def make_char_span(location_col, text_col, original_text):
"""
Convert a column of begin, end pairs to a SpanArray.
:param location_col: Arrow array containing (begin, end) tuples of character offset
:param text_col: Arrow array of strings that should match the target texts of the
spans in location_col. Used for reconstructing target text when it is not provided.
:param original_text: Target text for the spans. Optional. If not provided, this
function will reconstruct target text from the contents of `text_col`.
"""
# Replace location columns with char and token spans
if not (pa.types.is_list(location_col.type)
and pa.types.is_primitive(location_col.type.value_type)):
raise ValueError("Expected location column as a list of integers")
# TODO: assert location is fixed with 2 elements?
if isinstance(location_col, pa.ChunkedArray):
location_col = pa.concat_arrays(location_col.iterchunks())
# Flatten to get primitive array convertible to numpy
array = location_col.flatten()
values = array.to_numpy()
begins = values[0::2]
ends = values[1::2]
if original_text is None:
warnings.warn(
"Analyzed text was not provided, attempting to reconstruct from tokens, "
"however it will not be identical to the original analyzed text.")
if isinstance(text_col, pa.ChunkedArray):
text_col = pa.concat_arrays(text_col.iterchunks())
original_text = build_original_text(text_col, begins)
return SpanArray(original_text, begins, ends)
def arrow_to_token_span(extension_array: pa.ExtensionArray) -> TokenSpanArray:
"""
Convert a pyarrow.ExtensionArray with type ArrowTokenSpanType to
a TokenSpanArray.
:param extension_array: pyarrow.ExtensionArray with type ArrowTokenSpanType
:return: TokenSpanArray
"""
if isinstance(extension_array, pa.ChunkedArray):
if extension_array.num_chunks > 1:
raise ValueError("Only pyarrow.Array with a single chunk is supported")
extension_array = extension_array.chunk(0)
assert pa.types.is_struct(extension_array.storage.type)
# Get target text from the begins field metadata and decode string
metadata = extension_array.storage.type[ArrowTokenSpanType.BEGINS_NAME].metadata
target_text = metadata[ArrowCharSpanType.TARGET_TEXT_KEY]
if isinstance(target_text, bytes):
target_text = target_text.decode()
# Get the begins/ends pyarrow arrays
token_begins_array = extension_array.storage.field(ArrowTokenSpanType.BEGINS_NAME)
token_ends_array = extension_array.storage.field(ArrowTokenSpanType.ENDS_NAME)
# Check if CharSpans have been split
num_char_span_splits = extension_array.type.num_char_span_splits
if num_char_span_splits > 0:
char_begins_splits = []
char_ends_splits = []
for i in range(num_char_span_splits):
char_begins_splits.append(
extension_array.storage.field(ArrowCharSpanType.BEGINS_NAME + "_{}".format(i)))
char_ends_splits.append(
extension_array.storage.field(ArrowCharSpanType.ENDS_NAME + "_{}".format(i)))
char_begins_array = pa.concat_arrays(char_begins_splits)
char_ends_array = pa.concat_arrays(char_ends_splits)
else:
char_begins_array = extension_array.storage.field(ArrowCharSpanType.BEGINS_NAME)
char_ends_array = extension_array.storage.field(ArrowCharSpanType.ENDS_NAME)
# Remove any trailing padding
if char_begins_array.null_count > 0:
char_begins_array = char_begins_array[:-char_begins_array.null_count]
char_ends_array = char_ends_array[:-char_ends_array.null_count]
# Zero-copy convert arrays to numpy
token_begins = token_begins_array.to_numpy()
token_ends = token_ends_array.to_numpy()
char_begins = char_begins_array.to_numpy()
char_ends = char_ends_array.to_numpy()
# Create the CharSpanArray, then the TokenSpanArray
char_span = CharSpanArray(target_text, char_begins, char_ends)
return TokenSpanArray(char_span, token_begins, token_ends)
def take(self, indices, filtered=True):
gs = self.trim()
if gs.filtered and filtered:
# we translate the indices that refer to filters row indices to
# indices of the unfiltered row indices
indices = np.asarray(indices)
gs._df.count() # make sure the mask is filled
max_index = indices.max()
mask = gs._df._selection_masks['__filter__']
filtered_indices = mask.first(max_index + 1)
indices = filtered_indices[indices]
if len(indices) == 0:
return GeoSeries(geometry=pa.array([]), crs=gs._crs)
