annoy.Annoy legacy c-api with examples#
An example showing the Annoy, AnnoyIndex class.
See also
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_neighbors=5,
>>> on_disk_path=None,
>>> prefault=None,
>>> seed=None,
>>> verbose=None,
>>> schema_version=None,
>>> )
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_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).
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.
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.
prefault : bool, default=False
Stored prefault flag (see :meth:`load`/`:meth:`save` prefault parameters).
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 : dict | None, optional, default=None
If provided to fit(X, y), labels are stored here after a successful build.
You may also set this property manually. When possible, the setter enforces
that len(y) matches the current number of items (n_items).
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.
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 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
sys.modules["annoy"] = annoy # now `import annoy` will resolve to your module
import annoy
print(annoy.__doc__)
High-level Python interface for the C++ ANNoy backend.
Spotify ANNoy [0]_ (Approximate Nearest Neighbors Oh Yeah).
Exports:
* Annoy → low-level C-extension type (stable) `c-api powered new features <https://scikit-plots.github.io/dev/modules/generated/scikitplot.cexternals._annoy.Annoy.html>`_
* AnnoyIndex → alias of Annoy (legacy AnnoyIndex name)
.. seealso::
* :ref:`ANNoy <annoy-index>`
* :ref:`cexternals/ANNoy (experimental) <cexternals-annoy-index>`
* https://github.com/spotify/annoy
* https://pypi.org/project/annoy
References
----------
.. [0] `Spotify AB. (2013, Feb 20). "Approximate Nearest Neighbors Oh Yeah"
Github. https://pypi.org/project/annoy <https://pypi.org/project/annoy>`_
Examples
--------
>>> import random; random.seed(0)
>>> # from annoy import Annoy, AnnoyIndex
>>> from scikitplot.cexternals._annoy import Annoy, AnnoyIndex
>>> from scikitplot.annoy import Annoy, AnnoyIndex, Index
>>> f = 40 # vector dimensionality
>>> t = AnnoyIndex(f, "angular") # Length of item vector and metric
>>> t.add_item(0, [1] * f)
>>> t.build(10) # Build 10 trees
>>> t.get_nns_by_item(0, 1) # Find nearest neighbor
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
/home/circleci/repo/galleries/examples/annoy/plot_Annoy_legacy_c_api.py:60: FutureWarning:
The default argument for metric will be removed in a future version. Please pass metric='angular' explicitly.
(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()
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'>
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', 'add_item', 'build', 'deserialize', 'f', 'feature_names_in_', 'fit', 'fit_transform', 'get_distance', 'get_feature_names_out', 'get_item_vector', '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']
# 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
idx.metric = "dot"
idx
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})
# =============================================================
# 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})
# =============================================================
# 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})
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})
# =============================================================
# 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: 543076 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': 543076, 'memory_usage_mib': 0.5179176330566406}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 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': 543076, 'memory_usage_mib': 0.5179176330566406}
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 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})
