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- pytest-repeat>=0.9 ; extra == 'test' - - pytest>=8.2,<9 ; extra == 'test' - requires_python: '>=3.11' - editable: true - kind: pypi name: lenskit-funksvd version: 0.15.0.dev1407+g3c45716a @@ -13317,17 +13366,7 @@ packages: editable: true - kind: pypi name: lenskit-sklearn - version: 0.15.0.dev1549+g1caad4de.d20241121 - path: lenskit-sklearn - sha256: 18fa24b689f2924274287e83045bc80a1171d46d2d43383cf4eb35e7f0ebccdd - requires_dist: - - lenskit - - scikit-learn~=1.2 - requires_python: '>=3.11' - editable: true -- kind: pypi - name: lenskit-sklearn - version: 0.15.0.dev1550+g525fbe77 + version: 0.15.0.dev1561+g46813bfd path: lenskit-sklearn sha256: 18fa24b689f2924274287e83045bc80a1171d46d2d43383cf4eb35e7f0ebccdd requires_dist: @@ -19191,9 +19230,8 @@ packages: timestamp: 1728420305558 - kind: pypi name: progress-api - version: 0.1.0a9 - url: https://files.pythonhosted.org/packages/b4/e3/3f0fcbe30ccbd25d31e64afa3162b9f8e43d8d9333cc640c13b35deb7e08/progress_api-0.1.0a9-py3-none-any.whl - sha256: 3bdcf4e4bf176b6c02ed6ba4179edc341ae6172a48061c743e858ae52d873968 + version: 0.1.1.dev2+g7147712 + url: git+https://github.com/lenskit/progress-api.git@7147712bac9fb6991b68d5ffd7819681fdfa3592 requires_dist: - setuptools>=64 ; extra == 'dev' - setuptools-scm>=8 ; extra == 'dev' @@ -23839,21 +23877,21 @@ packages: timestamp: 1724956252424 - kind: conda name: tqdm - version: 4.66.5 + version: 4.67.0 build: pyhd8ed1ab_0 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.66.5-pyhd8ed1ab_0.conda - sha256: f2384902cef72048b0e9bad5c03d7a843de02ba6bc8618a9ecab6ff81a131312 - md5: c6e94fc2b2ec71ea33fe7c7da259acb4 + url: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.0-pyhd8ed1ab_0.conda + sha256: fb25b18cec1ebae56e7d7ebbd3e504f063b61a0fac17b1ca798fcaf205bdc874 + md5: 196a9e6ab4e036ceafa516ea036619b0 depends: - colorama - python >=3.7 license: MPL-2.0 or MIT purls: - pkg:pypi/tqdm?source=hash-mapping - size: 89519 - timestamp: 1722737568509 + size: 89434 + timestamp: 1730926216380 - kind: conda name: traitlets version: 5.14.3 @@ -24640,6 +24678,23 @@ packages: - pkg:pypi/wheel?source=hash-mapping size: 58585 timestamp: 1722797131787 +- kind: conda + name: widgetsnbextension + version: 4.0.13 + build: pyhd8ed1ab_0 + subdir: noarch + noarch: python + url: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.13-pyhd8ed1ab_0.conda + sha256: d155adc10f8c96f76d4468dbe37b33b4334dadf5cd4a95841aa009ca9bced5fa + md5: 6372cd99502721bd7499f8d16b56268d + depends: + - python >=3.7 + license: BSD-3-Clause + license_family: BSD + purls: + - pkg:pypi/widgetsnbextension?source=hash-mapping + size: 898656 + timestamp: 1724331433259 - kind: conda name: win_inet_pton version: 1.1.0 diff --git a/pixi.toml b/pixi.toml index 2cb44d30f..042745d45 100644 --- a/pixi.toml +++ b/pixi.toml @@ -14,7 +14,7 @@ pydantic = ">=2.8,<3" [pypi-dependencies] manylog = { git = "https://github.com/mdekstrand/manylog.git" } seedbank = ">=0.2.0a2" -progress-api = ">=0.1.0a9" +progress-api = { git = "https://github.com/lenskit/progress-api.git" } lenskit = { path = "lenskit", editable = true } [feature.py311.dependencies] @@ -102,6 +102,7 @@ nbformat = ">=5.2" ipython = ">=7" pyprojroot = ">=0.3" seaborn-base = ">=0.13.2,<0.14" +ipywidgets = ">=8.1.5,<9" [feature.reporting.dependencies] sqlite = "~=3.0" From 7869f4e27876e93c7d76b54bb3c083ed0c732564 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Fri, 22 Nov 2024 17:27:20 -0500 Subject: [PATCH 02/19] add units to ALS progress --- lenskit/lenskit/als/_common.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lenskit/lenskit/als/_common.py b/lenskit/lenskit/als/_common.py index bf9bb7059..b7b852d33 100644 --- a/lenskit/lenskit/als/_common.py +++ b/lenskit/lenskit/als/_common.py @@ -191,7 +191,7 @@ def fit_iters(self, data: Dataset, *, timer: util.Stopwatch | None = None) -> It ) start = timer.elapsed() - with make_progress(self.logger, "BiasedMF", self.epochs) as epb: + with make_progress(self.logger, "BiasedMF", self.epochs, unit="epoch") as epb: for epoch in range(self.epochs): epoch = epoch + 1 From 9ed28d05a46aa62d5ff77a31f1dae9dbee02076f Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Fri, 22 Nov 2024 17:27:28 -0500 Subject: [PATCH 03/19] add key-modifying add_from --- lenskit/lenskit/data/collection.py | 30 ++++++++++++++++++++++++--- lenskit/tests/data/test_collection.py | 12 +++++++++++ 2 files changed, 39 insertions(+), 3 deletions(-) diff --git a/lenskit/lenskit/data/collection.py b/lenskit/lenskit/data/collection.py index 4974b9811..83036e545 100644 --- a/lenskit/lenskit/data/collection.py +++ b/lenskit/lenskit/data/collection.py @@ -187,12 +187,36 @@ def key_type(self) -> type[K]: """ return self._key_class - def add(self, list: ItemList, *fields: ID): - key = self._key_class(*fields) # type: ignore + def add(self, list: ItemList, *fields: ID, **kwfields: ID): + """ + Add a single item list to this list. + + Args: + list: + The item list to add. + fields, kwfields: + The key fields for this list. + """ + key = self._key_class(*fields, **kwfields) # type: ignore self._add(key, list) - def add_from(self, other: ItemListCollection): + def add_from(self, other: ItemListCollection, **fields: ID): + """ + Add all collection from another collection to this collection. If field + values are supplied, they are used to supplement or overwrite the keys + in ``other``; a common use case is to add results from multiple + recommendation runs and save them a single field. + + Args: + other: + The item list collection to incorporate into this one. + fields: + Additional key fields (must be specified by name). + """ for key, list in other: + if fields: + fields = key._asdict() | fields + key = self._key_class(**fields) self._add(key, list) def _add(self, key: K, list: ItemList): diff --git a/lenskit/tests/data/test_collection.py b/lenskit/tests/data/test_collection.py index 8a19dd4b9..cbab50f9b 100644 --- a/lenskit/tests/data/test_collection.py +++ b/lenskit/tests/data/test_collection.py @@ -114,6 +114,18 @@ def test_lookup_projected(): assert np.all(il.ids() == ["a"]) +def test_add_from(): + ilc = ItemListCollection(["model", "user_id"]) + + ilc1 = ItemListCollection.from_dict({72: ItemList(["a", "b"])}, key="user_id") + ilc.add_from(ilc1, model="foo") + + assert len(ilc) == 1 + il = ilc.lookup(("foo", 72)) + assert il is not None + assert il.ids().tolist() == ["a", "b"] + + def test_from_df(rng, ml_ratings: pd.DataFrame): ml_ratings = ml_ratings.rename(columns={"user": "user_id", "item": "item_id"}) ilc = ItemListCollection.from_df(ml_ratings, UserIDKey) From cdeee37ef700200a717f83658f451f2199743ab7 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Fri, 22 Nov 2024 17:27:35 -0500 Subject: [PATCH 04/19] update Getting Started to new item list abilities --- docs/guide/GettingStarted.ipynb | 2040 ++++++++++++++++++++++++++++--- 1 file changed, 1839 insertions(+), 201 deletions(-) diff --git a/docs/guide/GettingStarted.ipynb b/docs/guide/GettingStarted.ipynb index 2c3fe0b31..dc6bbd90a 100644 --- a/docs/guide/GettingStarted.ipynb +++ b/docs/guide/GettingStarted.ipynb @@ -69,6 +69,23 @@ "from pyprojroot.here import here" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can set up some progress bars — those are convenient." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import progress_api\n", + "progress_api.set_backend('tqdm')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -80,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -159,7 +176,7 @@ "4 1 5 3.0 889751712" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -180,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -218,228 +235,1849 @@ "\n", "If memory is a concern, we can measure while generating, but we will not do that for now.\n", "\n", - "Let's start by creating and collecting the recommendations; we will collect all of them into a single `ItemListCollection`:" + "Let's start by creating and collecting the recommendations; we will generate 100 recommendations per user, and will collect all of them into a single {py:class}`~lenskit.data.ItemListCollection`:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/michael/Documents/LensKit/lkpy/lenskit/lenskit/als/_explicit.py:91: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /Users/runner/miniforge3/conda-bld/libtorch_1728232278409/work/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)\n", + "/Users/mde48/LensKit/lkpy/lenskit/lenskit/als/_explicit.py:91: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /Users/runner/miniforge3/conda-bld/libtorch_1728232278409/work/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)\n", " rmat = rmat.to_sparse_csr()\n" ] - } - ], - "source": [ - "all_test = ItemListCollection(UserIDKey)\n", - "ii_recs = ItemListCollection(UserIDKey)\n", - "als_recs = ItemListCollection(UserIDKey)\n", - "\n", - "for split in crossfold_users(ml100k, 5, SampleFrac(0.2)):\n", - " # collect the test data\n", - " all_test.add_from(split.test)\n", - "\n", - " # train the pipeline, cloning first so a fresh pipeline for each split\n", - " fit_als = pipe_als.clone()\n", - " fit_als.train(split.train)\n", - " # generate recs\n", - " als_recs.add_from(recommend(fit_als, split.test.keys(), 100))\n", - "\n", - " # do the same for item-item\n", - " fit_ii = pipe_ii.clone()\n", - " fit_ii.train(split.train)\n", - " ii_recs.add_from(recommend(fit_ii, split.test.keys(), 100))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Measuring Recommendations\n", - "\n", - "We analyze our recommendation lists with a `RunAnalysis` and some metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { "data": { - "text/html": [ - "
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", 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SpKqqKvX29gYfT0hI0EMPPaRhw/g/BAAA8SKq2O/YsUP/9m//poaGBknS8uXLNXr0aCUmnt1NV1eX7Ha7Fi1aNPgrBQAAFyWqU/CXXnpJHo8nZKyhoUHt7e1qb2/X2rVr9eqrr0a1gMrKSmVmZsput8vlcmnPnj0XnN/Q0CCXyyW73a6srCxVVVWdd+6OHTtks9k0b968qNYEAIBJoop9W1ubpk6det7HZ8+erb179w54f7W1tVq6dKnKy8vV0tIit9utuXPnqqOjI+L89vZ2FRYWyu12q6WlRatWrdKSJUu0c+fOsLmHDh3SsmXL5Ha7B7weAABMFFXsu7q6NGLEiOD9gwcP6rrrrgveT0pK0qlTpwa8v+eff16LFi3S4sWLlZ2drfXr1ys9PV2bN2+OOL+qqkoTJ07U+vXrlZ2drcWLF+u73/2u1q1bFzKvv79f9913n5588kllZWV97jp6enrU3d0dcgMAwBRRxd7pdGr//v3B+9dcc03IxXhtbW1KS0sb0L56e3vV3NysgoKCkPGCggI1NjZG3Mbn84XNnzNnjpqamnT69Ong2Jo1a3TNNdcM+NqBiooKORyO4C09PX1A2wEAcDmIKva33367fvjDH0Z8zLIsVVRU6Pbbbx/Qvrq6utTf3y+n0xky7nQ65ff7I27j9/sjzu/r61NXV5ck6f3331d1dbVefPHFAa1DksrKyhQIBIK3w4cPD3hbAADiXVRX45eXl2vGjBmaNWuWli1bpsmTJ8tms+mjjz7SunXrtH//fm3bti2qBdhstpD7lmWFjX3e/HPjJ06c0IIFC/Tiiy9q7NixA15DSkqKUlJSolg1AACXj6hiP2nSJNXX1+v+++9XUVFRMLyWZWnKlCmqq6vT9ddfP6B9jR07VgkJCWFn8UePHg07ez8nLS0t4vzExESlpqbqww8/1CeffKJvfvObwcfPnDkjSUpMTNT+/fs1adKkAR8vAAAmiCr2knTzzTfrN7/5jVpbW3XgwAFJ0pe+9CXddNNNUe0nOTlZLpdL9fX1+ru/+7vgeH19ve6+++6I2+Tl5emtt94KGaurq1Nubq6SkpI0ZcoU/e///m/I46tXr9aJEyf0r//6r3wWDwC4IkUd++7ubo0YMULTp0/X9OnTg+NnzpzRyZMnNWrUqAHvq7S0VAsXLlRubq7y8vK0ZcsWdXR0BL/LX1ZWpiNHjgQ/GvB4PNq4caNKS0v14IMPyufzqbq6Wtu3b5ck2e125eTkhDzH6NGjJSlsHACAK0VUF+i98cYbys3N1WeffRb22GeffaaZM2eGnXlfSFFRkdavX681a9Zo+vTp+tnPfiav16uMjAxJUmdnZ8h37jMzM+X1erV7925Nnz5dTz31lDZs2KD58+dHcxgAAFxRbNa5K9wGoKCgQN/+9re1ePHiiI/X1NSotrZW77zzzqAtMBa6u7vlcDgUCASieqfi87iWR3fxInCpNK8tjvUSBoTXEOJVvL+Gojqz37dvn2677bbzPn7rrbeGfWYOAABiK6rYHz9+XH19fed9/PTp0zp+/PgXXhQAABg8UcX+uuuuU1NT03kfb2pqCn7eDgAA4kNUsf/7v/97lZeX6w9/+EPYY36/X6tXr+ZiOQAA4kxUX71buXKl3nzzTX3pS1/SggULdMMNN8hms6mtrU0//vGPlZ6erpUrVw7VWgEAwEWIKvYjR47U+++/r7KyMtXW1gY/nx8zZowWLFigp59+WiNHjhyShQIAgIsT9Q/VcTgcqqys1KZNm9TV1SXLsnTNNddc8OfZAwCA2Ik69uccO3ZMhw4dks1mU0JCglJTUwdzXQAAYJBEdYGeJH344Ye69dZb5XQ6NWvWLN1888269tpr9bWvfS3kd90DAID4ENWZvd/v1+zZs3XNNdfo+eef15QpU2RZln7zm9/oxRdflNvt1r59+3TttdcO1XoBAECUoor9