annoy.Annoy legacy c-api with examples

An example showing the Annoy, AnnoyIndex class.

See also

• from_low_level

• https://docs.python.org/3/library/pickle.html#what-can-be-pickled-and-unpickled

import tempfile
from pathlib import Path

import numpy as np
import random; random.seed(0)

# from annoy import Annoy, AnnoyIndex
from scikitplot.cexternals._annoy import Annoy, AnnoyIndex
# from scikitplot.annoy import Annoy, AnnoyIndex, Index

print(AnnoyIndex.__doc__)

Compiled with GCC/Clang. Using 512-bit AVX instructions.

Approximate Nearest Neighbors index (Annoy) with a small, lazy C-extension wrapper.

::

>>> Annoy(
>>>     f=None,
>>>     metric=None,
>>>     *,
>>>     n_trees=-1,  # None = -1 = auto
>>>     n_neighbors=5,  # None = 5
>>>     on_disk_path=None,
>>>     prefault=None,
>>>     seed=None,
>>>     verbose=None,
>>>     schema_version=None,
>>>     n_jobs=None,  # None = -1
>>>     l1_ratio = 0.0,  # None = 0.0 Future
>>> )

Parameters
----------
f : int or None, optional, default=None
    Vector dimension. If ``0`` or ``None``, dimension may be inferred from the
    first vector passed to ``add_item`` (lazy mode).
    If None, treated as ``0`` (reset to default).
metric : {"angular", "cosine", "euclidean", "l2", "lstsq", "manhattan", "l1", "cityblock", "taxicab", "dot", "@", ".", "dotproduct", "inner", "innerproduct", "hamming"} or None, optional, default=None
    Distance metric (one of 'angular', 'euclidean', 'manhattan', 'dot', 'hamming').
    If omitted and ``f > 0``, defaults to ``'angular'`` (cosine-like).
    If omitted and ``f == 0``, metric may be set later before construction.
    If None, behavior depends on ``f``:

    * If ``f > 0``: defaults to ``'angular'`` (legacy behavior; may emit a
      :class:`FutureWarning`).
    * If ``f == 0``: leaves the metric unset (lazy). You may set
      :attr:`metric` later before construction, or it will default to
      ``'angular'`` on first :meth:`add_item`.
n_trees : int, default=-1
    Number of trees to build. If -1, auto-selects based on dimension.
    More trees = better accuracy but slower queries and more memory.
n_neighbors : int, default=5
    Non-negative integer Number of neighbors to retrieve for each query.
on_disk_path : str or None, optional, default=None
    If provided, configures the path for on-disk building. When the underlying
    index exists, this enables on-disk build mode (equivalent to calling
    :meth:`on_disk_build` with the same filename).

    Note: Annoy core truncates the target file when enabling on-disk build.
    This wrapper treats ``on_disk_path`` as strictly equivalent to calling
    :meth:`on_disk_build` with the same filename (truncate allowed).

    In lazy mode (``f==0`` and/or ``metric is None``), activation occurs once
    the underlying C++ index is created.
prefault : bool or None, optional, default=None
    If True, request page-faulting index pages into memory when loading
    (when supported by the underlying platform/backing).
    If None, treated as ``False`` (reset to default).
seed : int or None, optional, default=None
    Non-negative integer seed. If set before the index is constructed,
    the seed is stored and applied when the C++ index is created.
    Seed value ``0`` is treated as \"use Annoy's deterministic default seed\"
    (a :class:`UserWarning` is emitted when ``0`` is explicitly provided).
verbose : int or None, optional, default=None
    Verbosity level. Values are clamped to the range ``[-2, 2]``.
    ``level >= 1`` enables Annoy's verbose logging; ``level <= 0`` disables it.
    Logging level inspired by gradient-boosting libraries:

    * ``<= 0`` : quiet (warnings only)
    * ``1``    : info (Annoy's ``verbose=True``)
    * ``>= 2`` : debug (currently same as info, reserved for future use)
schema_version : int, optional, default=None
    Serialization/compatibility strategy marker.

    This does not change the Annoy on-disk format, but it *does* control
    how the index is snapshotted in pickles.

    * ``0`` or ``1``: pickle stores a ``portable-v1`` snapshot (fast restore,
      ABI-checked).
    * ``2``: pickle stores ``canonical-v1`` (portable across ABIs; restores by
      rebuilding deterministically).
    * ``>=3``: pickle stores both portable and canonical (canonical is used as
      a fallback if the ABI check fails).

    If None, treated as ``0`` (reset to default).
n_jobs : int or None, default=None
    Number of threads. If -1, uses all available cores.
    If None, treated as ``-1``.

Attributes
----------
f : int, default=0
    Vector dimension. ``0`` means "unknown / lazy".
metric : {'angular', 'euclidean', 'manhattan', 'dot', 'hamming'}, default="angular"
    Canonical metric name, or None if not configured yet (lazy).
n_neighbors : int, default=5
    Non-negative integer Number of neighbors to retrieve for each query.
on_disk_path : str or None, optional, default=None
    Configured on-disk build path. Setting this attribute enables on-disk
    build mode (equivalent to :meth:`on_disk_build`), with safety checks
    to avoid implicit truncation of existing files.
prefault : bool, default=False
    Stored prefault flag (see :meth:`load`/`:meth:`save` prefault parameters).
seed : int or None, optional, default=None
    Non-negative integer seed. Also provides :meth:`random_state`
verbose : int or None, optional, default=None
    Verbosity level.
schema_version : int, default=0
    Reserved schema/version marker (stored; does not affect on-disk format).
n_features : int
    Alias of :meth:`f` (dimension), provided for scikit-learn naming parity.
    Also provides :meth:`n_features_`, :meth:`n_features_in_`.
n_features_out_ : int
    Number of output features produced by transform.
feature_names_in_ : list-like
    Input feature names seen during fit.
    Set only when explicitly provided via fit(..., feature_names=...).
y : list-like | None, optional, default=None
    Dense label cache aligned to item ids (``0 .. n_items-1``). This is a
    convenience view for scikit-learn style APIs and may be derived from
    ``y_map`` lazily.
    The setter accepts sequences only (a dict is not allowed); when possible it
    validates that ``len(y) == n_items`` and updates ``y_map`` deterministically.
y_map : dict | None, optional, default=None
    Canonical sparse mapping ``{item_id -> label}``. Keys must be non-negative
    integers and (when an index exists) strictly less than ``n_items``.
    Setting this property invalidates the dense ``y`` cache; ``y`` is
    materialized lazily (missing keys become ``None``).

