towhee.functional.data_collection.DataCollection

class towhee.functional.data_collection.DataCollection(iterable: Iterable)

Bases: Iterable, DCMixins

A pythonic computation and processing framework.

DataCollection is a pythonic computation and processing framework for unstructured data in machine learning and data science. It allows a data scientist or researcher to assemble data processing pipelines and do their model work (embedding, transforming, or classification) with a method-chaining style API. It is also designed to behave as a python list or iterator. When created from a list, operations arent performed once all data has been stored from previous step. When created from an iterator, operations are performed streamwise, reading and operating on data one by one, and only progressing if its previous output has been consumed.

Examples

1. Create a DataCollection from list or iterator:

>>> dc = DataCollection([0, 1, 2, 3, 4])
>>> dc = DataCollection(iter([0, 1, 2, 3, 4]))

2. Chaining function invocations makes your code clean and fluent:

>>> (
...    dc.map(lambda x: x+1)
...      .map(lambda x: x*2)
... ).to_list()
[2, 4, 6, 8, 10]

3. Multi-line closures are also supported via decorator syntax:

>>> dc = DataCollection([1,2,3,4])
>>> @dc.map
... def add1(x):
...     return x+1
>>> @add1.map
... def mul2(x):
...     return x *2
>>> @mul2.filter
... def ge3(x):
...     return x>=7
>>> ge3.to_list()
[8, 10]

Methods

api
append	Append item to data collection.
as_function	Make the DataFrame as callable function
as_str
batch	Create batches from the DataCollection.
clear	Clear a DataCollection.
combine	Combine dataframes to be able to access schemas from seperate DF chains.
compile_dag	Compile the dag.
config	Set the parameters for the DC.
copy	Copy a DataCollection.
count	Count an element in DataCollection.
drop_empty	Unbox Option values and drop Empty.
exception_safe	Making the data collection exception-safe by warp elements with Option.
extend	Extend a DataCollection.
fill_empty	Unbox Option values and fill Empty with default values.
filter	Filter the DataCollection data based on function.
flatten	Flatten nested data within DataCollection.
from_df
from_glob	Generate a file list with pattern.
get_backend
get_config	Return the config of the DC, including parameters such as parallel, chunksize, jit and format_priority.
get_executor
get_formate_priority
get_num_worker
get_pipeline_config	Return the config of the DC, including parameters such as parallel, chunksize, jit and format_priority.
get_state	Get the state storage for DataCollection
group_by	Merge columns in DataCollection.
head	Return the first n values of a DataCollection.
image_imshow	Produce a CV2 imshow window.
insert	Insert data into a DataCollection.
jit_resolve
map	Apply a function across all values in a DataCollection.
mmap	Apply multiple unary_op to data collection.
netx	Show dags' relations.
notify_consumed	Notfify that a DataCollection was consumed.
pipeline_config	Set the parameters in DC.
pmap	Apply unary_op with parallel execution.
pop	Extend a DataCollection.
range	Generate DataCollection with range of values.
ray_resolve
ray_start	Start the ray service.
read_audio
read_camera	Read images from a camera.
read_csv
read_json
read_video	Load a video as a DataCollection.
read_zip	Load files from url/path.
register_dag	Function that can be called within the function trying to be added to dag.
remove	Remove element from DataCollection.
report	Report the metric scores, and if you are using 'confusion matrix' metric, please use jupyter to display the matrix.
resolve	Dispatch unknown operators.
reverse	Reverse a DataCollection.
rolling	Create rolling windows from DataCollection.
run	Iterate through the DataCollections data.
safe	Shortcut for exception_safe.
sample	Sample the data collection.
select_from	Select data from dc with list(self).
serve	Serve the DataFrame as a RESTful API
set_evaluating	Set evaluating mode for stateful operators
set_format_priority	Set format priority.
set_jit
set_parallel	Set parallel execution for following calls.
set_state	Set the state storage for DataCollection
set_training	Set training mode for stateful operators
show	Print the first n lines of a DataCollection.
shuffle	Shuffle an unstreamed data collection in place.
smap
sort	Sort a DataCollection.
split	Split a dataframe into multiple dataframes.
split_train_test	Split DataCollection to train and test data.
stream	Create a stream data collection.
to_csv	Save dc as a csv file.
to_df	Turn a DataCollection into a DataFrame.
to_list	Convert DataCollection to list.
to_video	Encode a video; with audio if provided.
unstream	Create a unstream data collection.
with_metrics
zip	Combine multiple data collections.

Attributes

control_plane
is_stream	Check whether the data collection is stream or unstream.

