.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/annoy/plot_annoy_to_NPY_CSV.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_annoy_plot_annoy_to_NPY_CSV.py: annoy.Index to NPY or CSV with examples ======================================= An example showing the :py:class:`~scikitplot.annoy.Index` class. .. seealso:: * :py:obj:`~scikitplot.annoy.Index.from_low_level` * https://docs.python.org/3/library/pickle.html#what-can-be-pickled-and-unpickled .. GENERATED FROM PYTHON SOURCE LINES 16-23 .. code-block:: Python import random; random.seed(0) # from annoy import Annoy, AnnoyIndex from scikitplot.annoy import Annoy, AnnoyIndex, Index print(Index.__doc__) .. rst-class:: sphx-glr-script-out .. code-block:: none High-level ANNoy index composed from mixins. 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, random_state : int or None, optional, default=None Non-negative integer seed. 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, n_features_, n_features_in_ : int Alias of `f` (dimension), provided for scikit-learn naming parity. 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). pickle_mode : PickleMode Pickle strategy used by :class:`~scikitplot.annoy._mixins._pickle.PickleMixin`. compress_mode : CompressMode or None Optional compression used by :class:`~scikitplot.annoy._mixins._pickle.PickleMixin` when serializing to bytes. Notes ----- This class is a direct subclass of the C-extension backend. It does not override ``__new__`` and does not rely on cooperative initialization across mixins. Mixins must be written so that their methods work even if they define no ``__init__`` at all. See Also -------- scikitplot.cexternals._annoy.Annoy Index.from_low_level .. GENERATED FROM PYTHON SOURCE LINES 24-40 .. code-block:: Python import random from pathlib import Path random.seed(0) HERE = Path.cwd().resolve() OUT = HERE / "../../../scikitplot/annoy/tests" / "test_v2.tree" f = 10 n = 1000 idx = Index(f, "angular") for i in range(n): v = [random.gauss(0, 1) for _ in range(f)] idx.add_item(i, v) .. GENERATED FROM PYTHON SOURCE LINES 41-72 .. code-block:: Python 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(10) plot(idx) .. image-sg:: /auto_examples/annoy/images/sphx_glr_plot_annoy_to_NPY_CSV_001.png :alt: plot annoy to NPY CSV :srcset: /auto_examples/annoy/images/sphx_glr_plot_annoy_to_NPY_CSV_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 73-79 .. code-block:: Python # idx.build(10) idx.save(str(OUT)) print("Wrote", OUT) idx .. rst-class:: sphx-glr-script-out .. code-block:: none Wrote /home/circleci/repo/galleries/examples/annoy/../../../scikitplot/annoy/tests/test_v2.tree .. raw:: html
Annoydev|0.4
Parameters
ParameterValue
f10
metric'angular'
n_neighbors5
on_disk_path'/home/circleci/repo/galleries/examples/annoy/../../../scikitplot/annoy/tests/test_v2.tree'
prefaultFalse
seedNone
verboseNone
schema_version0


.. GENERATED FROM PYTHON SOURCE LINES 80-90 .. code-block:: Python import random from scikitplot.utils._time import Timer n, f = 1_000, 10 X = [[random.gauss(0, 1) for _ in range(f)] for _ in range(n)] q = [[random.gauss(0, 1) for _ in range(f)]] q .. rst-class:: sphx-glr-script-out .. code-block:: none [[-0.3127546401869483, 0.9465724218669327, -0.3108358621823917, -1.112641318602492, 0.7006717421745847, -0.0728556513441296, -0.1340195780820892, -0.7864385964019445, 0.19071761022347317, 0.20808301694308412]] .. GENERATED FROM PYTHON SOURCE LINES 91-96 .. code-block:: Python # idx = Index().fit(X, feature_names=map("feature_{}".format, range(0,10))) idx = Index().fit(X, feature_names=map("col_{}".format, range(0,10))) idx .. raw:: html
Annoydev|0.4
Parameters
ParameterValue