See Also
--------
add_item : Add a vector to the index.
build : Build the forest after adding items.
unbuild : Remove trees to allow adding more items.
get_nns_by_item, get_nns_by_vector : Query nearest neighbours.
save, load : Persist the index to/from disk.
serialize, deserialize : Persist the index to/from bytes.
set_seed : Set the random seed deterministically.
set_verbose : Set verbosity level.
info : Return a structured summary of the current index.

Notes
-----
* Once the underlying C++ index is created, ``f`` and ``metric`` are immutable.
  This keeps the object consistent and avoids undefined behavior.
* The C++ index is created lazily when sufficient information is available:
  when both ``f > 0`` and ``metric`` are known, or when an operation that
  requires the index is first executed.
* If ``f == 0``, the dimensionality is inferred from the first non-empty vector
  passed to :meth:`add_item` and is then fixed for the lifetime of the index.
* Assigning ``None`` to :attr:`f` is not supported. Use ``0`` for lazy
  inference (this matches ``Annoy(f=None, ...)`` at construction time).
* If ``metric`` is omitted while ``f > 0``, the current behavior defaults to
  ``'angular'`` and may emit a :class:`FutureWarning`. To avoid warnings and
  future behavior changes, always pass ``metric=...`` explicitly.
* Items must be added *before* calling :meth:`build`. After :meth:`build`, the
  index becomes read-only; to add more items, call :meth:`unbuild`, add items
  again with :meth:`add_item`, then call :meth:`build` again.
* Very large indexes can be built directly on disk with :meth:`on_disk_build`
  and then memory-mapped with :meth:`load`.
* :meth:`info` returns a structured summary (dimension, metric, counts, and
  optional memory usage) suitable for programmatic inspection.
* This wrapper stores user configuration (e.g., seed/verbosity) even before the
  C++ index exists and applies it deterministically upon construction.

Developer Notes:

- Source of truth:

  * ``f`` (int) and ``metric_id`` (enum) describe configuration.
  * ``ptr`` is NULL when index is not constructed.

- Invariant:

  * ``ptr != NULL`` implies ``f > 0`` and ``metric_id != METRIC_UNKNOWN``.

Examples
--------
>>> from annoy import Annoy, AnnoyIndex

High-level API:

>>> from scikitplot.cexternals._annoy import Annoy, AnnoyIndex
>>> from scikitplot.annoy import Annoy, AnnoyIndex, Index

The lifecycle follows the examples in ``test.ipynb``:

1. **Construct the index**

>>> import random; random.seed(0)
>>> # from annoy import AnnoyIndex
>>> from scikitplot.cexternals._annoy import Annoy, AnnoyIndex
>>> from scikitplot.annoy import Annoy, AnnoyIndex, Index

>>> idx = Annoy(f=3, metric="angular")
>>> idx.f, idx.metric
(3, 'angular')

If you pass ``f=0`` the dimension can be inferred on the first
call to :meth:`add_item`.

2. **Add items**

>>> idx.add_item(0, [1.0, 0.0, 0.0])
>>> idx.add_item(1, [0.0, 1.0, 0.0])
>>> idx.add_item(2, [0.0, 0.0, 1.0])
>>> idx.get_n_items()
3

3. **Build the forest**

>>> idx.build(n_trees=-1)
>>> idx.get_n_trees()
10
>>> idx.memory_usage()  # byte
543076

After :meth:`build` the index becomes read-only.  You can still
query, save, load and serialize it.

4. **Query neighbours**

By stored item id:

>>> idx.get_nns_by_item(0, 5)
[0, 1, 2, ...]

With distances:

>>> idx.get_nns_by_item(0, 5, include_distances=True)
([0, 1, 2, ...], [0.0, 1.22, 1.26, ...])

Or by an explicit query vector:

>>> idx.get_nns_by_vector([0.1, 0.2, 0.3], 5, include_distances=True)
([103, 71, 160, 573, 672], [...])

5. **Persistence**

To work with memory-mapped indices on disk:

>>> idx.save("annoy_test.annoy")
>>> idx2 = Annoy(f=100, metric="angular")
>>> idx2.load("annoy_test.annoy")
>>> idx2.get_n_items()
1000

Or via raw byte:

>>> buf = idx.serialize()
>>> new_idx = Annoy(f=100, metric="angular")
>>> new_idx.deserialize(buf)
>>> new_idx.get_n_items()
1000

You can release OS resources with :meth:`unload` and drop the
current forest with :meth:`unbuild`.

from scikitplot.annoy._annoy import Index  # cython Total: 160 concrete index-data-metrics types 8 S × 4 T × 5 metrics
from scikitplot.annoy import AnnoyIndex  # Cpp 1 concrete index-data type (uint64 index float32 data)

# 32 bit int index
i = Index(10, "angular")
i.load(f"./test.tree")
# This might change in the future if we change the search algorithm, but in that case let's update the test
result1 = i.get_nns_by_item(0, 10)

# 64 bit uint index
j = AnnoyIndex(10, "angular")
for idx in range(i.get_n_items()):
    j.add_item(idx, i.get_item(idx))
j.build(10)
j.save("test64.tree")
result2 = i.get_nns_by_item(0, 10)

i = AnnoyIndex(10, "angular")
i.load(f"./test64.tree")
# This might change in the future if we change the search algorithm, but in that case let's update the test
result3 = i.get_nns_by_item(0, 10)

result1, result2, result3

([0, 85, 42, 11, 54, 38, 53, 66, 19, 31], [0, 85, 42, 11, 54, 38, 53, 66, 19, 31], [0, 85, 42, 11, 54, 38, 26, 53, 66, 3])

import sys

# TODO: change this import to wherever your modified AnnoyIndex lives
# e.g. scikitplot.cexternals._annoy or similar
import scikitplot.cexternals._annoy as annoy
# from scikitplot import annoy

# spotify/annoy Backward compatibility helper
sys.modules["annoy"] = annoy  # now `import annoy` will resolve to your module

import annoy

print(annoy.AnnoyIndex.__doc__)

Compiled with GCC/Clang. Using 512-bit AVX instructions.

Approximate Nearest Neighbors index (Annoy) with a small, lazy C-extension wrapper.