if isinstance(gs._geometry, pa.ChunkedArray):
offset = 0
chunks = []
for chunk in gs._geometry.chunks:
size = len(chunk)
chunk_indices = [
x for x in indices if offset <= x < size + offset
]
chunk_indices = pa.array([x - offset for x in chunk_indices])
if len(chunk_indices) > 0:
chunks.append(chunk.take(chunk_indices))
offset += size
if len(chunks) > 0:
geometry = pa.concat_arrays(chunks)
else:
raise IndexError('ERROR: Out of range')
elif isinstance(gs._geometry, pa.Array):
indices = pa.array(indices)
geometry = gs._geometry.take(indices)
else:
geometry = gs._geometry.take(indices)
return GeoSeries(geometry=geometry, crs=gs._crs)
def __init__(self, expression, values, keep_other=True, other_value=None, sort=False, label=None, df=None):
self.df = df or expression.df
self.sort = sort
self.pre_sort = True
self.expression = self.df[str(expression)]
self.label = label or self.expression._label
self.keep_other = keep_other
if isinstance(values, pa.ChunkedArray):
values = pa.concat_arrays(values.chunks)
if sort:
indices = pa.compute.sort_indices(values)
values = pa.compute.take(values, indices)
if self.keep_other:
self.bin_values = pa.array(vaex.array_types.tolist(values) + [other_value])
self.values = self.bin_values.slice(0, len(self.bin_values) - 1)
else:
raise NotImplementedError("not supported yet")
# although we can support this, it will fail with _combine, because of
# the mapping of the set to -1
self.bin_values = pa.array(vaex.array_types.tolist(values))
self.values = self.bin_values
self.N = len(self.bin_values)
fp = vaex.cache.fingerprint(values)
fingerprint = f"set-grouper-fixed-{fp}"
self.hash_map_unique = vaex.hash.HashMapUnique.from_keys(self.values, fingerprint=fingerprint)
self.basename = "hash_map_unique_%s" % vaex.utils._python_save_name(str(self.expression) + "_" + self.hash_map_unique.fingerprint)
self.sort_indices = None
self._promise = vaex.promise.Promise.fulfilled(None)
def _concat_same_type(cls, to_concat):
return cls(
pa.concat_arrays(
[ea.data for ea in to_concat]
),
dtype=to_concat[0].dtype
)
def token_span_to_arrow(token_span: TokenSpanArray) -> pa.ExtensionArray:
"""
Convert a TokenSpanArray to a pyarrow.ExtensionArray with a type
of ArrowTokenSpanType and struct as the storage type. The resulting
extension array can be serialized and transferred with standard
Arrow protocols.
:param token_span: A TokenSpanArray to be converted
:return: pyarrow.ExtensionArray containing TokenSpan data
"""
if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
raise NotImplementedError(
"Arrow serialization for TokenSpanArray is not supported with "
"PyArrow versions < 2.0.0")
# Create arrays for begins/ends
token_begins_array = pa.array(token_span.begin_token)
token_ends_array = pa.array(token_span.end_token)
# Filter out any empty SpanArrays
non_null_tokens = token_span.tokens[~token_span.isna()]
assert len(non_null_tokens) > 0
# Get either single document as a list or use a list of all if multiple docs
if all([token is non_null_tokens[0] for token in non_null_tokens]):
tokens_arrays = [non_null_tokens[0]]
tokens_indices = pa.array([0] * len(token_span.tokens),
mask=token_span.isna())
else:
raise NotImplementedError(
"TokenSpan Multi-doc serialization not yet implemented due to "
"ArrowNotImplementedError: Concat with dictionary unification NYI")
tokens_arrays = non_null_tokens
tokens_indices = np.zeros_like(token_span.tokens)
tokens_indices[~token_span.isna()] = range(len(tokens_arrays))
tokens_indices = pa.array(tokens_indices, mask=token_span.isna())
# Convert each token SpanArray to Arrow and get as raw storage
arrow_tokens_arrays = [span_to_arrow(sa).storage for sa in tokens_arrays]
# Create a list array with each element is an ArrowSpanArray
# TODO: pyarrow.lib.ArrowNotImplementedError: ('Sequence converter for type dictionary<values=string, indices=int8, ordered=0> not implemented', 'Conversion failed for column ts1 with type TokenSpanDtype')
#arrow_tokens_arrays_array = pa.array(arrow_tokens_arrays, type=pa.list_(arrow_tokens_arrays[0].type))
offsets = [0] + [len(a) for a in arrow_tokens_arrays]
values = pa.concat_arrays(
arrow_tokens_arrays) # TODO: can't concat extension arrays?
