necessary actors on worker
# and distribute them over processes
cuda_devices = [self.args.cuda_device] if self.args.cuda_device else None
self._service = WorkerService(
advertise_addr=self.args.advertise,
n_cpu_process=self.args.c... | https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def __init__(self, **kwargs):
self._plasma_store = None
self._storage_manager_ref = None
self._shared_holder_ref = None
self._task_queue_ref = None
self._mem_quota_ref = None
self._dispatch_ref = None
self._events_ref = None
self._status_ref = None
self._execution_ref = None
sel... | def __init__(self, **kwargs):
self._plasma_store = None
self._storage_manager_ref = None
self._shared_holder_ref = None
self._task_queue_ref = None
self._mem_quota_ref = None
self._dispatch_ref = None
self._events_ref = None
self._status_ref = None
self._execution_ref = None
sel... | https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def start(
self, endpoint, pool, distributed=True, discoverer=None, process_start_index=0
):
# create plasma key mapper
from .storage import PlasmaKeyMapActor
pool.create_actor(PlasmaKeyMapActor, uid=PlasmaKeyMapActor.default_uid())
# create vineyard key mapper
if options.vineyard.socket: # p... | def start(
self, endpoint, pool, distributed=True, discoverer=None, process_start_index=0
):
# create plasma key mapper
from .storage import PlasmaKeyMapActor
pool.create_actor(PlasmaKeyMapActor, uid=PlasmaKeyMapActor.default_uid())
# create vineyard key mapper
if options.vineyard.socket: # p... | https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def __init__(self, io_parallel_num=None, dispatched=True):
super().__init__()
self._work_items = deque()
self._max_work_item_id = 0
self._cur_work_items = dict()
self._io_parallel_num = io_parallel_num or options.worker.io_parallel_num
self._lock_work_items = dict()
self._dispatched = disp... | def __init__(self, lock_free=False, dispatched=True):
super().__init__()
self._work_items = deque()
self._max_work_item_id = 0
self._cur_work_items = dict()
self._lock_free = lock_free or options.worker.lock_free_fileio
self._lock_work_items = dict()
self._dispatched = dispatched
| https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def load_from(self, dest_device, session_id, data_keys, src_device, callback):
logger.debug(
"Copying %r from %s into %s submitted in %s",
data_keys,
src_device,
dest_device,
self.uid,
)
self._work_items.append(
(dest_device, session_id, data_keys, src_device,... | def load_from(self, dest_device, session_id, data_keys, src_device, callback):
logger.debug(
"Copying %r from %s into %s submitted in %s",
data_keys,
src_device,
dest_device,
self.uid,
)
self._work_items.append(
(dest_device, session_id, data_keys, src_device,... | https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def lock(self, session_id, data_keys, callback):
logger.debug("Requesting lock for %r on %s", data_keys, self.uid)
self._work_items.append((None, session_id, data_keys, None, True, callback))
if len(self._cur_work_items) < self._io_parallel_num:
self._submit_next()
| def lock(self, session_id, data_keys, callback):
logger.debug("Requesting lock for %r on %s", data_keys, self.uid)
self._work_items.append((None, session_id, data_keys, None, True, callback))
if self._lock_free or not self._cur_work_items:
self._submit_next()
| https://github.com/mars-project/mars/issues/1479 | Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 129, in __repr__
return self._data.__repr__()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/core.py", line 1083, in __repr__
return self._to_str(representation=True)
File "/Users/wenjun.swj/Co... | ModuleNotFoundError |
def tile(cls, op: "DataFrameDrop"):
inp = op.inputs[0]
out = op.outputs[0]
if len(op.inputs) > 1:
index_chunk = (
op.index.rechunk({0: (op.index.shape[0],)})._inplace_tile().chunks[0]
)
else:
index_chunk = op.index
col_to_args = OrderedDict()
chunks = []
... | def tile(cls, op: "DataFrameDrop"):
inp = op.inputs[0]
out = op.outputs[0]
if len(op.inputs) > 1:
index_chunk = (
op.index.rechunk({0: (op.index.shape[0],)})._inplace_tile().chunks[0]
)
else:
index_chunk = op.index
col_to_args = OrderedDict()
chunks = []
... | https://github.com/mars-project/mars/issues/1463 | Traceback (most recent call last):
File "/Users/qinxuye/Downloads/test_mars3.py", line 13, in <module>
print(c.execute())
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 579, in execute
self._data.execute(session, **kw)
File "/Users/qinxuye/Workspace/mars/mars/core.py", line 367, in execute
session.run(self, **... | TypeError |
def _execute_and_fetch(self, session=None, **kw):
if session is None and len(self._executed_sessions) > 0:
session = self._executed_sessions[-1]
try:
# fetch first, to reduce the potential cost of submitting a graph
return self.fetch(session=session)
except ValueError:
# not ... | def _execute_and_fetch(self, session=None, **kw):
try:
# fetch first, to reduce the potential cost of submitting a graph
return self.fetch(session=session)
except ValueError:
# not execute before
return self.execute(session=session, **kw).fetch(session=session)
| https://github.com/mars-project/mars/issues/1448 | D:\Anaconda3\envs\py37\python.exe E:/mycode/read_data.py
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine {}
2020-08-03 15:54:52,387 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
2020-08... | pymysql.err.InternalError |
def _process_pos(pos, length, is_start):
if pos is None:
return 0 if is_start else length
return pos + length if pos < 0 else pos
| def _process_pos(pos, length):
if pos is None:
return 0
return pos + length if pos < 0 else pos
| https://github.com/mars-project/mars/issues/1448 | D:\Anaconda3\envs\py37\python.exe E:/mycode/read_data.py
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine {}
2020-08-03 15:54:52,387 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
2020-08... | pymysql.err.InternalError |
def __getitem__(self, item):
has_take = hasattr(self._arrow_array, "take")
if not self._force_use_pandas and has_take:
if pd.api.types.is_scalar(item):
item = item + len(self) if item < 0 else item
return self._arrow_array.take([item]).to_pandas()[0]
elif self._can_proces... | def __getitem__(self, item):
has_take = hasattr(self._arrow_array, "take")
if not self._force_use_pandas and has_take:
if pd.api.types.is_scalar(item):
item = item + len(self) if item < 0 else item
return self._arrow_array.take([item]).to_pandas()[0]
elif self._can_proces... | https://github.com/mars-project/mars/issues/1448 | D:\Anaconda3\envs\py37\python.exe E:/mycode/read_data.py
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine {}
2020-08-03 15:54:52,387 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
2020-08... | pymysql.err.InternalError |
def _concat_same_type(
cls, to_concat: Sequence["ArrowStringArray"]
) -> "ArrowStringArray":
chunks = list(
itertools.chain.from_iterable(x._arrow_array.chunks for x in to_concat)
)
if len(chunks) == 0:
chunks = [pa.array([], type=pa.string())]
return cls(pa.chunked_array(chunks))
| def _concat_same_type(
cls, to_concat: Sequence["ArrowStringArray"]
) -> "ArrowStringArray":
chunks = list(
itertools.chain.from_iterable(x._arrow_array.chunks for x in to_concat)
)
return cls(pa.chunked_array(chunks))
| https://github.com/mars-project/mars/issues/1448 | D:\Anaconda3\envs\py37\python.exe E:/mycode/read_data.py
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine {}
2020-08-03 15:54:52,387 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
2020-08... | pymysql.err.InternalError |
def tile(cls, op):
check_chunks_unknown_shape(op.inputs, TilesError)
out = op.outputs[0]
new_chunk_size = op.chunk_size
if isinstance(out, DATAFRAME_TYPE):
itemsize = max(getattr(dt, "itemsize", 8) for dt in out.dtypes)
else:
itemsize = out.dtype.itemsize
steps = plan_rechunks(
... | def tile(cls, op):
check_chunks_unknown_shape(op.inputs, TilesError)
out = op.outputs[0]
new_chunk_size = op.chunk_size
if isinstance(out, DATAFRAME_TYPE):
itemsize = max(dt.itemsize for dt in out.dtypes)
else:
itemsize = out.dtype.itemsize
steps = plan_rechunks(
op.input... | https://github.com/mars-project/mars/issues/1448 | D:\Anaconda3\envs\py37\python.exe E:/mycode/read_data.py
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine {}
2020-08-03 15:54:52,387 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
2020-08... | pymysql.err.InternalError |
def rechunk(a, chunk_size, threshold=None, chunk_size_limit=None):
if isinstance(a, DATAFRAME_TYPE):
itemsize = max(getattr(dt, "itemsize", 8) for dt in a.dtypes)
else:
itemsize = a.dtype.itemsize
chunk_size = get_nsplits(a, chunk_size, itemsize)
if chunk_size == a.nsplits:
retur... | def rechunk(a, chunk_size, threshold=None, chunk_size_limit=None):
if isinstance(a, DATAFRAME_TYPE):