j370I2VkZOj999+X3W4Pjt9555166KGH9NWvflU/+tGPVFFRMegLBQAAFyeqt/Hr6+u1YsWKkNCfc9VVV2n58uWX/c/FBwDANFHF/uDBg5oxY8Z5H8/NzdXBgwe/8KIAAMDgiSr2J06cuOBvgRs5cqROnjz5hRcFAAAGT9RfvTtx4kTEt/Gls78aNorfmAsAAC6BqGJvWZYmT558wcf54ToAAMSXqGL/3nvvDdU6AADAEIkq9rNnzx6qdQAAgCESVeyHDRv2uW/T22w29fX1faFFAQCAwRNV7N94443zPtbY2KgXXniBC/QAAIgzUcX+7rvvDhv76KOPVFZWprfeekv33XefnnrqqUFbHAAA+OKi/kU45/z+97/Xgw8+qGnTpqmvr0+tra165ZVXNHHixMFcHwAA+IKijn0gENCKFSt0/fXX68MPP9S7776rt956Szk5OUOxPgAA8AVF9Tb+s88+q2eeeUZpaWnavn17xLf1AQBAfIkq9itXrtRVV12l66+/Xq+88opeeeWViPNef/31QVkcAAD44qKKfXFxMT8hDwCAy0xUsX/55ZeHaBkAAGCoXPTV+AAA4PJA7AEAMByxBwDAcMQeAADDEXsAAAxH7AEAMByxBwDAcMQeAADDEXsAAAxH7AEAMByxBwDAcMQeAADDEXsAAAxH7AEAMByxBwDAcMQeAADDEXsAAAxH7AEAMFzMY19ZWanMzEzZ7Xa5XC7t2bPngvMbGhrkcrlkt9uVlZWlqqqqkMdff/115ebmavTo0Ro+fLimT5+uf//3fx/KQwAAIK7FNPa1tbVaunSpysvL1dLSIrfbrblz56qjoyPi/Pb2dhUWFsrtdqulpUWrVq3SkiVLtHPnzuCcq6++WuXl5fL5fPr1r3+tBx54QA888IDeeeedS3VYAADEFZtlWVasnnzWrFmaMWOGNm/eHBzLzs7WvHnzVFFRETZ/xYoV2rVrl9ra2oJjHo9He/fulc/nO+/zzJgxQ3fddZeeeuqpiI/39PSop6cneL+7u1vp6ekKBAIaNWrUxRxaRK7l2wZtX8Bgal5bHOslDAivIcSreH8NxezMvre3V83NzSooKAgZLygoUGNjY8RtfD5f2Pw5c+aoqalJp0+fDptvWZbeffdd7d+/X7feeut511JRUSGHwxG8paenX8QRAQAQn2IW+66uLvX398vpdIaMO51O+f3+iNv4/f6I8/v6+tTV1RUcCwQCGjFihJKTk3XXXXfphRde0Ne//vXzrqWsrEyBQCB4O3z48Bc4MgAA4ktirBdgs9lC7luWFTb2efP/enzkyJFqbW3VyZMn9e6776q0tFRZWVm67bbbIu4zJSVFKSkpF3kEAADEt5jFfuzYsUpISAg7iz969GjY2fs5aWlpEecnJiYqNTU1ODZs2DBdf/31kqTp06erra1NFRUV5409AAAmi9nb+MnJyXK5XKqvrw8Zr6+vV35+fsRt8vLywubX1dUpNzdXSUlJ530uy7JCLsADAOBKEtO38UtLS7Vw4ULl5uYqLy9PW7ZsUUdHhzwej6Szn6UfOXJE27advQLX4/Fo48aNKi0t1YMPPiifz6fq6mpt3749uM+Kigrl5uZq0qRJ6u3tldfr1bZt20Ku+AcA4EoS09gXFRXp2LFjWrNmjTo7O5WTkyOv16uMjAxJUmdnZ8h37jMzM+X1elVSUqJNmzZp/Pjx2rBhg+bPnx+cc+rUKT388MP63e9+p6uuukpTpkzRq6++qqKiokt+fAAAxIOYfs8+XnV3d8vhcPA9e1wx4v07wufwGkK8ivfXUMx/XC4AABhaxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwMY99ZWWlMjMzZbfb5XK5tGfPngvOb2hokMvlkt1uV1ZWlqqqqkIef/HFF+V2uzVmzBiNGTNGd9xxhz744IOhPAQAAOJaTGNfW1urpUuXqry8XC0tLXK73Zo7d646Ojoizm9vb1dhYaHcbrdaWlq0atUqLVmyRDt37gzO2b17t77zne/ovffek8/n08SJE1VQUKAjR45cqsMCACCu2CzLsmL15LNmzdKMGTO0efPm4Fh2drbmzZunioqKsPkrVqzQrl271NbWFhzzeDzau3evfD5fxOfo7+/XmDFjtHHjRhUXF0ec09PTo56enuD97u5upaenKxAIaNSoURd7eGFcy7cN2r6AwdS8NvJrI97wGkK8ivfXUMzO7Ht7e9Xc3KyCgoKQ8YKCAjU2Nkbcxufzhc2fM2eOmpqadPr06YjbfPrppzp9+rSuvvrq866loqJCDocjeEtPT4/yaAAAiF8xi31XV5f6+/vldDpDxp1Op/x+f8Rt/H5/xPl9fX3q6uqKuM3KlSs1YcIE3XHHHeddS1lZmQKBQPB2+PDhKI8GAID4lRjrBdhstpD7lmWFjX3e/EjjkvTss89q+/bt2r17t+x2+3n3mZKSopSUlGiWDQDAZSNmsR87dqwSEhLCzuKPHj0advZ+TlpaWsT5iYmJSk1NDRlft26dnn76af30pz/VtGnTBnfxAABcRmL2Nn5ycrJcLpfq6+tDxuvr65Wfnx9xm7y8vLD5dXV1ys3NVVJSUnBs7dq1euqpp/Rf//Vfys3NHfzFAwBwGYnpV+9KS0u1detW1dTUqK2tTSUlJero6JDH45F09rP0v7yC3uPx6NChQyotLVVbW5tqampUXV2tZcuWBec8++yzWr16tWpqanTdddfJ7/fL7/fr5MmTl/z4AACIBzH9zL6oqEjHjh3TmjVr1NnZqZycHHm9XmVkZEiSOjs7Q75zn5mZKa/Xq5KSEm3atEnjx4/Xhg0bNH/+/OCcyspK9fb26lvf+lbIcz3++ON64oknLslxAQAQT2L6Pft41d3dLYfDwffsccWI9+8In8NrCPEq3l9DMf9xuQAAYGgRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMPFPPaVlZXKzMyU3W6Xy+XSnj17Lji/oaFBLpdLdrtdWVlZqqqqCnn8ww8/1Pz583XdddfJZrNp/fr1Q7h6AADiX0xjX1tbq6VLl6q8vFwtLS1yu92aO3euOjo6Is5vb29XYWGh3G63WlpatGrVKi1ZskQ7d+4Mzvn000+VlZWlf/mXf1FaWtqlOhQAAOJWTGP//PPPa9GiRVq8eLGys7O1fv16paena/PmzRHnV1VVaeLEiVq/fr2ys7O1ePFiffe739W6deuCc2bOnKm1a9fq3nvvVUpKyqU6FAAA4lbMYt/b26vm5mYVFBSEjBcUFKixsTHiNj6fL2z+nDlz1NTUpNOnT1/0Wnp6etTd3R1yAwDAFDGLfVdXl/r7++V0OkPGnU6n/H5/xG38fn/E+X19ferq6rrotVRUVMjhcARv6enpF70vAADiTcwv0LPZbCH3LcsKG/u8+ZHGo1FWVqZAIBC8HT58+KL3BQBAvEmM1ROPHTtWCQkJYWfxR48eDTt7PyctLS3i/MTERKWmpl70WlJSUvh8HwBgrJid2ScnJ8vlcqm+vj5kvL6+Xvn5+RG3ycvLC5tfV1en3NxcJSUlDdlaAQC4nMX0bfzS0lJt3bpVNTU1amtrU0lJiTo6OuTxeCSdfXu9uLg4ON/j8ejQoUMqLS1VW1ubampqVF1drWXLlgXn9Pb2qrW1Va2trert7dWRI0fU2tqqjz/++JIfHwAA8SBmb+NLUlFRkY4dO6Y1a9aos7NTOTk58nq9ysjIkCR1dnaGfOc+MzNTXq9XJSUl2rRpk8aPH68NGzZo/vz5wTm///3vddNNNwXvr1u3TuvWrdPs2bO1e/fuS3ZsAADEC5t17go3BHV3d8vhcCgQCGjUqFGDtl/X8m2Dti9gMDWvLf78SXGA1xDiVby/hmJ+NT4AABhaxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwxB4AAMMRewAADEfsAQAwHLEHAMBwMY99ZWWlMjMzZbfb5XK5tGfPngvOb2hokMvlkt1uV1ZWlqqqqsLm7Ny5U1OnTlVKSoqmTp2qN954Y6iWDwBA3Itp7Gtra7V06VKVl5erpaVFbrdbc+fOVUdHR8T57e3tKiwslNvtVktLi1atWqUlS5Zo586dwTk+n09FRUVauHCh9u7dq4ULF+rb3/62/ud//udSHRYAAHHFZlmWFasnnzVrlmbMmKHNmzcHx7KzszVv3jxVVFSEzV+xYoV27dqltra24JjH49HevXvl8/kkSUVFReru7tbbb78dnHPnnXdqzJgx2r59+4DW1d3dLYfDoUAgoFGjRl3s4YVxLd82aPsCBlPz2uJYL2FAeA0hXsX7aygxVk/c29ur5uZmrVy5MmS8oKBAjY2NEbfx+XwqKCgIGZszZ46qq6t1+vRpJSUlyefzqaSkJGzO+vXrz7uWnp4e9fT0BO8HAgFJZ6M/mPp7/t+g7g8YLIP9d32o8BpCvBqq19DIkSNls9m+8H5iFvuuri719/fL6XSGjDudTvn9/ojb+P3+iPP7+vrU1dWlcePGnXfO+fYpSRUVFXryySfDxtPT0wd6OMBlzfGCJ9ZLAC5rQ/UaGqx3mGMW+3P++n8slmVd8H8xkeb/9Xi0+ywrK1NpaWnw/pkzZ/SnP/1Jqampg/I/Kgy+7u5upaen6/Dhw4P6UQtwpeA1dHkYOXLkoOwnZrEfO3asEhISws64jx49GnZmfk5aWlrE+YmJiUpNTb3gnPPtU5JSUlKUkpISMjZ69OiBHgpiaNSoUfxDBXwBvIauDDG7Gj85OVkul0v19fUh4/X19crPz4+4TV5eXtj8uro65ebmKikp6YJzzrdPAABMF9O38UtLS7Vw4ULl5uYqLy9PW7ZsUUdHhzyes599lJWV6ciRI9q27ewVuB6PRxs3blRpaakefPBB+Xw+VVdXh1xl/9hjj+nWW2/VM888o7vvvltvvvmmfvrTn+rnP/95TI4RAIBYi2nsi4qKdOzYMa1Zs0adnZ3KycmR1+tVRkaGJKmzszPkO/eZmZnyer0qKSnRpk2bNH78eG3YsEHz588PzsnPz9eOHTu0evVq/fM//7MmTZqk2tpazZo165IfH4ZOSkqKHn/88bCPXwAMDK+hK0tMv2cPAACGXsx/XC4AABhaxB4AAMMRewAADEfsAQAwHLFH3GlsbFRCQoLuvPPOkPFPPvlENptNra2tEbfr7+9XRUWFpkyZoquuukpXX321vvKVr+ill166BKsGLg/333+/5s2bF/ZnmC3mPy4X+Gs1NTV69NFHtXXrVnV0dGjixIkD2u6JJ57Qli1btHHjRuXm5qq7u1tNTU06fvz4EK8YAOIbsUdcOXXqlF577TX98pe/lN/v18svv6zvf//7A9r2rbfe0sMPP6x77rknOHbjjTcO1VIB4LLB2/iIK7W1tbrhhht0ww03aMGCBXrppZc00B8FkZaWpv/+7//WH//4xyFeJQBcXog94kp1dbUWLFggSbrzzjt18uRJvfvuuwPa9vnnn9cf//hHpaWladq0afJ4PHr77beHcrkAcFkg9ogb+/fv1wcffKB7771XkpSYmKiioiLV1NQMaPupU6dq3759+sUvfqEHHnhAf/jDH/TNb35TixcvHsplA0Dc4zN7xI3q6mr19fVpwoQJwTHLspSUlDTgi+yGDRummTNnaubMmSopKdGrr76qhQsXqry8XJmZmUO1dACIa5zZIy709fVp27Zteu6559Ta2hq87d27VxkZGfrxj398UfudOnWqpLMX/gHAlYoze8SF//iP/9Dx48e1aNEiORyOkMe+9a1vqbq6Wt/4xjcknX27/69NnTpV//AP/6BbbrlF+fn5SktLU3t7u8rKyjR58mRNmTLlkhwHAMQjYo+4UF1drTvuuCMs9JI0f/58Pf300/rTn/4kScHP9P9Se3u75syZo+3bt6uiokKBQEBpaWn62te+pieeeEKJifxVB3Dl4lfcAgBgOD6zBwDAcMQeAADDEXsAAAxH7AEAMByxBwDAcMQeAADDEXsAAAxH7AEAMByxB3BJ3XbbbVq6dOmA57/88ssaPXr0kK0HuBIQewAADEfsAQAwHLEHIOns2+uPPvqoli5dqjFjxsjpdGrLli06deqUHnjgAY0cOVKTJk3S22+/HdymoaFBN998s1JSUjRu3DitXLlSfX19wcdPnTql4uJijRgxQuPGjdNzzz0X9ry9vb36p3/6J02YMEHDhw/XrFmztHv37ktxyMAVg9gDCHrllVc0duxYffDBB3r00Uf10EMP6Z577lF+fr5+9atfac6cOVq4cKE+/fRTHTlyRIWFhZo5c6b27t2rzZs3q7q6Wj/4wQ+C+1u+fLnee+89vfHGG6qrq9Pu3bvV3Nwc8pwPPPCA3n//fe3YsUO//vWvdc899+jOO+/Ub3/720t9+IC5LACwLGv27NnWV7/61eD9vr4+a/jw4dbChQuDY52dnZYky+fzWatWrbJuuOEG68yZM8HHN23aZI0YMcLq7++3Tpw4YSUnJ1s7duwIPn7s2DHrqquush577DHLsizr448/tmw2m3XkyJGQtdx+++1WWVmZZVmW9dJLL1kOh2MIjhi4cvBLvgEETZs2LfjnhIQEpaam6stf/nJwzOl0SpKOHj2qtrY25eXlyWazBR+/5ZZbdPLkSf3ud7/T8ePH1dvbq7y8vODjV199tW644Ybg/V/96leyLEuTJ08OWUdPT49SU1MH/fiAKxWxBxCUlJQUct9ms4WMnQv7mTNnZFlWSOglybKs4Lxzf76QM2fOKCEhQc3NzUpISAh5bMSIERd1DADCEXsAF2Xq1KnauXNnSPQbGxs1cuRITZgwQWPGjFFSUpJ+8YtfaOLEiZKk48eP68CBA5o9e7Yk6aabblJ/f7+OHj0qt9sds2MBTMcFegAuysMPP6zDhw/r0Ucf1UcffaQ333xTjz/+uEpLSzVs2DCNGDFCixYt0vLly/Xuu+9q3759uv/++zVs2P/9szN58mTdd999Ki4u1uuvv6729nb98pe/1DPPPCOv1xvDowPMwpk9gIsyYcIEeb1eLV++XDfeeKOuvvpqLVq0SKtXrw7OWbt2rU6ePKm//du/1ciRI/W9731PgUAgZD8vvfSSfvCDH+h73/uejhw5otTUVOXl5amwsPBSHxJgLJs1kA/WAADAZYu38QEAMByxBwDAcMQeAADDEXsAAAxH7AEAMByxBwDAcMQeAADDEXsAAAxH7AEAMByxBwDAcMQeAADD/X+uohvQ8h1rfAAAAABJRU5ErkJggg==", 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" ] @@ -449,14 +2087,14 @@ } ], "source": [ - "sns.catplot(all_list_metrics.reset_index(), x=\"Model\", y=\"NDCG\", kind=\"bar\")\n", + "sns.catplot(results.list_metrics().reset_index(), x=\"model\", y=\"NDCG\", kind=\"bar\")\n", "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "dev-full", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -474,5 +2112,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From fb769c75a151097a5be00dd6277292b59d9e4f02 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Fri, 22 Nov 2024 17:34:01 -0500 Subject: [PATCH 05/19] fix header font --- docs/_static/css/custom.css | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css index 43dab9947..0c3d59ea3 100644 --- a/docs/_static/css/custom.css +++ b/docs/_static/css/custom.css @@ -1,7 +1,8 @@ -@import url(https://fonts.bunny.net/css?family=source-sans-3:400,400i,700,700i|source-code-pro:300,300i,400,400i,500,500i,700,700i); +@import url(https://fonts.bunny.net/css?family=source-sans-3:400,400i,500,500i,700,700i|source-code-pro:300,300i,400,400i,500,500i,700,700i); html { --pst-font-family-base: "Source Sans 3", var(--pst-font-family-base-system); - --pst-font-family-header: "Source Sans 3", var(--pst-font-family-base-system); + --pst-font-family-heading: "Source Sans 3", var(--pst-font-family-base-system); + --pst-font-weight-heading: 500; --pst-font-family-monospace: "Source Code Pro", var(--pst-font-family-monospace-system); } From 5d25428f96bd4147f2b9ea30909cee8eced1f458 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Fri, 22 Nov 2024 17:34:07 -0500 Subject: [PATCH 06/19] update install instructions --- docs/guide/install.rst | 37 +++++++++++++++++++++++++++++++------ 1 file changed, 31 insertions(+), 6 deletions(-) diff --git a/docs/guide/install.rst b/docs/guide/install.rst index ddc956bb0..6021fe442 100644 --- a/docs/guide/install.rst +++ b/docs/guide/install.rst @@ -1,11 +1,40 @@ Installing LensKit ---------------- +================== + +We distribute LensKit through both PyPI and Conda (in the ``conda-forge`` channel). + +Once you have installed LensKit, see `Getting Started`_. + +.. _`Getting Started`: GettingStarted.html + + +Conda and Pixi +-------------- To install the current release with Anaconda (recommended):: conda install -c conda-forge lenskit -You can also use ``pip`` to install LensKit in a stock Python environment, +If you are using Pixi_, you can add LensKit as a dependency to your project:: + + pixi add lenskit + +.. _Pixi: https://pixi.sh + +.. note:: + + To use the latest development LensKit in a Pixi project, add the following + to your ``pixi.toml``: + + .. code-block:: toml + + [pypi-dependencies] + lenskit = { git = "https://github.com/lenskit/lkpy.git" } + +Pip +--- + +You can also use ``pip`` to install LensKit in a standard Python environment, such as a virtual environment:: pip install lenskit @@ -13,7 +42,3 @@ such as a virtual environment:: To use the latest development version, install directly from GitHub:: pip install git+https://github.com/lenskit/lkpy - -Then see `Getting Started`_. - -.. _`Getting Started`: GettingStarted.html From cd2b052646a6233af6b8be33b320cb8123625b89 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Fri, 22 Nov 2024 19:01:22 -0500 Subject: [PATCH 07/19] start the migration guide --- .vscode/ltex.dictionary.en-US.txt | 2 + .vscode/ltex.hiddenFalsePositives.en-US.txt | 2 + docs/api/index.rst | 3 + docs/guide/data.rst | 2 + docs/guide/evaluation/index.rst | 2 + docs/guide/index.rst | 2 + docs/guide/migrating.rst | 179 ++++++++++++++++++++ docs/guide/parallelism.rst | 2 + docs/guide/queries.rst | 6 + docs/guide/splitting.rst | 2 + 10 files changed, 202 insertions(+) create mode 100644 docs/guide/migrating.rst create mode 100644 docs/guide/queries.rst diff --git a/.vscode/ltex.dictionary.en-US.txt b/.vscode/ltex.dictionary.en-US.txt index 3c8ce998f..4ad9b9375 100644 --- a/.vscode/ltex.dictionary.en-US.txt +++ b/.vscode/ltex.dictionary.en-US.txt @@ -20,3 +20,5 @@ LKPY FunkSVD RecSys PyArrow +Numba +DuckDB diff --git a/.vscode/ltex.hiddenFalsePositives.en-US.txt b/.vscode/ltex.hiddenFalsePositives.en-US.txt index 394fdfe8d..828bb60f5 100644 --- a/.vscode/ltex.hiddenFalsePositives.en-US.txt +++ b/.vscode/ltex.hiddenFalsePositives.en-US.txt @@ -1,2 +1,4 @@ {"rule":"COMMA_COMPOUND_SENTENCE","sentence":"^\\QThis combination does mean that we may sometimes release a minor revision with the previous\nyear's major version number, if there are breaking changes in progress but not yet ready for\nrelease and we need to release new features or fixes for the current major version.