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.
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 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 [1]_ (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 <cexternals-annoy-index>`
* https://github.com/spotify/annoy
* https://pypi.org/project/annoy
References
----------
.. [1] `Spotify AB. (2013). "Approximate Nearest Neighbors Oh Yeah"
Github. https://github.com/spotify/annoy <https://github.com/spotify/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()
9
/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', '_y_map', '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', '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
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, 429, 39, 598, 47], [0.0, 1.2052730321884155, 1.212796926498413, 1.2235209941864014, 1.2287578582763672])
# =============================================================
# 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: ([336, 499, 237, 815, 839], [1.2275598049163818, 1.2495390176773071, 1.2670350074768066, 1.2672923803329468, 1.269050121307373])
# =============================================================
# 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, 429]
Low-level tuple return: [0, 429] [0.0, 1.2052730321884155]
# =============================================================
# 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': 542252, 'memory_usage_mib': 0.5171318054199219}, 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': 542252, 'memory_usage_mib': 0.5171318054199219}
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': 542252, 'memory_usage_mib': 0.5171318054199219}
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': 542252, 'memory_usage_mib': 0.5171318054199219}
idx.get_nns_by_item(0, 10), len(idx.get_item_vector(0))
([0, 183, 293, 429, 39, 598, 47, 329, 715, 639], 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 [[[-0.5852223634719849, -0.06997305154800415, 1.2184703350067139, -0.09014776349067688, 0.7037280201911926, -0.27357056736946106, 0.02893211506307125, 2.1964058876037598, -0.8043721318244934, -0.15248392522335052, 0.26651036739349365, -0.5819159746170044, -1.6376222372055054, -1.3551329374313354, -0.29166996479034424, -0.05728413537144661, -0.5338978171348572, -0.0791354775428772, -0.42755749821662903, -0.7031083106994629, 0.6068969964981079, -0.09360741823911667, -2.29099702835083, -2.807333469390869, 3.0869858264923096, -1.0233874320983887, 2.316859483718872, 0.3059225380420685, -0.056303709745407104, 0.10747287422418594, 0.2755567133426666, -0.2559686601161957, -0.16010361909866333, 1.2887382507324219, -0.16888262331485748, 0.8505357503890991, 0.36626678705215454, -1.4574270248413086, 0.2088029533624649, -0.17387014627456665, 1.7004467248916626, -1.1651887893676758, 0.2514297366142273, -0.8753154277801514, -0.8871513605117798, -1.5481376647949219, -0.0053722369484603405, 1.5539630651474, 1.5736793279647827, 1.9340739250183105, -0.20129401981830597, -0.2948918044567108, -1.6812671422958374, -0.550592303276062, -1.363808512687683, -0.4154496490955353, 1.087469220161438, -0.9726617932319641, -1.6748194694519043, 1.9257829189300537, -1.3774793148040771, -0.08865544199943542, 0.7990171313285828, -1.474295973777771, -0.00898695457726717, -1.4802848100662231, -0.09519857913255692, 0.955003559589386, -0.48075321316719055, -0.8610109090805054, 0.20951880514621735, 0.4359440207481384, -0.5855584144592285, 0.31691423058509827, -1.8223170042037964, -0.9998979568481445, 0.5954018235206604, -1.8510982990264893, -0.21347999572753906, 0.6668511629104614, 1.7222942113876343, -0.22747622430324554, -1.888601303100586, 1.544604778289795, 