f10
metric'angular'
n_neighbors5
on_disk_pathNone
prefaultFalse
seedNone
verboseNone
schema_version0


.. GENERATED FROM PYTHON SOURCE LINES 97-100 .. code-block:: Python idx.feature_names_in_ .. rst-class:: sphx-glr-script-out .. code-block:: none ('col_0', 'col_1', 'col_2', 'col_3', 'col_4', 'col_5', 'col_6', 'col_7', 'col_8', 'col_9') .. GENERATED FROM PYTHON SOURCE LINES 101-104 .. code-block:: Python idx.get_feature_names_out() .. rst-class:: sphx-glr-script-out .. code-block:: none ('neighbor_0', 'neighbor_1', 'neighbor_2', 'neighbor_3', 'neighbor_4') .. GENERATED FROM PYTHON SOURCE LINES 105-108 .. code-block:: Python idx.transform(X[:5]) .. rst-class:: sphx-glr-script-out .. code-block:: none [[[0.5474572777748108, -1.30021333694458, -0.24908927083015442, -0.338500440120697, -1.4725918769836426, -0.6193925142288208, 0.4619145691394806, 0.32358142733573914, 0.2247430980205536, 0.7160129547119141], [0.3795432150363922, -1.0814439058303833, -0.6971868872642517, -0.5686206817626953, -1.103886365890503, -0.7541236281394958, 0.705725371837616, 0.943377673625946, 0.6119086742401123, 1.3435934782028198], [0.21156643331050873, -0.8643466234207153, -0.5849316120147705, -0.783858597278595, -1.9358190298080444, -0.04689274728298187, 0.5601489543914795, 0.011728908866643906, 0.9475083351135254, 0.488138347864151], [0.7672842741012573, -0.9102413058280945, -1.3365751504898071, -0.3575524091720581, -1.0078351497650146, -0.27656781673431396, 0.4459843337535858, 0.25284719467163086, 0.712553083896637, 0.3709689974784851], [0.3964042365550995, -0.42005616426467896, -0.5673843622207642, -0.6945840716362, -0.9052101373672485, -0.6205509305000305, -0.058636922389268875, 0.6826471090316772, 0.9898018836975098, 0.7490196228027344]], [[0.05573755502700806, -0.4706576466560364, -0.6597297191619873, 0.16312913596630096, -0.9790469408035278, -0.8275525569915771, -2.060459852218628, -2.729288101196289, -0.8268195986747742, 0.6338273882865906], [0.649846613407135, -1.2302342653274536, 0.013532223179936409, -0.8263025283813477, -1.2967811822891235, -1.2061113119125366, -0.7416226267814636, -2.066455841064453, -1.0455116033554077, 1.7582625150680542], [1.2927970886230469, -0.8755640387535095, -0.5056493282318115, -0.09927254915237427, -0.16237357258796692, 0.2741217613220215, -1.6539034843444824, -1.5960801839828491, -0.2236139178276062, 1.1900722980499268], [0.4064362943172455, -1.0276243686676025, -0.757765531539917, 0.7894313931465149, -0.6055637001991272, -0.41537636518478394, -0.7599490880966187, -0.7554258704185486, -0.9755097031593323, 0.27309876680374146], [1.2435444593429565, -1.0532466173171997, -0.26311859488487244, -1.1512625217437744, -1.0769168138504028, -2.0363476276397705, -2.6408212184906006, -1.356844186782837, -1.0705242156982422, -1.0091755390167236]], [[1.037416696548462, 0.6505188941955566, 0.9112770557403564, 0.40074265003204346, 1.7090003490447998, -1.8210971355438232, -1.0613712072372437, -1.0616240501403809, -0.001068173092789948, -0.2159004658460617], [0.9943563938140869, 0.151925727725029, 2.238942861557007, 0.8607289791107178, 1.2040303945541382, -2.3748667240142822, 0.5421305298805237, -1.2962846755981445, -1.0256778001785278, 0.3658820688724518], [1.0757293701171875, 1.6776785850524902, -0.4844566285610199, -0.2801186442375183, 0.7964460253715515, -1.5309860706329346, -0.9653468132019043, -1.1346079111099243, -0.48670199513435364, 0.3066943287849426], [0.4567016661167145, 1.339646816253662, 0.9898688793182373, -0.25645315647125244, 1.4672883749008179, -0.6375895142555237, -0.6386777758598328, 0.18234391510486603, -0.4200282394886017, 0.0998697504401207], [0.7255332469940186, 0.22458761930465698, 