\\E$"} {"rule":"MISSING_GENITIVE","sentence":"^\\QPyTorch tensors, including those on CUDA devices, are shared.\\E$"} +{"rule":"EN_A_VS_AN","sentence":"^\\QThis method returns an \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q containing the\nrecommended items.\\E$"} +{"rule":"EN_A_VS_AN","sentence":"^\\QYou can optionally specify candidate items with an \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q\nparameter to \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q (it takes an \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q), or a list\nlength with \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q (you can also bake a default list length into the pipeline\nwhen you call \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q).\\E$"} diff --git a/docs/api/index.rst b/docs/api/index.rst index 41fd14c5a..75f3922a8 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -34,6 +34,9 @@ them need to be installed separately. lenskit.algorithms lenskit.basic + lenskit.knn + lenskit.als + lenskit.sklearn lenskit.funksvd lenskit.implicit lenskit.hpf diff --git a/docs/guide/data.rst b/docs/guide/data.rst index f47e0a416..12c85388e 100644 --- a/docs/guide/data.rst +++ b/docs/guide/data.rst @@ -1,3 +1,5 @@ +.. _datasets: + Datasets ======== diff --git a/docs/guide/evaluation/index.rst b/docs/guide/evaluation/index.rst index 0f8bc9b23..e15be91bb 100644 --- a/docs/guide/evaluation/index.rst +++ b/docs/guide/evaluation/index.rst @@ -1,3 +1,5 @@ +.. _evaluation: + Evaluating Recommender Output ============================= diff --git a/docs/guide/index.rst b/docs/guide/index.rst index 9fb8e08e2..6ac5846de 100644 --- a/docs/guide/index.rst +++ b/docs/guide/index.rst @@ -18,6 +18,7 @@ guide to how to use LensKit for research, education, and other purposes. data item-lists + queries pipeline .. toctree:: @@ -39,5 +40,6 @@ guide to how to use LensKit for research, education, and other purposes. :caption: Tips and Help :maxdepth: 1 + migrating performance impl-tips diff --git a/docs/guide/migrating.rst b/docs/guide/migrating.rst new file mode 100644 index 000000000..95c41243b --- /dev/null +++ b/docs/guide/migrating.rst @@ -0,0 +1,179 @@ +.. _migrating: + +Migrating from LensKit 0.x +========================== + +.. versionchanged:: 2025.1 + + Everything. + +LensKit 2025.1 brings the largest changes to LensKit's APIs and operation since +moving to Python. These changes are a result of years of experience using and +teaching the current Python APIs, talking with users (and non-users), and +understanding limitations of the previous design in the modern recommendation +and machine learning landscape, and “pathway limitations” — things that were +technically possible, but the design did not make it clear how to achieve them. + +The recommendation models and evaluation metrics retain their behavior from +previous versions of LensKit, but their interfaces and the surrounding +infrastructure are significantly different. + +Change Highlights +----------------- + +The major changes that client or research code will need to adapt to largely +fall into the following categories: + +- **Data structures** — LensKit now uses recommender-oriented data abstractions, + such as :class:`~lenskit.data.Dataset` (see :ref:`datasets`) and + :class:`~lenskit.data.ItemList`, instead of storing everything directly in + Pandas data frames. All data structures support round-tripping with data + frames (and often other structures); the default user and item column names + have changed (to ``user_id`` and ``item_id``). + +- **Computational infrastructure** — LensKit models now mostly use PyTorch + instead of Numba-accelerated NumPy code. This speeds forward compatibility, + as Numba was often a release bottleneck. There are no limits on what can be + used in models, however — TensorFlow and Jax models should work just fine in + LensKit. + +- **Pipelines** — LensKit recommendation is now built around *pipelines* (see + :ref:`pipeline`) consisting of individual *components*, such as scoring models + and rankers. + +- **Queries** — requests for recommendations are now encapsulated in a “query” + (implemented by :py:class:`~lenskit.data.RecQuery`) containing the different + features to identify and describe a recommendation request, such as a user + identifier, historical ratings, or session information (see :ref:`queries`). + +- **Reorganization** — the ``lenskit.algorithms`` package is gone, and + recommendation components are directly in packages such as + :py:mod:`lenskit.basic` and :py:mod:`lenskit.als`. + +- **Evaluation** — the new :py:class:`~lenskit.metrics.RunAnalysis` class + provides unified handling of both prediction and recommendation metrics, + summary statistics, and better defaults and handling for missing data. It + also directly supports “global” metrics that are computed over an entire run + instead of one list at a time. + +Loading Data +------------ + +New code should use :py:func:`lenskit.data.from_interactions_df` to convert a Pandas +data frame into a :py:func:`~lenskit.data.Dataset`, or one of the standard loaders +such as :py:func:`lenskit.data.load_movielens`. + +Additional dataset construction support and possible implementations (e.g. +database-backed datasets) are coming, but this is the migration path for the +typical code patterns used in LensKit 0.14 and earlier. + +.. tip:: + + The :py:func:`~lenskit.data.load_movielens` function can now directly load + MovieLens data from the ``.zip`` files distributed by GroupLens, without + needing to extract them first. It also automatically detects which version + of the MovieLens data you are loading. + +Configuring Recommenders +------------------------ + +In LensKit 0.3 through 0.14, you configured a recommender by instantiating an +*algorithm*, and then calling ``Recommender.adapt`` to make sure it implemented +the ``Recommender`` interface. + +LensKit 2025 introduces the *pipeline* design; you configure the core +recommendation model in a very similar way (constructor arguments), and pass it +to :py:func:`~lenskit.pipeline.topn_pipeline` instead of ``Recommender.adapt``. +The resulting pipeline object can be directly used by the batch inference +facilities. + +The model and pipeline training method is now named ``train``, so after creating +the pipeline, you will call :py:meth:`~lenskit.pipeline.Pipeline.train`:: + + pipe.train(dataset) + +See :ref:`pipeline` for more details on pipelines and how you can reconfigure +them for very different ways of turning scoring models into full recommenders. + +.. note:: + + Since 2025, we no longer use the term “algorithm” in LensKit, as it is + ambiguous and promotes confusion about very different things. Instead we + have “pipelines” consisting of ”components”, some of which may be ”models” + (for scoring, ranking, etc.). + +Obtaining Recommendations +------------------------- + +In previous LensKit versions, you would get recommendations by calling the +`recommend` method and providing the user ID, recommendation count, and +optionally the user's current historical ratings. + +In LensKit 2025, you invoke the *pipeline* to obtain recommendations. In a +standard recommendation pipeline, the recommendations are produced by a +component called ``recommender``; you can obtain them with: + +.. code:: python + + recs = pipeline.run('recommender', query=user_id) + +This method returns an :py:class:`~lenskit.data.ItemList` containing the +recommended items. You can optionally specify candidate items with an ``items=`` +parameter to ``run`` (it takes an :py:class:`~lenskit.data.ItemList`), or a list +length with ``n=`` (you can also bake a default list length into the pipeline +when you call :py:func:`~lenskit.pipeline.topn_pipeline`). + +.. important:: + + The input specifying the user identifier is now called a ``query``, in order + to support recommendation tasks beyond simple user-based recommendation such + as context-based or session-based recommendation. + +.. note:: + + We are considering adding a more ergonomic interface to obtain + recommendations from pipelines. + +Batch Inference +--------------- + +The :py:func:`~lenskit.batch.recommend` and :py:func:`~lenskit.batch.predict` +functions still exist, and now work on pipelines instead of “algorithms”. They +no longer return data frames; instead, they return an +:py:class:`~lenskit.data.ItemListCollection` containing the item lists produced +by the recommender or predictor / scorer components. + +You can also use the more flexible +:py:class:`~lenskit.batch.BatchPipelineRunner` to do things like extract +multiple component outputs for each test user (e.g. both rating predictions and +top-*N* recommendations, or rankings before and after a reranking stage). + +All batch inference interfaces support parallel processing over users, and the +same parallel configuration (see :ref:`parallelism`). The resulting item list +collections can be converted to data frames +(:py:meth:`~lenskit.data.ItemListCollection.to_df`) to be saved in any format +supported by Pandas; future LensKit versions will add support for directly +storing them in other formats such as DuckDB, and loading them from such +formats. + +Evaluating Recommendations +-------------------------- + +The evaluation logic has seen significant updates and improvements and API +changes. The :py:mod:`lenskit.splitting` module contains various facilities for +data splitting, including equivalents of the splitting strategies that used to +live in ``lenskit.crossfold``; see :ref:`splitting` for details on data +splitting. These functions now operate on data sets and return item list +collections instead of data frames. + +To measure recommendations, use the various metrics in +:py:mod:`lenskit.metrics`, and the :py:mod:`lenskit.metrics.RunAnalysis` class +provides support for analyzing *runs* (sequences of recommendation lists +produced by an algorithm in an experimental condition). It handles both ranking +and prediction accuracy metrics in a single analysis interface, and also +supports both listwise and global metrics (e.g. exposure metrics). We will be +quickly building out additional metrics that take advantage of this +functionality. See :ref:`evaluation` for details on metrics and analysis. + +:py:mod:`lenskit.metrics.RunAnalysis` replaces the old ``RecListAnalysis``, and +provides better defaults (e.g. how users without recommendations are handled). diff --git a/docs/guide/parallelism.rst b/docs/guide/parallelism.rst index 4adda3aab..7cd01aa74 100644 --- a/docs/guide/parallelism.rst +++ b/docs/guide/parallelism.rst @@ -1,3 +1,5 @@ +.. _parallelism: + Parallel Processing =================== diff --git a/docs/guide/queries.rst b/docs/guide/queries.rst new file mode 100644 index 000000000..b206035b2 --- /dev/null +++ b/docs/guide/queries.rst @@ -0,0 +1,6 @@ +.. _queries: + +Queries +======= + +.. py:currentmodule:: lenskit.data diff --git a/docs/guide/splitting.rst b/docs/guide/splitting.rst index 0ae8d4d17..3eece7bc7 100644 --- a/docs/guide/splitting.rst +++ b/docs/guide/splitting.rst @@ -1,3 +1,5 @@ +.. _splitting: + Splitting Data ============== From 1a4a6afdd987e153674fe75a03c9c532531869a1 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 11:33:00 -0500 Subject: [PATCH 08/19] add Mermaid support --- docs/conf.py | 1 + pixi.lock | 28 ++++++++++++++++++++++++++++ pixi.toml | 1 + 3 files changed, 30 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index b3406365c..084816a21 