arrow_tokens_arrays_array = pa.ListArray.from_arrays(offsets, values)
# Create a dictionary array mapping each token SpanArray index used to the list of ArrowSpanArrays
tokens_dict_array = pa.DictionaryArray.from_arrays(
tokens_indices, arrow_tokens_arrays_array)
typ = ArrowTokenSpanType(token_begins_array.type, tokens_dict_array.type)
fields = list(typ.storage_type)
storage = pa.StructArray.from_arrays(
[token_begins_array, token_ends_array, tokens_dict_array],
fields=fields)
return pa.ExtensionArray.from_storage(typ, storage)
def test_arrow_array_modifies_data(self,
test_array_chunked_nulls): # noqa: F811
expected = pa.concat_arrays(test_array_chunked_nulls.data.iterchunks())
id1 = id(test_array_chunked_nulls.data)
real = test_array_chunked_nulls.__arrow_array__()
assert id1 != id(test_array_chunked_nulls.data)
assert real.equals(expected)
def _collect_as_arrow(self, split_batches: bool = False) -> List["pa.RecordBatch"]:
"""
Returns all records as a list of ArrowRecordBatches, pyarrow must be installed
and available on driver and worker Python environments.
This is an experimental feature.
:param split_batches: split batches such that each column is in its own allocation, so
that the selfDestruct optimization is effective; default False.
.. note:: Experimental.
"""
from pyspark.sql.dataframe import DataFrame
assert isinstance(self, DataFrame)
with SCCallSiteSync(self._sc):
(
port,
auth_secret,
jsocket_auth_server,
) = self._jdf.collectAsArrowToPython()
# Collect list of un-ordered batches where last element is a list of correct order indices
try:
batch_stream = _load_from_socket((port, auth_secret), ArrowCollectSerializer())
if split_batches:
# When spark.sql.execution.arrow.pyspark.selfDestruct.enabled, ensure
# each column in each record batch is contained in its own allocation.
# Otherwise, selfDestruct does nothing; it frees each column as its
# converted, but each column will actually be a list of slices of record
# batches, and so no memory is actually freed until all columns are
# converted.
import pyarrow as pa
results = []
for batch_or_indices in batch_stream:
if isinstance(batch_or_indices, pa.RecordBatch):
batch_or_indices = pa.RecordBatch.from_arrays(
[
# This call actually reallocates the array
pa.concat_arrays([array])
for array in batch_or_indices
],
schema=batch_or_indices.schema,
)
results.append(batch_or_indices)
else:
results = list(batch_stream)
finally:
# Join serving thread and raise any exceptions from collectAsArrowToPython
jsocket_auth_server.getResult()
# Separate RecordBatches from batch order indices in results
batches = results[:-1]
batch_order = results[-1]
# Re-order the batch list using the correct order
return [batches[i] for i in batch_order]
def _set_single_index_in_chunk(chunk: pa.Array, index: int,
value: Any) -> pa.Array:
"""Set a single position in a pyarrow array."""
assert is_scalar(value)
return pa.concat_arrays([
chunk[:index],
pa.array([value], type=pa.string()),
chunk[index + 1:],
])
def test_group_by(df):
udaf = f.udaf(Accumulator, pa.float64(), pa.float64(), [pa.float64()])
df = df.aggregate([f.col("b")], [udaf(f.col("a"))])
batches = df.collect()
arrays = [batch.column(1) for batch in batches]
joined = pa.concat_arrays(arrays)
assert joined == pa.array([1.0 + 2.0, 3.0])
def pd_nanop(nanop: Callable, arr: Union[pa.ChunkedArray, pa.Array],
skipna: bool):
"""Use pandas.core.nanops to provide a reduction."""
if isinstance(arr, pa.ChunkedArray):
data = pa.concat_arrays(arr.iterchunks())
else:
data = arr
np_arr = _extract_data_buffer_as_np_array(data)
mask = extract_isnull_bytemap(data)
return nanop(np_arr, skipna=skipna, mask=mask)
def _replace_with_indices(
cls,
chunk: pa.Array,
indices: npt.NDArray[np.intp],
value: npt.NDArray[Any],
) -> pa.Array:
"""
Replace items selected with a set of positional indices.