__add__(other) → DataCollection

Concat two DataCollections.

Parameters:

other (DataCollection) – The DataCollection being appended to the calling DataFrame.

Returns:

A new DataCollection of the concated DataCollections.

Return type:

DataCollection

Examples

>>> dc0 = DataCollection.range(5)
>>> dc1 = DataCollection.range(5)
>>> dc2 = DataCollection.range(5)
>>> (dc0 + dc1 + dc2)
[0, 1, 2, 3, 4, 0, ...]

__getattr__(name) → DataCollection

Unknown method dispatcher.

When an unknown method is invoked on a DataCollection object, the function call will be dispatched to a method resolver. By registering function to the resolver, you are able to extend DataCollection’s API at runtime without modifying its code.

Parameters:

name (str) – The unknown attribute.

Returns:

Returns a new DataCollection for the output of attribute

call.

Return type:

DataCollection

Examples

>>> from towhee import register
>>> dc = DataCollection([1,2,3,4])
>>> @register(name='test/add1')
... def add1(x):
...     return x+1
>>> dc.test.add1().to_list()
[2, 3, 4, 5]

__getitem__(index) → any

Index based access of element in DataCollection.

Access the element at the given index, similar to accessing list[at_index]. Does not work with streamed DataCollections.

Parameters:

index (int) – The index location of the element being accessed.

Raises:

TypeError – If function called on streamed DataCollection

Returns:

The object at index.

Return type:

any

Examples

1. Usage with non-streamed:

   >>> dc = DataCollection([0, 1, 2, 3, 4])
   >>> dc[2]
   2
   

2. Usage with streamed:

   >>> dc.stream()[1] 
   Traceback (most recent call last):
   TypeError: indexing is only supported for DataCollection created from list
       or pandas DataFrame.
   

__init__(iterable: Iterable) → None

Initializes a new DataCollection instance.

Parameters:

iterable (Iterable) – The iterable data that is stored in the DataCollection.

__repr__() → str

String representation of the DataCollection

Returns:

String representation of the DataCollection.

Return type:

str

Examples

1. Usage with non-streamed:

   >>> DataCollection([1, 2, 3]).unstream()
   [1, 2, 3]
   

2. Usage with streamed:

   >>> DataCollection([1, 2, 3]).stream() 
   <list_iterator object at...>
   

__setitem__(index, value)

Index based setting of element in DataCollection.

Assign the value of the element at the given index, similar to list[at_index]=val. Does not work with streamed DataCollections.

Parameters:

• index (int) – The index location of the element being set.

• val (any) – The value to be set.

Raises:

TypeError – If function called on streamed DataCollection

Examples

1. Usage with non-streamed:

   >>> dc = DataCollection([0, 1, 2, 3, 4])
   >>> dc[2] = 3
   >>> dc.to_list()
   [0, 1, 3, 3, 4]
   

2. Usage with streamed:

   >>> dc.stream()[1] 
   Traceback (most recent call last):
   TypeError: indexing is only supported for DataCollection created from list
       or pandas DataFrame.
   

append(*args) → DataCollection

Append item to data collection.

Parameters:

item (Any) – the item to append

Returns:

self

Return type:

DataCollection

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([0, 1, 2])
>>> dc.append(3).append(4)
[0, 1, 2, 3, 4]

as_function()

Make the DataFrame as callable function

Returns:

a callable function

Return type:

_type_

Examples:

>>> import towhee
>>> with towhee.api() as api:
...     func1 = (
...         api.map(lambda x: x+' -> 1')
...            .map(lambda x: x+' => 1')
...            .as_function()
...     )

>>> with towhee.api['x']() as api:
...     func2 = (
...         api.runas_op['x', 'x_plus_1'](func=lambda x: x+' -> 2')
...            .runas_op['x_plus_1', 'y'](func=lambda x: x+' => 2')
...            .select['y']()
...            .as_raw()
...            .as_function()
...     )

>>> with towhee.api() as api:
...     func3 = (
...         api.parse_json()
...            .runas_op['x', 'x_plus_1'](func=lambda x: x+' -> 3')
...            .runas_op['x_plus_1', 'y'](func=lambda x: x+' => 3')
...            .select['y']()
...            .as_json()
...            .as_function()
...     )

>>> func1('1')
'1 -> 1 => 1'
>>> func2('2')
'2 -> 2 => 2'
>>> func3('{"x": "3"}')
'{"y": "3 -> 3 => 3"}'

batch(size, drop_tail=False)

Create batches from the DataCollection.