itemsize = max(dt.itemsize for dt in a.dtypes)
else:
itemsize = a.dtype.itemsize
chunk_size = get_nsplits(a, chunk_size, itemsize)
if chunk_size == a.nsplits:
return a
op = D... | https://github.com/mars-project/mars/issues/1448 | D:\Anaconda3\envs\py37\python.exe E:/mycode/read_data.py
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine {}
2020-08-03 15:54:52,387 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
2020-08... | pymysql.err.InternalError |
def _get_selectable(self, engine_or_conn, columns=None):
import sqlalchemy as sa
from sqlalchemy import sql
from sqlalchemy.exc import SQLAlchemyError
# process table_name
if self._selectable is not None:
selectable = self._selectable
else:
if isinstance(self._table_or_sql, sa.T... | def _get_selectable(self, engine_or_conn, columns=None):
import sqlalchemy as sa
from sqlalchemy import sql
from sqlalchemy.exc import NoSuchTableError
# process table_name
if self._selectable is not None:
selectable = self._selectable
else:
if isinstance(self._table_or_sql, sa.... | https://github.com/mars-project/mars/issues/1448 | D:\Anaconda3\envs\py37\python.exe E:/mycode/read_data.py
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine {}
2020-08-03 15:54:52,387 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
2020-08... | pymysql.err.InternalError |
def arrow_table_to_pandas_dataframe(arrow_table, use_arrow_dtype=True, **kw):
if not use_arrow_dtype:
# if not use arrow string, just return
return arrow_table.to_pandas(**kw)
from .arrays import ArrowStringArray
table: pa.Table = arrow_table
schema: pa.Schema = arrow_table.schema
... | def arrow_table_to_pandas_dataframe(arrow_table, use_arrow_string=True, **kw):
if not use_arrow_string:
# if not use arrow string, just return
return arrow_table.to_pandas(**kw)
from .arrays import ArrowStringArray
table: pa.Table = arrow_table
schema: pa.Schema = arrow_table.schema
... | https://github.com/mars-project/mars/issues/1448 | D:\Anaconda3\envs\py37\python.exe E:/mycode/read_data.py
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
2020-08-03 15:54:52,383 INFO sqlalchemy.engine.base.Engine {}
2020-08-03 15:54:52,387 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
2020-08... | pymysql.err.InternalError |
def to_pandas(self):
data = getattr(self, "_data", None)
if data is None:
sortorder = getattr(self, "_sortorder", None)
return pd.MultiIndex.from_arrays(
[[] for _ in range(len(self._names))],
sortorder=sortorder,
names=self._names,
)
return pd.Mul... | def to_pandas(self):
data = getattr(self, "_data", None)
if data is None:
return pd.MultiIndex.from_arrays(
[[] for _ in range(len(self._names))],
sortorder=self._sortorder,
names=self._names,
)
return pd.MultiIndex.from_tuples(
[tuple(d) for d in ... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def _tile_offset(cls, op: "DataFrameReadSQL"):
df = op.outputs[0]
if op.row_memory_usage is not None:
# Data selected
chunk_size = df.extra_params.raw_chunk_size or options.chunk_size
if chunk_size is None:
chunk_size = (
int(options.chunk_store_limit / op.ro... | def _tile_offset(cls, op: "DataFrameReadSQL"):
df = op.outputs[0]
if op.row_memory_usage is not None:
# Data selected
chunk_size = df.extra_params.raw_chunk_size or options.chunk_size
if chunk_size is None:
chunk_size = (
int(options.chunk_store_limit / op.ro... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def _calc_bool_index_param(
cls, input_index_value: IndexValue, pd_index: pd.Index, inp, index, axis: int
) -> Dict:
param = dict()
if input_index_value.has_value():
if isinstance(index, np.ndarray):
filtered_index = pd_index[index]
param["shape"] = len(filtered_index)
... | def _calc_bool_index_param(
cls, input_index_value: IndexValue, pd_index: pd.Index, inp, index, axis: int
) -> Dict:
param = dict()
if input_index_value.has_value():
if isinstance(index, np.ndarray):
filtered_index = pd_index[index]
param["shape"] = len(filtered_index)
... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def shuffle(*arrays, **options):
arrays = [convert_to_tensor_or_dataframe(ar) for ar in arrays]
axes = options.pop("axes", (0,))
if not isinstance(axes, Iterable):
axes = (axes,)
elif not isinstance(axes, tuple):
axes = tuple(axes)
random_state = check_random_state(options.pop("rando... | def shuffle(*arrays, **options):
arrays = [convert_to_tensor_or_dataframe(ar) for ar in arrays]
axes = options.pop("axes", (0,))
if not isinstance(axes, Iterable):
axes = (axes,)
elif not isinstance(axes, tuple):
axes = tuple(axes)
random_state = check_random_state(options.pop("rando... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def __call__(self, a, repeats):
axis = self._axis
a = astensor(a)
if axis is None:
a = ravel(a)
ax = axis or 0
if not isinstance(repeats, Integral):
if not isinstance(repeats, Tensor):
repeats = np.asarray(repeats)
if repeats.size == 1:
repea... | def __call__(self, a, repeats):
axis = self._axis
a = astensor(a)
if axis is None:
a = ravel(a)
ax = axis or 0
if not isinstance(repeats, Integral):
if not isinstance(repeats, Tensor):
repeats = np.asarray(repeats)
if repeats.size == 1:
repea... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def tile(cls, op):
a = op.input
repeats = op.repeats
axis = op.axis
ax = axis or 0
out = op.outputs[0]
check_chunks_unknown_shape(op.inputs, TilesError)
if isinstance(repeats, TENSOR_TYPE):
a, repeats = unify_chunks(a, (repeats, (ax,)))
nsplit = a.nsplits[axis or 0]
if is... | def tile(cls, op):
a = op.input
repeats = op.repeats
axis = op.axis
ax = axis or 0
out = op.outputs[0]
check_chunks_unknown_shape(op.inputs, TilesError)
if isinstance(repeats, TENSOR_TYPE):
a, repeats = unify_chunks(a, (repeats, (ax,)))
nsplit = a.nsplits[axis or 0]
if is... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def _tile_via_shuffle(cls, op):
# rechunk the axes except the axis to do unique into 1 chunk
inp = op.inputs[0]
if inp.ndim > 1:
new_chunk_size = dict()
for axis in range(inp.ndim):
if axis == op.axis:
continue
if np.isnan(inp.shape[axis]):
... | def _tile_via_shuffle(cls, op):
# rechunk the axes except the axis to do unique into 1 chunk
inp = op.inputs[0]
if inp.ndim > 1:
new_chunk_size = dict()
for axis in range(inp.ndim):
if axis == op.axis:
continue
if np.isnan(inp.shape[axis]):
... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def tile(cls, op):
if op.inputs:
check_chunks_unknown_shape(op.inputs, TilesError)
tensor = op.outputs[0]
# op can be TensorDiag or TensorEye
k = op.k
nsplits = op._get_nsplits(op)
fx = lambda x, y: x - y + k
cum_size = [np.cumsum(s).tolist() for s in nsplits]
out_chunks = []
... | def tile(cls, op):
if op.inputs:
check_chunks_unknown_shape(op.inputs, TilesError)
tensor = op.outputs[0]
# op can be TensorDiag or TensorEye
k = op.k
nsplits = op._get_nsplits(op)
fx = lambda x, y: x - y + k
cum_size = [np.cumsum(s) for s in nsplits]
out_chunks = []
for ou... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def tile(cls, op):
tensor = op.outputs[0]
v = op.input
k = op.k
idx = itertools.count(0)
if v.ndim == 2:
check_chunks_unknown_shape(op.inputs, TilesError)
chunks = []
nsplit = []
fx = lambda x, y: x - y + k
in_nsplits = v.nsplits
cum_size = [np.cumsu... | def tile(cls, op):
tensor = op.outputs[0]
v = op.input
k = op.k
idx = itertools.count(0)
if v.ndim == 2:
check_chunks_unknown_shape(op.inputs, TilesError)
chunks = []
nsplit = []
fx = lambda x, y: x - y + k
in_nsplits = v.nsplits
cum_size = [np.cumsu... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def fromtiledb(uri, ctx=None, key=None, timestamp=None, gpu=False):
import tiledb
raw_ctx = ctx
if raw_ctx is None:
ctx = tiledb.Ctx()
# get metadata from tiledb
try:
tiledb_arr = tiledb.DenseArray(uri=uri, ctx=ctx, key=key, timestamp=timestamp)
sparse = False
except Va... | def fromtiledb(uri, ctx=None, key=None, timestamp=None, gpu=False):
import tiledb
raw_ctx = ctx
if raw_ctx is None:
ctx = tiledb.Ctx()
# get metadata from tiledb
try:
tiledb_arr = tiledb.DenseArray(uri=uri, ctx=ctx, key=key, timestamp=timestamp)
sparse = False
except Va... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def tile(cls, op):
check_chunks_unknown_shape(op.inputs, TilesError)
tensor = op.outputs[0]
m = op.input
k = op.k
is_triu = type(op) == TensorTriu
fx = lambda x, y: x - y + k
nsplits = m.nsplits
cum_size = [np.cumsum(s).tolist() for s in nsplits]
out_chunks = []
for out_idx in... | def tile(cls, op):
check_chunks_unknown_shape(op.inputs, TilesError)
tensor = op.outputs[0]
m = op.input
k = op.k
is_triu = type(op) == TensorTriu
fx = lambda x, y: x - y + k
nsplits = m.nsplits
cum_size = [np.cumsum(s) for s in nsplits]
out_chunks = []
for out_idx in itertool... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def calc_shape(tensor_shape, index):
shape = []
in_axis = 0
out_axis = 0