::

>>> Annoy(
>>>     f=None,
>>>     metric=None,
>>>     *,
>>>     n_trees=-1,  # None = -1 = auto
>>>     n_neighbors=5,  # None = 5
>>>     on_disk_path=None,
>>>     prefault=None,
>>>     seed=None,
>>>     verbose=None,
>>>     schema_version=None,
>>>     n_jobs=None,  # None = -1
>>>     l1_ratio = 0.0,  # None = 0.0 Future
>>> )

Parameters
----------
f : int or None, optional, default=None
    Vector dimension. If ``0`` or ``None``, dimension may be inferred from the
    first vector passed to ``add_item`` (lazy mode).
    If None, treated as ``0`` (reset to default).
metric : {"angular", "cosine", "euclidean", "l2", "lstsq", "manhattan", "l1", "cityblock", "taxicab", "dot", "@", ".", "dotproduct", "inner", "innerproduct", "hamming"} or None, optional, default=None
    Distance metric (one of 'angular', 'euclidean', 'manhattan', 'dot', 'hamming').
    If omitted and ``f > 0``, defaults to ``'angular'`` (cosine-like).
    If omitted and ``f == 0``, metric may be set later before construction.
    If None, behavior depends on ``f``:

    * If ``f > 0``: defaults to ``'angular'`` (legacy behavior; may emit a
      :class:`FutureWarning`).
    * If ``f == 0``: leaves the metric unset (lazy). You may set
      :attr:`metric` later before construction, or it will default to
      ``'angular'`` on first :meth:`add_item`.
n_trees : int, default=-1
    Number of trees to build. If -1, auto-selects based on dimension.
    More trees = better accuracy but slower queries and more memory.
n_neighbors : int, default=5
    Non-negative integer Number of neighbors to retrieve for each query.
on_disk_path : str or None, optional, default=None
    If provided, configures the path for on-disk building. When the underlying
    index exists, this enables on-disk build mode (equivalent to calling
    :meth:`on_disk_build` with the same filename).

    Note: Annoy core truncates the target file when enabling on-disk build.
    This wrapper treats ``on_disk_path`` as strictly equivalent to calling
    :meth:`on_disk_build` with the same filename (truncate allowed).

    In lazy mode (``f==0`` and/or ``metric is None``), activation occurs once
    the underlying C++ index is created.
prefault : bool or None, optional, default=None
    If True, request page-faulting index pages into memory when loading
    (when supported by the underlying platform/backing).
    If None, treated as ``False`` (reset to default).
seed : int or None, optional, default=None
    Non-negative integer seed. If set before the index is constructed,
    the seed is stored and applied when the C++ index is created.
    Seed value ``0`` is treated as \"use Annoy's deterministic default seed\"
    (a :class:`UserWarning` is emitted when ``0`` is explicitly provided).
verbose : int or None, optional, default=None
    Verbosity level. Values are clamped to the range ``[-2, 2]``.
    ``level >= 1`` enables Annoy's verbose logging; ``level <= 0`` disables it.
    Logging level inspired by gradient-boosting libraries:

    * ``<= 0`` : quiet (warnings only)
    * ``1``    : info (Annoy's ``verbose=True``)
    * ``>= 2`` : debug (currently same as info, reserved for future use)
schema_version : int, optional, default=None
    Serialization/compatibility strategy marker.

    This does not change the Annoy on-disk format, but it *does* control
    how the index is snapshotted in pickles.

    * ``0`` or ``1``: pickle stores a ``portable-v1`` snapshot (fast restore,
      ABI-checked).
    * ``2``: pickle stores ``canonical-v1`` (portable across ABIs; restores by
      rebuilding deterministically).
    * ``>=3``: pickle stores both portable and canonical (canonical is used as
      a fallback if the ABI check fails).

    If None, treated as ``0`` (reset to default).
n_jobs : int or None, default=None
    Number of threads. If -1, uses all available cores.
    If None, treated as ``-1``.

Attributes
----------
f : int, default=0
    Vector dimension. ``0`` means "unknown / lazy".
metric : {'angular', 'euclidean', 'manhattan', 'dot', 'hamming'}, default="angular"
    Canonical metric name, or None if not configured yet (lazy).
n_neighbors : int, default=5
    Non-negative integer Number of neighbors to retrieve for each query.
on_disk_path : str or None, optional, default=None
    Configured on-disk build path. Setting this attribute enables on-disk
    build mode (equivalent to :meth:`on_disk_build`), with safety checks
    to avoid implicit truncation of existing files.
prefault : bool, default=False
    Stored prefault flag (see :meth:`load`/`:meth:`save` prefault parameters).
seed : int or None, optional, default=None
    Non-negative integer seed. Also provides :meth:`random_state`
verbose : int or None, optional, default=None
    Verbosity level.
schema_version : int, default=0
    Reserved schema/version marker (stored; does not affect on-disk format).
n_features : int
    Alias of :meth:`f` (dimension), provided for scikit-learn naming parity.
    Also provides :meth:`n_features_`, :meth:`n_features_in_`.
n_features_out_ : int
    Number of output features produced by transform.
feature_names_in_ : list-like
    Input feature names seen during fit.
    Set only when explicitly provided via fit(..., feature_names=...).
y : list-like | None, optional, default=None
    Dense label cache aligned to item ids (``0 .. n_items-1``). This is a
    convenience view for scikit-learn style APIs and may be derived from
    ``y_map`` lazily.
    The setter accepts sequences only (a dict is not allowed); when possible it
    validates that ``len(y) == n_items`` and updates ``y_map`` deterministically.
y_map : dict | None, optional, default=None
    Canonical sparse mapping ``{item_id -> label}``. Keys must be non-negative
    integers and (when an index exists) strictly less than ``n_items``.
    Setting this property invalidates the dense ``y`` cache; ``y`` is
    materialized lazily (missing keys become ``None``).

See Also
--------
add_item : Add a vector to the index.
build : Build the forest after adding items.
unbuild : Remove trees to allow adding more items.
get_nns_by_item, get_nns_by_vector : Query nearest neighbours.
save, load : Persist the index to/from disk.
serialize, deserialize : Persist the index to/from bytes.
set_seed : Set the random seed deterministically.
set_verbose : Set verbosity level.
info : Return a structured summary of the current index.

Notes
-----
* Once the underlying C++ index is created, ``f`` and ``metric`` are immutable.
  This keeps the object consistent and avoids undefined behavior.
* The C++ index is created lazily when sufficient information is available:
  when both ``f > 0`` and ``metric`` are known, or when an operation that
  requires the index is first executed.
* If ``f == 0``, the dimensionality is inferred from the first non-empty vector
  passed to :meth:`add_item` and is then fixed for the lifetime of the index.
* Assigning ``None`` to :attr:`f` is not supported. Use ``0`` for lazy
  inference (this matches ``Annoy(f=None, ...)`` at construction time).
* If ``metric`` is omitted while ``f > 0``, the current behavior defaults to
  ``'angular'`` and may emit a :class:`FutureWarning`. To avoid warnings and
  future behavior changes, always pass ``metric=...`` explicitly.
* Items must be added *before* calling :meth:`build`. After :meth:`build`, the
  index becomes read-only; to add more items, call :meth:`unbuild`, add items
  again with :meth:`add_item`, then call :meth:`build` again.
* Very large indexes can be built directly on disk with :meth:`on_disk_build`
  and then memory-mapped with :meth:`load`.
* :meth:`info` returns a structured summary (dimension, metric, counts, and
  optional memory usage) suitable for programmatic inspection.
* This wrapper stores user configuration (e.g., seed/verbosity) even before the
  C++ index exists and applies it deterministically upon construction.