Parameters:

• size (int) – Window size.

• drop_tail (bool) – Drop trailing window that is not full, defaults to False.

Returns:

Batched DataCollection.

Return type:

DataCollection

Examples

>>> from towhee import DataCollection
>>> dc = DataCollection(range(10))
>>> [list(batch) for batch in dc.batch(2)]
[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]

>>> dc = DataCollection(range(10))
>>> dc.batch(3)
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]

>>> dc = DataCollection(range(10))
>>> dc.batch(3, drop_tail=True)
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]

>>> from towhee import Entity
>>> dc = DataCollection([Entity(a=a, b=b) for a,b in zip(['abc', 'vdfvcd', 'cdsc'], [1,2,3])])
>>> dc.batch(2)
[[<Entity dict_keys(['a', 'b'])>, <Entity dict_keys(['a', 'b'])>], [<Entity dict_keys(['a', 'b'])>]]

clear(*args) → DataCollection

Clear a DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 2, 3])
>>> dc.clear()
[]

classmethod combine(*datacollections)

Combine dataframes to be able to access schemas from seperate DF chains.

Parameters:

datacollections (DataFrame) – DataFrames to combine.

Examples

>>> import towhee
>>> a = towhee.range['a'](1,5)
>>> b = towhee.range['b'](5,10)
>>> c = towhee.range['c'](10, 15)
>>> z = towhee.DataFrame.combine(a, b, c)
>>> z.as_raw().to_list()
[(1, 5, 10), (2, 6, 11), (3, 7, 12), (4, 8, 13)]

compile_dag()

Compile the dag.

Runs a schema of commands that removes unecessary steps and cleans the DAG.

Returns:

The compiled DAG.

Return type:

dict

config(parallel: Optional[int] = None, chunksize: Optional[int] = None, jit: Optional[Union[str, dict]] = None, format_priority: Optional[List[str]] = None)

Set the parameters for the DC.

Parameters:

• parallel (int, optional) – Set the number of parallel execution for the following calls, defaults to None.

• chunksize (int, optional) – Set the chunk size for arrow, defaults to None.

• jit (Union[str, dict], optional) – Can be set to “numba”, this mode will speed up the Operator’s function, but it may also need to return to python mode due to JIT failure, which will take longer, so please set it carefully, defaults to None.

• format_priority (List[str], optional) – The priority list of formats, defaults to None.

Returns:

Self.

Return type:

DataCollection

copy(*args) → DataCollection

Copy a DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 2, 3])
>>> dc_1 = dc.copy()
>>> dc_1._iterable.append(4)
>>> dc, dc_1
([1, 2, 3], [1, 2, 3, 4])

count(*args) → int

Count an element in DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 2, 3])
>>> dc.count(1)
1

drop_empty(callback: Callable = None) → DataCollection

Unbox Option values and drop Empty.

Parameters:

callback (Callable) – handler for empty values;

Returns:

A DataCollection that drops empty values;

Return type:

DataCollection

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection.range(5)
>>> dc.safe().map(lambda x: x / (0 if x == 3 else 2)).drop_empty().to_list()
[0.0, 0.5, 1.0, 2.0]

Get inputs that case exceptions:

>>> exception_inputs = []
>>> result = dc.safe().map(lambda x: x / (0 if x == 3 else 2)).drop_empty(lambda x: exception_inputs.append(x.get().value))
>>> exception_inputs
[3]

exception_safe()

Making the data collection exception-safe by warp elements with Option.

Examples:

1. Exception breaks pipeline execution:

>>> from towhee import DataCollection
>>> dc = DataCollection.range(5)
>>> dc.map(lambda x: x / (0 if x == 3 else 2)).to_list()
Traceback (most recent call last):
ZeroDivisionError: division by zero

2. Exception-safe execution

>>> dc.exception_safe().map(lambda x: x / (0 if x == 3 else 2)).to_list()
[Some(0.0), Some(0.5), Some(1.0), Empty(), Some(2.0)]

>>> dc.exception_safe().map(lambda x: x / (0 if x == 3 else 2)).filter(lambda x: x < 1.5).to_list()
[Some(0.0), Some(0.5), Some(1.0), Empty()]

>>> dc.exception_safe().map(lambda x: x / (0 if x == 3 else 2)).filter(lambda x: x < 1.5, drop_empty=True).to_list()
[Some(0.0), Some(0.5), Some(1.0)]

extend(*args) → DataCollection

Extend a DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 2, 3])
>>> dc.extend([4, 5])
[1, 2, 3, 4, 5]

fill_empty(default: Any = None) → DataCollection

Unbox Option values and fill Empty with default values.