fancy_index = None
fancy_index_shapes = []
for ind in index:
if (
isinstance(ind, TENSOR_TYPE + TENSOR_CHUNK_TYPE + (np.ndarray,))
and ind.dtype == np.bool_
):
# bool
... | def calc_shape(tensor_shape, index):
shape = []
in_axis = 0
out_axis = 0
fancy_index = None
fancy_index_shapes = []
for ind in index:
if (
isinstance(ind, TENSOR_TYPE + TENSOR_CHUNK_TYPE + (np.ndarray,))
and ind.dtype == np.bool_
):
# bool
... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def process(self, index_info: IndexInfo, context: IndexHandlerContext) -> None:
tileable = context.tileable
input_axis = index_info.input_axis
is_first_bool_index = self._is_first_bool_index(context, index_info)
axes = list(range(input_axis, input_axis + index_info.raw_index.ndim))
cum_sizes = []
... | def process(self, index_info: IndexInfo, context: IndexHandlerContext) -> None:
tileable = context.tileable
input_axis = index_info.input_axis
is_first_bool_index = self._is_first_bool_index(context, index_info)
axes = list(range(input_axis, input_axis + index_info.raw_index.ndim))
cum_sizes = []
... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def tile(cls, op):
from ..merge.concatenate import TensorConcatenate
from ..indexing.slice import TensorSlice
from .dot import TensorDot
from .qr import TensorQR
from .svd import TensorSVD
calc_svd = getattr(op, "_is_svd", lambda: None)() or False
a = op.input
tinyq, tinyr = np.linalg... | def tile(cls, op):
from ..merge.concatenate import TensorConcatenate
from ..indexing.slice import TensorSlice
from .dot import TensorDot
from .qr import TensorQR
from .svd import TensorSVD
calc_svd = getattr(op, "_is_svd", lambda: None)() or False
a = op.input
tinyq, tinyr = np.linalg... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def _handle_size(cls, size):
if size is None:
return size
try:
return tuple(int(s) for s in size)
except TypeError:
return (size,)
| def _handle_size(cls, size):
if size is None:
return size
try:
return tuple(size)
except TypeError:
return (size,)
| https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def tile(cls, op):
tensor = op.outputs[0]
chunk_size = tensor.extra_params.raw_chunk_size or options.chunk_size
nsplits = decide_chunk_sizes(tensor.shape, chunk_size, tensor.dtype.itemsize)
fields = getattr(op, "_input_fields_", [])
to_one_chunk_fields = set(getattr(op, "_into_one_chunk_fields_", li... | def tile(cls, op):
tensor = op.outputs[0]
chunk_size = tensor.extra_params.raw_chunk_size or options.chunk_size
nsplits = decide_chunk_sizes(tensor.shape, chunk_size, tensor.dtype.itemsize)
fields = getattr(op, "_input_fields_", [])
to_one_chunk_fields = set(getattr(op, "_into_one_chunk_fields_", li... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def _gen_reshape_rechunk_nsplits(old_shape, new_shape, nsplits):
old_idx = len(old_shape) - 1
new_idx = len(new_shape) - 1
rechunk_nsplists = [None for _ in old_shape]
reshape_nsplists = [None for _ in new_shape]
while old_idx >= 0 or new_idx >= 0:
old_dim_size = old_shape[old_idx]
... | def _gen_reshape_rechunk_nsplits(old_shape, new_shape, nsplits):
old_idx = len(old_shape) - 1
new_idx = len(new_shape) - 1
rechunk_nsplists = [None for _ in old_shape]
reshape_nsplists = [None for _ in new_shape]
while old_idx >= 0 or new_idx >= 0:
old_dim_size = old_shape[old_idx]
... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def _tile_chunks(cls, op, in_tensor, w, v, vi):
out_tensor = op.outputs[0]
extra_inputs = []
for val in [w, v, vi]:
if val is not None:
extra_inputs.append(val.chunks[0])
n = in_tensor.shape[0]
aggregate_size = op.aggregate_size
if aggregate_size is None:
aggregate_s... | def _tile_chunks(cls, op, in_tensor, w, v, vi):
out_tensor = op.outputs[0]
extra_inputs = []
for val in [w, v, vi]:
if val is not None:
extra_inputs.append(val.chunks[0])
n = in_tensor.shape[0]
aggregate_size = op.aggregate_size
if aggregate_size is None:
aggregate_s... | https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def gen_random_seeds(n, random_state):
assert isinstance(random_state, np.random.RandomState)
return tuple(np.frombuffer(random_state.bytes(n * 4), dtype=np.uint32).tolist())
| def gen_random_seeds(n, random_state):
assert isinstance(random_state, np.random.RandomState)
return np.frombuffer(random_state.bytes(n * 4), dtype=np.uint32)
| https://github.com/mars-project/mars/issues/1433 | In [3]: import pandas as pd
In [4]: data = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [6]: df = md.DataFrame(data)
In [7]: df.groupby(['a','b']).size().execute()
Unexpected exception occurred in enter_build_mode.<locals>.inner.
Traceback (most recent call last):
File "/User... | AttributeError |
def _infer_df_func_returns(self, in_dtypes, dtypes):
if self.output_types[0] == OutputType.dataframe:
empty_df = build_empty_df(in_dtypes, index=pd.RangeIndex(2))
try:
with np.errstate(all="ignore"):
if self.call_agg:
infer_df = empty_df.agg(
... | def _infer_df_func_returns(self, in_dtypes, dtypes):
if self.output_types[0] == OutputType.dataframe:
empty_df = build_empty_df(in_dtypes, index=pd.RangeIndex(2))
with np.errstate(all="ignore"):
if self.call_agg:
infer_df = empty_df.agg(
self._func, ax... | https://github.com/mars-project/mars/issues/1423 | In [1]: import pandas as pd
...: import mars.dataframe as md
...: mdf = md.Series(pd.Series(list('abc')))
...: mdf.transform(lambda x: x + 's').execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/miniconda3/l... | TypeError |
def get_chunk_metas(self, chunk_keys, filter_fields=None):
metas = []
for chunk_key in chunk_keys:
chunk_data = self.get(chunk_key)
if chunk_data is None:
metas.append(None)
continue
if hasattr(chunk_data, "nbytes"):
# ndarray
size = chunk_... | def get_chunk_metas(self, chunk_keys, filter_fields=None):
if filter_fields is not None: # pragma: no cover
raise NotImplementedError("Local context doesn't support filter fields now")
metas = []
for chunk_key in chunk_keys:
chunk_data = self.get(chunk_key)
if chunk_data is None:
... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def __init__(self, scheduler_address, session_id, actor_ctx=None, **kw):
from .worker.api import WorkerAPI
from .scheduler.resource import ResourceActor
from .scheduler.utils import SchedulerClusterInfoActor
from .actors import new_client
self._session_id = session_id
self._scheduler_address = ... | def __init__(self, scheduler_address, session_id, actor_ctx=None, **kw):
from .worker.api import WorkerAPI
from .scheduler.api import MetaAPI
from .scheduler.resource import ResourceActor
from .scheduler.utils import SchedulerClusterInfoActor
from .actors import new_client
self._session_id = se... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def get_tileable_metas(self, tileable_keys, filter_fields: List[str] = None) -> List:
return self.meta_api.get_tileable_metas(
self._session_id, tileable_keys, filter_fields
)
| def get_tileable_metas(self, tileable_keys, filter_fields: List[str] = None) -> List:
return self._meta_api.get_tileable_metas(
self._session_id, tileable_keys, filter_fields
)
| https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def get_chunk_metas(self, chunk_keys, filter_fields: List[str] = None) -> List:
return self.meta_api.get_chunk_metas(self._session_id, chunk_keys, filter_fields)
| def get_chunk_metas(self, chunk_keys, filter_fields: List[str] = None) -> List:
return self._meta_api.get_chunk_metas(self._session_id, chunk_keys, filter_fields)
| https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def get_named_tileable_infos(self, name: str):
tileable_key = self.meta_api.get_tileable_key_by_name(self._session_id, name)
nsplits = self.get_tileable_metas([tileable_key], filter_fields=["nsplits"])[0][0]
shape = tuple(sum(s) for s in nsplits)
return TileableInfos(tileable_key, shape)
| def get_named_tileable_infos(self, name: str):
tileable_key = self._meta_api.get_tileable_key_by_name(self._session_id, name)
nsplits = self.get_tileable_metas([tileable_key], filter_fields=["nsplits"])[0][0]
shape = tuple(sum(s) for s in nsplits)
return TileableInfos(tileable_key, shape)
| https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
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, Cache... | def _install():
from ..core import DATAFRAME_TYPE, SERIES_TYPE, INDEX_TYPE
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",... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def tile(cls, op: "DataFrameGroupByAgg"):
if op.method == "auto":
ctx = get_context()
if (
ctx is not None and ctx.running_mode == RunningMode.distributed
): # pragma: no cover
return cls._tile_with_shuffle(op)
else:
return cls._tile_with_tree(op)... | def tile(cls, op: "DataFrameGroupByAgg"):
if op.method == "shuffle":
return cls._tile_with_shuffle(op)
elif op.method == "tree":
return cls._tile_with_tree(op)