Developer Notes:

- Source of truth:

  * ``f`` (int) and ``metric_id`` (enum) describe configuration.
  * ``ptr`` is NULL when index is not constructed.

- Invariant:

  * ``ptr != NULL`` implies ``f > 0`` and ``metric_id != METRIC_UNKNOWN``.

Examples
--------
>>> from annoy import Annoy, AnnoyIndex

High-level API:

>>> from scikitplot.cexternals._annoy import Annoy, AnnoyIndex
>>> from scikitplot.annoy import Annoy, AnnoyIndex, Index

The lifecycle follows the examples in ``test.ipynb``:

1. **Construct the index**

>>> import random; random.seed(0)
>>> # from annoy import AnnoyIndex
>>> from scikitplot.cexternals._annoy import Annoy, AnnoyIndex
>>> from scikitplot.annoy import Annoy, AnnoyIndex, Index

>>> idx = Annoy(f=3, metric="angular")
>>> idx.f, idx.metric
(3, 'angular')

If you pass ``f=0`` the dimension can be inferred on the first
call to :meth:`add_item`.

2. **Add items**

>>> idx.add_item(0, [1.0, 0.0, 0.0])
>>> idx.add_item(1, [0.0, 1.0, 0.0])
>>> idx.add_item(2, [0.0, 0.0, 1.0])
>>> idx.get_n_items()
3

3. **Build the forest**

>>> idx.build(n_trees=-1)
>>> idx.get_n_trees()
10
>>> idx.memory_usage()  # byte
543076

After :meth:`build` the index becomes read-only.  You can still
query, save, load and serialize it.

4. **Query neighbours**

By stored item id:

>>> idx.get_nns_by_item(0, 5)
[0, 1, 2, ...]

With distances:

>>> idx.get_nns_by_item(0, 5, include_distances=True)
([0, 1, 2, ...], [0.0, 1.22, 1.26, ...])

Or by an explicit query vector:

>>> idx.get_nns_by_vector([0.1, 0.2, 0.3], 5, include_distances=True)
([103, 71, 160, 573, 672], [...])

5. **Persistence**

To work with memory-mapped indices on disk:

>>> idx.save("annoy_test.annoy")
>>> idx2 = Annoy(f=100, metric="angular")
>>> idx2.load("annoy_test.annoy")
>>> idx2.get_n_items()
1000

Or via raw byte:

>>> buf = idx.serialize()
>>> new_idx = Annoy(f=100, metric="angular")
>>> new_idx.deserialize(buf)
>>> new_idx.get_n_items()
1000

You can release OS resources with :meth:`unload` and drop the
current forest with :meth:`unbuild`.

import gc

x = annoy.Annoy()
assert gc.is_tracked(x) in (True, False)  # must NOT crash
gc.is_tracked(x)

True

x = annoy.Annoy()
del x
import gc; gc.collect()

12

Annoy(), \
Annoy(None), \
Annoy(None, None), \
Annoy(1), \
Annoy(1,".", seed=1, verbose=1), \
Annoy(1,"@", seed=1, verbose=1).set_verbose(2).set_seed(2)

/home/circleci/repo/galleries/examples/annoy/plot_Annoy_legacy_c_api.py:90: FutureWarning: The default argument for metric will be removed in a future version. Please pass metric='angular' explicitly.
  Annoy(1), \

(Annoy(**{'f': 0, 'metric': None, 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0}), Annoy(**{'f': 0, 'metric': None, 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0}), Annoy(**{'f': 0, 'metric': None, 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0}), Annoy(**{'f': 1, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0}), Annoy(**{'f': 1, 'metric': 'dot', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': 1, 'verbose': 1, 'schema_version': 0}), Annoy(**{'f': 1, 'metric': 'dot', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': 2, 'verbose': 2, 'schema_version': 0}))

Annoy()

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	0
🗗 Copy	metric	None
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

AnnoyIndex()

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	0
🗗 Copy	metric	None
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

print(AnnoyIndex())

Annoy(**{'f': 0, 'metric': None, 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

# from IPython.display import display
# from IPython.core.display import HTML
# display(HTML('<h1>Hello, world!</h1>'))

# display(AnnoyIndex())

AnnoyIndex().info()

{'f': 0, 'metric': None, 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 0, 'n_trees': 0}

AnnoyIndex().repr_info()

"Annoy(**{'f': 0, 'metric': None, 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 0, 'n_trees': 0})"

# =============================================================
# 1. Construction
# =============================================================
idx = AnnoyIndex()
idx = AnnoyIndex(None, None)
print("Index dimension:", idx.f)
print("Metric         :", idx.metric)
print(idx.info())
print(idx)
print(type(idx))
idx
# help(idx.info)

Index dimension: 0
Metric         : None
{'f': 0, 'metric': None, 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 0, 'n_trees': 0}
Annoy(**{'f': 0, 'metric': None, 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})
<class 'scikitplot.cexternals._annoy.Annoy'>

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	0
🗗 Copy	metric	None
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

dir(idx)

['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setstate__', '__sizeof__', '__sklearn_clone__', '__sklearn_is_fitted__', '__sklearn_tags__', '__str__', '__subclasshook__', '_f', '_metric_id', '_on_disk_path', '_prefault', '_repr_html_', '_schema_version', '_y', '_y_map', 'add_item', 'build', 'deserialize', 'f', 'feature_names_in_', 'fit', 'fit_transform', 'get_distance', 'get_feature_names_out', 'get_item', 'get_n_items', 'get_n_trees', 'get_nns_by_item', 'get_nns_by_vector', 'get_params', 'info', 'load', 'memory_usage', 'metric', 'n_features', 'n_features_', 'n_features_in_', 'n_features_out_', 'n_neighbors', 'on_disk_build', 'on_disk_path', 'prefault', 'random_state', 'rebuild', 'repr_info', 'save', 'schema_version', 'seed', 'serialize', 'set_params', 'set_seed', 'set_verbose', 'set_verbosity', 'transform', 'unbuild', 'unload', 'verbose', 'y', 'y_map']

# AttributeError: readonly attribute
# idx._metric_id = 1
idx._f, idx._metric_id, idx._on_disk_path

(0, 0, None)

idx.f, idx.metric, idx.on_disk_path
idx

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	0
🗗 Copy	metric	None
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

idx.metric = "dot"
idx

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	0
🗗 Copy	metric	'dot'
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

idx.f, idx.metric, idx.on_disk_path

(0, 'dot', None)

type(idx)