0.38222336769104004, 1.3151862621307373, -0.8726229071617126, 0.8770172595977783, -1.1032590866088867, 0.2238825410604477, -1.2375348806381226, 2.2704989910125732, -1.2606971263885498, -0.5247965455055237, 0.16898253560066223, 0.008365029469132423, 0.4257911443710327, 1.0053831338882446, 1.835474967956543, 0.6478879451751709, -1.1636625528335571, -0.2991185486316681, 0.19694799184799194, -1.3127686977386475, 0.5093865990638733, 0.6312392950057983, 0.5026051998138428, 0.5021654963493347, -2.553457021713257, -0.410479873418808, -0.011404084041714668, -0.04036523029208183, 0.7172057032585144, -0.0049101863987743855, -0.36783087253570557, 0.3358911871910095, 1.4558547735214233, 0.9229565858840942, -2.06430721282959, 1.617226004600525, 0.8536306023597717, 0.38264620304107666, 0.7369701862335205, 0.3113294541835785, 0.7516992688179016, 0.33670079708099365, -0.13040494918823242, -0.9880680441856384, 1.0848816633224487, -0.791896402835846, 0.22310541570186615, -0.1458144187927246, -1.1611623764038086, -0.38528773188591003, -0.1137700304389, -1.5636883974075317, 0.11271324008703232, -1.1621451377868652, 0.1889561414718628, -0.45856496691703796, 0.10726162046194077, -2.311999797821045, -0.37764453887939453, 1.3237669467926025, 0.27350667119026184, -1.4241372346878052, -1.6028268337249756, 0.6166134476661682, 0.6531920433044434, -0.9208390712738037, 0.5844190716743469, 0.044250328093767166, -1.3680284023284912, 0.17411787807941437, -1.2855583429336548, -1.4097923040390015, 1.7543505430221558, -0.6126699447631836, -0.17387224733829498, 0.9873214960098267, 0.16437797248363495, -0.6472386717796326, -0.8803995847702026, 1.152353048324585, 1.5996297597885132, -0.5093165636062622, -0.03821102902293205, -0.3143315613269806, 0.1580912470817566, 0.9765365719795227, 2.228876829147339, 1.5893086194992065, -0.09719809144735336, -0.5123807191848755, 0.12931960821151733, 0.6357356309890747, -0.5834897756576538, -1.004830002784729, 0.06734662503004074, 0.014827278442680836, -0.12283983081579208, 1.0458078384399414, 2.1872100830078125, 1.4239492416381836, 0.6324941515922546, -1.0691243410110474, -0.9356611967086792, 0.35638609528541565, 1.2468799352645874, 0.12321098148822784, -0.2701984643936157, 0.5435382723808289, -1.4574129581451416, -0.7536880373954773, -0.880316436290741, 0.2207304835319519, 0.11189109832048416, -0.30788180232048035, 0.6243005990982056, -1.292657732963562, 0.8660392761230469, -0.36422693729400635, 0.4366716146469116, -1.224618673324585, 0.039644770324230194, -2.0310428142547607, -0.9044390916824341, -0.5007391571998596, -0.887691855430603, 0.42837411165237427, -0.5180718898773193, 0.1125124916434288, -0.45988354086875916, 0.7958732843399048, -1.4305331707000732, 1.5430775880813599, 1.4611092805862427, 0.015438525006175041, -0.7725175619125366, -0.33856698870658875, 0.9318331480026245, 0.7469621300697327, 0.9386345744132996, -1.0800398588180542, -0.011329632252454758, 1.3285614252090454, 1.2996822595596313, 0.684899628162384, 0.10528364777565002, 1.758423924446106, -0.7110488414764404, 0.8813236355781555, 0.3632878065109253, 0.3558633029460907, -0.9129589200019836, -0.17712099850177765, -0.6232520341873169, -0.3830803334712982, 1.363985538482666, 0.09769579023122787, 1.2618311643600464, -0.5846014618873596, -0.8102043271064758, 1.7746957540512085, 0.5222225785255432, -0.008272343315184116, -0.1883300542831421, -1.7398364543914795, -0.8305545449256897, -0.2253527194261551, -0.4434520900249481, -0.06013656407594681, 0.2016211301088333, 0.7577903866767883, 0.01590796746313572, -1.0056815147399902, -0.05971517786383629, 1.223648190498352, 1.731424331665039, -0.1177595779299736, 0.5431265830993652, -0.5981659889221191, -0.3381914794445038, 1.2552522420883179, -0.7022705078125, 