0.5582488775253296, -0.4674156904220581, 0.3457297086715698, -1.1519341468811035, -2.4280519485473633, -1.0988396406173706, 0.20553182065486908, 0.07200413197278976]], [[1.2054249048233032, -1.0638865232467651, 0.9474580883979797, -0.8452785015106201, -1.4825248718261719, -0.8566842079162598, 1.247633934020996, 0.8291140198707581, -1.2124615907669067, -0.5157737135887146], [0.16599434614181519, -1.2545446157455444, 1.5510746240615845, -0.5211600661277771, -1.1034409999847412, -0.6353161334991455, 1.226678490638733, -0.0911865234375, -0.9819154143333435, 0.2407008856534958], [1.115717887878418, -0.44695305824279785, 1.3250070810317993, -0.6017792820930481, -1.2686055898666382, -2.6183433532714844, 0.22277195751667023, 0.7337615489959717, -1.1620653867721558, -0.5668874979019165], [0.8928369879722595, -1.1051188707351685, 0.9232276082038879, -0.4902592599391937, -0.8964894413948059, -0.51762455701828, 1.4229260683059692, -0.7730417251586914, -0.4338323771953583, -0.10377947986125946], [1.015687108039856, 0.18673701584339142, -0.21417959034442902, -0.668981671333313, -1.3195013999938965, -0.4612681567668915, 0.8830868005752563, 0.11312589794397354, -1.3212906122207642, -1.1506803035736084]], [[-0.4644966423511505, 0.8524463772773743, 1.1260707378387451, -0.6392664909362793, -0.06072111055254936, 0.18059048056602478, 0.13483808934688568, 0.13324356079101562, -0.12403132766485214, 1.2627049684524536], [-0.2865630090236664, 0.267973929643631, 1.3443444967269897, -1.2862900495529175, -0.4104452431201935, 0.22263342142105103, 0.896701455116272, 0.44988250732421875, -0.6043241024017334, 1.5569214820861816], [-0.551535964012146, 0.6913226246833801, 1.466260552406311, -0.6457347869873047, -0.9011657238006592, -0.019423335790634155, 0.10446380078792572, 1.3888795375823975, -0.5449660420417786, 1.5216526985168457], [-0.8694427013397217, 0.9422611594200134, 1.529123306274414, -0.21110539138317108, -0.5779234766960144, 0.2732229232788086, 0.29372403025627136, -0.1075374186038971, 1.0344274044036865, 1.3135284185409546], [-0.9806204438209534, 0.6720618605613708, 1.1598405838012695, -1.156262755393982, -0.08514151722192764, 0.3562352657318115, 0.6620896458625793, 1.5465965270996094, 0.9621661305427551, 1.5791634321212769]]] .. GENERATED FROM PYTHON SOURCE LINES 109-112 .. code-block:: Python idx.transform(X[:5], output_type="item") .. rst-class:: sphx-glr-script-out .. code-block:: none [[0, 382, 470, 226, 862], [1, 664, 736, 352, 797], [2, 607, 113, 87, 460], [3, 663, 583, 943, 618], [4, 999, 805, 750, 503]] .. GENERATED FROM PYTHON SOURCE LINES 113-116 .. code-block:: Python idx.transform(q, output_type="item") .. rst-class:: sphx-glr-script-out .. code-block:: none [[934, 267, 840, 680, 214]] .. GENERATED FROM PYTHON SOURCE LINES 117-120 .. code-block:: Python idx.transform(q, output_type="vector") .. rst-class:: sphx-glr-script-out .. code-block:: none [[[-0.7293733358383179, 0.6688253879547119, -1.352664589881897, -1.600181221961975, 1.0919655561447144, -0.20712751150131226, -0.45299363136291504, -2.3112924098968506, 0.730546236038208, -0.029609087854623795], [0.002563339425250888, 1.6989046335220337, 0.6126339435577393, -1.3617113828659058, 1.986482858657837, 0.48036810755729675, -0.1901821792125702, -0.8359416127204895, 0.4078412652015686, -0.1793580800294876], [-1.1475645303726196, 1.8878971338272095, -2.107212543487549, -2.4899961948394775, 0.2224663347005844, -2.656973123550415, -0.055160533636808395, -1.3886394500732422, 0.24803723394870758, 0.8768548965454102], [-0.17549194395542145, 0.49046894907951355, -0.42784786224365234, -3.2699573040008545, 0.002341537270694971, 0.3177329897880554, -0.27455103397369385, -0.9180266857147217, 