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': 543076, 'memory_usage_mib': 0.5179176330566406}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 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})
# =============================================================
# 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})
# =============================================================
# 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: 611056 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': 611056, 'memory_usage_mib': 0.5827484130859375}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': None, 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 0})
# =============================================================
# 4. Query — return NNSResult
# =============================================================
res = idx.get_nns_by_item(
0,
5,
# search_k = -1,
include_distances=True,
)
print(res)
([0, 762, 535, 864, 306], [0.0, 1.2786411046981812, 1.282088041305542, 1.2963494062423706, 1.3046419620513916])
# =============================================================
# 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: ([566, 336, 695, 237, 408], [1.2055667638778687, 1.2275598049163818, 1.243502140045166, 1.2670350074768066, 1.2830111980438232])
# =============================================================
# 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, 762]
Low-level tuple return: [0, 762] [0.0, 1.2786411046981812]
# =============================================================
# 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
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': 543076, 'memory_usage_mib': 0.5179176330566406}, 1000, 10)
np.array_equal(a.get_item_vector(0), idx2.get_item_vector(0))
True
np.array_equal(a.get_item_vector(0), idx.get_item_vector(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': 543076, 'memory_usage_mib': 0.5179176330566406}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 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})
# 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': 543076, 'memory_usage_mib': 0.5179176330566406}
Annoy(**{'f': 100, 'metric': 'angular', 'n_neighbors': 5, 'on_disk_path': 'annoy_test_1.annoy', 'prefault': False, 'seed': None, 'verbose': None, 'schema_version': 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': 543076, 'memory_usage_mib': 0.5179176330566406}
idx.get_nns_by_item(0, 10), len(idx.get_item_vector(0))
([0, 592, 293, 47, 499, 762, 727, 535, 928, 864], 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 [[[0.8759568333625793, -0.8257683515548706, -0.6117377281188965, -0.054673369973897934, -0.7532786130905151, -0.10367351770401001, -0.20036563277244568, 0.33989110589027405, 1.6375612020492554, -1.1355922222137451, 0.9982355237007141, 0.9751926064491272, -1.9678866863250732, 0.35627955198287964, 0.5527481436729431, -0.18547746539115906, 1.766366958618164, -0.04073218256235123, -0.5461446642875671, -0.20987272262573242, -0.3330865800380707, -1.1802864074707031, 0.10983637720346451, 0.308281272649765, -3.0143771171569824, -2.106659173965454, 0.2842836380004883, -1.504733920097351, -0.7107263207435608, 1.208106517791748, -0.8389620780944824, 1.2150828838348389, -0.8291031718254089, -1.035062551498413, -2.063636302947998, -0.4144243896007538, -1.363772988319397, 0.08441708981990814, -0.6420859694480896, 0.037681929767131805, -0.24156181514263153, -0.9609965682029724, 2.5695266723632812, 0.33478835225105286, 1.0115375518798828, -0.574651300907135, -1.3004822731018066, -1.837820053100586, -0.75947505235672, -0.34443655610084534, -0.17779691517353058, -0.650153398513794, 0.4361475706100464, -0.22853122651576996, 1.1992310285568237, 0.36555972695350647, 0.5030422210693359, -0.5236008763313293, 0.11255718767642975, 0.6291269659996033, -0.4917486011981964, -0.6188151836395264, 0.9393750429153442, 