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annoy.Annoy legacy c-api with examples#
An example showing the Annoy, AnnoyIndex class.
See also
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 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 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)
/work/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(**{'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()
AnnoyIndex()
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', '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: 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})
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})
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': 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})
# =============================================================
# 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: 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})
# =============================================================
# 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
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})
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': 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})
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, 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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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Total running time of the script: (1 minutes 5.531 seconds)
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