else: # pragma: no cover
raise NotImplementedError
| https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
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 aggreg... | def agg(groupby, func, method="tree", *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 aggreg... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
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)
n = op.n_partition
if a.shape[op.axis] < n:
num = n // a.shape[op.... | def execute(cls, ctx, op):
a = ctx[op.inputs[0].key]
n = op.n_partition
w = int(a.shape[op.axis] // n)
slc = (slice(None),) * op.axis + (slice(0, n * w, w),)
if op.sort_type == "sort_values":
ctx[op.outputs[0].key] = res = execute_sort_values(a, op)
# do regular sample
if o... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
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 = [... | 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 = [... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def get_dependent_data_keys(self):
return [dep.key for dep in self.inputs or ()]
| def get_dependent_data_keys(self):
return [
dep.key
for dep, has_dep in zip(self.inputs or (), self.prepare_inputs)
if has_dep
]
| https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def get_dependent_data_keys(self):
if self.stage == OperandStage.reduce:
inputs = self.inputs or ()
deps = []
for inp in inputs:
if isinstance(inp.op, (ShuffleProxy, FetchShuffle)):
deps.extend(
[(chunk.key, self._shuffle_key) for chunk in inp.... | def get_dependent_data_keys(self):
if self.stage == OperandStage.reduce:
inputs = self.inputs or ()
return [
(chunk.key, self._shuffle_key)
for proxy in inputs
for chunk in proxy.inputs or ()
]
return super().get_dependent_data_keys()
| https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def execute(cls, ctx, op: "RemoteFunction"):
from ..session import Session
session = ctx.get_current_session()
prev_default_session = Session.default
mapping = {
inp: ctx[inp.key]
for inp, prepare_inp in zip(op.inputs, op.prepare_inputs)
if prepare_inp
}
for to_search i... | def execute(cls, ctx, op: "RemoteFunction"):
from ..session import Session
session = ctx.get_current_session()
prev_default_session = Session.default
mapping = {
inp: ctx[inp.key]
for inp, prepare_inp in zip(op.inputs, op.prepare_inputs)
if prepare_inp
}
for to_search i... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def preprocess(cls, op, in_data=None):
if in_data is None:
in_data = op.inputs[0]
axis_shape = in_data.shape[op.axis]
axis_chunk_shape = in_data.chunk_shape[op.axis]
# rechunk to ensure all chunks on axis have rough same size
has_unknown_shape = False
for ns in in_data.nsplits:
... | def preprocess(cls, op, in_data=None):
in_data = in_data or op.inputs[0]
axis_shape = in_data.shape[op.axis]
axis_chunk_shape = in_data.chunk_shape[op.axis]
# rechunk to ensure all chunks on axis have rough same size
axis_chunk_shape = min(axis_chunk_shape, int(np.sqrt(axis_shape)))
if np.isnan... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def plan_rechunks(
tileable, new_chunk_size, itemsize, threshold=None, chunk_size_limit=None
):
threshold = threshold or options.rechunk.threshold
chunk_size_limit = chunk_size_limit or options.rechunk.chunk_size_limit
if len(new_chunk_size) != tileable.ndim:
raise ValueError(
"Prov... | def plan_rechunks(
tileable, new_chunk_size, itemsize, threshold=None, chunk_size_limit=None
):
threshold = threshold or options.rechunk.threshold
chunk_size_limit = chunk_size_limit or options.rechunk.chunk_size_limit
if len(new_chunk_size) != tileable.ndim:
raise ValueError(
"Prov... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def is_object_dtype(dtype):
try:
return (
np.issubdtype(dtype, np.object_)
or np.issubdtype(dtype, np.unicode_)
or np.issubdtype(dtype, np.bytes_)
)
except TypeError: # pragma: no cover
return False
| def is_object_dtype(dtype):
return (
np.issubdtype(dtype, np.object_)
or np.issubdtype(dtype, np.unicode_)
or np.issubdtype(dtype, np.bytes_)
)
| https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
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... | 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()
local_context_dict = DistributedDictContext(
self.get_scheduler(sel... | https://github.com/mars-project/mars/issues/1413 | In [25]: merge_df = parsing_df.append(pFold_4_df)
In [26]: mDf_g = merge_df.groupby(["query","template"])
In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/py37/lib/... | AttributeError |
def _get_selectable(self, engine_or_conn, columns=None):
import sqlalchemy as sa
from sqlalchemy import sql
from sqlalchemy.exc import NoSuchTableError
# process table_name
if self._selectable is not None:
selectable = self._selectable
else:
if isinstance(self._table_or_sql, sa.... | def _get_selectable(self, engine_or_conn, columns=None):
import sqlalchemy as sa
from sqlalchemy import sql
from sqlalchemy.exc import NoSuchTableError
# process table_name
if self._selectable is not None:
selectable = self._selectable
else:
if isinstance(self._table_or_sql, sa.... | https://github.com/mars-project/mars/issues/1415 | Traceback (most recent call last):
File "D:\Anaconda3\envs\py37\lib\site-packages\sqlalchemy\engine\base.py", line 1249, in _execute_context
cursor, statement, parameters, context
File "D:\Anaconda3\envs\py37\lib\site-packages\sqlalchemy\engine\default.py", line 580, in do_execute
cursor.execute(statement, parameters)
... | mysql.connector.errors.ProgrammingError |
def estimate_size(cls, ctx, op):
from .dataframe.core import (
DATAFRAME_CHUNK_TYPE,
SERIES_CHUNK_TYPE,
INDEX_CHUNK_TYPE,
)
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
)... | 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)
for inp in op.inputs or ():
t... | https://github.com/mars-project/mars/issues/1415 | Traceback (most recent call last):
File "D:\Anaconda3\envs\py37\lib\site-packages\sqlalchemy\engine\base.py", line 1249, in _execute_context
cursor, statement, parameters, context
File "D:\Anaconda3\envs\py37\lib\site-packages\sqlalchemy\engine\default.py", line 580, in do_execute
cursor.execute(statement, parameters)
... | mysql.connector.errors.ProgrammingError |
def _estimate_calc_memory(self, session_id, graph_key):
graph_record = self._graph_records[(session_id, graph_key)]
data_metas = graph_record.data_metas
size_ctx = dict((k, (v.chunk_size, v.chunk_size)) for k, v in data_metas.items())
# update shapes
for n in graph_record.graph:
if isinstan... | def _estimate_calc_memory(self, session_id, graph_key):
graph_record = self._graph_records[(session_id, graph_key)]
size_ctx = dict(
(k, (v.chunk_size, v.chunk_size)) for k, v in graph_record.data_metas.items()
)
executor = Executor(
storage=size_ctx, sync_provider_type=Executor.SyncProv... | https://github.com/mars-project/mars/issues/1415 | Traceback (most recent call last):
File "D:\Anaconda3\envs\py37\lib\site-packages\sqlalchemy\engine\base.py", line 1249, in _execute_context
cursor, statement, parameters, context
File "D:\Anaconda3\envs\py37\lib\site-packages\sqlalchemy\engine\default.py", line 580, in do_execute
cursor.execute(statement, parameters)
... | mysql.connector.errors.ProgrammingError |
def tile(cls, op: "LGBMTrain"):
ctx = get_context()
if ctx.running_mode != RunningMode.distributed:
assert all(len(inp.chunks) == 1 for inp in op.inputs)
chunk_op = op.copy().reset_key()
out_chunk = chunk_op.new_chunk(
[inp.chunks[0] for inp in op.inputs], shape=(1,), index=... | def tile(cls, op: "LGBMTrain"):
ctx = get_context()
if ctx.running_mode != RunningMode.distributed:
assert all(len(inp.chunks) == 1 for inp in op.inputs)
chunk_op = op.copy().reset_key()
out_chunk = chunk_op.new_chunk(
[inp.chunks[0] for inp in op.inputs], shape=(1,), index=... | https://github.com/mars-project/mars/issues/1404 | Error
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/scheduler/graph.py", line 395, in _execute_graph
self.prepare_graph(compose=compose)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 353, in _wrapped
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line... | TypeError |
def tile(cls, op: "LGBMAlign"):
inputs = [
d for d in [op.data, op.label, op.sample_weight, op.init_score] if d is not None
]
data = op.data
# check inputs to make sure no unknown chunk shape exists
check_chunks_unknown_shape(inputs, TilesError)
ctx = get_context()
if ctx.running_m... | def tile(cls, op: "LGBMAlign"):
inputs = [
d for d in [op.data, op.label, op.sample_weight, op.init_score] if d is not None
]
data = op.data
ctx = get_context()
if ctx.running_mode != RunningMode.distributed:
outputs = [
inp.rechunk(tuple((s,) for s in inp.shape))._inpla... | https://github.com/mars-project/mars/issues/1395 | Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.4.2.zip/mars/scheduler/graph.py", line 371, in _execute_graph