0.39710578322410583, -1.1380581855773926, -0.5239026546478271, -0.5070082545280457, -0.5284774303436279, -0.06200645864009857, 0.5758079886436462, 0.9073389768600464, -1.0080615282058716, 1.3390525579452515, 1.8656874895095825, -0.1486872434616089, -0.7573816776275635, -0.7238391637802124, 1.4987576007843018, -0.8140237927436829, 0.8057752251625061, 0.9932642579078674, 0.4195168614387512, 0.31545552611351013, 0.26319390535354614, 0.48133811354637146, 0.12515771389007568, 0.7969937920570374, 0.4245471954345703, -0.07147283852100372, -0.2340237945318222, -2.309811592102051, 1.4716286659240723, 0.15781566500663757, 0.24365969002246857, -0.9004893898963928, -0.36243152618408203, 1.0158402919769287, -1.2174711227416992, -0.3681541681289673, -0.669802188873291, -0.8226082921028137, 1.190089225769043, -0.1930418759584427, -2.1911208629608154, 0.22482310235500336, 0.21519958972930908, 0.13406914472579956, -2.3838133811950684, -1.3679859638214111, 0.6247382760047913, -0.31102171540260315, 1.5214515924453735, 0.7210867404937744, 0.8750216960906982, -0.03232559934258461, -1.5435991287231445, -0.4749925136566162, 1.4911119937896729, -0.8694575428962708, -0.21462315320968628, 0.7291505336761475, -0.19501739740371704, -0.28306567668914795, -2.3837099075317383, 0.33987030386924744, -2.003281593322754, 1.2394639253616333, 2.045551300048828, -1.597883701324463, 0.7087885141372681, 1.4939590692520142, -1.6972546577453613, 1.232345461845398, 1.089432954788208, 1.4478448629379272, -0.26021090149879456, 0.11446040123701096, -2.3858256340026855, -0.5348974466323853, -0.01167392823845148, 1.7480318546295166, 6.069190567359328e-05, 0.6839897632598877, 0.324935644865036, -0.32482725381851196, -1.4107718467712402, 0.1171431690454483, -1.0761001110076904, -1.5221760272979736, 1.4327443838119507, -0.6225391030311584, 0.5918840765953064, 1.0577820539474487, -0.15193796157836914, 0.9591099619865417, 2.039391040802002, 0.2511647939682007, -0.2237270325422287, 2.09381365776062, -1.0510139465332031, -0.33617350459098816, 1.3672325611114502, 1.4088788032531738, 0.23611532151699066, 2.2052249908447266, 0.44233614206314087, 0.6714766621589661, 0.28911876678466797, -1.9191639423370361, -0.58568274974823, 0.03547396883368492, -1.4152220487594604, -0.252334326505661, -0.7120935320854187, 0.18890440464019775, 0.2784223258495331, 0.028118114918470383, -0.7846288084983826, -0.19774991273880005, 1.4656800031661987, 2.459409475326538, 0.22879166901111603, 0.2612644135951996, -1.0508824586868286, 0.09378433972597122, 0.481904536485672, -0.3039703071117401, -0.9913929104804993, 0.49571600556373596, 0.3293627202510834, 0.08203315734863281, 0.3829917311668396, 1.2803928852081299, -0.64882892370224, 0.23912888765335083, -0.6883891820907593, 0.33714669942855835, -1.1520437002182007, -0.9520249962806702, -0.7911269664764404, -0.15673445165157318, 0.9396101236343384, -0.5591180920600891, 0.831312894821167, 0.5490581393241882, -1.1655031442642212, 0.6421690583229065, 1.6500248908996582, -1.7270599603652954, -0.5616922378540039, 1.6628799438476562, 0.41845014691352844, -0.18142810463905334, 0.820519208908081, 1.035209059715271, 0.029357248917222023, 2.242732524871826, 0.13108457624912262, -1.6272064447402954, 0.6417461633682251, 0.27630630135536194, 0.2452455759048462, -0.9956232905387878, -0.7252262830734253, -0.7710791230201721, 0.9019526839256287, 0.4062894880771637, 0.5662549138069153, 0.27886658906936646, -0.7616106271743774, 0.7255157828330994, -0.8029681444168091, 0.09892597794532776, 0.17540660500526428, 1.392168402671814, -1.012643575668335, -0.5942782163619995, -1.3369299173355103, -2.2129321098327637, -0.980577290058136, 1.6887507438659668, 0.9517069458961487, -0.4241516590118408, 0.5721056461334229, 1.1342462301254272, 1.099729299545288, -0.7264867424964905, 1.1703025102615356, -0.04995039477944374, 0.6318587064743042, -1.9034953117370605, -0.8697640299797058, -1.4195481538772583, -0.2694734036922455, 1.5532009601593018, 