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -32,6 +32,7 @@ "sphinx_togglebutton", "sphinxext.opengraph", "sphinxcontrib.bibtex", + "sphinxcontrib.mermaid", ] # set up our filenames diff --git a/pixi.lock b/pixi.lock index d7e60b36e..a233a7e02 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1102,6 +1102,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -1488,6 +1489,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -1897,6 +1899,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -2271,6 +2274,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -2623,6 +2627,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -3046,6 +3051,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -3432,6 +3438,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -3675,6 +3682,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -3870,6 +3878,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda @@ -23191,6 +23200,25 @@ packages: - pkg:pypi/sphinxcontrib-jsmath?source=hash-mapping size: 10431 timestamp: 1691604844204 +- kind: conda + name: sphinxcontrib-mermaid + version: 1.0.0 + build: pyhd8ed1ab_0 + subdir: noarch + noarch: python + url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-mermaid-1.0.0-pyhd8ed1ab_0.conda + sha256: 419e221a58330dececc14151c7fe5acb80d303c11879fa406638c6d1a4c3dff1 + md5: a906d0a778a54834ffd15d22bdda9ddd + depends: + - python >=3.7 + - pyyaml + - sphinx + license: BSD-2-Clause + license_family: BSD + purls: + - pkg:pypi/sphinxcontrib-mermaid?source=hash-mapping + size: 14981 + timestamp: 1728887781136 - kind: conda name: sphinxcontrib-qthelp version: 2.0.0 diff --git a/pixi.toml b/pixi.toml index 042745d45..cf04c81fd 100644 --- a/pixi.toml +++ b/pixi.toml @@ -70,6 +70,7 @@ pydata-sphinx-theme = "~=0.16.0" myst-nb = ">=0.13" just = ">=1.2" sphinx-togglebutton = ">=0.3.2,<0.4" +sphinxcontrib-mermaid = ">=1.0.0,<2" [feature.dev.dependencies] hatchling = "~=1.24" From 7206af0823491154ae041f3bb8061ccb36c1f145 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 11:33:13 -0500 Subject: [PATCH 09/19] improve pipeline documentation --- docs/api/pipeline.rst | 9 +- docs/guide/pipeline.rst | 114 +++++++++++++++++-------- docs/guide/std-pred-pipeline.mmd | 41 +++++++++ docs/guide/std-topn-pipeline.mmd | 33 +++++++ lenskit/lenskit/pipeline/__init__.py | 1 + lenskit/lenskit/pipeline/common.py | 9 +- lenskit/lenskit/pipeline/components.py | 11 +-- 7 files changed, 169 insertions(+), 49 deletions(-) create mode 100644 docs/guide/std-pred-pipeline.mmd create mode 100644 docs/guide/std-topn-pipeline.mmd diff --git a/docs/api/pipeline.rst b/docs/api/pipeline.rst index 32d4272cc..a5bac12b9 100644 --- a/docs/api/pipeline.rst +++ b/docs/api/pipeline.rst @@ -15,7 +15,9 @@ Pipeline Classes :caption: Data Sets ~lenskit.pipeline.Pipeline + ~lenskit.pipeline.PipelineState ~lenskit.pipeline.Node + ~lenskit.pipeline.Lazy Component Interface ------------------- @@ -27,9 +29,9 @@ LensKit components. :toctree: . :nosignatures: - ~lenskit.pipeline.ConfigurableComponent - ~lenskit.pipeline.TrainableComponent - ~lenskit.pipeline.AutoConfig + ~lenskit.pipeline.Component + ~lenskit.pipeline.Trainable + ~lenksit.pipeline.Configurable Standard Pipelines ------------------ @@ -38,4 +40,5 @@ Standard Pipelines :toctree: . :nosignatures: + ~lenskit.pipeline.RecPipelineBuilder ~lenskit.pipeline.topn_pipeline diff --git a/docs/guide/pipeline.rst b/docs/guide/pipeline.rst index 68530906e..71fee6bf7 100644 --- a/docs/guide/pipeline.rst +++ b/docs/guide/pipeline.rst @@ -18,39 +18,10 @@ definitions of different composition classes. The pipeline lives in the :class:`Pipeline` class. If all you want to do is build a standard top-N recommendation pipeline from an -item scorer, see :func:`topn_pipeline`; this is the equivalent to -``Recommender.adapt`` in the old LensKit API. If you want more flexibility, you -can write out the pipeline configuration yourself; the equivalent to -``topn_pipeline(scorer)`` is: - -.. code:: python - - pipe = Pipeline() - # define an input parameter for the user ID - user = pipe.create_input('user', EntityId) - # allow candidate items to be optionally specified - items = pipe.create_input('items', list[EntityId], None) - # look up a user's history in the training data - history = pipe.add_component('lookup-user', LookupTrainingHistory(), user=user) - # find candidates from the training data - lookup_candidates = pipe.add_component( - 'select-candidates', - UnratedTrainingItemsCandidateSelector(), - user=history, - ) - # if the client provided items as a pipeline input, use those; otherwise - # use the candidate selector we just configured. - candidates = pipe.use_first_of('candidates', items, lookup_candidates) - # score the candidate items using the specified scorer - score = pipe.add_component('score', scorer, user=user, items=candidates) - # rank the items by score - recommend = pipe.add_component('recommend', TopNRanker(50), items=score) - -You can then run this pipeline to produce recommendations with: - -.. code:: python - - user_recs = pipe.run(recommend, user=user_id) +item scorer, see :func:`topn_pipeline`. :class:`RecPipelineBuilder` provides +some more flexibility in configuring a recommendation pipeline with a standard +design, and you can always fully configure the pipeline yourself for maximum +flexibility. .. todo:: Redo some of those types with user & item data, etc. @@ -72,7 +43,8 @@ pipeline can use some components (especially the scorer) trained from training data, and other components that query a database or REST services for things like user history and candidate set lookup. -.. note:: Acknowledgements +.. admonition:: Acknowledgements + :class: note The LensKit pipeline design is heavily inspired by the pipeline abstraction Karl Higley originally created for POPROX_ (available in the git history), @@ -81,6 +53,77 @@ like user history and candidate set lookup. .. _Haystack: https://docs.haystack.deepset.ai/docs/pipelines .. _POPROX: https://ccri-poprox.github.io/poprox-researcher-manual/reference/recommender/poprox_recommender.pipeline.html + +Constructing Pipelines +~~~~~~~~~~~~~~~~~~~~~~ + +The simplest way to make a pipeline is to construct a ``topn_pipeline`` — the +following will create a top-*N* recommendation pipeline using implicit-feedback +matrix factorization: + +.. code:: python + + als = ImplicitMF(50) + pipe = topn_pipeline(als) + +The :class:`RecPipelineBuilder` class provides a more flexible mechanism to +create standard recommendation pipelines; to implement the same pipeline with +that class, do: + +.. code:: python + + als = ImplicitMF(50) + builder = RecPipelineBuilder() + builder.scorer(als) + pipe = builder.build('ALS') + +For maximum flexibility, you can directly construct and wire the pipeline +yourself: + +.. code:: python + + pipe = Pipeline() + # define an input parameter for the user ID (the 'query') + query = pipe.create_input('query', ID) + # allow candidate items to be optionally specified + items = pipe.create_input('items', ItemList, None) + # look up a user's history in the training data + history = pipe.add_component('lookup-user', LookupTrainingHistory(), query=query) + # find candidates from the training data + lookup_candidates = pipe.add_component( + 'select-candidates', + UnratedTrainingItemsCandidateSelector(), + query=history, + ) + # if the client provided items as a pipeline input, use those; otherwise + # use the candidate selector we just configured. + candidates = pipe.use_first_of('candidates', items, lookup_candidates) + # score the candidate items using the specified scorer + score = pipe.add_component('score', scorer, query=query, items=candidates) + # rank the items by score + recommend = pipe.add_component('recommend', TopNRanker(50), items=score) + +After any of these methods, you can run the pipeline to produce recommendations +with: + +.. code:: python + + user_recs = pipe.run(recommend, query=user_id) + +Standard Pipeline Layout +------------------------ + +The standard recommendation pipeline, produced by any of the above approaches, +looks like this: + +.. mermaid:: std-topn-pipeline.mmd + :caption: Top-N recommendation pipeline. + +A pipeline configured for rating prediction, with a fallback predictor, looks like: + +.. mermaid:: std-pred-pipeline.mmd + :caption: Pipeline for top-N recommendation and rating prediction, with predictions falling back to a baseline scorer. + .. _pipeline-model: Pipeline Model @@ -256,7 +299,8 @@ three ways to save a pipeline or part thereof: the learned parameters but **not** the configuration or connections. The parameters can be reloaded into a compatible pipeline with :meth:`Pipeline.load_params`; a compatible pipeline can be created by - running the pipeline setup code or using a saved pipeline configuration. + running the same pipeline setup code or using a saved pipeline + configuration. These can be mixed and matched; if you pickle an untrained pipeline, you can unpickle it and use :meth:`~Pipeline.load_params` to infuse it with parameters. diff --git a/docs/guide/std-pred-pipeline.mmd b/docs/guide/std-pred-pipeline.mmd new file mode 100644 index 000000000..3a54bc874 --- /dev/null +++ b/docs/guide/std-pred-pipeline.mmd @@ -0,0 +1,41 @@ +flowchart LR + subgraph input["Inputs"] + QUERY[/"Query
(query: QueryInput)"/] + ITEMS[/"Candidate Items
(items: ItemList)"/] + N[/"List Length
(n: int)"/] + class ITEMS optional; + class N optional; + end + + subgraph prep["Data Preparation"] + HLOOK["History Lookup"] + CSEL["Candidate Selector"] + end + + subgraph rank["Scoring and Ranking"] + SCORE["Scorer"] + FALLBACK["Baseline Scorer"] + FILL["Fallback"] + RANK["Top-K Ranker"] + end + + RESULT[\"Recommendations
(ordered ItemList)"\] + PREDS[\"Predictions
(ItemList)"\] + + QUERY --> HLOOK + HLOOK -- RecQuery --> CSEL + ITEMS --> CSEL + + HLOOK -- RecQuery --> SCORE + CSEL -- ItemList --> SCORE + HLOOK -- RecQuery --> FALLBACK + CSEL -- ItemList --> FALLBACK + SCORE -- ItemList --> FILL + FALLBACK -- ItemList --> FILL + SCORE -- ItemList --> RANK + N --> RANK + + RANK --> RESULT + FILL --> PREDS + + classDef optional stroke-dasharray: 5 5; diff --git a/docs/guide/std-topn-pipeline.mmd b/docs/guide/std-topn-pipeline.mmd new file mode 100644 index 000000000..656e6a6d8 --- /dev/null +++ b/docs/guide/std-topn-pipeline.mmd @@ -0,0 +1,33 @@ +flowchart LR + subgraph input["Inputs"] + QUERY[/"Query
(query: QueryInput)"/] + ITEMS[/"Candidate Items
(items: ItemList)"/] + N[/"List Length
(n: int)"/] + class ITEMS optional; + class N optional; + end + + subgraph prep["Data Preparation"] + HLOOK["History Lookup"] + CSEL["Candidate Selector"] + end + + subgraph rank["Scoring and Ranking"] + SCORE["Scorer"] + RANK["Top-K Ranker"] + end + + RESULT[\"Recommendations