Analogous to pyarrow.compute.replace_with_mask, except that replacement
positions are identified via indices rather than a mask.
Parameters
----------
chunk : pa.Array
indices : npt.NDArray[np.intp]
value : npt.NDArray[Any]
Replacement value(s).
Returns
-------
pa.Array
"""
n = len(indices)
if n == 0:
return chunk
start, stop = indices[[0, -1]]
if (stop - start) == (n - 1):
# fast path for a contiguous set of indices
arrays = [
chunk[:start],
pa.array(value, type=chunk.type, from_pandas=True),
chunk[stop + 1:],
]
arrays = [arr for arr in arrays if len(arr)]
if len(arrays) == 1:
return arrays[0]
return pa.concat_arrays(arrays)
mask = np.zeros(len(chunk), dtype=np.bool_)
mask[indices] = True
if pa_version_under5p0:
arr = chunk.to_numpy(zero_copy_only=False)
arr[mask] = value
return pa.array(arr, type=chunk.type)
if isna(value).all():
return pc.if_else(mask, None, chunk)
return pc.replace_with_mask(chunk, mask, value)
def trim_offsets(offset, length, null_buffer, offsets_buffer, large=False):
if offset == 0:
return null_buffer, offsets_buffer
if large:
offsets = np.frombuffer(offsets_buffer, np.int64, length + 1 + offset)
else:
offsets = np.frombuffer(offsets_buffer, np.int32, length + 1 + offset)
nulls = pa.BooleanArray.from_buffers(pa.bool_(), length, [None, null_buffer], offset=offset)
nulls = pa.concat_arrays([nulls])
assert nulls.offset == 0
assert len(nulls) == length
offsets = offsets[offset:] - offsets[offset]
return nulls.buffers()[1], pa.py_buffer(offsets)
def test_pa_array(self, array_inhom_chunks):
npt.assert_array_equal(array_inhom_chunks.offsets, [0, 3, 8])
expected = pa.concat_arrays(array_inhom_chunks.data.iterchunks())
real = pa.array(array_inhom_chunks)
assert isinstance(real, pa.Array)
assert real.equals(expected)
if pa.__version__ < "0.15":
npt.assert_array_equal(array_inhom_chunks.offsets, [0, 3, 8])
else:
npt.assert_array_equal(array_inhom_chunks.offsets, [0])
def __init__(self, array, dtype=None, copy=None):
# Choose default dtype for empty arrays
try:
if len(array) == 0 and dtype is None:
dtype = 'float64'
except:
# len failed
pass
# See if we can determine arrow array type
if isinstance(dtype, GeometryDtype):
# Use arrow type as-is
arrow_dtype = dtype.arrow_dtype
elif isinstance(dtype, pa.DataType):
arrow_dtype = dtype
elif dtype is not None and dtype != np.dtype('object'):
# Scalar element dtype
arrow_dtype = self._arrow_type_from_numpy_element_dtype(dtype)
else:
# Let arrow infer type
arrow_dtype = None
# Unwrap GeometryList elements to numpy arrays
if is_array_like(array) or isinstance(array, list):
array = [_unwrap_geometry(el, self._element_type) for el in array]
array = pa.array(array, type=arrow_dtype)
elif isinstance(array, pa.Array):
# Nothing to do
pass
elif isinstance(array, pa.ChunkedArray):
array = pa.concat_arrays(array.chunks)
else:
raise ValueError(
"Unsupported type passed for {}: {}".format(
self.__class__.__name__, type(array)
)
)
# Save off pyarrow array
self.data = array
# Compute types
np_type = self._numpy_element_dtype_from_arrow_type(self.data.type)
self._numpy_element_type = np.dtype(np_type)
self._dtype = self._dtype_class(np_type)
# Initialize backing property for spatial index
self._sindex = None
def _get_field(struct_array: pa.StructArray, field: Union[str,
int]) -> pa.Array:
"""Returns struct_array.field(field) with null propagation.