Parameters:

default (Any) – default value to replace empty values;

Returns:

data collection with empty values filled with default;

Return type:

DataCollection

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection.range(5)
>>> dc.safe().map(lambda x: x / (0 if x == 3 else 2)).fill_empty(-1.0).to_list()
[0.0, 0.5, 1.0, -1.0, 2.0]

filter(unary_op: Callable, drop_empty=False) → DataCollection

Filter the DataCollection data based on function.

Filters the DataCollection based on the function provided. If data is stored as an Option (see towhee.functional.option.py), drop empty will decide whether to remove the element or set it to empty.

Parameters:

• unary_op (Callable) – Function that dictates filtering.

• drop_empty (bool, optional) – Whether to drop empty fields. Defaults to False.

Returns:

Resulting DataCollection after filter.

Return type:

DataCollection

flatten(*args) → DataCollection

Flatten nested data within DataCollection.

Returns:

Flattened DataCollection.

Return type:

DataCollection

Examples

>>> from towhee import DataCollection, Entity
>>> dc = DataCollection(range(10))
>>> nested_dc = dc.batch(2)
>>> nested_dc.flatten().to_list()
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

>>> g = (i for i in range(3))
>>> e = Entity(a=1, b=2, c=g)
>>> dc = DataCollection([e]).flatten('c')
>>> [str(i) for i in dc]
["{'a': 1, 'b': 2, 'c': 0}", "{'a': 1, 'b': 2, 'c': 1}", "{'a': 1, 'b': 2, 'c': 2}"]

classmethod from_glob(*args)

Generate a file list with pattern.

get_config()

Return the config of the DC, including parameters such as parallel, chunksize, jit and format_priority.

Returns:

A dict of config parameters.

Return type:

dict

get_pipeline_config()

Return the config of the DC, including parameters such as parallel, chunksize, jit and format_priority.

Returns:

A dict of config parameters.

Return type:

dict

get_state()

Get the state storage for DataCollection

Returns:

the state storage

Return type:

State

group_by(index) → DataCollection

Merge columns in DataCollection. Unstreamed data only.

Examples

>>> import towhee
>>> dc = towhee.dc['a']([1,1,2,2,3,3])
>>> [i.a for i in dc]
[1, 1, 2, 2, 3, 3]

>>> dc = dc.group_by('a')
>>> [i.a for i in dc]
[1, 2, 3]

head(n: int = 5)

Return the first n values of a DataCollection.

Parameters:

n (int, optional) – The amount to select, defaults to 5.

Returns:

DataCollection with the selected values.

Return type:

DataCollection

image_imshow(title='image')

Produce a CV2 imshow window.

Parameters:

title (str, optional) – The title for the image. Defaults to ‘image’.

insert(*args) → DataCollection

Insert data into a DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 2, 3])
>>> dc.insert(0, 0)
[0, 1, 2, 3]

property is_stream

Check whether the data collection is stream or unstream.

Examples:

>>> from towhee import DataCollection
>>> from typing import Iterable
>>> dc = DataCollection([0,1,2,3,4])
>>> dc.is_stream
False

>>> result = dc.map(lambda x: x+1)
>>> result.is_stream
False
>>> result._iterable
[1, 2, 3, 4, 5]

>>> dc = DataCollection(iter(range(5)))
>>> dc.is_stream
True

>>> result = dc.map(lambda x: x+1)
>>> result.is_stream
True
>>> isinstance(result._iterable, Iterable)
True

map(*arg) → DataCollection

Apply a function across all values in a DataCollection.

Can apply multiple functions to the DataCollection. If multiple functions supplied, the same amount of new DataCollections will be returend.

Parameters:

*arg (Callable) – One or multiple functions to apply to the DataCollection.

Returns:

New DataCollection containing computation results.

Return type:

DataCollection

Examples

1. Single Function:

   >>> dc = DataCollection([1,2,3,4])
   >>> dc.map(lambda x: x+1).map(lambda x: x*2).to_list()
   [4, 6, 8, 10]
   

2. Multiple Functions:

   >>> dc = DataCollection([1,2,3,4])
   >>> a, b = dc.map(lambda x: x+1, lambda x: x*2)
   >>> (a.to_list(), b.to_list())
   ([2, 3, 4, 5], [2, 4, 6, 8])
   

mmap(ops: list, num_worker=None, backend=None)

Apply multiple unary_op to data collection. Currently supports two backends, ray and thread.