(ordered ItemList)"\] + + QUERY --> HLOOK + HLOOK -- RecQuery --> CSEL + ITEMS --> CSEL + + HLOOK -- RecQuery --> SCORE + CSEL -- ItemList --> SCORE + SCORE -- ItemList --> RANK + N --> RANK + + RANK --> RESULT + + classDef optional stroke-dasharray: 5 5; diff --git a/lenskit/lenskit/pipeline/__init__.py b/lenskit/lenskit/pipeline/__init__.py index 031c38ca6..394e4a6a0 100644 --- a/lenskit/lenskit/pipeline/__init__.py +++ b/lenskit/lenskit/pipeline/__init__.py @@ -39,6 +39,7 @@ "Pipeline", "PipelineError", "PipelineWarning", + "PipelineState", "Node", "PipelineFunction", "Configurable", diff --git a/lenskit/lenskit/pipeline/common.py b/lenskit/lenskit/pipeline/common.py index 93f5e7ba0..c79b518e2 100644 --- a/lenskit/lenskit/pipeline/common.py +++ b/lenskit/lenskit/pipeline/common.py @@ -121,18 +121,15 @@ def build(self, name: str | None = None) -> Pipeline: def topn_pipeline(scorer: Component, *, predicts_ratings: bool = False, n: int = -1) -> Pipeline: """ - Create a pipeline that produces top-N recommendations using the specified - scorer. The scorer should have the following call signature:: - - def scorer(user: UserHistory, items: ItemList) -> pd.Series: ... + Create a pipeline that produces top-N recommendations using a scoring model. Args: scorer: The scorer to use in the pipeline (it will added with the component name ``scorer``, see :ref:`pipeline-names`). predicts_ratings: - If ``True``, make ``rating-predictor`` an alias for ``scorer`` so that - evaluation components know this pipeline can predict ratings. + If ``True``, make ``rating-predictor`` an alias for ``scorer`` so + that evaluation components know this pipeline can predict ratings. n: The recommendation list length to configure in the pipeline. """ diff --git a/lenskit/lenskit/pipeline/components.py b/lenskit/lenskit/pipeline/components.py index 264ecab75..6ca20691c 100644 --- a/lenskit/lenskit/pipeline/components.py +++ b/lenskit/lenskit/pipeline/components.py @@ -39,9 +39,9 @@ class Configurable(Protocol): # pragma: nocover :meth:`from_config`. An object must implement both of these methods to be considered - configurable. Components extending the :class:`Component` class - automatically have working versions of these methods if they define their - constructor parameters and fields appropriately. + configurable. Components extending the :class:`Component` automatically + have working versions of these methods if they define their constructor + parameters and fields appropriately. .. note:: @@ -65,8 +65,9 @@ def get_config(self) -> dict[str, object]: @runtime_checkable class Trainable(Protocol): # pragma: nocover """ - Interface for components that can learn parameters from training data, and - expose those parameters for serialization as an alternative to pickling + Interface for components that can learn parameters from training data. It + supports trainig, checking if a component has already been trained, and + exposing trained parameters for serialization as an alternative to pickling (components also need to be picklable). .. note:: From 25e18993b5e855488a5edcf3bc24725bedee6839 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 11:41:19 -0500 Subject: [PATCH 10/19] fix module typo --- docs/api/pipeline.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/api/pipeline.rst b/docs/api/pipeline.rst index a5bac12b9..c2f039ae6 100644 --- a/docs/api/pipeline.rst +++ b/docs/api/pipeline.rst @@ -31,7 +31,7 @@ LensKit components. ~lenskit.pipeline.Component ~lenskit.pipeline.Trainable - ~lenksit.pipeline.Configurable + ~lenskit.pipeline.Configurable Standard Pipelines ------------------ From a378de9663e55fc875c7844e71c40dd877a8f5fb Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 12:08:37 -0500 Subject: [PATCH 11/19] clean up figure display --- docs/_static/css/custom.css | 6 ++- docs/conf.py | 2 + docs/guide/pipeline.rst | 87 ++++++++++++++++++-------------- docs/guide/std-pred-pipeline.mmd | 12 +++-- docs/guide/std-topn-pipeline.mmd | 10 +++- 5 files changed, 73 insertions(+), 44 deletions(-) diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css index 0c3d59ea3..8f0c2403e 100644 --- a/docs/_static/css/custom.css +++ b/docs/_static/css/custom.css @@ -1,4 +1,4 @@ -@import url(https://fonts.bunny.net/css?family=source-sans-3:400,400i,500,500i,700,700i|source-code-pro:300,300i,400,400i,500,500i,700,700i); +@import url(https://fonts.bunny.net/css?family=source-sans-3:300,300i,400,400i,500,500i,700,700i|source-code-pro:300,300i,400,400i,500,500i,700,700i); html { --pst-font-family-base: "Source Sans 3", var(--pst-font-family-base-system); @@ -6,3 +6,7 @@ html { --pst-font-weight-heading: 500; --pst-font-family-monospace: "Source Code Pro", var(--pst-font-family-monospace-system); } + +.mermaid tt { + font-family: var(--pst-font-family-monospace); +} diff --git a/docs/conf.py b/docs/conf.py index 084816a21..5ca2ca34c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -126,6 +126,8 @@ bibtex_bibfiles = ["lenskit.bib"] nb_execution_mode = "off" +mermaid_d3_zoom = True + # -- external links extlinks = { diff --git a/docs/guide/pipeline.rst b/docs/guide/pipeline.rst index 71fee6bf7..a504a0c18 100644 --- a/docs/guide/pipeline.rst +++ b/docs/guide/pipeline.rst @@ -53,6 +53,7 @@ like user history and candidate set lookup. .. _Haystack: https://docs.haystack.deepset.ai/docs/pipelines .. _POPROX: https://ccri-poprox.github.io/poprox-researcher-manual/reference/recommender/poprox_recommender.pipeline.html +.. _pipeline-construct: Constructing Pipelines ~~~~~~~~~~~~~~~~~~~~~~ @@ -78,30 +79,7 @@ that class, do: pipe = builder.build('ALS') For maximum flexibility, you can directly construct and wire the pipeline -yourself: - -.. code:: python - - pipe = Pipeline() - # define an input parameter for the user ID (the 'query') - query = pipe.create_input('query', ID) - # allow candidate items to be optionally specified - items = pipe.create_input('items', ItemList, None) - # look up a user's history in the training data - history = pipe.add_component('lookup-user', LookupTrainingHistory(), query=query) - # find candidates from the training data - lookup_candidates = pipe.add_component( - 'select-candidates', - UnratedTrainingItemsCandidateSelector(), - query=history, - ) - # if the client provided items as a pipeline input, use those; otherwise - # use the candidate selector we just configured. - candidates = pipe.use_first_of('candidates', items, lookup_candidates) - # score the candidate items using the specified scorer - score = pipe.add_component('score', scorer, query=query, items=candidates) - # rank the items by score - recommend = pipe.add_component('recommend', TopNRanker(50), items=score) +yourself; this is described in :ref:`standard-pipelines`. After any of these methods, you can run the pipeline to produce recommendations with: @@ -110,20 +88,6 @@ with: user_recs = pipe.run(recommend, query=user_id) -Standard Pipeline Layout ------------------------- - -The standard recommendation pipeline, produced by any of the above approaches, -looks like this: - -.. mermaid:: std-topn-pipeline.mmd - :caption: Top-N recommendation pipeline. - -A pipeline configured for rating prediction, with a fallback predictor, looks like: - -.. mermaid:: std-pred-pipeline.mmd - :caption: Pipeline for top-N recommendation and rating prediction, with predictions falling back to a baseline scorer. - .. _pipeline-model: Pipeline Model @@ -326,6 +290,53 @@ pipeline will emit a warning (or fail, if ``allow_pickle=False`` is passed to The load/save parameter operations are modeled after PyTorch's :meth:`~torch.nn.Module.state_dict` and the needs of ``safetensors``. +.. _standard-pipelines: + +Standard Layouts +~~~~~~~~~~~~~~~~ + +The standard recommendation pipeline, produced by either of the approaches +described above in :ref:`pipeline-construct`, looks like this: + +.. mermaid:: std-topn-pipeline.mmd + :caption: Top-N recommendation pipeline. + +The convenience methods are equivalent to the following pipeline code: + +.. code:: python + + pipe = Pipeline() + # define an input parameter for the user ID (the 'query') + query = pipe.create_input('query', ID) + # allow candidate items to be optionally specified + items = pipe.create_input('items', ItemList, None) + # look up a user's history in the training data + history = pipe.add_component('history-lookup', LookupTrainingHistory(), query=query) + # find candidates from the training data + default_candidates = pipe.add_component( + 'candidate-selector', + UnratedTrainingItemsCandidateSelector(), + query=history, + ) + # if the client provided items as a pipeline input, use those; otherwise + # use the candidate selector we just configured. + candidates = pipe.use_first_of('candidates', items, default_candidates) + # score the candidate items using the specified scorer + score = pipe.add_component('scorer', scorer, query=query, items=candidates) + # rank the items by score + recommend = pipe.add_component('ranker', TopNRanker(50), items=score) + pipe.alias('recommender', recommend) + + +If we want to also emit rating predictions, with fallback to a baseline model to +predict ratings for items the primary scorer cannot score (e.g. they are not in +an item neighborhood), we use the following pipeline (created by +:class:`RecPipelineBuilder` when rating prediction is enabled): + +.. mermaid:: std-pred-pipeline.mmd + :caption: Pipeline for top-N recommendation and rating prediction, with predictions falling back to a baseline scorer. + + Component Interface ~~~~~~~~~~~~~~~~~~~ diff --git a/docs/guide/std-pred-pipeline.mmd b/docs/guide/std-pred-pipeline.mmd index 3a54bc874..702d8854c 100644 --- a/docs/guide/std-pred-pipeline.mmd +++ b/docs/guide/std-pred-pipeline.mmd @@ -1,3 +1,7 @@ +--- +config: + fontFamily: '"Source Sans 3", Verdana, Helvetica, Arial, sans-serif' +--- flowchart LR subgraph input["Inputs"] QUERY[/"Query
(query: QueryInput)"/] @@ -10,6 +14,7 @@ flowchart LR subgraph prep["Data Preparation"] HLOOK["History Lookup"] CSEL["Candidate Selector"] + CPICK(["Pick Cand. Source"]) end subgraph rank["Scoring and Ranking"] @@ -24,12 +29,13 @@ flowchart LR QUERY --> HLOOK HLOOK -- RecQuery --> CSEL - ITEMS --> CSEL + ITEMS --> CPICK + CSEL -.->|"ItemList
(if needed)"| CPICK HLOOK -- RecQuery --> SCORE - CSEL -- ItemList --> SCORE + CPICK -- ItemList --> SCORE HLOOK -- RecQuery --> FALLBACK - CSEL -- ItemList --> FALLBACK + CPICK -- ItemList --> FALLBACK SCORE -- ItemList --> FILL FALLBACK -- ItemList --> FILL SCORE -- ItemList --> RANK diff --git a/docs/guide/std-topn-pipeline.mmd b/docs/guide/std-topn-pipeline.mmd index 656e6a6d8..6d7923a4c 100644 --- a/docs/guide/std-topn-pipeline.mmd +++ b/docs/guide/std-topn-pipeline.mmd @@ -1,3 +1,7 @@ +--- +config: + fontFamily: '"Source Sans 3", Verdana, Helvetica, Arial, sans-serif' +--- flowchart LR subgraph input["Inputs"] QUERY[/"Query
(query: QueryInput)"/] @@ -10,6 +14,7 @@ flowchart LR subgraph prep["Data Preparation"] HLOOK["History Lookup"] CSEL["Candidate Selector"] + CPICK(["Pick Cand. Source"]) end subgraph rank["Scoring and Ranking"] @@ -21,10 +26,11 @@ flowchart LR QUERY --> HLOOK HLOOK -- RecQuery --> CSEL - ITEMS --> CSEL + CSEL -.->|"ItemList
(if needed)"| CPICK + ITEMS --> CPICK HLOOK -- RecQuery --> SCORE - CSEL -- ItemList --> SCORE + CPICK -- ItemList --> SCORE SCORE -- ItemList --> RANK N --> RANK From 0de35e28f88d50c7433e923ed8f1d1369835c00e Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 12:11:17 -0500 Subject: [PATCH 12/19] clean up pipeline diagrams --- docs/guide/std-pred-pipeline.mmd | 4 +++- docs/guide/std-topn-pipeline.mmd | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/guide/std-pred-pipeline.mmd b/docs/guide/std-pred-pipeline.mmd index 702d8854c..79015e267 100644 --- a/docs/guide/std-pred-pipeline.mmd +++ b/docs/guide/std-pred-pipeline.mmd @@ -21,7 +21,8 @@ flowchart LR SCORE["Scorer"] FALLBACK["Baseline Scorer"] FILL["Fallback"] - RANK["Top-K Ranker"] + RANK["`Top-*N* Ranker`"] + class SCORE config; end RESULT[\"Recommendations
(ordered ItemList)"\] @@ -45,3 +46,4 @@ flowchart LR FILL --> PREDS classDef optional stroke-dasharray: 5 5; + classDef config font-weight:bold,stroke-width:4px; diff --git a/docs/guide/std-topn-pipeline.mmd b/docs/guide/std-topn-pipeline.mmd index 6d7923a4c..28b2b8bdf 100644 --- a/docs/guide/std-topn-pipeline.mmd +++ b/docs/guide/std-topn-pipeline.mmd @@ -19,7 +19,8 @@ flowchart LR subgraph rank["Scoring and Ranking"] SCORE["Scorer"] - RANK["Top-K Ranker"] + RANK["`Top-*N* Ranker`"] + class SCORE config; end RESULT[\"Recommendations