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 343, in _wrapped
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 483, in inner
File "/home/admin/wo... | ValueError |
def fit(
self,
X,
y,
sample_weight=None,
init_score=None,
eval_set=None,
eval_sample_weight=None,
eval_init_score=None,
session=None,
run_kwargs=None,
**kwargs,
):
check_consistent_length(X, y, session=session, run_kwargs=run_kwargs)
params = self.get_params(True)
... | def fit(
self,
X,
y,
sample_weight=None,
init_score=None,
eval_set=None,
eval_sample_weight=None,
eval_init_score=None,
**kwargs,
):
params = self.get_params(True)
model = train(
params,
self._wrap_train_tuple(X, y, sample_weight, init_score),
eval_set... | https://github.com/mars-project/mars/issues/1395 | Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.4.2.zip/mars/scheduler/graph.py", line 371, in _execute_graph
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 343, in _wrapped
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 483, in inner
File "/home/admin/wo... | ValueError |
def fit(
self,
X,
y,
sample_weight=None,
init_score=None,
group=None,
eval_set=None,
eval_sample_weight=None,
eval_init_score=None,
session=None,
run_kwargs=None,
**kwargs,
):
check_consistent_length(X, y, session=session, run_kwargs=run_kwargs)
params = self.get_... | def fit(
self,
X,
y,
sample_weight=None,
init_score=None,
group=None,
eval_set=None,
eval_sample_weight=None,
eval_init_score=None,
**kwargs,
):
params = self.get_params(True)
model = train(
params,
self._wrap_train_tuple(X, y, sample_weight, init_score),
... | https://github.com/mars-project/mars/issues/1395 | Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.4.2.zip/mars/scheduler/graph.py", line 371, in _execute_graph
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 343, in _wrapped
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 483, in inner
File "/home/admin/wo... | ValueError |
def fit(
self,
X,
y,
sample_weight=None,
init_score=None,
eval_set=None,
eval_sample_weight=None,
eval_init_score=None,
session=None,
run_kwargs=None,
**kwargs,
):
check_consistent_length(X, y, session=session, run_kwargs=run_kwargs)
params = self.get_params(True)
... | def fit(
self,
X,
y,
sample_weight=None,
init_score=None,
eval_set=None,
eval_sample_weight=None,
eval_init_score=None,
**kwargs,
):
params = self.get_params(True)
model = train(
params,
self._wrap_train_tuple(X, y, sample_weight, init_score),
eval_set... | https://github.com/mars-project/mars/issues/1395 | Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.4.2.zip/mars/scheduler/graph.py", line 371, in _execute_graph
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 343, in _wrapped
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 483, in inner
File "/home/admin/wo... | ValueError |
def train(params, train_set, eval_sets=None, **kwargs):
eval_sets = eval_sets or []
model_type = kwargs.pop("model_type", LGBMModelType.CLASSIFIER)
evals_result = kwargs.pop("evals_result", dict())
session = kwargs.pop("session", None)
run_kwargs = kwargs.pop("run_kwargs", None)
if run_kwargs i... | def train(params, train_set, eval_sets=None, **kwargs):
eval_sets = eval_sets or []
model_type = kwargs.pop("model_type", LGBMModelType.CLASSIFIER)
evals_result = kwargs.pop("evals_result", dict())
session = kwargs.pop("session", None)
run_kwargs = kwargs.pop("run_kwargs", dict())
timeout = kwa... | https://github.com/mars-project/mars/issues/1395 | Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.4.2.zip/mars/scheduler/graph.py", line 371, in _execute_graph
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 343, in _wrapped
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 483, in inner
File "/home/admin/wo... | ValueError |
def _execute_graph(self, compose=True):
try:
self.prepare_graph(compose=compose)
self._detect_cancel()
self._dump_graph()
self.analyze_graph()
self._detect_cancel()
if self.state == GraphState.SUCCEEDED:
self._graph_meta_ref.set_graph_end(_tell=True, _w... | def _execute_graph(self, compose=True):
try:
self.prepare_graph(compose=compose)
self._detect_cancel()
self._dump_graph()
self.analyze_graph()
self._detect_cancel()
self.create_operand_actors()
self._detect_cancel(self.stop_graph)
except ExecutionInterr... | https://github.com/mars-project/mars/issues/1395 | Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.4.2.zip/mars/scheduler/graph.py", line 371, in _execute_graph
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 343, in _wrapped
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 483, in inner
File "/home/admin/wo... | ValueError |
def analyze_graph(self, **kwargs):
operand_infos = self._operand_infos
chunk_graph = self.get_chunk_graph()
# remove fetch chunk if exists
if any(isinstance(c.op, Fetch) for c in chunk_graph):
chunk_graph = chunk_graph.copy()
for c in list(chunk_graph):
if isinstance(c.op, F... | def analyze_graph(self, **kwargs):
operand_infos = self._operand_infos
chunk_graph = self.get_chunk_graph()
# remove fetch chunk if exists
if any(isinstance(c.op, Fetch) for c in chunk_graph):
chunk_graph = chunk_graph.copy()
for c in list(chunk_graph):
if isinstance(c.op, F... | https://github.com/mars-project/mars/issues/1395 | Traceback (most recent call last):
File "/home/admin/work/_public-mars-0.4.2.zip/mars/scheduler/graph.py", line 371, in _execute_graph
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 343, in _wrapped
File "/home/admin/work/_public-mars-0.4.2.zip/mars/utils.py", line 483, in inner
File "/home/admin/wo... | ValueError |
def _calc_chunk_params(
cls, in_chunk, axes, chunk_shape, output, output_type, chunk_op, no_shuffle: bool
):
params = {"index": in_chunk.index}
if output_type == OutputType.tensor:
shape_c = list(in_chunk.shape)
for ax in axes:
if not no_shuffle and chunk_shape[ax] > 1:
... | def _calc_chunk_params(
cls, in_chunk, axes, chunk_shape, output, output_type, chunk_op, no_shuffle: bool
):
params = {"index": in_chunk.index}
if output_type == OutputType.tensor:
shape_c = list(in_chunk.shape)
for ax in axes:
if not no_shuffle and chunk_shape[ax] > 1:
... | https://github.com/mars-project/mars/issues/1393 | In [1]: import mars.dataframe as md
In [2]: from mars.deploy.local import new_cluster
In [3]: cluster = new_cluster()
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0710 12:01:39.413233 286952896 store.cc:1149] Allowing the Plasma store to use up to 3.43597GB of memory.
I0710 12:01:39.414255 286952... | ValueError |
def __getstate__(self):
fetch_op = self.tileable.op
fetch_tileable = self.tileable
chunk_infos = [
(type(c.op), c.op.output_types, c.key, c.id, c.params)
for c in fetch_tileable.chunks
]
return (
type(fetch_op),
fetch_op.id,
fetch_op.output_types,
fetc... | def __getstate__(self):
fetch_op = self.tileable.op
fetch_tileable = self.tileable
chunk_infos = [(type(c.op), c.key, c.id, c.params) for c in fetch_tileable.chunks]
return (
type(fetch_op),
fetch_op.id,
fetch_tileable.params,
fetch_tileable.nsplits,
chunk_infos,
... | https://github.com/mars-project/mars/issues/1393 | In [1]: import mars.dataframe as md
In [2]: from mars.deploy.local import new_cluster
In [3]: cluster = new_cluster()
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0710 12:01:39.413233 286952896 store.cc:1149] Allowing the Plasma store to use up to 3.43597GB of memory.
I0710 12:01:39.414255 286952... | ValueError |
def __setstate__(self, state):
fetch_op_type, fetch_op_id, output_types, params, nsplits, chunk_infos = state
params["nsplits"] = nsplits
chunks = []
for ci in chunk_infos:
chunk_op_type, chunk_op_output_types, chunk_key, chunk_id, chunk_params = ci
chunk = chunk_op_type(output_types=chu... | def __setstate__(self, state):
fetch_op_type, fetch_op_id, params, nsplits, chunk_infos = state
params["nsplits"] = nsplits
chunks = []
for ci in chunk_infos:
chunk = ci[0]().new_chunk(None, _key=ci[1], _id=ci[2], kws=[ci[3]])
chunks.append(chunk)
params["chunks"] = chunks
self.t... | https://github.com/mars-project/mars/issues/1393 | In [1]: import mars.dataframe as md
In [2]: from mars.deploy.local import new_cluster
In [3]: cluster = new_cluster()
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0710 12:01:39.413233 286952896 store.cc:1149] Allowing the Plasma store to use up to 3.43597GB of memory.
I0710 12:01:39.414255 286952... | ValueError |
def spawn(func, args=(), kwargs=None, retry_when_fail=False, n_output=None):
"""
Spawn a function and return a Mars Object which can be executed later.
Parameters
----------
func : function
Function to spawn.
args: tuple
Args to pass to function
kwargs: dict
Kwargs to ... | def spawn(func, args=(), kwargs=None, retry_when_fail=True, n_output=None):
"""
Spawn a function and return a Mars Object which can be executed later.