This function is equivalent to struct_array.field() but correctly handles
null propagation (the parent struct's null values are propagated to children).
Args:
struct_array: A struct array which should be queried.
field: The request field to retrieve.
Returns:
A pa.Array containing the requested field.
Raises:
KeyError: If field is not a child field in struct_array.
"""
child_array = struct_array.field(field)
# In case all values are present then there's no need for special handling.
# We can return child_array as is to avoid a performance penalty caused by
# constructing and flattening the returned array.
if struct_array.null_count == 0:
return child_array
# is_valid returns a BooleanArray with two buffers the buffer at offset
# 0 is always None and buffer 1 contains the data on which fields are
# valid/not valid.
# (https://arrow.apache.org/docs/format/Columnar.html#buffer-listing-for-each-layout)
validity_bitmap_buffer = struct_array.is_valid().buffers()[1]
# Construct a new struct array with a single field. Calling flatten() on the
# new array guarantees validity bitmaps are merged correctly.
new_type = pa.struct([pa.field(field, child_array.type)])
if (child_array.null_count == 0 and child_array.offset != 0):
# TODO(https://issues.apache.org/jira/browse/ARROW-14156): Remove this
# special handling once flattening a struct that has children that were
# sliced produces arrays with a correct validity bitmap.
child_array = pa.concat_arrays(
[pa.nulls(0, child_array.type), child_array])
filtered_struct = pa.StructArray.from_buffers(
new_type,
len(struct_array), [validity_bitmap_buffer],
null_count=struct_array.null_count,
children=[child_array])
return filtered_struct.flatten()[0]
def _concatenate_extension_column(ca: "pyarrow.ChunkedArray") -> "pyarrow.Array":
"""Concatenate chunks of an extension column into a contiguous array.
This concatenation is required for creating copies and for .take() to work on
extension arrays.
See https://issues.apache.org/jira/browse/ARROW-16503.
"""
if not _is_column_extension_type(ca):
raise ValueError("Chunked array isn't an extension array: {ca}")
if ca.num_chunks == 0:
# No-op for no-chunk chunked arrays, since there's nothing to concatenate.
return ca
chunk = ca.chunk(0)
return type(chunk).from_storage(
chunk.type, pyarrow.concat_arrays([c.storage for c in ca.chunks])
)
def _copy_table(table: "pyarrow.Table") -> "pyarrow.Table":
"""Copy the provided Arrow table."""
import pyarrow as pa
# Copy the table by copying each column and constructing a new table with
# the same schema.
cols = table.columns
new_cols = []
for col in cols:
if col.num_chunks > 0 and isinstance(col.chunk(0), pa.ExtensionArray):
# If an extension array, we copy the underlying storage arrays.
chunk = col.chunk(0)
arr = type(chunk).from_storage(
chunk.type, pa.concat_arrays([c.storage for c in col.chunks]))
else:
# Otherwise, we copy the top-level chunk arrays.
arr = col.combine_chunks()
new_cols.append(arr)
return pa.Table.from_arrays(new_cols, schema=table.schema)
def __arrow_array__(self, type=None):
# type: (pa.DataType,) -> pa.Array
"""
Implement pyarrow array interface (requires pyarrow>=0.15).