(ordered ItemList)"\] @@ -37,3 +38,4 @@ flowchart LR RANK --> RESULT classDef optional stroke-dasharray: 5 5; + classDef config font-weight:bold,stroke-width:4px; From 2a7b855fc9a2a88d944f38c021aa674c74f3baa6 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 12:50:06 -0500 Subject: [PATCH 13/19] document loading/saving --- docs/guide/pipeline.rst | 68 ++++++++++++++++++----------------------- 1 file changed, 29 insertions(+), 39 deletions(-) diff --git a/docs/guide/pipeline.rst b/docs/guide/pipeline.rst index a504a0c18..ac69db567 100644 --- a/docs/guide/pipeline.rst +++ b/docs/guide/pipeline.rst @@ -205,14 +205,15 @@ itself, e.g.: * ``recommender`` * ``reranker`` * ``scorer`` -* ``user-history-resolver`` +* ``history-lookup`` * ``item-embedder`` Component nodes can also have *aliases*, allowing them to be accessed by more than one name. Use :meth:`Pipeline.alias` to define these aliases. -Various LensKit facilities recognize several standard component names that we -recommend you use when applicable: +Various LensKit facilities recognize several standard component names used by +the standard pipeline builders, and we recommend you use them in your own +pipelines when applicable: * ``scorer`` — compute (usually personalized) scores for items for a given user. * ``ranker`` — compute a (ranked) list of recommendations for a user. If you @@ -224,11 +225,10 @@ recommend you use when applicable: alias for ``ranker``, as in a top-*N* recommender, but may return other formats such as grids or unordered slates. * ``rating-predictor`` — predict a user's ratings for the specified items. When - present, this is usually an alias for ``scorer``, but in some pipelines it - will be a different component that transforms the scores into rating - predictions. + present, this may be an alias for ``scorer``, or it may be another component + that fills in missing scores with a baseline prediction. -These component names replace the task-specific interfaces in pre-2024 LensKit; +These component names replace the task-specific interfaces in pre-2025 LensKit; a ``Recommender`` is now just a pipeline with ``recommender`` and/or ``ranker`` components. @@ -241,8 +241,8 @@ Pipelines are defined by the following: * The components and inputs (nodes) * The component input connections (edges) -* The component configurations (see :class:`ConfigurableComponent`) -* The components' learned parameters (see :class:`TrainableComponent`) +* The component configurations (see :class:`Configurable` and :class:`Component`) +* The components' learned parameters (see :class:`Trainable`) .. todo:: Serialization support other than ``pickle`` is not yet implemented. @@ -251,10 +251,10 @@ LensKit supports serializing both pipeline descriptions (components, connections, and configurations) and pipeline parameters. There are three ways to save a pipeline or part thereof: -1. Pickle the entire pipeline. This is easy, and saves everything pipeline; it - has the usual downsides of pickling (arbitrary code execution, etc.). - LensKit uses pickling to share pipelines with worker processes for parallel - batch operations. +1. Pickle the entire pipeline. This is easy, and saves everything in the + pipeline; it has the usual downsides of pickling (arbitrary code execution, + etc.). LensKit uses pickling to share pipelines with worker processes for + parallel batch operations. 2. Save the pipeline configuration with :meth:`Pipeline.save_config`. This saves the components, their configurations, and their connections, but **not** any learned parameter data. A new pipeline can be constructed from such a @@ -266,7 +266,7 @@ three ways to save a pipeline or part thereof: running the same pipeline setup code or using a saved pipeline configuration. -These can be mixed and matched; if you pickle an untrained pipeline, you can +These can be mixed and matched: if you pickle an untrained pipeline, you can unpickle it and use :meth:`~Pipeline.load_params` to infuse it with parameters. Component implementations need to support the configuration and/or parameter @@ -274,12 +274,12 @@ values, as needed, in addition to functioning correctly with pickle (no specific logic is usually needed for this). LensKit knows how to safely save the following object types from -:meth:`Component.get_params`: +:meth:`Trainable.get_params`: * :class:`torch.Tensor` (dense, CSR, and COO tensors). * :class:`numpy.ndarray`. -* :class:`scipy.sparse.csr_array`, :class:`scipy.sparse.~coo_array`, - :class:`scipy.sparse.~csc_array`, and the corresponding ``*_matrix`` +* :class:`scipy.sparse.csr_array`, :class:`~scipy.sparse.coo_array`, + :class:`~scipy.sparse.csc_array`, and the corresponding ``*_matrix`` versions. Other objects (including Pandas dataframes) are serialized by pickling, and the @@ -287,8 +287,11 @@ pipeline will emit a warning (or fail, if ``allow_pickle=False`` is passed to :meth:`~Pipeline.save_params`). .. note:: + The load/save parameter operations are modeled after PyTorch's - :meth:`~torch.nn.Module.state_dict` and the needs of ``safetensors``. + :meth:`~torch.nn.Module.state_dict` and the needs of safetensors_. + +.. _safetensors: https://huggingface.co/docs/safetensors/ .. _standard-pipelines: @@ -340,25 +343,12 @@ an item neighborhood), we use the following pipeline (created by Component Interface ~~~~~~~~~~~~~~~~~~~ -Pipeline components are callable objects that can optionally provide training -and serialization capabilities. In the simplest case, a component that requires -no training or configuration can simply be a Python function; more sophisticated -components can implement the :class:`TrainableComponent` and/or -:class:`ConfigurableComponent` protocols to support flexible model training and -pipeline serialization. - -Components also need to be pickleable, as LensKit uses pickling for shared -memory parallelism in its batch-inference code. - -.. note:: - - The component interfaces are simply protocol definitions (defined using - :class:`typing.Protocol` with :func:`~typing.runtime_checkable`), so - implementations can directly implement the specified methods and do not need - to explicitly inherit from the protocol classes, although they are free to - do so. - -.. todo:: +Pipeline components are callable objects that can optionally provide +configuration, training, and serialization capabilities. In the simplest case, +a component that requires no training or configuration can simply be a Python +function; most components will extend the :class:`Component` base class to +expose configuration capabilities, and implement the :class:`Trainable` protocol +if they contain a model that needs to be trained. - Is it clear to write these capabilities as separate protocols, or would it be - better to write a single ``Component`` :class:`~abc.ABC`? +Components also must be pickleable, as LensKit uses pickling for shared memory +parallelism in its batch-inference code. From a08ef23b781eb562113417c9f714bd9b18f495c0 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 13:09:30 -0500 Subject: [PATCH 14/19] improve data structure docs --- .vscode/ltex.hiddenFalsePositives.en-US.txt | 1 + docs/guide/migrating.rst | 74 +++++++++++++++++++++ lenskit/lenskit/data/collection.py | 5 ++ 3 files changed, 80 insertions(+) diff --git a/.vscode/ltex.hiddenFalsePositives.en-US.txt b/.vscode/ltex.hiddenFalsePositives.en-US.txt index 828bb60f5..5278fb5be 100644 --- a/.vscode/ltex.hiddenFalsePositives.en-US.txt +++ b/.vscode/ltex.hiddenFalsePositives.en-US.txt @@ -2,3 +2,4 @@ {"rule":"MISSING_GENITIVE","sentence":"^\\QPyTorch tensors, including those on CUDA devices, are shared.\\E$"} {"rule":"EN_A_VS_AN","sentence":"^\\QThis method returns an \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q containing the\nrecommended items.\\E$"} {"rule":"EN_A_VS_AN","sentence":"^\\QYou can optionally specify candidate items with an \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q\nparameter to \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q (it takes an \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q), or a list\nlength with \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q (you can also bake a default list length into the pipeline\nwhen you call \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q).\\E$"} +{"rule":"POSSESSIVE_APOSTROPHE","sentence":"^\\QWhere older versions of LensKit used Pandas data frames and series as the primary\ndata structures for interfacing with components\\E$"} diff --git a/docs/guide/migrating.rst b/docs/guide/migrating.rst index 95c41243b..7419ca171 100644 --- a/docs/guide/migrating.rst +++ b/docs/guide/migrating.rst @@ -74,6 +74,80 @@ typical code patterns used in LensKit 0.14 and earlier. needing to extract them first. It also automatically detects which version of the MovieLens data you are loading. +Data Structures +--------------- + +Where older versions of LensKit used Pandas data frames and series as the +primary data structures for interfacing with components, LensKit 2025 introduces +new data abstractions specifically for handling recommender data, but that support +conversion to and from data frames. The core ones are: + +- :class:`~lenskit.data.ItemList` represents a list of items, optionally with + scores or other fields (e.g. ratings). Item lists can convert between item + IDs and item numbers, using a vocabulary, and can be converted to and from + Pandas data frames. Their fields (including the item numbers) can also be + retrieved in multiple formats, including NumPy arrays (the default), Pandas + :class:`~pandas.Series`, and PyTorch tensors. Format conversions are + zero-copy whenever possible. + +- :class:`~lenskit.data.Vocabulary` represents a collection of item or user IDs + (or other ID-like things, such as tags), and supports bidirectional mapping + between such IDs and contiguous 0-based indices (numbers) for indexing into + arrays and matrices. This was not used as a part of an API in LensKit before, + but was implemented internally by many components using the Pandas + :class:`~pandas.Index` data structure. Vocabularies centralize that logic + (and use :class:`~pandas.Index` under the hood), so that we don't duplicate it + so much across the codebase and to enable multiple models trained on the same + data to share the same index. If you are implementing a model component that + needs to store vectors or matrices of user or item data, consider using the + vocabulary to associate those with user and item IDs. + +- :class:`~lenskit.data.ItemListCollection` represents a collection of item + lists indexed by keys, such as the test items for users a test data split, or + the recommendation lists for users in an experiment. It supports conversion + to and from Pandas data frames. Future releases will support additional + formats, such as DuckDB. + +Motivation +.......... + +These data structures, and the data set abstraction, are something of a +departure from one of the design principles originally set out for LensKit for +Python :cite:p:`lkpy`; specifically, to use standard data structures for +interchange between components. + +There are three primary reason for this change: + +* While Pandas data frames and series are widely used and supported by many + libraries, they are not self-documenting: a Python method returning a + :class:`~pandas.DataFrame` is not enough to know what columns in that data + frame. Things are further complicated with Pandas indexes, requiring + elaborate discussions of exact data frame and series layouts in the + documentation. This also sometimes resulted in bugs with incorrect layouts, + particularly if an index was incorrectly configured. Dedicated abstractions + are more self-documenting, particularly in modern Python with type annotations + and good IDE support. + +* Many libraries work directly with arrays and sparse matrices instead of Pandas + data structures, requiring data conversion and translation