Parameters
----------
func : function
Function to spawn.
args: tuple
Args to pass to function
kwargs: dict
Kwargs to p... | https://github.com/mars-project/mars/issues/1393 | In [1]: import mars.dataframe as md
In [2]: from mars.deploy.local import new_cluster
In [3]: cluster = new_cluster()
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0710 12:01:39.413233 286952896 store.cc:1149] Allowing the Plasma store to use up to 3.43597GB of memory.
I0710 12:01:39.414255 286952... | ValueError |
def _collect_info(self, engine_or_conn, selectable, columns, test_rows):
from sqlalchemy import sql
# fetch test DataFrame
if columns:
query = sql.select([sql.column(c) for c in columns], from_obj=selectable).limit(
test_rows
)
else:
query = sql.select("*", from_obj=... | def _collect_info(self, engine_or_conn, selectable, columns, test_rows):
from sqlalchemy import sql
# fetch test DataFrame
if columns:
query = sql.select([sql.column(c) for c in columns], from_obj=selectable).limit(
test_rows
)
else:
query = sql.select("*", from_obj=... | https://github.com/mars-project/mars/issues/1368 | In [1]: import mars.dataframe as md
In [7]: import sqlalchemy as sa
In [9]: con = sa.create_engine('sqlite:///database.sqlite', echo=False)
In [10]: df = md.read_sql('loan', con)
In [11]: df.head().execute()
Out[11]:
id member_id loan_amnt funded_amnt funded_amnt_inv term int_rate installment grade sub_grade... | OverflowError |
def _tile_offset(cls, op: "DataFrameReadSQL"):
df = op.outputs[0]
if op.row_memory_usage is not None:
# Data selected
chunk_size = df.extra_params.raw_chunk_size or options.chunk_size
if chunk_size is None:
chunk_size = (
int(options.chunk_store_limit / op.ro... | def _tile_offset(cls, op: "DataFrameReadSQL"):
df = op.outputs[0]
chunk_size = df.extra_params.raw_chunk_size or options.chunk_size
if chunk_size is None:
chunk_size = (int(options.chunk_store_limit / op.row_memory_usage), df.shape[1])
row_chunk_sizes = normalize_chunk_sizes(df.shape, chunk_siz... | https://github.com/mars-project/mars/issues/1368 | In [1]: import mars.dataframe as md
In [7]: import sqlalchemy as sa
In [9]: con = sa.create_engine('sqlite:///database.sqlite', echo=False)
In [10]: df = md.read_sql('loan', con)
In [11]: df.head().execute()
Out[11]:
id member_id loan_amnt funded_amnt funded_amnt_inv term int_rate installment grade sub_grade... | OverflowError |
def analyze_graph(self, **kwargs):
operand_infos = self._operand_infos
chunk_graph = self.get_chunk_graph()
# remove fetch chunk if exists
if any(isinstance(c.op, Fetch) for c in chunk_graph):
chunk_graph = chunk_graph.copy()
for c in list(chunk_graph):
if isinstance(c.op, F... | def analyze_graph(self, **kwargs):
operand_infos = self._operand_infos
chunk_graph = self.get_chunk_graph()
# remove fetch chunk if exists
if any(isinstance(c.op, Fetch) for c in chunk_graph):
chunk_graph = chunk_graph.copy()
for c in list(chunk_graph):
if isinstance(c.op, F... | https://github.com/mars-project/mars/issues/1368 | In [1]: import mars.dataframe as md
In [7]: import sqlalchemy as sa
In [9]: con = sa.create_engine('sqlite:///database.sqlite', echo=False)
In [10]: df = md.read_sql('loan', con)
In [11]: df.head().execute()
Out[11]:
id member_id loan_amnt funded_amnt funded_amnt_inv term int_rate installment grade sub_grade... | OverflowError |
def build_empty_df(dtypes, index=None):
columns = dtypes.index
# duplicate column may exist,
# so use RangeIndex first
df = pd.DataFrame(columns=pd.RangeIndex(len(columns)), index=index)
for i, d in enumerate(dtypes):
df[i] = pd.Series(dtype=d, index=index)
df.columns = columns
retur... | def build_empty_df(dtypes, index=None):
columns = dtypes.index
df = pd.DataFrame(columns=columns, index=index)
for c, d in zip(columns, dtypes):
df[c] = pd.Series(dtype=d, index=index)
return df
| https://github.com/mars-project/mars/issues/1312 | KeyError
Traceback (most recent call last)
<ipython-input-73-3d10a0dadb7d> in <module>
5 data = pd.merge(data,data.groupby(['c']).size().reset_index(),on = ['c'],how='left')
6 data = pd.merge(data,data.groupby(['d']).size().reset_index(),on = ['d'],how='left')
----> 7 data = pd.merge(data,data.groupby(['e']).size().res... | KeyError |
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 empty_df.dtypes]
if isinstance(empty_df.index, pd.MultiIndex):
index = tuple(
... | 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 empty_df.dtypes]
if isinstance(empty_df.index, pd.MultiIndex):
index = tuple(
... | https://github.com/mars-project/mars/issues/1312 | KeyError
Traceback (most recent call last)
<ipython-input-73-3d10a0dadb7d> in <module>
5 data = pd.merge(data,data.groupby(['c']).size().reset_index(),on = ['c'],how='left')
6 data = pd.merge(data,data.groupby(['d']).size().reset_index(),on = ['d'],how='left')
----> 7 data = pd.merge(data,data.groupby(['e']).size().res... | KeyError |
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
if len(self._inputs) == 2:
self._lhs = self._inputs[0]
self._rhs = self._inputs[1]
else:
if isinstance(self._lhs, (DATAFRAME_TYPE, SERIES_TYPE)):
self._lhs = self._inputs[0]
elif pd.api.types.is_scalar(sel... | def _set_inputs(self, inputs):
super()._set_inputs(inputs)
if len(self._inputs) == 2:
self._lhs = self._inputs[0]
self._rhs = self._inputs[1]
else:
if isinstance(self._lhs, (DATAFRAME_TYPE, SERIES_TYPE)):
self._lhs = self._inputs[0]
elif np.isscalar(self._lhs):
... | https://github.com/mars-project/mars/issues/1286 | In [25]: df = md.DataFrame(mt.random.rand(10, 3), chunk_size=3)
In [26]: df.sort_values(0).reset_index(drop=True).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-e0c111d55eb4> in <module>... | TypeError |
def _tile_scalar(cls, op):
tileable = op.rhs if pd.api.types.is_scalar(op.lhs) else op.lhs
df = op.outputs[0]
out_chunks = []
for chunk in tileable.chunks:
out_op = op.copy().reset_key()
if isinstance(chunk, DATAFRAME_CHUNK_TYPE):
out_chunk = out_op.new_chunk(
... | def _tile_scalar(cls, op):
tileable = op.rhs if np.isscalar(op.lhs) else op.lhs
df = op.outputs[0]
out_chunks = []
for chunk in tileable.chunks:
out_op = op.copy().reset_key()
if isinstance(chunk, DATAFRAME_CHUNK_TYPE):
out_chunk = out_op.new_chunk(
[chunk],
... | https://github.com/mars-project/mars/issues/1286 | In [25]: df = md.DataFrame(mt.random.rand(10, 3), chunk_size=3)
In [26]: df.sort_values(0).reset_index(drop=True).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-e0c111d55eb4> in <module>... | TypeError |
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, ... | 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, ... | https://github.com/mars-project/mars/issues/1286 | In [25]: df = md.DataFrame(mt.random.rand(10, 3), chunk_size=3)
In [26]: df.sort_values(0).reset_index(drop=True).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-e0c111d55eb4> in <module>... | 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):
dt... | def _calc_properties(cls, x1, x2=None, axis="columns"):
if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and (
x2 is None or np.isscalar(x2) or isinstance(x2, TENSOR_TYPE)
):
if x2 is None:
dtypes = x1.dtypes
elif np.isscalar(x2):
dtypes = infer_dtypes(
... | https://github.com/mars-project/mars/issues/1286 | In [25]: df = md.DataFrame(mt.random.rand(10, 3), chunk_size=3)
In [26]: df.sort_values(0).reset_index(drop=True).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-e0c111d55eb4> in <module>... | TypeError |
def _process_input(x):
if isinstance(x, (DATAFRAME_TYPE, SERIES_TYPE)) or pd.api.types.is_scalar(x):
return x
elif isinstance(x, pd.Series):
return Series(x)
elif isinstance(x, pd.DataFrame):
return DataFrame(x)
elif isinstance(x, (list, tuple, np.ndarray, TENSOR_TYPE)):
... | def _process_input(x):
if isinstance(x, (DATAFRAME_TYPE, SERIES_TYPE)) or np.isscalar(x):
return x
elif isinstance(x, pd.Series):
return Series(x)
elif isinstance(x, pd.DataFrame):
return DataFrame(x)
elif isinstance(x, (list, tuple, np.ndarray, TENSOR_TYPE)):
return aste... | https://github.com/mars-project/mars/issues/1286 | In [25]: df = md.DataFrame(mt.random.rand(10, 3), chunk_size=3)