0.49631237983703613, 0.6146805882453918, 0.23137420415878296, -0.28263363242149353, 0.9537917375564575, -1.8719439506530762, 0.16318471729755402, 0.963492751121521, 1.1482292413711548, 1.3265115022659302, -1.1526623964309692, -3.107818365097046, 1.6318018436431885, 0.43704891204833984, 0.5950964093208313, 0.4074109196662903, 1.4285892248153687, -1.1975822448730469, 1.7079814672470093, 0.170357808470726, 1.6790461540222168, 1.9306504726409912, -0.7515353560447693, -1.0156656503677368, 0.20570600032806396, 0.9292925596237183, -0.3942773640155792, -0.09334316849708557, -0.06012045964598656, 0.5035783648490906, 0.8410118818283081, -0.14592495560646057, -1.2113624811172485, 1.0257267951965332, 0.22508706152439117, -0.6252666115760803, 1.11430823802948, 0.9868940114974976, -0.5477070808410645, 1.1195842027664185, -1.4427789449691772, -1.8555560111999512, 1.1554665565490723, 0.4591805934906006, -1.2228862047195435, 1.0442982912063599, -0.06568099558353424, 0.015386263839900494, 0.5383734107017517, -0.0814717710018158, 0.3027256727218628, 0.6065313816070557, 0.4248938262462616, -1.3536893129348755, -1.2856488227844238, 0.30365705490112305, -0.8737351298332214, -2.5856833457946777, 0.33592715859413147, -0.5618422627449036, -1.1408299207687378, -0.3357028067111969, 0.1901841014623642, -0.5294017195701599, 0.6665403246879578, 1.4802324771881104, -0.10958694666624069, -1.1159411668777466, 0.8750089406967163, -0.4906030297279358, 0.21727487444877625, -0.9688804745674133, 0.6772571802139282, -0.1111614778637886, 0.9090520143508911, -0.08869472146034241, 0.8911523222923279, 2.032668113708496, 0.3293518126010895, -0.9819841384887695, 1.182323694229126, -2.5350606441497803, -1.5342202186584473, -0.8086175322532654, 0.4178858995437622, -0.5513831377029419, 0.21785758435726166, -1.2498959302902222, 0.28313717246055603, 0.4277152419090271, -1.526110053062439, -0.2856155037879944, 0.05347670242190361, 0.3974883556365967, -1.1840213537216187, 0.09443794935941696, 1.0633258819580078, 0.21520337462425232, -2.0829310417175293, 2.5548899173736572, -1.0507452487945557, 0.5001829862594604, -0.5826883912086487, -1.5301460027694702, -0.1535222977399826, 0.08106902241706848, 0.9342889189720154, 1.5823466777801514, 1.0515393018722534, -1.5947949886322021, -1.6862176656723022, -0.36385753750801086, -0.09993504732847214, -1.4609335660934448, -0.9995895028114319, 1.1870701313018799, -0.3468925952911377, -0.5738797187805176, -0.2160625159740448, -0.169235497713089, 0.48533499240875244, -1.1583342552185059, 0.3735024333000183, 0.025177668780088425, -0.6104589700698853, -0.8706676959991455, 0.22825340926647186, 0.8711127638816833, 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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 [[[0.8759568333625793, -0.8257683515548706, -0.6117377281188965, -0.054673369973897934, -0.7532786130905151, -0.10367351770401001, -0.20036563277244568, 0.33989110589027405, 1.6375612020492554, -1.1355922222137451, 0.9982355237007141, 0.9751926064491272, -1.9678866863250732, 0.35627955198287964, 0.5527481436729431, -0.18547746539115906, 1.766366958618164, -0.04073218256235123, -0.5461446642875671, -0.20987272262573242, -0.3330865800380707, -1.1802864074707031, 0.10983637720346451, 0.308281272649765, -3.0143771171569824, -2.106659173965454, 0.2842836380004883, -1.504733920097351, -0.7107263207435608, 1.208106517791748, -0.8389620780944824, 1.2150828838348389, -0.8291031718254089, -1.035062551498413, -2.063636302947998, -0.4144243896007538, -1.363772988319397, 0.08441708981990814, -0.6420859694480896, 0.037681929767131805, -0.24156181514263153, -0.9609965682029724, 2.5695266723632812, 0.33478835225105286, 1.0115375518798828, -0.574651300907135, -1.3004822731018066, 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Total running time of the script: (0 minutes 10.097 seconds)
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