Parameters:

• unary_op (func) – the op to be mapped;

• num_worker (int) – how many threads to reserve for this op;

• backend (str) – whether to use ray or thread

# TODO: the test is broken with pytest # Examples:

# 1. Using mmap:

# >>> from towhee import DataCollection # >>> dc1 = DataCollection([0,1,2,’3’,4]).stream() # >>> a1, b1 = dc1.mmap([lambda x: x+1, lambda x: x*2]) # >>> c1 = a1.map(lambda x: x+1) # >>> c1.zip(b1).to_list() # [(2, 0), (3, 2), (4, 4), (Empty(), ‘33’), (6, 8)]

# 2. Using map instead of mmap:

# >>> from towhee import DataCollection # >>> dc2 = DataCollection.range(5).stream() # >>> a2, b2, c2 = dc2.map(lambda x: x+1, lambda x: x*2, lambda x: int(x/2)) # >>> d2 = a2.map(lambda x: x+1) # >>> d2.zip(b2, c2).to_list() # [(2, 0, 0), (3, 2, 0), (4, 4, 1), (5, 6, 1), (6, 8, 2)]

# 3. DAG execution:

# >>> dc3 = DataCollection.range(5).stream() # >>> a3, b3, c3 = dc3.map(lambda x: x+1, lambda x: x*2, lambda x: int(x/2)) # >>> d3 = a3.map(lambda x: x+1) # >>> d3.zip(b3, c3).map(lambda x: x[0]+x[1]+x[2]).to_list() # [2, 5, 9, 12, 16]

netx()

Show dags’ relations.

Returns:

The dags’ relations

Return type:

image

notify_consumed(new_id)

Notfify that a DataCollection was consumed.

When a DataCollection is consumed by a call to another DataCollection, that Dag needs to be aware of this, so any functions that consume more than the DataCollection calling the function need to use this function, e.g. zip().

Parameters:

new_id (str) – The ID of the DataCollection that did the consuming.

pipeline_config(parallel: Optional[int] = None, chunksize: Optional[int] = None, jit: Optional[Union[str, dict]] = None, format_priority: Optional[List[str]] = None)

Set the parameters in DC.

Parameters:

• parallel (int, optional) – Set the number of parallel executions for the following calls, defaults to None.

• chunksize (int, optional) – Set the chunk size for arrow, defaults to None.

• jit (Union[str, dict], optional) – Can be set to “numba”, this mode will speed up the Operator’s function, but it may also need to return to python mode due to JIT failure, which will take longer, so please set it carefully, defaults to None.

• format_priority (List[str], optional) – The priority list of format, defaults to None.

Returns:

Self

Return type:

DataCollection

pmap(unary_op, num_worker=None, backend=None)

Apply unary_op with parallel execution. Currently supports two backends, ray and thread.

Parameters:

• unary_op (func) – the op to be mapped;

• num_worker (int) – how many threads to reserve for this op;

• backend (str) – whether to use ray or thread

Examples:

>>> from towhee import DataCollection
>>> import threading
>>> stage_1_thread_set = {threading.current_thread().ident}
>>> stage_2_thread_set = {threading.current_thread().ident}
>>> result = (
...     DataCollection.range(1000).stream()
...     .pmap(lambda x: stage_1_thread_set.add(threading.current_thread().ident), 5)
...     .pmap(lambda x: stage_2_thread_set.add(threading.current_thread().ident), 4).to_list()
... )
>>> len(stage_1_thread_set) > 1
True
>>> len(stage_2_thread_set) > 1
True

pop(*args) → DataCollection

Extend a DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 2, 3])
>>> dc.pop()
[1, 2]

static range(*arg, **kws) → DataCollection

Generate DataCollection with range of values.

Generate DataCollection with a range of numbers as the data. Functions in same way as Python range() function.

Returns:

Returns a new DataCollection.

Return type:

DataCollection

Examples

>>> DataCollection.range(5).to_list()
[0, 1, 2, 3, 4]

ray_start(address=None, local_packages: Optional[list] = None, pip_packages: Optional[list] = None, silence=True)

Start the ray service. When using a remote cluster, all dependencies for custom functions and operators defined locally will need to be sent to the ray cluster. If using ray locally, within the runtime, avoid passing in any arguments.

Parameters:

• address (str) – The address for the ray service being connected to. If using ray cluster remotely with kubectl forwarded port, the most likely address will be “ray://localhost:10001”.

• local_packages (list[str]) – Whichever locally defined modules that are used within a custom function supplied to the pipeline, whether it be in lambda functions, locally registered operators, or functions themselves.