0.4636206030845642, -0.27691906690597534], [-0.21622367203235626, 2.9266834259033203, -1.4076400995254517, -1.0336885452270508, 0.2131737470626831, -1.2547216415405273, 0.5574899911880493, -1.1752636432647705, -0.2801768183708191, -0.09265698492527008]]] .. GENERATED FROM PYTHON SOURCE LINES 121-124 .. code-block:: Python idx.kneighbors(q, n_neighbors=5, output_type="vector") .. rst-class:: sphx-glr-script-out .. code-block:: none (array([[[-7.2937334e-01, 6.6882539e-01, -1.3526646e+00, -1.6001812e+00, 1.0919656e+00, -2.0712751e-01, -4.5299363e-01, -2.3112924e+00, 7.3054624e-01, -2.9609088e-02], [ 2.5633394e-03, 1.6989046e+00, 6.1263394e-01, -1.3617114e+00, 1.9864829e+00, 4.8036811e-01, -1.9018218e-01, -8.3594161e-01, 4.0784127e-01, -1.7935808e-01], [-1.1475645e+00, 1.8878971e+00, -2.1072125e+00, -2.4899962e+00, 2.2246633e-01, -2.6569731e+00, -5.5160534e-02, -1.3886395e+00, 2.4803723e-01, 8.7685490e-01], [-1.7549194e-01, 4.9046895e-01, -4.2784786e-01, -3.2699573e+00, 2.3415373e-03, 3.1773299e-01, -2.7455103e-01, -9.1802669e-01, 4.6362060e-01, -2.7691907e-01], [-2.1622367e-01, 2.9266834e+00, -1.4076401e+00, -1.0336885e+00, 2.1317375e-01, -1.2547216e+00, 5.5748999e-01, -1.1752636e+00, -2.8017682e-01, -9.2656985e-02]]], dtype=float32), array([[0.5031697 , 0.5779903 , 0.6861234 , 0.69044447, 0.7136054 ]], dtype=float32)) .. GENERATED FROM PYTHON SOURCE LINES 125-128 .. code-block:: Python idx.kneighbors(X[:5], n_neighbors=5, include_distances=False).shape .. rst-class:: sphx-glr-script-out .. code-block:: none (5, 5, 10) .. GENERATED FROM PYTHON SOURCE LINES 129-136 .. code-block:: Python import numpy as np arr = idx.to_numpy() arr .. rst-class:: sphx-glr-script-out .. code-block:: none array([[ 5.4745728e-01, -1.3002133e+00, -2.4908927e-01, ..., 3.2358143e-01, 2.2474310e-01, 7.1601295e-01], [ 5.5737555e-02, -4.7065765e-01, -6.5972972e-01, ..., -2.7292881e+00, -8.2681960e-01, 6.3382739e-01], [ 1.0374167e+00, 6.5051889e-01, 9.1127706e-01, ..., -1.0616241e+00, -1.0681731e-03, -2.1590047e-01], ..., [-6.9389485e-02, -2.2415810e+00, 4.8493934e-01, ..., -8.3359528e-01, -8.7853217e-01, -1.1443168e+00], [ 1.5495661e+00, 8.8046187e-01, -7.7849793e-01, ..., -1.1908487e+00, 1.9838655e+00, -4.4115126e-01], [-2.8656301e-01, 2.6797393e-01, 1.3443445e+00, ..., 4.4988251e-01, -6.0432410e-01, 1.5569215e+00]], shape=(1000, 10), dtype=float32) .. GENERATED FROM PYTHON SOURCE LINES 137-142 .. code-block:: Python # save, savez np.save("annoy_vectors.npy", arr) np.load("annoy_vectors.npy") .. rst-class:: sphx-glr-script-out .. code-block:: none array([[ 5.4745728e-01, -1.3002133e+00, -2.4908927e-01, ..., 3.2358143e-01, 2.2474310e-01, 7.1601295e-01], [ 5.5737555e-02, -4.7065765e-01, -6.5972972e-01, ..., -2.7292881e+00, -8.2681960e-01, 6.3382739e-01], [ 1.0374167e+00, 6.5051889e-01, 9.1127706e-01, ..., -1.0616241e+00, -1.0681731e-03, -2.1590047e-01], ..., [-6.9389485e-02, -2.2415810e+00, 4.8493934e-01, ..., -8.3359528e-01, -8.7853217e-01, -1.1443168e+00], [ 1.5495661e+00, 8.8046187e-01, -7.7849793e-01, ..., -1.1908487e+00, 1.9838655e+00, -4.4115126e-01], [-2.8656301e-01, 2.6797393e-01, 1.3443445e+00, ..., 4.4988251e-01, -6.0432410e-01, 1.5569215e+00]], shape=(1000, 10), dtype=float32) .. GENERATED FROM PYTHON SOURCE LINES 143-146 .. code-block:: Python idx.to_scipy_csr() .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 147-150 .. code-block:: Python idx.to_pandas(id_location="index") .. raw:: html
col_0 col_1 col_2 col_3 col_4 col_5 col_6 col_7 col_8 col_9
id
0 0.547457 -1.300213 -0.249089 -0.338500 -1.472592 -0.619393 0.461915 0.323581 0.224743 0.716013
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