# =============================================================
# 1. Construction
# =============================================================
# idx = AnnoyIndex(f=3)
idx.add_item(0, [1, 0, 0])
print("Index dimension:", idx.f)
print("Metric         :", idx.metric)
print(idx.info())
print(idx)
idx
# idx.on_disk_path is None

Index dimension: 3
Metric         : dot
{'f': 3, 'metric': 'dot', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1, 'n_trees': 0}
Annoy(**{'f': 3, 'metric': 'dot', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	3
🗗 Copy	metric	'dot'
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

# =============================================================
# 1. Construction
# =============================================================
idx = AnnoyIndex(f=3, metric="angular")
print("Index dimension:", idx.f)
print("Metric         :", idx.metric)
print(idx.info())
print(idx)
idx

Index dimension: 3
Metric         : angular
{'f': 3, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 0, 'n_trees': 0}
Annoy(**{'f': 3, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	3
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

# =============================================================
# 2. Add items
# =============================================================
idx.add_item(0, [0, 0, 0])

idx.add_item(1, [1, 0, 0])
idx.add_item(2, [0, 1, 0])
idx.add_item(3, [0, 0, 1])

idx.add_item(4, [2, 0, 0])
idx.add_item(5, [0, 2, 0])
idx.add_item(6, [0, 0, 2])

idx.add_item(7, [3, 0, 0])
idx.add_item(8, [0, 3, 0])
idx.add_item(9, [0, 0, 3])

idx.add_item(10, [4, 0, 0])
idx.add_item(11, [0, 4, 0])
idx.add_item(12, [0, 0, 4])

idx.add_item(12, [4, 0, 0])
idx.add_item(13, [0, 4, 0])
idx.add_item(14, [0, 0, 4])

print("Number of items:", idx.get_n_items())
print("Index dimension:", idx.f)
print("Metric         :", idx.metric)
print(idx.info())
print(idx)
idx

Number of items: 15
Index dimension: 3
Metric         : angular
{'f': 3, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 15, 'n_trees': 0}
Annoy(**{'f': 3, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	3
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

def plot(idx, y=None, **kwargs):
    import numpy as np
    import matplotlib.pyplot as plt
    import scikitplot.cexternals._annoy._plotting as utils

    single = np.zeros(idx.get_n_items(), dtype=int)
    if y is None:
        double = np.random.uniform(0, 1, idx.get_n_items()).round()

    # single vs double
    fig, ax = plt.subplots(ncols=2, figsize=(12, 5))
    alpha = kwargs.pop("alpha", 0.8)
    y2 = utils.plot_annoy_index(
        idx,
        dims = list(range(idx.f)),
        plot_kwargs={"draw_legend": False},
        ax=ax[0],
    )[0]
    utils.plot_annoy_knn_edges(
        idx,
        y2,
        k=1,
        line_kwargs={"alpha": alpha},
        ax=ax[1],
    )

idx.unbuild()
idx.build(100)
plot(idx)

# =============================================================
# 1. Construction
# =============================================================
idx = AnnoyIndex(100, metric="angular")
print("Index dimension:", idx.f)
print("Metric         :", idx.metric)
idx.on_disk_build("annoy_test_1.annoy"), idx#.on_disk_path
# help(idx.on_disk_build)

Index dimension: 100
Metric         : angular

(Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0}), Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0}))

# =============================================================
# 2. Add items
# =============================================================
f=100
n=1000
for i in range(n):
    if(i % (n//10) == 0): print(f"{i} / {n} = {1.0 * i / n}")
    # v = []
    # for z in range(f):
    #     v.append(random.gauss(0, 1))
    v = [random.gauss(0, 1) for _ in range(f)]
    idx.add_item(i, v)

print("Number of items:", idx.get_n_items())
print("Index dimension:", idx.f)
print("Metric         :", idx.metric)
print(idx.info())
print(idx)
idx

0 / 1000 = 0.0
100 / 1000 = 0.1
200 / 1000 = 0.2
300 / 1000 = 0.3
400 / 1000 = 0.4
500 / 1000 = 0.5
600 / 1000 = 0.6
700 / 1000 = 0.7
800 / 1000 = 0.8
900 / 1000 = 0.9
Number of items: 1000
Index dimension: 100
Metric         : angular
{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 0}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	'annoy_test_1.annoy'
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

# =============================================================
# 3. Build index
# =============================================================
idx.build(10)
print("Trees:", idx.get_n_trees())
print("Memory usage:", idx.memory_usage(), "bytes")
print(idx.info())
print(idx)
idx
# help(idx.build)

Trees: 10
Memory usage: 688688 bytes
{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 10, 'memory_usage_byte': 688688, 'memory_usage_mib': 0.6567840576171875}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	10
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	'annoy_test_1.annoy'
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

def plot(idx, y=None, **kwargs):
    import numpy as np
    import matplotlib.pyplot as plt
    import scikitplot.cexternals._annoy._plotting as utils

    single = np.zeros(idx.get_n_items(), dtype=int)
    if y is None:
        double = np.random.uniform(0, 1, idx.get_n_items()).round()

    # single vs double
    fig, ax = plt.subplots(ncols=2, figsize=(12, 5))
    alpha = kwargs.pop("alpha", 0.8)
    y2 = utils.plot_annoy_index(
        idx,
        plot_kwargs={"draw_legend": False},
        ax=ax[0],
    )[0]
    utils.plot_annoy_knn_edges(
        idx,
        y2,
        k=1,
        line_kwargs={"alpha": alpha},
        ax=ax[1],
    )

print(idx.info())
print(idx)
idx

{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 10, 'memory_usage_byte': 688688, 'memory_usage_mib': 0.6567840576171875}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	10
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	'annoy_test_1.annoy'
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

import numpy as np
import scikitplot.cexternals._annoy._plotting as utils

# labels = np.random.uniform(0, 1, idx.get_n_items()).round()
labels = np.zeros(idx.get_n_items(), dtype=int)

nth = 1
ids_subset = np.arange(0, idx.get_n_items(), nth)  # every 1th item

y2, ids, ax = utils.plot_annoy_index(
    idx,
    labels=labels[ids_subset],    # must match ids length
    ids=ids_subset,
    projection="pca",
    plot_kwargs={"draw_legend": True},
)

utils.plot_annoy_knn_edges(idx, y2, ids=ids, k=nth)