• pip_packages (list[str]) – Whichever pip installed modules that are used within a custom function supplied to the pipeline, whether it be in lambda functions, locally registered operators, or functions themselves.

classmethod read_camera(device_id=0, limit=-1)

Read images from a camera.

Parameters:

• device_id (int, optional) – The camera device ID. Defaults to 0.

• limit (int, optional) – The amount of images to capture. Defaults to -1.

Returns:

Collection with images.

Return type:

DataCollection

classmethod read_video(path, format='rgb24')

Load a video as a DataCollection.

Parameters:

• path (str) – The path of the video.

• format (str, optional) – The color format of video. Defaults to ‘rgb24’.

Returns:

DataCollection with the video.

Return type:

DataCollection

classmethod read_zip(url, pattern, mode='r')

Load files from url/path.

Parameters:

• zip_src (Union[str, path]) – The path leads to the image.

• pattern (str) – The filename pattern to extract.

• mode (str) – file open mode.

Returns:

The file handler for file in the zip file.

Return type:

(File)

register_dag(children)

Function that can be called within the function trying to be added to dag.

Parameters:

children (DataCollecton or list) – List of children DataCollection’s or singular child DataCollection.

Returns:

The resulting child DataCollections.

Return type:

DataCollection or list

remove(*args) → DataCollection

Remove element from DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 2, 3])
>>> dc.remove(1)
[2, 3]

report()

Report the metric scores, and if you are using ‘confusion matrix’ metric, please use jupyter to display the matrix.

Examples:

>>> from towhee import DataCollection
>>> from towhee import Entity
>>> dc1 = DataCollection([Entity(a=a, b=b, c=c) for a, b, c in zip([0,1,1,0,0], [0,1,1,1,0], [0,1,1,0,0])])
>>> dc1.with_metrics(['accuracy', 'recall']).evaluate['a', 'c'](name='lr').evaluate['a', 'b'](name='rf').report()
    accuracy  recall
lr       1.0     1.0
rf       0.8     0.8
{'lr': {'accuracy': 1.0, 'recall': 1.0}, 'rf': {'accuracy': 0.8, 'recall': 0.8}}
>>> dc1.with_metrics(['confusion_matrix']).evaluate['a', 'c'](name='lr').evaluate['a', 'b'](name='rf').report() 
<IPython.core.display.HTML object>
{'lr': {'confusion_matrix': array([[3, 0],
       [0, 2]])}, 'rf': {'confusion_matrix': array([[2, 1],
       [0, 2]])}}
>>> dc2 = DataCollection([Entity(pred=[1,6,2,7,8,3,9,10,4,5], act=[1,2,3,4,5])])
>>> dc2.with_metrics(['mean_average_precision', 'mean_hit_ratio']).evaluate['act', 'pred'](name='test').report()
      mean_average_precision  mean_hit_ratio
test                0.622222             1.0
{'test': {'mean_average_precision': 0.6222222222222221, 'mean_hit_ratio': 1.0}}

resolve(path, index, *arg, **kws)

Dispatch unknown operators.

Parameters:

• path (str) – The operator name.

• index (str) – The index of data being called on.

Returns:

The operator that corresponds to the path.

Return type:

_OperatorLazyWrapper

reverse(*args) → DataCollection

Reverse a DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 2, 3])
>>> dc.reverse()
[3, 2, 1]

rolling(size: int, step: int = 1, drop_head=True, drop_tail=True)

Create rolling windows from DataCollection.

Parameters:

• size (int) – Window size.

• drop_head (bool) – Drop head windows that are not full.

• drop_tail (bool) – Drop trailing windows that are not full.

Returns:

DataCollection of rolling windows.

Return type:

DataCollection

Examples

>>> from towhee import DataCollection
>>> dc = DataCollection(range(5))
>>> [list(batch) for batch in dc.rolling(3)]
[[0, 1, 2], [1, 2, 3], [2, 3, 4]]

>>> dc = DataCollection(range(5))
>>> [list(batch) for batch in dc.rolling(3, drop_head=False)]
[[0], [0, 1], [0, 1, 2], [1, 2, 3], [2, 3, 4]]

>>> dc = DataCollection(range(5))
>>> [list(batch) for batch in dc.rolling(3, drop_tail=False)]
[[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4], [4]]

>>> from towhee import DataCollection
>>> dc = DataCollection(range(5))
>>> dc.rolling(2, 2, drop_head=False, drop_tail=False)
[[0], [0, 1], [2, 3], [4]]

>>> from towhee import DataCollection
>>> dc = DataCollection(range(5))
>>> dc.rolling(2, 4, drop_head=False, drop_tail=False)
[[0], [0, 1], [4]]

run()

Iterate through the DataCollections data.