that is often + repeated in different model components. First-class support for multiple data + formats in a single abstraction reduces the work needed to implement a model + with PyTorch, Scikit-Learn, or any other library. + +* When chaining together multiple components, data always needed to be converted + to and from Pandas at the component interface boundary. This meant that two + components both using PyTorch needed to convert to Pandas (possibly moving + from GPU to CPU) at the interface, and then convert back to PyTorch. A + unified interface with lazy, zero-copy conversion means that two components + using the same compute support do not need to convert data in order to + interface, while still supporting composition with arbitrary components using + different compute layers. + +Since the new data structures, particularly :class:`ItemList`, are thin +abstractions on top of arrays, these are hopefully still as easy (or easier) to +use and integrate, and provide much easier support for implementing new +components with your choice of support libraries. + Configuring Recommenders ------------------------ diff --git a/lenskit/lenskit/data/collection.py b/lenskit/lenskit/data/collection.py index 83036e545..6da11a94e 100644 --- a/lenskit/lenskit/data/collection.py +++ b/lenskit/lenskit/data/collection.py @@ -167,6 +167,11 @@ def from_df(cls, df: pd.DataFrame, key: type[K] | Sequence[Column] | Column, *ot def to_df(self) -> pd.DataFrame: """ Convert this item list collection to a data frame. + + .. warning:: + + If this item list collection has any keys with empty lists, those + lists will be excluded from the output. """ fields = self._key_class._fields # type: ignore return ( From 473d44a732f781cba30f7f944cc6f0a0e018c85a Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 13:10:28 -0500 Subject: [PATCH 15/19] more migration notes --- docs/guide/migrating.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/guide/migrating.rst b/docs/guide/migrating.rst index 7419ca171..ce5b98d67 100644 --- a/docs/guide/migrating.rst +++ b/docs/guide/migrating.rst @@ -18,6 +18,11 @@ The recommendation models and evaluation metrics retain their behavior from previous versions of LensKit, but their interfaces and the surrounding infrastructure are significantly different. +The new changes will also enable new functionality that is planned for upcoming +versions of LensKit, such as first-class content-based and hybrid models, and +support for additional recommendation tasks such as session- or context-based +recommendation. + Change Highlights ----------------- From b743aa4342f29fbc344d82b26c6abd751cb56528 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 13:10:47 -0500 Subject: [PATCH 16/19] note that query docs are TODO --- docs/guide/queries.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/guide/queries.rst b/docs/guide/queries.rst index b206035b2..1ca7ce6d2 100644 --- a/docs/guide/queries.rst +++ b/docs/guide/queries.rst @@ -4,3 +4,7 @@ Queries ======= .. py:currentmodule:: lenskit.data + +.. todo:: + + Document queries. From 6039270485f3debc094bee4c811a987cafdc899f Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 13:17:16 -0500 Subject: [PATCH 17/19] update release notes --- docs/releases/2025.rst | 84 ++++++++++++++---------------------------- 1 file changed, 27 insertions(+), 57 deletions(-) diff --git a/docs/releases/2025.rst b/docs/releases/2025.rst index d57839dec..1d58b8a0e 100644 --- a/docs/releases/2025.rst +++ b/docs/releases/2025.rst @@ -7,7 +7,8 @@ date is set yet. 2025 will bring breaking changes to several LensKit APIs to improve ergonomics, correctness-by-default, and flexibility. It also adopts SPEC0_, a standard for supported versions of scientific Python libraries, and changes the LensKit -version number scheme to “:ref:`SemCalver`”. +version number scheme to “:ref:`SemCalver`”. See :ref:`migrating` for +information on how to upgrade your code. .. _SPEC0: https://scientific-python.org/specs/spec-0000/ @@ -18,74 +19,44 @@ version number scheme to “:ref:`SemCalver`”. The first 2025 release is currently in-progress. -This document presents the highlights for this release. The full changelog for this release is available in the `Git history `_ -and `issue/PR milestone `_. - -Significant Changes -~~~~~~~~~~~~~~~~~~~ - -2025.1 brings substantial changes to LensKit. - -* :class:`~lenskit.data.Dataset`. LensKit now provides an abstraction for - training data instead of working with Pandas data frames directly, that - allows components to reduce code duplication and recomputation, access data - in multiple formats (Pandas, NumPy, and PyTorch), and provided standardized - structures like mappings of user or item IDs to array indices. This also - supersedes the old bespoke dataset loading support, with functions like - :func:`~lenskit.data.load_movielens` to load standard datasets. - -* New classes like :class:`~lenskit.data.ItemList` for routing item data - instead of using Pandas data frames and series. This makes component return - types more self-documenting (rather than requiring developers to remember - what is on the index, what the column names are, etc.), and facilitates more - efficient data transfer between components that do not use Pandas (e.g. data - passed between components using PyTorch can leave the data in tensors - without round-tripping through Pandas and NumPy, and keep this transparent - to client code). - -* Data splitting for offline evaluation has been moved into - :mod:`lenskit.splitting`, updated to work with data sets and item lists - instead of raw data frames, and splitting functions have been renamed (e.g. - ``rows`` to ``records``) and had parameters updated for clarity and - consistency. +The changes in this release are too numerous and fundamental to fully document +in traditional release notes. See the following for release update documentation: + +- :ref:`migrating` for conceptual changes and how to upgrade your code. +- The notes below for *behavior* changes (e.g. new defaults, new metric + capabilities), and small bits not covered in the migration guide. +- The full changelog in the `Git history + `_ and `issue/PR + milestone `_. + +Breaking Changes +~~~~~~~~~~~~~~~~ + +LensKit 2025 has many breaking changes, with the migration guide +(:ref:`migrating`) documenting the major ones. Below are some smaller ones not +covered by that document: * Where Pandas data frames are still used, the standard user and item columns have been renamed to ``user_id`` and ``item_id`` respectively, with ``user_num`` and ``item_num`` for 0-based user and item numbers. This is to remove ambiguity about how users and items are being referenced. -* **PyTorch**. LensKit now uses PyTorch to implement most of its algorithms, - instead of Numba-accelerated NumPy code. Algorithms using PyTorch are: - - * :py:class:`~lenskit.algorithms.knn.ItemItem` - * :py:class:`~lenskit.algorithms.als.ImplicitMF` - * :py:class:`~lenskit.algorithms.als.BiasedMF` - -* Many LensKit components (batch running, model training, etc.) now report progress with - :py:mod:`progress_api`, and can be connected to TQDM or Enlighten. - -* Algorithms refactored to more consistent package locations, without the - ``algorithms`` subpackage. - * LensKit no longer has top-level exports (and is now in fact a namespace package). Classes and functions must be imported from appropriate subpackages. * The ``Popular`` recommender has been removed in favor of :class:`~lenskit.basic.PopScore`. +* The DCG metric has been removed, as it is basically never used and was not + useful as a part of the NDCG implementation. + New Features (incremental) ~~~~~~~~~~~~~~~~~~~~~~~~~~ -* Added RBP top-N metric (:pr:`334`). -* Added command-line tool to fetch datasets (:pr:`347`). - -Evaluation Changes -~~~~~~~~~~~~~~~~~~ - -* The DCG metric has been removed, as it is basically never used and was not - useful as a part of the NDCG implementation. - -* Train-test split results are no longer tuples. +* Many LensKit components (batch running, model training, etc.) now report progress with + :py:mod:`progress_api`, and can be connected to TQDM or Enlighten. +* Added RBP top-N metric (:pr:`334`). +* Added command-line tool to fetch datasets (:pr:`347`). Model Behavior Changes ~~~~~~~~~~~~~~~~~~~~~~ @@ -113,13 +84,12 @@ Bug Fixes ~~~~~~~~~ * Fixed bug in NDCG list truncation (:issue:`309`, :pr:`312`). -* :py:func:`lenskit.util.clone` now properly clones tuples (:pr:`358`). -* Corrected documentation errors for :py:func:`~lenskit.metrics.topn.recall` and :py:func:`~lenskit.metrics.topn.hit` (:pr:`369` by :user:`lukas-wegmeth`). +* Corrected documentation errors for :py:func:`~lenskit.metrics.recall` and :py:func:`~lenskit.metrics.hit` (:pr:`369` by :user:`lukas-wegmeth`). Dependencies and Maintenance ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -* Bumped minimum supported dependencies as per SPEC0_ (Python 3.10, NumPy 1.23, Pandas 1.5, SciPy 1.9). +* Bumped minimum supported dependencies as per SPEC0_ (Python 3.11, NumPy 1.24, Pandas 2.0, SciPy 1.10). * Added support for Pandas 2 (:pr:`364`) and Python 3.12. * Improved Apple testing to include vanilla Python and Apple Silicon (:pr:`366`). * Updated build environment, dependency setup, taskrunning, and CI to more consistent and maintainable. From a13051e4adac3b37fabe7118cefae7c20dfe7b09 Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 13:18:09 -0500 Subject: [PATCH 18/19] update Getting Started again --- docs/guide/GettingStarted.ipynb | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/guide/GettingStarted.ipynb b/docs/guide/GettingStarted.ipynb index dc6bbd90a..1e0a6ae4b 100644 --- a/docs/guide/GettingStarted.ipynb +++ b/docs/guide/GettingStarted.ipynb @@ -44,12 +44,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", "import seaborn as sns" ] }, @@ -78,12 +77,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import progress_api\n", - "progress_api.set_backend('tqdm')" + "\n", + "progress_api.set_backend(\"tqdm\")" ] }, { @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1936,7 +1936,7 @@ "# test data is organized by user\n", "all_test = ItemListCollection(UserIDKey)\n", "# recommendations will be organized by model and user ID\n", - "all_recs = ItemListCollection(['model', 'user_id'])\n", + "all_recs = ItemListCollection([\"model\", \"user_id\"])\n", "\n", "for split in crossfold_users(ml100k, 5, SampleFrac(0.2)):\n", " # collect the test data\n", @@ -1947,13 +1947,13 @@ " fit_als.train(split.train)\n", " # generate recs\n", " als_recs = recommend(fit_als, split.test.keys(), 100)\n", - " all_recs.add_from(als_recs, model='ALS')\n", + " all_recs.add_from(als_recs, model=\"ALS\")\n", "\n", " # do the same for item-item\n", " fit_ii = pipe_ii.clone()\n", " fit_ii.train(split.train)\n", " ii_recs = recommend(fit_ii, split.test.keys(), 100)\n", - " all_recs.add_from(ii_recs, model='II')" + " all_recs.add_from(ii_recs, model=\"II\")" ] }, { @@ -2002,7 +2002,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2067,7 +2067,7 @@ } ], "source": [ - "results.list_metrics().groupby('model').mean()" + "results.list_metrics().groupby(\"model\").mean()" ] }, { From c08f3330a71085b9ee142f3734d085d8ae35943d Mon Sep 17 00:00:00 2001 From: Michael Ekstrand Date: Sat, 23 Nov 2024 15:04:02 -0500 Subject: [PATCH 19/19] re-execute getting started guide --- docs/guide/GettingStarted.ipynb | 43935 +++++++++++++++++++++++++++++- 1 file changed, 43790 insertions(+), 145 deletions(-) diff --git a/docs/guide/GettingStarted.ipynb b/docs/guide/GettingStarted.ipynb index 1e0a6ae4b..ccd69d804 100644 --- a/docs/guide/GettingStarted.ipynb +++ b/docs/guide/GettingStarted.ipynb @@ -23,7 +23,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:55:17.798408Z", + "iopub.status.busy": "2024-11-23T19:55:17.797778Z", + "iopub.status.idle": "2024-11-23T19:55:19.388735Z", + "shell.execute_reply": "2024-11-23T19:55:19.388489Z" + } + }, "outputs": [], "source": [ "from lenskit.als import BiasedMF\n", @@ -44,8 +51,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:55:19.390119Z", + "iopub.status.busy": "2024-11-23T19:55:19.390004Z", + "iopub.status.idle": "2024-11-23T19:55:20.087582Z", + "shell.execute_reply": "2024-11-23T19:55:20.087321Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -62,7 +76,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:55:20.088991Z", + "iopub.status.busy": "2024-11-23T19:55:20.088869Z", + "iopub.status.idle": "2024-11-23T19:55:20.093162Z", + "shell.execute_reply": "2024-11-23T19:55:20.092955Z" + } + }, "outputs": [], "source": [ "from pyprojroot.here import here" @@ -77,8 +98,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:55:20.094168Z", + "iopub.status.busy": "2024-11-23T19:55:20.094081Z", + "iopub.status.idle": "2024-11-23T19:55:20.115432Z", + "shell.execute_reply": "2024-11-23T19:55:20.115202Z" + } + }, "outputs": [], "source": [ "import progress_api\n", @@ -98,7 +126,14 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:55:20.116608Z", + "iopub.status.busy": "2024-11-23T19:55:20.116512Z", + "iopub.status.idle": "2024-11-23T19:55:20.166933Z", + "shell.execute_reply": "2024-11-23T19:55:20.166683Z" + } + }, "outputs": [ { "data": { @@ -198,7 +233,14 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:55:20.186762Z", + "iopub.status.busy": "2024-11-23T19:55:20.186645Z", + "iopub.status.idle": "2024-11-23T19:55:20.188393Z", + "shell.execute_reply": "2024-11-23T19:55:20.188163Z" + } + }, "outputs": [], "source": [ "model_ii = ItemKNNScorer(20)\n", @@ -215,7 +257,14 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:55:20.189403Z", + "iopub.status.busy": "2024-11-23T19:55:20.189315Z", + "iopub.status.idle": "2024-11-23T19:55:20.191314Z", + "shell.execute_reply": "2024-11-23T19:55:20.191094Z" + } + }, "outputs": [], "source": [ "pipe_ii = topn_pipeline(model_ii)\n", @@ -240,8 +289,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:55:20.192310Z", + "iopub.status.busy": "2024-11-23T19:55:20.192230Z", + "iopub.status.idle": "2024-11-23T19:56:20.526743Z", + "shell.execute_reply": "2024-11-23T19:56:20.525891Z" + } + }, "outputs": [ { "name": "stderr", @@ -254,7 +310,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2d07a34198db4ea1a7c0bbb5d5615455", "version_major": 2, "version_minor": 0 }, @@ -268,7 +324,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "deca8701747f42639f154ccf6773c805", "version_major": 2, "version_minor": 0 }, @@ -282,7 +338,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ed17fc7625d54d55a2c5c89b25a84f6c", "version_major": 2, "version_minor": 0 }, @@ -296,7 +352,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "31df5e02b6e1491ba8e74ba47820898a", "version_major": 2, "version_minor": 0 }, @@ -310,7 +366,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "89de5884c683485081de76d94101e6d5", "version_major": 2, "version_minor": 0 }, @@ -324,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e127578dcc674e55b2022833d1754b0f", "version_major": 2, "version_minor": 0 }, @@ -338,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "6fbc15e854bb453ba8fe420a51c0d0d8", "version_major": 2, "version_minor": 0 }, @@ -352,7 +408,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "83d2b3ad273944f6a5f928d1c81bae0e", "version_major": 2, "version_minor": 0 }, @@ -366,7 +422,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "3c6cf5e566ac4511bdebe18d9bc3e4c0", "version_major": 2, "version_minor": 0 }, @@ -380,7 +436,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "5f4060efc7f64cc5a5789ac68ddfbf0c", "version_major": 2, "version_minor": 0 }, @@ -394,7 +450,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "392af90af7c94b6abd1b5e599bb0d294", "version_major": 2, "version_minor": 0 }, @@ -408,7 +464,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "d0da46f0160347b6bedd1f677ca69e60", "version_major": 2, "version_minor": 0 }, @@ -422,7 +478,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "fb19786b19c8430ab5924685d1f54b4a", "version_major": 2, "version_minor": 0 }, @@ -436,7 +492,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "9413f0022b2247c4b185b50684091e1c", "version_major": 2, "version_minor": 0 }, @@ -450,7 +506,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "3eb5c47905994ea88e6383edf29ffba0", "version_major": 2, "version_minor": 0 }, @@ -464,7 +520,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "84330fb84bad40829849415d4910b5fb", "version_major": 2, "version_minor": 0 }, @@ -478,7 +534,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "7fea4713168e436ca5a8d5ea516e1e47", "version_major": 2, "version_minor": 0 }, @@ -492,7 +548,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "03e9f565b5174f4db08f5de2468255ed", "version_major": 2, "version_minor": 0 }, @@ -506,7 +562,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "a3cd29b915a742838af32bee85a6d920", "version_major": 2, "version_minor": 0 }, @@ -520,7 +576,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "34c7bc3309b54158b1ac3572e65e4594", "version_major": 2, "version_minor": 0 }, @@ -534,7 +590,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8db7158aa9e14f229440e56dfdab89af", 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@@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b25414aa185d48f3965092d6997b0433", "version_major": 2, "version_minor": 0 }, @@ -632,7 +688,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8ca000031b9e44b19fc521ccd3af7f2e", "version_major": 2, "version_minor": 0 }, @@ -646,7 +702,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "0da7eddb29734d7b8330ee8ffb622001", "version_major": 2, "version_minor": 0 }, @@ -660,7 +716,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "0876321cb678448ba2b17818314fd43c", "version_major": 2, "version_minor": 0 }, @@ -674,7 +730,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "36621faa919843c0bc9b586d07039d00", "version_major": 2, "version_minor": 0 }, @@ -688,7 +744,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "9c95c99148504077a169d0b8b35a4061", "version_major": 2, "version_minor": 0 }, @@ -702,7 +758,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "285cb01be17043e3a418c4753607956b", "version_major": 2, "version_minor": 0 }, @@ -716,7 +772,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "f4d8a1210c394dd2927b6a16a0d6a087", "version_major": 2, "version_minor": 0 }, @@ -730,7 +786,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "35af744f34894d55a064347575e4c764", "version_major": 2, "version_minor": 0 }, @@ -744,7 +800,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "d301ac16853544c8822cd616295008c7", "version_major": 2, "version_minor": 0 }, @@ -758,7 +814,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": 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"application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "d46377dc8cff49d4b9d2c172e30052ac", "version_major": 2, "version_minor": 0 }, @@ -1654,7 +1710,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "7d743cace6994df3a9de6f0b8939c3fa", "version_major": 2, "version_minor": 0 }, @@ -1668,7 +1724,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "f207195245794f4991da0cffb4421bf3", "version_major": 2, "version_minor": 0 }, @@ -1682,7 +1738,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "88c77f64966841e7be644eed9a0e9607", "version_major": 2, "version_minor": 0 }, @@ -1696,7 +1752,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b41d12de1b14434d82be098c6b12adda", "version_major": 2, "version_minor": 0 }, @@ -1710,7 +1766,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4cd67e627b1a446895e97efba7b77968", "version_major": 2, "version_minor": 0 }, @@ -1724,7 +1780,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e0526b8948f54a67ad6133880dda402c", "version_major": 2, "version_minor": 0 }, @@ -1738,7 +1794,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "d9d2bc7451fa44778e66079d9cc405ac", "version_major": 2, "version_minor": 0 }, @@ -1752,7 +1808,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ea1fec75e8794cf09269df493469f9fc", "version_major": 2, "version_minor": 0 }, @@ -1766,7 +1822,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "709dd78277494e078421622ec1fb4b8f", "version_major": 2, "version_minor": 0 }, @@ -1780,7 +1836,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "32e9c046c79248b2a836f09ac0f72047", "version_major": 2, "version_minor": 0 }, @@ -1794,7 +1850,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "38af23457a7741719be8326a88fe5bd6", "version_major": 2, "version_minor": 0 }, @@ -1808,7 +1864,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4e950650dccf4b99802e9bc91858d506", "version_major": 2, "version_minor": 0 }, @@ -1822,7 +1878,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "58a3d6403c034e35ac11b84de878b223", "version_major": 2, "version_minor": 0 }, @@ -1836,7 +1892,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "fd3c13acfbe94caca47382a107132e0b", "version_major": 2, "version_minor": 0 }, @@ -1850,7 +1906,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "cbacaba10340421cbafcb337627c7817", "version_major": 2, "version_minor": 0 }, @@ -1864,7 +1920,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "49566972bc634fd3836d8475fa48a401", "version_major": 2, "version_minor": 0 }, @@ -1878,7 +1934,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2c19c8ff41b34e82817696947868d9f5", "version_major": 2, "version_minor": 0 }, @@ -1892,7 +1948,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "0c666f7801c74d178ed577e96e799107", "version_major": 2, "version_minor": 0 }, @@ -1906,7 +1962,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "bed9f35658f74fa28ea70c0f2c30f9b9", "version_major": 2, "version_minor": 0 }, @@ -1920,7 +1976,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "f8250eba802643c2acdf831f34cbaee1", "version_major": 2, "version_minor": 0 }, @@ -1968,12 +2024,19 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:56:20.529805Z", + "iopub.status.busy": "2024-11-23T19:56:20.529106Z", + "iopub.status.idle": "2024-11-23T19:56:20.770645Z", + "shell.execute_reply": "2024-11-23T19:56:20.770346Z" + } + }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b5e32c837fa440099bb4ef7fd6240d1d", "version_major": 2, "version_minor": 0 }, @@ -2002,8 +2065,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:56:20.772086Z", + "iopub.status.busy": "2024-11-23T19:56:20.771831Z", + "iopub.status.idle": "2024-11-23T19:56:20.777213Z", + "shell.execute_reply": "2024-11-23T19:56:20.776996Z" + } + }, "outputs": [ { "data": { @@ -2040,15 +2110,15 @@ " \n", " \n", " ALS\n", - " 0.068825\n", - " 0.055435\n", - " 0.150510\n", + " 0.061874\n", + " 0.031195\n", + " 0.098237\n", " \n", " \n", " II\n", - " 0.053497\n", - " 0.017592\n", - " 0.078011\n", + " 0.046237\n", + " 0.007525\n", + " 0.046818\n", " \n", " \n", "\n", @@ -2057,8 +2127,8 @@ "text/plain": [ " NDCG RBP RecipRank\n", "model \n", - "ALS 0.068825 0.055435 0.150510\n", - "II 0.053497 0.017592 0.078011" + "ALS 0.061874 0.031195 0.098237\n", + "II 0.046237 0.007525 0.046818" ] }, "execution_count": 10, @@ -2073,11 +2143,18 @@ { "cell_type": "code", "execution_count": 11, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-23T19:56:20.778382Z", + "iopub.status.busy": "2024-11-23T19:56:20.778285Z", + "iopub.status.idle": "2024-11-23T19:56:20.905048Z", + "shell.execute_reply": "2024-11-23T19:56:20.904784Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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