0.17468072474002838, 1.7975713014602661, 1.7819279432296753, -1.6310954093933105, -0.31914573907852173, 0.03554270789027214, -0.9402187466621399, 1.2840111255645752, 0.5496805310249329, -0.09583765268325806, 1.3004330396652222, -1.1487514972686768, -1.0062705278396606, 0.39578863978385925, 2.2287232875823975, -0.44148603081703186, -0.3265489339828491, 0.7999597191810608, -1.4074881076812744, -0.25988373160362244, 1.5550426244735718, -0.9438595771789551, 0.28619128465652466, 1.0702682733535767, 0.513883650302887, -0.06752568483352661, 1.2217216491699219, 2.1651527881622314, -1.2781291007995605, 0.04277460649609566, 0.2850567102432251, 1.5030596256256104, -0.6076725125312805, -0.15280020236968994, 1.1545284986495972, -0.16878147423267365, 1.197860836982727, 0.6361761093139648, -0.8560751676559448, -0.039097923785448074, 1.2457482814788818, -0.837630569934845, 0.7944231033325195, 0.12009931355714798, 0.4838590919971466, -0.14619943499565125, 1.1109545230865479, 0.5216962695121765, -0.4401313364505768, 1.2020376920700073, 1.8600841760635376, -0.3580828011035919, -0.6569845676422119, -0.0014957451494410634, 0.9722772836685181, 0.3212682604789734, -0.037839822471141815, 1.0113320350646973, -0.8280234336853027, 1.002508282661438, -1.0137624740600586, -0.5991739630699158, 1.728485345840454, -0.034829504787921906, 1.493249535560608, 1.3901538848876953, 0.38320428133010864, 0.31390181183815, -0.9445761442184448, -0.7683161497116089, 0.6629788279533386, -1.3548563718795776, -0.783187747001648, 0.10072319209575653, -1.5910993814468384, 0.9559831023216248, -2.2996551990509033, 1.2446871995925903, 0.7517240047454834, 0.08904055505990982, -0.20847100019454956, -0.25555434823036194, 0.21895471215248108, -2.0171751976013184, -0.6085904240608215, 0.8713491559028625, 0.586746871471405, 0.6029092073440552, -1.9506436586380005, 0.22406284511089325, -0.709825336933136, 0.10306411236524582, 2.218141555786133, -0.21944783627986908, -0.11253349483013153, 0.055979467928409576, 1.135956048965454, 0.8754609227180481, 1.2589964866638184, -1.8491978645324707, 0.11299671977758408, -0.8521704077720642, -0.03363073989748955, 0.007703689858317375, -0.9924276471138, 0.22753499448299408, 0.7874380350112915, 0.3949046730995178, -0.4241640865802765, 0.646275520324707, 0.7090029120445251, 1.1907099485397339, 0.6247155070304871, -1.4224501848220825, 0.5465995669364929, -0.5201015472412109, 0.36975613236427307, 0.7544838190078735, 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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 [[[-0.5852223634719849, -0.06997305154800415, 1.2184703350067139, -0.09014776349067688, 0.7037280201911926, -0.27357056736946106, 0.02893211506307125, 2.1964058876037598, -0.8043721318244934, -0.15248392522335052, 0.26651036739349365, -0.5819159746170044, -1.6376222372055054, -1.3551329374313354, -0.29166996479034424, -0.05728413537144661, -0.5338978171348572, -0.0791354775428772, -0.42755749821662903, -0.7031083106994629, 0.6068969964981079, -0.09360741823911667, -2.29099702835083, -2.807333469390869, 3.0869858264923096, -1.0233874320983887, 2.316859483718872, 0.3059225380420685, -0.056303709745407104, 0.10747287422418594, 0.2755567133426666, -0.2559686601161957, -0.16010361909866333, 1.2887382507324219, -0.16888262331485748, 0.8505357503890991, 0.36626678705215454, -1.4574270248413086, 0.2088029533624649, -0.17387014627456665, 1.7004467248916626, -1.1651887893676758, 0.2514297366142273, -0.8753154277801514, -0.8871513605117798, -1.5481376647949219, -0.0053722369484603405, 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Total running time of the script: (0 minutes 11.412 seconds)
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