Returns
-------
pa.Array
"""
if self._has_single_chunk:
data = self.data.chunks[0]
else:
data = pa.concat_arrays(self.data.iterchunks())
self.data = pa.chunked_array([data
]) # modify a data pointer inplace
if type is not None and type != data.type:
return data.cast(type, safe=False)
else:
return data
def take(self, indices):
lz = self.copy()
if isinstance(lz._obj, ChunkedArray):
offset = 0
chunks = []
for chunk in lz._obj.chunks:
size = len(chunk)
chunk_indices = [x for x in indices if offset <= x < size + offset]
chunk_indices = array([x - offset for x in chunk_indices])
if len(chunk_indices) > 0:
chunks.append(chunk.take(chunk_indices))
offset += size
if len(chunks) > 0:
obj = concat_arrays(chunks)
else:
raise IndexError('ERROR: Out of range')
else:
indices = array(indices)
obj = lz._obj.take(indices)
lz._obj = obj
return lz
def take(self, indices):
lz = self.copy()
if isinstance(lz._obj, ChunkedArray):
offset = 0
chunks = []
for chunk in lz._obj.chunks:
size = len(chunk)
chunk_indices = list(
filter(lambda x: offset <= x < size + offset, indices))
chunk_indices = array(map(lambda x: x - offset, chunk_indices))
if len(chunk_indices) > 0:
chunks.append(chunk.take(chunk_indices))
offset += size
if len(chunks) > 0:
obj = concat_arrays(chunks)
else:
raise IndexError('ERROR: Out of range')
else:
indices = array(indices)
obj = lz._obj.take(indices)
lz._obj = obj
return lz
def _take_on_chunks(self, indices, limits_idx, cum_lengths, sort_idx=None):
def take_in_one_chunk(i_chunk):
indices_chunk = indices[limits_idx[i_chunk]:limits_idx[i_chunk +
1]]
indices_chunk -= cum_lengths[i_chunk]
if (self.dtype.is_list
and self.data.chunk(i_chunk).flatten().null_count == 0
and self.data.chunk(i_chunk).null_count == 0
and self.flatten().dtype._is_numeric):
return take_indices_on_pyarrow_list(self.data.chunk(i_chunk),
indices_chunk)
else:
return self.data.chunk(i_chunk).take(pa.array(indices_chunk))
# this is a pyarrow.Array
result = [take_in_one_chunk(i) for i in range(self.data.num_chunks)]
# we know that self.data.num_chunks >1
if sort_idx is None:
return FletcherArray(
pa.chunked_array(filter(len, result), type=self.data.type))
else:
return FletcherArray(
pa.concat_arrays(result).take(pa.array(sort_idx)))
def assert_content_equals_array(result, expected):
"""Assert that the result is an Arrow structure and the content matches an array."""
assert isinstance(result, (pa.Array, pa.ChunkedArray))
if isinstance(result, pa.ChunkedArray):
result = pa.concat_arrays(result.iterchunks())
assert result.equals(expected)
def _make_entity_dataframes(
entities: List, original_text: str) -> (pd.DataFrame, pd.DataFrame):
"""
Create the entities and entity_mentions DataFrames.
:param entities: The "entities" section of a parsed NLU response
:param original_text: Text of the document. This argument must be provided if there
are entity mention spans.
"""
if len(entities) == 0:
return pd.DataFrame(), pd.DataFrame()
table = util.make_table(entities)
# Check if response includes entity mentions
mention_name_cols = [(name, table.column(name))
for name in table.column_names
if name.lower().startswith("mentions")]
# Make entities and entity mentions (optional) DataFrames
if len(mention_name_cols) > 0:
mention_names, mention_cols = zip(*mention_name_cols)
# Create the entities DataFrame with mention arrays dropped
table = table.drop(mention_names)
pdf = table.to_pandas()
# Flatten the mention arrays to be put in separate table
mention_arrays = [
pa.concat_arrays(col.iterchunks()) for col in mention_cols
]
flat_mention_arrays = [a.flatten() for a in mention_arrays]
table_mentions = pa.Table.from_arrays(flat_mention_arrays,
names=mention_names)
# Convert location/text columns to span
location_col, location_name = util.find_column(table_mentions,
"location")
text_col, text_name = util.find_column(table_mentions, "text")
if original_text is None:
raise ValueError(
"Unable to construct target text for converting entity mentions to spans"
)
char_span = util.make_char_span(location_col, text_col, original_text)