<Axes: >

import numpy as np
import scikitplot.cexternals._annoy._plotting as utils

# labels = np.random.uniform(0, 1, idx.get_n_items()).round()
labels = np.zeros(idx.get_n_items(), dtype=int)

X, ids = utils.annoy_index_to_array(idx)  # X: (n_items, f), ids: (n_items,)
ax = utils.plot(
    x=X,
    y=labels,  # must match y2 rows labels[ids]
    title="2D embedding",
    figsize=(7, 7),
    draw_legend=True,
    scatter_kwargs={"s": 6, "alpha": 0.7},
)

idx.unbuild()
print(idx.info())
print(idx)
idx

{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 0}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	10
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	'annoy_test_1.annoy'
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

idx.build(10)
print(idx.info())
print(idx)
idx

{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 10, 'memory_usage_byte': 688688, 'memory_usage_mib': 0.6567840576171875}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	10
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	'annoy_test_1.annoy'
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

# =============================================================
# 1. Construction
# =============================================================
idx = AnnoyIndex(0, metric="angular")
print("Index dimension:", idx.f)
print("Metric         :", idx.metric)
print(idx.info())
print(idx)
idx

Index dimension: 0
Metric         : angular
{'f': 0, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 0, 'n_trees': 0}
Annoy(**{'f': 0, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	0
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

# =============================================================
# 2. Add items
# =============================================================
f=100
n=1000
for i in range(n):
    if(i % (n//10) == 0): print(f"{i} / {n} = {1.0 * i / n}")
    # v = []
    # for z in range(f):
    #     v.append(random.gauss(0, 1))
    v = [random.gauss(0, 1) for _ in range(f)]
    idx.add_item(i, v)

print("Number of items:", idx.get_n_items())
print("Index dimension:", idx.f)
print("Metric         :", idx.metric)
print(idx.info())
print(idx)
idx

0 / 1000 = 0.0
100 / 1000 = 0.1
200 / 1000 = 0.2
300 / 1000 = 0.3
400 / 1000 = 0.4
500 / 1000 = 0.5
600 / 1000 = 0.6
700 / 1000 = 0.7
800 / 1000 = 0.8
900 / 1000 = 0.9
Number of items: 1000
Index dimension: 100
Metric         : angular
{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 0}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

# =============================================================
# 3. Build index
# =============================================================
idx.build(10)
print("Trees:", idx.get_n_trees())
print("Memory usage:", idx.memory_usage(), "bytes")
print(idx.info())
print(idx)
idx
# help(idx.get_n_trees)

Trees: 10
Memory usage: 818008 bytes
{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 10, 'memory_usage_byte': 818008, 'memory_usage_mib': 0.7801132202148438}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	10
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

# =============================================================
# 4. Query — return NNSResult
# =============================================================
res = idx.get_nns_by_item(
    0,
    5,
    # search_k = -1,
    include_distances=True,
)

print(res)

([0, 183, 596, 293, 132], [0.0, 1.1197848320007324, 1.2014238834381104, 1.201889991760254, 1.2221797704696655])

# =============================================================
# 8. Query using vector
# =============================================================
res2 = idx.get_nns_by_vector(
    [random.gauss(0, 1) for _ in range(f)],
    5,
    include_distances=True
)
print("\nQuery by vector:", res2)

Query by vector: ([543, 406, 539, 833, 868], [1.244363784790039, 1.2754391431808472, 1.2776145935058594, 1.2818483114242554, 1.2914206981658936])

# =============================================================
# 9. Low-level (non-result) mode
# =============================================================
items = idx.get_nns_by_item(0, 2, include_distances=False)
print("\nLow-level items only:", items)

items_low, d_low = idx.get_nns_by_item(0, 2, include_distances=True)
print("Low-level tuple return:", items_low, d_low)

Low-level items only: [0, 293]
Low-level tuple return: [0, 293] [0.0, 1.201889991760254]

# =============================================================
# 10. Persistence
# =============================================================
print("\n=== Saving with binary annoy ===")
print(idx)
idx.save("annoy_test_1.annoy")
print(idx)

print("Loading...")
idx2 = AnnoyIndex(100, metric='angular').load("annoy_test_1.annoy")
print("Loaded index:", idx2)

=== Saving with binary annoy ===
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})
Loading...
Loaded index: Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

import joblib

joblib.dump(idx, "test.joblib")
a = joblib.load("test.joblib")
a

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	-1
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	'annoy_test_1.annoy'
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

a.info(), a.get_n_items(), a.get_n_trees()

({'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 10, 'memory_usage_byte': 687840, 'memory_usage_mib': 0.655975341796875}, 1000, 10)

np.array_equal(a.get_item(0), idx2.get_item(0))

True

np.array_equal(a.get_item(0), idx.get_item(0))

True

# =============================================================
# 11. Raw serialize / deserialize
# =============================================================
print("\n=== Raw serialize ===")
buf = idx.serialize()
new_idx = AnnoyIndex(100, metric='angular')
new_idx.deserialize(buf)
print("Deserialized index n_items:", new_idx.get_n_items())
print(idx.info())
print(idx)
idx

=== Raw serialize ===
Deserialized index n_items: 1000
{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 10, 'memory_usage_byte': 687840, 'memory_usage_mib': 0.655975341796875}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	10
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	'annoy_test_1.annoy'
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

idx.unload()
print(idx.info())
print(idx)
idx

{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 0, 'n_trees': 0}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	10
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	None
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

# idx.build(10)
idx.load("annoy_test_1.annoy")
print(idx.info())
print(idx)
idx

{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 10, 'memory_usage_byte': 687840, 'memory_usage_mib': 0.655975341796875}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})

Annoydev|0.4

Parameters

	Parameter	Value
🗗 Copy	f	100
🗗 Copy	metric	'angular'
🗗 Copy	n_trees	10
🗗 Copy	n_neighbors	5
🗗 Copy	on_disk_path	'annoy_test_1.annoy'
🗗 Copy	prefault	False
🗗 Copy	seed	None
🗗 Copy	verbose	None
🗗 Copy	schema_version	0
🗗 Copy	n_jobs	-1
🗗 Copy	l1_ratio	0.0

idx.info()

{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0, 'n_items': 1000, 'n_trees': 10, 'memory_usage_byte': 687840, 'memory_usage_mib': 0.655975341796875}

idx.get_nns_by_item(0, 10), len(idx.get_item(0))