In [26]: df.sort_values(0).reset_index(drop=True).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-e0c111d55eb4> in <module>... | TypeError |
def _call(self, x1, x2):
self._check_inputs(x1, x2)
if isinstance(x1, DATAFRAME_TYPE) or isinstance(x2, DATAFRAME_TYPE):
df1, df2 = (x1, x2) if isinstance(x1, DATAFRAME_TYPE) else (x2, x1)
setattr(self, "_object_type", ObjectType.dataframe)
kw = self._calc_properties(df1, df2, axis=self.... | def _call(self, x1, x2):
self._check_inputs(x1, x2)
if isinstance(x1, DATAFRAME_TYPE) or isinstance(x2, DATAFRAME_TYPE):
df1, df2 = (x1, x2) if isinstance(x1, DATAFRAME_TYPE) else (x2, x1)
setattr(self, "_object_type", ObjectType.dataframe)
kw = self._calc_properties(df1, df2, axis=self.... | https://github.com/mars-project/mars/issues/1286 | In [25]: df = md.DataFrame(mt.random.rand(10, 3), chunk_size=3)
In [26]: df.sort_values(0).reset_index(drop=True).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-e0c111d55eb4> in <module>... | TypeError |
def _tile_dataframe(cls, op):
in_df = op.inputs[0]
out_df = op.outputs[0]
added_columns_num = len(out_df.dtypes) - len(in_df.dtypes)
out_chunks = []
index_has_value = out_df.index_value.has_value()
chunk_has_nan = any(np.isnan(s) for s in in_df.nsplits[0])
cum_range = np.cumsum((0,) + in_df.... | def _tile_dataframe(cls, op):
in_df = op.inputs[0]
out_df = op.outputs[0]
added_columns_num = len(out_df.dtypes) - len(in_df.dtypes)
out_chunks = []
is_range_index = out_df.index_value.has_value()
cum_range = np.cumsum((0,) + in_df.nsplits[0])
for c in in_df.chunks:
if is_range_index... | https://github.com/mars-project/mars/issues/1286 | In [25]: df = md.DataFrame(mt.random.rand(10, 3), chunk_size=3)
In [26]: df.sort_values(0).reset_index(drop=True).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-e0c111d55eb4> in <module>... | TypeError |
def _call_dataframe(self, a):
if self.drop:
shape = a.shape
columns_value = a.columns_value
dtypes = a.dtypes
range_value = -1 if np.isnan(a.shape[0]) else a.shape[0]
index_value = parse_index(pd.RangeIndex(range_value))
else:
empty_df = build_empty_df(a.dtypes)
... | def _call_dataframe(self, a):
if self.drop:
shape = a.shape
columns_value = a.columns_value
dtypes = a.dtypes
range_value = -1 if np.isnan(a.shape[0]) else a.shape[0]
index_value = parse_index(pd.RangeIndex(range_value))
else:
empty_df = build_empty_df(a.dtypes)
... | https://github.com/mars-project/mars/issues/1286 | In [25]: df = md.DataFrame(mt.random.rand(10, 3), chunk_size=3)
In [26]: df.sort_values(0).reset_index(drop=True).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-e0c111d55eb4> in <module>... | TypeError |
def calc_data_size(dt):
if dt is None:
return 0
if isinstance(dt, tuple):
return sum(calc_data_size(c) for c in dt)
if hasattr(dt, "nbytes"):
return max(sys.getsizeof(dt), dt.nbytes)
if hasattr(dt, "shape") and len(dt.shape) == 0:
return 0
if hasattr(dt, "memory_usa... | def calc_data_size(dt):
if dt is None:
return 0
if isinstance(dt, tuple):
return sum(calc_data_size(c) for c in dt)
if hasattr(dt, "nbytes"):
return max(sys.getsizeof(dt), dt.nbytes)
if hasattr(dt, "shape") and len(dt.shape) == 0:
return 0
if hasattr(dt, "memory_usa... | https://github.com/mars-project/mars/issues/1306 | Attempt 1: Unexpected error AttributeError occurred in executing operand b4e4bc5f7b31094fb234d9ea949251a1 in 0.0.0.0:46150
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/promise.py", line 100, in _wrapped
result = func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/worker/calc.py", l... | AttributeError |
def estimate_graph_finish_time(
self, session_id, graph_key, calc_fetch=True, base_time=None
):
"""
Calc predictions for given chunk graph
"""
session_graph_key = (session_id, graph_key)
if session_graph_key not in self._graph_records:
return
graph_record = self._graph_records[sessio... | def estimate_graph_finish_time(
self, session_id, graph_key, calc_fetch=True, base_time=None
):
"""
Calc predictions for given chunk graph
"""
session_graph_key = (session_id, graph_key)
if session_graph_key not in self._graph_records:
return
graph_record = self._graph_records[sessio... | https://github.com/mars-project/mars/issues/1306 | Attempt 1: Unexpected error AttributeError occurred in executing operand b4e4bc5f7b31094fb234d9ea949251a1 in 0.0.0.0:46150
Traceback (most recent call last):
File "/Users/wenjun.swj/Code/mars/mars/promise.py", line 100, in _wrapped
result = func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/worker/calc.py", l... | AttributeError |
def tile(cls, op):
x = astensor(op.input)
axis = op.axis
ord = op.ord
keepdims = op.keepdims
axis_chunk_shapes = tuple(x.chunk_shape[i] for i in axis)
can_apply_norm = all(s == 1 for s in axis_chunk_shapes)
if can_apply_norm:
axis_set = set(axis)
get_shape = lambda shape: t... | def tile(cls, op):
x = op.input
axis = op.axis
ord = op.ord
keepdims = op.keepdims
axis_chunk_shapes = tuple(x.chunk_shape[i] for i in axis)
can_apply_norm = all(s == 1 for s in axis_chunk_shapes)
if can_apply_norm:
axis_set = set(axis)
get_shape = lambda shape: tuple(
... | https://github.com/mars-project/mars/issues/1301 | In [2]: import mars.tensor as mt
In [3]: t = mt.random.rand(10, 10, chunk_size=5)
In [4]: mt.linalg.norm(t).execute()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-900a2d2bec75> in <module>
---->... | TypeError |
def start(self, event=None, block=False):
self._configure_loop()
self._try_start_web_server()
if not block:
self._server_thread = threading.Thread(target=self._server.io_loop.start)
self._server_thread.daemon = True
self._server_thread.start()
if event:
event.se... | def start(self, event=None, block=False):
self._configure_loop()
self._try_start_web_server()
if not block:
self._server_thread = threading.Thread(target=self._server.io_loop.start)
self._server_thread.daemon = True
self._server_thread.start()
if event:
event.se... | https://github.com/mars-project/mars/issues/1270 | In [1]: import mars.remote as mr
In [2]: from mars.deploy.local import new_cluster
In [3]: c = new_cluster()
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0604 14:30:01.529353 132840896 store.cc:1149] Allowing the Plasma store to use up to 3.43597GB of memory.
I0604 14:30:01.534931 132840896 store... | KeyError |
def stop(self):
try:
destroy_futures = []
for actor in (
self._cpu_calc_actors
+ self._sender_actors
+ self._inproc_holder_actors
+ self._inproc_io_runner_actors
+ self._cuda_calc_actors
+ self._cuda_holder_actors
+ ... | def stop(self):
try:
for actor in (
self._cpu_calc_actors
+ self._sender_actors
+ self._inproc_holder_actors
+ self._inproc_io_runner_actors
+ self._cuda_calc_actors
+ self._cuda_holder_actors
+ self._receiver_actors
... | https://github.com/mars-project/mars/issues/1270 | In [1]: import mars.remote as mr
In [2]: from mars.deploy.local import new_cluster
In [3]: c = new_cluster()
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0604 14:30:01.529353 132840896 store.cc:1149] Allowing the Plasma store to use up to 3.43597GB of memory.
I0604 14:30:01.534931 132840896 store... | KeyError |
def copy_to(self, session_id, data_keys, device_order, ensure=True, pin_token=None):
device_order = self._normalize_devices(device_order)
existing_devs = self._manager_ref.get_data_locations(session_id, data_keys)
data_sizes = self._manager_ref.get_data_sizes(session_id, data_keys)
device_to_keys = def... | def copy_to(self, session_id, data_keys, device_order, ensure=True, pin_token=None):
device_order = self._normalize_devices(device_order)
existing_devs = self._manager_ref.get_data_locations(session_id, data_keys)
data_sizes = self._manager_ref.get_data_sizes(session_id, data_keys)
device_to_keys = def... | https://github.com/mars-project/mars/issues/1270 | In [1]: import mars.remote as mr
In [2]: from mars.deploy.local import new_cluster
In [3]: c = new_cluster()
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0604 14:30:01.529353 132840896 store.cc:1149] Allowing the Plasma store to use up to 3.43597GB of memory.