table_mentions = table_mentions.drop([location_name, text_name])
# Create the entity_mentions DataFrame
pdf_mentions = table_mentions.to_pandas()
pdf_mentions["span"] = char_span
# Align index of parent entities DataFrame with flattened DataFrame and ffill
# values
mention_offsets = mention_arrays[0].offsets.to_numpy()
pdf_parent = pdf.set_index(mention_offsets[:-1])
pdf_parent = pdf_parent.reindex(pdf_mentions.index, method="ffill")
# Add columns from entities parent DataFrame
pdf_mentions["text"] = pdf_parent["text"]
pdf_mentions["type"] = pdf_parent["type"]
# Remove "mentions" from column names
pdf_mentions.rename(columns={
c: c.split("mentions.")[-1]
for c in pdf_mentions.columns
},
inplace=True)
else:
pdf = table.to_pandas()
pdf_mentions = pd.DataFrame()
return pdf, pdf_mentions
def _make_relations_dataframe_zero_copy(relations):
if len(relations) == 0:
return pd.DataFrame()
table = util.make_table(relations)
# Separate each argument into a column
flattened_arguments = []
drop_cols = []
for name in table.column_names:
if name.lower().startswith("arguments"):
col = pa.concat_arrays(table.column(name).iterchunks())
assert pa.types.is_list(col.type)
is_nested_list = pa.types.is_list(col.type.value_type)
name_split = name.split('.', maxsplit=1)
first_list = col[0]
num_arguments = len(first_list)
null_count = 0
# Get the flattened raw values
raw = col
offset_arrays = []
while pa.types.is_list(raw.type):
offset_arrays.append(raw.offsets)
null_count += raw.null_count
raw = raw.flatten()
# TODO handle lists with null values
if null_count > 0:
continue
# Convert values to numpy
values = raw.to_numpy(zero_copy_only=False) # string might copy
offsets_list = [o.to_numpy() for o in offset_arrays]
# Compute the length of each list in the array
value_offsets = offsets_list.pop()
value_lengths = value_offsets[1:] - value_offsets[:-1]
# Separate the arguments into individual columns
for i in range(num_arguments):
arg_name = "{}.{}.{}".format(name_split[0], i, name_split[1])
arg_lengths = value_lengths[i::num_arguments]
# Fixed length arrays can be sliced
if not is_nested_list or len(np.unique(arg_lengths)) == 1:
num_elements = len(first_list[i]) if is_nested_list else 1
# Only 1 element so leave in primitive array
if not is_nested_list or num_elements == 1:
arg_values = values[i::num_arguments]
arg_array = pa.array(arg_values)
# Multiple elements so put back in a list array
else:
arg_values = values.reshape(
[len(col) * num_arguments, num_elements])
arg_values = arg_values[i::num_elements]
arg_values = arg_values.flatten()
arg_offsets = np.cumsum(arg_lengths)
arg_offsets = np.insert(arg_offsets, 0, 0)
arg_array = pa.ListArray.from_arrays(
arg_offsets, arg_values)
else:
# TODO Argument properties with variable length arrays not currently
# supported
continue
flattened_arguments.append((arg_array, arg_name))
drop_cols.append(name)
# Add the flattened argument columns
for arg_array, arg_name in flattened_arguments:
table = table.append_column(arg_name, arg_array)
# Drop columns that have been flattened
table = table.drop(drop_cols)
return table.to_pandas()
def _make_relations_dataframe(relations, original_text, sentence_span_series):
if len(relations) == 0:
return pd.DataFrame()
table = util.make_table(relations)
location_cols = {
} # Type: Dict[int, Tuple[Union[Array, ChunkedArray], str]]
# Separate each argument into a column
flattened_arguments = []
drop_cols = []
for name in table.column_names:
if name.lower().startswith("arguments"):
col = pa.concat_arrays(table.column(name).iterchunks())
assert pa.types.is_list(col.type)
name_split = name.split('.', maxsplit=1)
num_arguments = len(col[0])
value_series = col.values.to_pandas()
# Separate the arguments into individual columns
for i in range(num_arguments):
arg_name = "{}.{}.{}".format(name_split[0], i, name_split[1])
arg_series = value_series[i::num_arguments]
arg_array = pa.array(arg_series)
# If list array is fixed length with 1 element, it can be flattened
temp = arg_array
while pa.types.is_list(temp.type):
temp = temp.flatten()
if len(temp) == len(arg_array):