Stream-based DataCollections will not run if the data is not a datasink. This function is a datasink that consumes the data without any operations.

safe()

Shortcut for exception_safe.

sample(ratio=1.0) → DataCollection

Sample the data collection.

Parameters:

ratio (float) – sample ratio.

Returns:

Sampled data collection.

Return type:

DataCollection

Examples

>>> from towhee import DataCollection
>>> dc = DataCollection(range(10000))
>>> result = dc.sample(0.1)
>>> ratio = len(result.to_list()) / 10000.
>>> 0.09 < ratio < 0.11
True

select_from(other)

Select data from dc with list(self).

Parameters:

other (DataCollection) – DataCollection to select from.

Examples

>>> from towhee import DataCollection
>>> dc1 = DataCollection([0.8, 0.9, 8.1, 9.2])
>>> dc2 = DataCollection([[1, 2, 0], [2, 3, 0]])

>>> dc3 = dc2.select_from(dc1)
>>> list(dc3)
[[0.9, 8.1, 0.8], [8.1, 9.2, 0.8]]

serve(path='/', app=None)

Serve the DataFrame as a RESTful API

Parameters:

• path (str, optional) – API path. Defaults to ‘/’.

• app (_type_, optional) – The FastAPI app the API bind to, will create one if None.

Returns:

the app that bind to

Return type:

_type_

Examples:

>>> from fastapi import FastAPI
>>> from fastapi.testclient import TestClient
>>> app = FastAPI()

>>> import towhee
>>> with towhee.api() as api:
...     app1 = (
...         api.map(lambda x: x+' -> 1')
...            .map(lambda x: x+' => 1')
...            .serve('/app1', app)
...     )

>>> with towhee.api['x']() as api:
...     app2 = (
...         api.runas_op['x', 'x_plus_1'](func=lambda x: x+' -> 2')
...            .runas_op['x_plus_1', 'y'](func=lambda x: x+' => 2')
...            .select['y']()
...            .serve('/app2', app)
...     )

>>> with towhee.api() as api:
...     app2 = (
...         api.parse_json()
...            .runas_op['x', 'x_plus_1'](func=lambda x: x+' -> 3')
...            .runas_op['x_plus_1', 'y'](func=lambda x: x+' => 3')
...            .select['y']()
...            .serve('/app3', app)
...     )

>>> client = TestClient(app)
>>> client.post('/app1', '1').text
'"1 -> 1 => 1"'
>>> client.post('/app2', '2').text
'{"y":"2 -> 2 => 2"}'
>>> client.post('/app3', '{"x": "3"}').text
'{"y":"3 -> 3 => 3"}'

set_evaluating(state=None)

Set evaluating mode for stateful operators

Parameters:

state (State, optional) – Update the state storage. Defaults to None.

Returns:

data collection itself

Return type:

DataCollection

set_format_priority(format_priority: List[str])

Set format priority.

Parameters:

format_priority (List[str]) – The priority queue of format.

Returns:

DataCollection with format_priorty set.

Return type:

DataCollection

set_parallel(num_worker=2, backend='thread')

Set parallel execution for following calls.

Examples:

>>> from towhee import DataCollection
>>> import threading
>>> stage_1_thread_set = set()
>>> stage_2_thread_set = set()
>>> result = (
...     DataCollection.range(1000).stream().set_parallel(4)
...     .map(lambda x: stage_1_thread_set.add(threading.current_thread().ident))
...     .map(lambda x: stage_2_thread_set.add(threading.current_thread().ident)).to_list()
... )

>>> len(stage_2_thread_set)>1
True

set_state(state)

Set the state storage for DataCollection

Parameters:

state (State) – state storage

Returns:

data collection itself

Return type:

DataCollection

set_training(state=None)

Set training mode for stateful operators

Parameters:

state (State, optional) – Update the state storage. Defaults to None.

Returns:

data collection itself

Return type:

DataCollection

show(limit=5, header=None, tablefmt='html', formatter={})

Print the first n lines of a DataCollection.

Parameters:

• limit (int, optional) – The number of lines to print. Prints all if limit is negative. Defaults to 5.

• header (_type_, optional) – The field names. Defaults to None.

• tablefmt (str, optional) – The format of the output, supports html, plain, grid.. Defaults to ‘html’.

shuffle() → DataCollection

Shuffle an unstreamed data collection in place.