([0, 183, 596, 293, 429, 132, 839, 595, 826, 58], 100)

import random
from scikitplot.utils._time import Timer

n, f = 10_000, 1_000
X = [[random.gauss(0, 1) for _ in range(f)] for _ in range(n)]
q = [[random.gauss(0, 1) for _ in range(f)]]

with Timer("set_params"):
    for m in ["angular", "l1", "l2", ".", "hamming"]:
        idx = AnnoyIndex().set_params(metric=m).fit(X)
        print(m, idx.transform(q))

angular [[[-0.8404051661491394, -0.3567480742931366, -0.5828819274902344, 0.5630024671554565, -0.41118302941322327, -1.0003893375396729, -0.6599777340888977, -0.017333874478936195, 2.1777536869049072, 0.8183628916740417, 0.6165456771850586, -0.6283175349235535, 0.2394079715013504, -0.05144694074988365, -0.8132093548774719, -1.2057366371154785, -0.03679826483130455, 1.101664423942566, 0.5282793045043945, 0.5268701314926147, -0.941550076007843, -0.3203383684158325, -2.1472787857055664, 0.08458570390939713, 0.34597912430763245, -0.5624266862869263, -0.5241606831550598, -0.5700066685676575, 0.5406630635261536, -0.1798732876777649, -0.6182960867881775, 0.027622109279036522, 0.21553601324558258, -0.09789566695690155, -1.1120609045028687, -0.19454607367515564, 1.4317952394485474, 1.8457533121109009, -0.3132607042789459, 0.6609426736831665, -0.19408200681209564, 0.09128856658935547, -2.3590404987335205, 0.4779016375541687, 0.45970284938812256, 0.38343730568885803, -0.33656570315361023, 1.558239221572876, -1.2236266136169434, 0.7774333953857422, 0.0013093138113617897, 0.7900688648223877, 0.908488392829895, -0.43184685707092285, -0.742366373538971, 0.7288059592247009, -1.13115656375885, 0.6645299792289734, -0.2966386675834656, 0.01282721757888794, -1.0625876188278198, -1.2925615310668945, -0.621612012386322, -0.40936824679374695, -1.146467924118042, 0.9479307532310486, 0.5066787600517273, 0.7212688326835632, 0.05310507491230965, 1.063809871673584, -1.049269676208496, 0.46660760045051575, 0.7894355058670044, -0.45211875438690186, 0.010652130469679832, 0.3597492277622223, 0.5762432217597961, 0.21204325556755066, 1.2014909982681274, -1.8989863395690918, -0.38933905959129333, -0.36203333735466003, 0.42108964920043945, 0.13808314502239227, -1.5324199199676514, 1.096292495727539, -0.7304741740226746, 0.7399131655693054, -0.9543105959892273, -1.0294846296310425, 0.6492841839790344, 0.2902883291244507, -0.5978260040283203, -0.07863572239875793, -3.364499807357788, -0.9912970066070557, -0.7219445705413818, -2.316514730453491, 0.24605681002140045, -0.4464213252067566, 0.25218909978866577, -0.7235556244850159, -0.2823408842086792, 0.48625004291534424, 1.2218204736709595, -0.9700833559036255, -0.2113341987133026, 1.0854326486587524, 0.30108290910720825, 0.06867432594299316, 1.58292555809021, -0.8737212419509888, -0.508452296257019, -0.5410763025283813, -0.6390085220336914, -1.114478349685669, -0.9481443762779236, -1.0686426162719727, 0.066779226064682, -0.9352723360061646, -2.060772180557251, 2.587737560272217, -1.4947298765182495, -0.5097038745880127, 1.1426734924316406, -1.2741408348083496, 0.9477977752685547, 0.5573841333389282, 1.0378336906433105, -0.6626720428466797, -0.6817185282707214, 2.1484873294830322, -0.567000150680542, -0.04186654090881348, -0.7496365308761597, 0.8617992401123047, 0.6475389003753662, 1.1275204420089722, 0.47881555557250977, 0.8205405473709106, -0.23895369470119476, 0.8053026795387268, 1.7843663692474365, 0.23470458388328552, 0.9361766576766968, 0.4377741515636444, 0.4701552987098694, 0.660527765750885, -0.4926680326461792, 0.04709523543715477, -0.8041443824768066, 0.8957918882369995, -0.6522700786590576, 0.812373161315918, -0.3580611050128937, 2.3244001865386963, -1.622830867767334, -1.2867152690887451, -1.1337225437164307, -0.5026916265487671, -0.37896308302879333, 0.6007441282272339, -1.7489114999771118, -0.06613706797361374, -0.06680890917778015, 0.23946425318717957, -0.4073755741119385, 3.003225088119507, 0.8940473794937134, 0.04209991171956062, 0.2829328179359436, -0.5078552961349487, 0.44078436493873596, -0.8965771198272705, -0.9004817008972168, 0.19720491766929626, -0.1371823400259018, 0.4457332193851471, -0.21810166537761688, 1.3854719400405884, -0.13314926624298096, -1.3974746465682983, -0.3643653690814972, 0.12132058292627335, 0.7834147214889526, 1.3747116327285767, 1.6990333795547485, -0.19974705576896667, -1.5922822952270508, 0.738659143447876, -0.910927414894104, 0.2452296018600464, 0.05406717583537102, -0.4327041506767273, 0.40800243616104126, 1.0160549879074097, 1.67372465133667, -2.513425588607788, -0.287596732378006, 1.2756651639938354, 0.7072378993034363, 0.35489580035209656, 1.3072835206985474, 0.45431235432624817, -1.1855854988098145, 0.06071998178958893, -0.9111825823783875, 1.8921117782592773, -1.933346152305603, 0.29685959219932556, 0.6570219397544861, -0.5726526379585266, 0.985984742641449, -0.04435976222157478, 0.8584916591644287, -1.7557135820388794, 0.0046134875155985355, 0.11619225144386292, -1.2644333839416504, 0.13637791574001312, -1.7756022214889526, 0.3509737551212311, -1.4250792264938354, 1.0339288711547852, -1.0505783557891846, -0.17909279465675354, 1.6418377161026, 0.6516522169113159, -0.576279878616333, 1.9658418893814087, -0.24370653927326202, 0.08383779972791672, 0.12130512297153473, 0.21867524087429047, 0.8315665125846863, 1.0123388767242432, 0.34826353192329407, -1.0176949501037598, 2.311384677886963, 1.1691925525665283, -0.39595827460289, 1.4744263887405396, 0.0823749527335167, 0.6737703680992126, 1.9162570238113403, 0.27771422266960144, -0.4544117748737335, -0.825042724609375, 0.9209718108177185, -1.1573928594589233, -0.15618419647216797, -1.0674307346343994, 2.1307730674743652, 0.7255173325538635, -0.4366719424724579, -1.3672053813934326, -0.8494060039520264, -1.1287716627120972, 0.6012473702430725, -0.5025030374526978, 1.7794660329818726, 0.6953580975532532, -1.115909218788147, 0.7231637835502625, 0.464972585439682, 1.7386937141418457, 0.9538461565971375, 2.3032901287078857, -0.3566778898239136, 0.7778507471084595, -0.18674138188362122, -0.7213646173477173, -0.38136520981788635, -0.4451839327812195, -1.4352935552597046, 0.4507434070110321, 0.751031219959259, -0.49608197808265686, -2.243246078491211, -0.3345547616481781, -1.4572312831878662, 0.7634375691413879, 0.5120847225189209, -0.0606134831905365, -1.8794276714324951, 0.8215690851211548, 1.0366772413253784, 3.044180393218994, 0.08522429317235947, -0.08120160549879074, -1.4637914896011353, 0.4798848628997803, 0.6649668216705322, -0.14236198365688324, -0.5501486659049988, -1.8532779216766357, 1.281381607055664, -0.49306485056877136, -1.1123508214950562, -0.4262082576751709, 0.007320111151784658, 1.1748557090759277, -0.4605483412742615, 0.38613519072532654, -1.2897928953170776, 1.0140942335128784, 0.6093806028366089, -0.2506104111671448, -0.5159895420074463, -0.8712422847747803, -0.715129554271698, -1.247249960899353, 1.091418743133545, 0.06736534833908081, 1.3876234292984009, 0.9311904907226562, 0.14009644091129303, -0.624022901058197, -0.5237289071083069, -1.6462774276733398, -0.5594687461853027, 1.2310551404953003, 0.3717689514160156, 0.5744857788085938, -1.175563097000122, 0.1036151796579361, 0.16881445050239563, -0.27769559621810913, -1.1694902181625366, 0.8130870461463928, 0.8560495376586914, -0.36376163363456726, -0.16503633558750153, 2.037205219268799, 0.09321153163909912, -0.419127881526947, 1.0980265140533447, 1.8586517572402954, 2.1986403465270996, 0.3063684105873108, 0.018141239881515503, -0.9138724207878113, -1.1580965518951416, 1.4116151332855225, -1.540875792503357, 0.07584752887487411, 0.2383289933204651, 0.7470635771751404, -1.094212293624878, -0.33827003836631775, 0.7677735686302185, 1.562336802482605, -0.15447349846363068, -1.3190242052078247, -0.988477885723114, -0.5948179960250854, -1.8631601333618164, 1.0365021228790283, -2.030287981033325, 0.6003307104110718, 0.4771883487701416, 1.3421714305877686, 1.410262107849121, -0.9741681814193726, -0.4066395163536072, -0.09322650730609894, -1.7389010190963745, -1.5307843685150146, -0.7040114998817444, -0.5058687329292297, -0.1897384524345398, 0.5650601387023926, 0.561312735080719, -1.7156689167022705, 2.0689914226531982, -0.32564401626586914, 0.870003879070282, -0.20078712701797485, 2.0276272296905518, -1.5905619859695435, 1.6649997234344482, -1.1061416864395142, 1.2021739482879639, 0.9040942192077637, -0.2270762324333191, -0.8807844519615173, 1.4589738845825195, 1.777664065361023, -0.9527832865715027, 0.008986644446849823, 0.943607747554779, -2.0289628505706787, -1.5192584991455078, 0.5659216642379761, 1.5079435110092163, 0.3827572464942932, 0.9230945110321045, -0.6696222424507141, -0.6417369842529297, -0.6741232872009277, -1.1814091205596924, -1.2593026161193848, -1.6853569746017456, 0.3627128601074219, 1.5868158340454102, 0.5916765332221985, -0.14993447065353394, 0.8399901986122131, -1.4144943952560425, -1.0382739305496216, 0.43464037775993347, 0.6855374574661255, 1.3649137020111084, 0.3370160162448883, -0.050901178270578384, -0.784500241279602, 0.9827625155448914, -0.09838301688432693, -0.5063295364379883, -1.7754915952682495, -0.011680977419018745, 0.5122619867324829, -2.451040744781494, -0.4431766867637634, -0.7305312156677246, 1.4025871753692627, -0.017908470705151558, 0.08187534660100937, 0.05771744251251221, -0.33082908391952515, -0.07892892509698868, 0.3358418047428131, 0.5253825187683105, -2.092559814453125, -0.14629466831684113, -0.6213732361793518, 0.02207041345536709, -0.6211398243904114, -0.15400680899620056, -0.03180698677897453, 0.8215034008026123, -0.13626044988632202, 1.1337487697601318, 1.0693507194519043, -0.8759941458702087, 0.08029980212450027, -1.5592080354690552, -0.22276461124420166, -0.41250690817832947, 0.4973497986793518, 0.27984127402305603, 0.04075890779495239, 0.5679999589920044, 0.2242124378681183, 0.48308107256889343, -1.084287405014038, 1.9132741689682007, 0.15252844989299774, -0.830274224281311, 1.1221274137496948, 0.9123688340187073, 0.5511978268623352, -0.06740228086709976, -0.5738367438316345, 1.4569295644760132, 1.7604151964187622, -1.9825825691223145, -2.186793804168701, 0.5286375284194946, -1.237276554107666, 1.40077805519104, 2.4117112159729004, 0.6555858850479126, -0.5975332260131836, 0.6955858469009399, 0.34710511565208435, -0.681893527507782, -0.7515091896057129, -0.5947948694229126, -1.0486327409744263, 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1.0, 1.0, 0.0, 1.0, 0.0, 0.0]]]