# TODO also need to verify each offset inc by 1?
arg_array = temp
if name.lower().endswith("location"):
location_cols[i] = (arg_array,
"{}.{}".format(name_split[0], i))
flattened_arguments.append((arg_array, arg_name))
drop_cols.append(name)
# Add the flattened argument columns
for arg_array, arg_name in flattened_arguments:
table = table.append_column(arg_name, arg_array)
# Replace argument location and text columns with spans
arg_span_cols = {}
for arg_i, (location_col, arg_prefix) in location_cols.items():
text_col, text_name = util.find_column(table,
"{}.text".format(arg_prefix))
arg_span_cols["{}.span".format(arg_prefix)] = util.make_char_span(
location_col, text_col, original_text)
drop_cols.extend(["{}.location".format(arg_prefix), text_name])
add_cols = arg_span_cols.copy()
# Build the sentence span and drop plain text sentence col
sentence_col, sentence_name = util.find_column(table, "sentence")
arg_col_names = list(arg_span_cols.keys())
if len(arg_col_names) > 0:
first_arg_span_array = arg_span_cols[arg_col_names[0]]
sentence_matches = []
for i, arg_span in enumerate(first_arg_span_array):
arg_begin = arg_span.begin
arg_end = arg_span.end
j = len(sentence_span_series) // 2
found = False
while not found:
sentence_span = sentence_span_series[j]
if arg_begin >= sentence_span.end:
j += 1
elif arg_end <= sentence_span.begin:
j -= 1
else:
contains = [
sentence_span.contains(a[i])
for a in arg_span_cols.values()
]
if not (all(contains) and sentence_span.covered_text
== sentence_col[i].as_py()):
msg = f"Mismatched sentence span for: {sentence_span}"
if not all(contains):
msg += f"\nContains Args: {all(contains)}"
if sentence_span.covered_text != sentence_col[i].as_py(
):
msg += f"\nSpanText: '{sentence_span.covered_text}'" \
f"\nSentence: '{sentence_col[i]}'"
warnings.warn(msg)
sentence_matches.append(j)
found = True
relations_sentence = sentence_span_series[sentence_matches]
add_cols["sentence_span"] = relations_sentence.reset_index(drop=True)
drop_cols.append(sentence_name)
else:
warnings.warn("Could not make sentence span column for Re")
# Drop columns that have been flattened or replaced by spans
table = table.drop(drop_cols)
df = table.to_pandas()
# Insert additional columns
for col_name, col in add_cols.items():
df[col_name] = col
return df
def test_concat_array_different_types():
with pytest.raises(pa.ArrowInvalid):
pa.concat_arrays([pa.array([1]), pa.array([2.])])
def test_concat_array():
concatenated = pa.concat_arrays([pa.array([1, 2]), pa.array([3, 4])])
assert concatenated.equals(pa.array([1, 2, 3, 4]))
def list_of_pa_arrays_to_pyarrow_listarray(
l_arr: List[pa.Array]) -> pa.ListArray:
offsets = pa.array(np.cumsum([0] + [len(arr) for arr in l_arr]),
type=pa.int32())
values = pa.concat_arrays(l_arr)
return pa.ListArray.from_arrays(offsets, values)
def __init__(self,
expression,
values,
keep_other=True,
other_value=None,
sort=False,
label=None,
df=None):
self.df = df or expression.df
self.sort = sort
self.pre_sort = True
self.expression = self.df[str(expression)]
self.label = label or self.expression._label
self.keep_other = keep_other
if isinstance(values, pa.ChunkedArray):
values = pa.concat_arrays(values.chunks)
if sort:
indices = pa.compute.sort_indices(values)
values = pa.compute.take(values, indices)
if self.keep_other:
self.bin_values = pa.array(
vaex.array_types.tolist(values) + [other_value])
self.values = self.bin_values.slice(0, len(self.bin_values) - 1)
else:
raise NotImplementedError("not supported yet")
# although we can support this, it will fail with _combine, because of
# the mapping of the set to -1
self.bin_values = pa.array(vaex.array_types.tolist(values))
self.values = self.bin_values
self.N = len(self.bin_values)
dtype = vaex.dtype_of(self.values)
set_type = vaex.hash.ordered_set_type_from_dtype(dtype)
values_list = self.values.tolist()
try:
null_value = values_list.index(None)
null_count = 1
except ValueError:
null_value = -1
null_count = 0
if vaex.dtype_of(self.values) == float:
nancount = np.isnan(self.values).sum()
else:
nancount = 0
fp = vaex.cache.fingerprint(values)
fingerprint = f"set-grouper-fixed-{fp}"
if dtype.is_string:
values = vaex.column.ColumnStringArrow.from_arrow(self.values)
string_sequence = values.string_sequence
self.set = set_type(string_sequence, null_value, nancount,
null_count, fingerprint)
else:
self.set = set_type(self.values, null_value, nancount, null_count,
fingerprint)
self.basename = "set_%s" % vaex.utils._python_save_name(
str(self.expression) + "_" + self.set.fingerprint)
self.binby_expression = expression
self.sort_indices = None
self._promise = vaex.promise.Promise.fulfilled(None)