Returns:

Shuffled data collection.

Return type:

DataCollection

Examples

1. Shuffle: >>> from towhee import DataCollection >>> dc = DataCollection([0, 1, 2, 3, 4]) >>> a = dc.shuffle() >>> tuple(a) == tuple(range(5)) False

2. Streamed data collection is not supported: >>> dc = DataCollection([0, 1, 2, 3, 4]).stream() >>> _ = dc.shuffle() Traceback (most recent call last): TypeError: shuffle is not supported for streamed data collection.

sort(*args) → DataCollection

Sort a DataCollection.

Examples:

>>> from towhee import DataCollection
>>> dc = DataCollection([1, 4, 3])
>>> dc.sort()
[1, 3, 4]

split(count)

Split a dataframe into multiple dataframes.

Parameters:

count (int) – how many resulting DCs;

Returns:

copies of DC;

Return type:

[DataCollection, …]

Examples:

1. Split:

>>> from towhee import DataCollection
>>> dc = DataCollection([0, 1, 2, 3, 4]).stream()
>>> a, b, c = dc.split(3)
>>> a.zip(b, c).to_list()
[(0, 0, 0), (1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4)]

split_train_test(size: list = [0.9, 0.1], **kws)

Split DataCollection to train and test data.

Parameters:

size (list) – The size of the train and test.

Examples:

>>> from towhee.functional import DataCollection
>>> dc = DataCollection.range(10)
>>> train, test = dc.split_train_test(shuffle=False)
>>> train.to_list()
[0, 1, 2, 3, 4, 5, 6, 7, 8]
>>> test.to_list()
[9]

stream()

Create a stream data collection.

Examples: 1. Convert a data collection to streamed version

>>> from towhee import DataCollection
>>> dc = DataCollection([0, 1, 2, 3, 4])
>>> dc.is_stream
False

>>> dc = dc.stream()
>>> dc.is_stream
True

to_csv(csv_path: Union[str, Path], encoding: str = 'utf-8-sig')

Save dc as a csv file.

Parameters:

• csv_path (Union[str, Path]) – The path to save the dc to.

• encoding (str) – The encoding to use in the output file.

to_df() → DataFrame

Turn a DataCollection into a DataFrame.

Returns:

Resulting converted DataFrame.

Return type:

DataFrame

Examples

>>> from towhee import DataCollection, Entity
>>> e = [Entity(a=a, b=b) for a,b in zip(['abc', 'def', 'ghi'], [1,2,3])]
>>> dc = DataCollection(e)
>>> type(dc)
<class 'towhee.functional.data_collection.DataCollection'>

>>> type(dc.to_df())
<class 'towhee.functional.data_collection.DataFrame'>

to_list() → list

Convert DataCollection to list.

Returns:

List of values stored in DataCollection.

Return type:

list

Examples

>>> DataCollection.range(5).to_list()
[0, 1, 2, 3, 4]

to_video(output_path, codec=None, rate=None, width=None, height=None, format=None, template=None, audio_src=None)

Encode a video; with audio if provided.

Parameters:

• output_path (str) – Path to output the video to.

• codec (str, optional) – Which codec to use for encoding. Defaults to None.

• rate (int, optional) – The framrate of the video. Defaults to None.

• width (int, optional) – The width of the video image. Defaults to None.

• height (int, optional) – The height of the video image. Defaults to None.

• format (str, optional) – The color format of the video. Defaults to None.

• template (str, optional) – The template video stream of the ouput video stream. Defaults to None.

• audio_src (str, optional) – Audio path to include in video. Defaults to None.

unstream()

Create a unstream data collection.

Examples:

1. Create a unstream data collection

>>> from towhee import DataCollection
>>> dc = DataCollection(iter(range(5))).unstream()
>>> dc.is_stream
False

2. Convert a streamed data collection to unstream version

>>> dc = DataCollection(iter(range(5)))
>>> dc.is_stream
True
>>> dc = dc.unstream()
>>> dc.is_stream
False

zip(*others) → DataCollection

Combine multiple data collections.

Parameters:

*others (DataCollection) – The other data collections.

Returns:

Data collection with zipped values.

Return type:

DataCollection

Examples

>>> from towhee import DataCollection
>>> dc1 = DataCollection([1,2,3,4])
>>> dc2 = DataCollection([1,2,3,4]).map(lambda x: x+1)
>>> dc3 = dc1.zip(dc2)
>>> list(dc3)
[(1, 2), (2, 3), (3, 4), (4, 5)]