with Timer("rebuild"):
    base = AnnoyIndex(metric="l2").fit(X)

    for m in ["angular", "l1", "l2", "dot", "hamming"]:
        idx_m = base.rebuild(metric=m)          # rebuild-from-index
        print(m, idx_m.transform(q))            # no .fit(X) here

angular [[[-0.8404051661491394, -0.3567480742931366, -0.5828819274902344, 0.5630024671554565, -0.41118302941322327, -1.0003893375396729, -0.6599777340888977, -0.017333874478936195, 2.1777536869049072, 0.8183628916740417, 0.6165456771850586, -0.6283175349235535, 0.2394079715013504, -0.05144694074988365, -0.8132093548774719, -1.2057366371154785, -0.03679826483130455, 1.101664423942566, 0.5282793045043945, 0.5268701314926147, -0.941550076007843, -0.3203383684158325, -2.1472787857055664, 0.08458570390939713, 0.34597912430763245, -0.5624266862869263, -0.5241606831550598, -0.5700066685676575, 0.5406630635261536, -0.1798732876777649, -0.6182960867881775, 0.027622109279036522, 0.21553601324558258, -0.09789566695690155, -1.1120609045028687, -0.19454607367515564, 1.4317952394485474, 1.8457533121109009, -0.3132607042789459, 0.6609426736831665, -0.19408200681209564, 0.09128856658935547, -2.3590404987335205, 0.4779016375541687, 0.45970284938812256, 0.38343730568885803, -0.33656570315361023, 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Tags: model-workflow: vector-db level: beginner purpose: showcase

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