I0604 14:30:01.534931 132840896 store... | KeyError |
def build_fetch_graph(self, tileable_key):
"""
Convert single tileable node to tiled fetch tileable node and
put into a graph which only contains one tileable node
:param tileable_key: the key of tileable node
"""
tileable = self._get_tileable_by_key(tileable_key)
graph = DAG()
new_tile... | def build_fetch_graph(self, tileable_key):
"""
Convert single tileable node to tiled fetch tileable node and
put into a graph which only contains one tileable node
:param tileable_key: the key of tileable node
"""
tileable = self._get_tileable_by_key(tileable_key)
graph = DAG()
new_tile... | https://github.com/mars-project/mars/issues/1260 | import numpy as np
import mars.tensor as mt
from mars.learn.neighbors import NearestNeighbors
from mars.deploy.local import new_cluster
with new_cluster(scheduler_n_process=2, worker_n_process=2, shared_memory='20M', web=False) as cluster:
rs = np.random.RandomState(0)
raw_X = rs.rand(10, 5)
raw_Y = rs.rand(8, 5)
X = ... | IndexError |
def _start_cluster(endpoint, event, n_process=None, shared_memory=None, **kw):
modules = kw.pop("modules", None) or []
for m in modules:
__import__(m, globals(), locals(), [])
cluster = LocalDistributedCluster(
endpoint, n_process=n_process, shared_memory=shared_memory, **kw
)
clust... | def _start_cluster(endpoint, event, n_process=None, shared_memory=None, **kw):
cluster = LocalDistributedCluster(
endpoint, n_process=n_process, shared_memory=shared_memory, **kw
)
cluster.start_service()
event.set()
try:
cluster.serve_forever()
finally:
cluster.stop_serv... | https://github.com/mars-project/mars/issues/1231 | Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/Users/wenjun.swj/miniconda3/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "mars/lib/gipc.pyx", line 419, ... | AttributeError |
def _start_cluster_process(endpoint, n_process, shared_memory, **kw):
event = _mp_spawn_context.Event()
kw = kw.copy()
kw["n_process"] = n_process
kw["shared_memory"] = shared_memory or "20%"
process = _mp_spawn_context.Process(
target=_start_cluster, args=(endpoint, event), kwargs=kw
)... | def _start_cluster_process(endpoint, n_process, shared_memory, **kw):
event = multiprocessing.Event()
kw = kw.copy()
kw["n_process"] = n_process
kw["shared_memory"] = shared_memory or "20%"
process = gipc.start_process(_start_cluster, args=(endpoint, event), kwargs=kw)
while True:
even... | https://github.com/mars-project/mars/issues/1231 | Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/Users/wenjun.swj/miniconda3/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "mars/lib/gipc.pyx", line 419, ... | AttributeError |
def _start_web_process(scheduler_endpoint, web_endpoint):
ui_port = int(web_endpoint.rsplit(":", 1)[1])
web_event = _mp_spawn_context.Event()
web_process = _mp_spawn_context.Process(
target=_start_web, args=(scheduler_endpoint, ui_port, web_event), daemon=True
)
web_process.start()
whi... | def _start_web_process(scheduler_endpoint, web_endpoint):
web_event = multiprocessing.Event()
ui_port = int(web_endpoint.rsplit(":", 1)[1])
web_process = gipc.start_process(
_start_web, args=(scheduler_endpoint, ui_port, web_event), daemon=True
)
while True:
web_event.wait(5)
... | https://github.com/mars-project/mars/issues/1231 | Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/Users/wenjun.swj/miniconda3/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "mars/lib/gipc.pyx", line 419, ... | AttributeError |
def deserialize_graph(ser_graph, graph_cls=None):
from google.protobuf.message import DecodeError
from .serialize.protos.graph_pb2 import GraphDef
from .graph import DirectedGraph
graph_cls = graph_cls or DirectedGraph
ser_graph_bin = to_binary(ser_graph)
g = GraphDef()
try:
ser_gra... | def deserialize_graph(ser_graph, graph_cls=None):
from google.protobuf.message import DecodeError
from .serialize.protos.graph_pb2 import GraphDef
from .graph import DirectedGraph
graph_cls = graph_cls or DirectedGraph
ser_graph_bin = to_binary(ser_graph)
g = GraphDef()
try:
ser_gra... | https://github.com/mars-project/mars/issues/1231 | Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/Users/wenjun.swj/miniconda3/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "mars/lib/gipc.pyx", line 419, ... | AttributeError |
def _calc_chunk_params(
cls, in_chunk, axes, output, output_type, chunk_op, no_shuffle: bool
):
params = {"index": in_chunk.index}
if output_type == OutputType.tensor:
chunk_shape = list(in_chunk.shape)
for ax in axes:
if not no_shuffle:
chunk_shape[ax] = np.nan
... | def _calc_chunk_params(cls, in_chunk, axes, output, output_type, chunk_op):
params = {"index": in_chunk.index}
if output_type == OutputType.tensor:
chunk_shape = list(in_chunk.shape)
for ax in axes:
chunk_shape[ax] = np.nan
params["shape"] = tuple(chunk_shape)
params[... | https://github.com/mars-project/mars/issues/1184 | In [14]: from mars.learn.utils import shuffle
In [15]: X, y = shuffle(X, y)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/Workspace/mars/mars/serialize/jsonserializer.pyx in mars.serialize.jsonserializer.JsonSer... | TypeError |
def tile(cls, op):
inputs = op.inputs
check_chunks_unknown_shape(inputs, TilesError)
axis_to_nsplits = defaultdict(list)
has_dataframe = any(
output_type == OutputType.dataframe for output_type in op.output_types
)
for ax in op.axes:
if has_dataframe and ax == 1:
# if... | def tile(cls, op):
inputs = op.inputs
check_chunks_unknown_shape(inputs, TilesError)
axis_to_nsplits = defaultdict(list)
has_dataframe = any(
output_type == OutputType.dataframe for output_type in op.output_types
)
for ax in op.axes:
if has_dataframe and ax == 1:
# if... | https://github.com/mars-project/mars/issues/1184 | In [14]: from mars.learn.utils import shuffle
In [15]: X, y = shuffle(X, y)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/Workspace/mars/mars/serialize/jsonserializer.pyx in mars.serialize.jsonserializer.JsonSer... | TypeError |
def shuffle(*arrays, **options):
arrays = [convert_to_tensor_or_dataframe(ar) for ar in arrays]
axes = options.pop("axes", (0,))
if not isinstance(axes, Iterable):
axes = (axes,)
elif not isinstance(axes, tuple):
axes = tuple(axes)
random_state = check_random_state(options.pop("rando... | def shuffle(*arrays, **options):
arrays = [convert_to_tensor_or_dataframe(ar) for ar in arrays]
axes = options.pop("axes", (0,))
if not isinstance(axes, Iterable):
axes = (axes,)
elif not isinstance(axes, tuple):
axes = tuple(axes)
random_state = check_random_state(options.pop("rando... | https://github.com/mars-project/mars/issues/1184 | In [14]: from mars.learn.utils import shuffle
In [15]: X, y = shuffle(X, y)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/Workspace/mars/mars/serialize/jsonserializer.pyx in mars.serialize.jsonserializer.JsonSer... | TypeError |
def __init__(
self,
obj,
groupby_obj=None,
keys=None,
axis=0,
level=None,
grouper=None,
exclusions=None,
selection=None,
as_index=True,
sort=True,
group_keys=True,
squeeze=False,
observed=False,
mutated=False,
grouper_cache=None,
):
def fill_value(v, k... | def __init__(
self,
obj,
groupby_obj=None,
keys=None,
axis=0,
level=None,
grouper=None,
exclusions=None,
selection=None,
as_index=True,
sort=True,
group_keys=True,
squeeze=False,
observed=False,
mutated=False,
grouper_cache=None,
):
def fill_value(v, k... | https://github.com/mars-project/mars/issues/1154 | In [1]: import pandas as pd; import numpy as np
In [2]: df = pd.DataFrame(np.random.rand(4, 3), index=np.arange(5, 1, -1))
In [4]: import mars.dataframe as md
In [5]: mdf = md.DataFrame(df)
In [6]: mdf.groupby(0).execute()
---------------------------------------------------------------------------
AttributeError ... | AttributeError |
def execute_map(cls, ctx, op):
chunk = op.outputs[0]
df = ctx[op.inputs[0].key]
shuffle_on = op.shuffle_on
if shuffle_on is not None:
# shuffle on field may be resident in index
to_reset_index_names = []
if not isinstance(shuffle_on, (list, tuple)):
if shuffle_on not... | def execute_map(cls, ctx, op):
chunk = op.outputs[0]
df = ctx[op.inputs[0].key]
filters = hash_dataframe_on(df, op.shuffle_on, op.index_shuffle_size)
# shuffle on index
for index_idx, index_filter in enumerate(filters):
group_key = ",".join([str(index_idx), str(chunk.index[1])])
if... | https://github.com/mars-project/mars/issues/1110 | In [4]: df = pd.DataFrame({'a': np.arange(10), 'b': np.random.rand(10)})
In [5]: df2 = df.copy()
In [6]: df2.set_index('a', inplace=True)
In [7]: df2
Out[7]:
b
a
0 0.984265
1 0.544014
2 0.592392
3 0.269762
4 0.236130
5 0.846061
6 0.308780
7 0.604834
8 0.973824
9 0.867099
In [8]: df.merge(df2, on='a') # c... | KeyError |
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