{
 "cells": [
  {
   "cell_type": "raw",
   "id": "f7e954ff",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/adriangb/scikeras/blob/docs-deploy/refs/heads/master/notebooks/AutoEncoders.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Run in Google Colab</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de04154",
   "metadata": {},
   "source": [
    "# Sparse Inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9309c95f",
   "metadata": {},
   "source": [
    "SciKeras supports sparse inputs (`X`/features).\n",
    "You don't have to do anything special for this to work, you can just pass a sparse matrix to `fit()`.\n",
    "\n",
    "In this notebook, we'll demonstrate how this works and compare memory consumption of sparse inputs to dense inputs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c52384",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8402edeb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:06.989751Z",
     "iopub.status.busy": "2024-12-12T21:42:06.989428Z",
     "iopub.status.idle": "2024-12-12T21:42:08.632601Z",
     "shell.execute_reply": "2024-12-12T21:42:08.631787Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting memory_profiler\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Downloading memory_profiler-0.61.0-py3-none-any.whl.metadata (20 kB)\r\n",
      "Requirement already satisfied: psutil in /home/runner/work/scikeras/scikeras/.venv/lib/python3.12/site-packages (from memory_profiler) (6.1.0)\r\n",
      "Downloading memory_profiler-0.61.0-py3-none-any.whl (31 kB)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: memory_profiler\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed memory_profiler-0.61.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install memory_profiler\n",
    "%load_ext memory_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c4d284d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:08.635714Z",
     "iopub.status.busy": "2024-12-12T21:42:08.635170Z",
     "iopub.status.idle": "2024-12-12T21:42:11.898831Z",
     "shell.execute_reply": "2024-12-12T21:42:11.898080Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1734039728.905851    3633 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1734039728.910544    3633 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "from tensorflow import get_logger\n",
    "get_logger().setLevel('ERROR')\n",
    "warnings.filterwarnings(\"ignore\", message=\"Setting the random state for TF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de4a87aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:11.902305Z",
     "iopub.status.busy": "2024-12-12T21:42:11.901571Z",
     "iopub.status.idle": "2024-12-12T21:42:11.909446Z",
     "shell.execute_reply": "2024-12-12T21:42:11.908549Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import scikeras\n",
    "except ImportError:\n",
    "    !python -m pip install scikeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08f8f3dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:11.912423Z",
     "iopub.status.busy": "2024-12-12T21:42:11.911861Z",
     "iopub.status.idle": "2024-12-12T21:42:12.416644Z",
     "shell.execute_reply": "2024-12-12T21:42:12.415734Z"
    }
   },
   "outputs": [],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "from scikeras.wrappers import KerasRegressor\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3661d5",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The dataset we'll be using is designed to demostrate a worst-case/best-case scenario for dense and sparse input features respectively.\n",
    "It consists of a single categorical feature with equal number of categories as rows.\n",
    "This means the one-hot encoded representation will require as many columns as it does rows, making it very ineffienct to store as a dense matrix but very efficient to store as a sparse matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4004157",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:12.420260Z",
     "iopub.status.busy": "2024-12-12T21:42:12.419464Z",
     "iopub.status.idle": "2024-12-12T21:42:12.424606Z",
     "shell.execute_reply": "2024-12-12T21:42:12.423766Z"
    }
   },
   "outputs": [],
   "source": [
    "N_SAMPLES = 20_000  # hand tuned to be ~4GB peak\n",
    "\n",
    "X = np.arange(0, N_SAMPLES).reshape(-1, 1)\n",
    "y = np.random.uniform(0, 1, size=(X.shape[0],))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee2ca756",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "The model here is nothing special, just a basic multilayer perceptron with one hidden layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0333ced",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:12.427123Z",
     "iopub.status.busy": "2024-12-12T21:42:12.426905Z",
     "iopub.status.idle": "2024-12-12T21:42:12.431507Z",
     "shell.execute_reply": "2024-12-12T21:42:12.430750Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_clf(meta) -> keras.Model:\n",
    "    n_features_in_ = meta[\"n_features_in_\"]\n",
    "    model = keras.models.Sequential()\n",
    "    model.add(keras.layers.Input(shape=(n_features_in_,)))\n",
    "    # a single hidden layer\n",
    "    model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
    "    model.add(keras.layers.Dense(1))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8343932",
   "metadata": {},
   "source": [
    "## Pipelines\n",
    "\n",
    "Here is where it gets interesting.\n",
    "We make two Scikit-Learn pipelines that use `OneHotEncoder`: one that uses `sparse_output=False` to force a dense matrix as the output and another that uses `sparse_output=True` (the default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c78b9d79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:12.434130Z",
     "iopub.status.busy": "2024-12-12T21:42:12.433833Z",
     "iopub.status.idle": "2024-12-12T21:42:12.438761Z",
     "shell.execute_reply": "2024-12-12T21:42:12.437851Z"
    }
   },
   "outputs": [],
   "source": [
    "dense_pipeline = Pipeline(\n",
    "    [\n",
    "        (\"encoder\", OneHotEncoder(sparse_output=False)),\n",
    "        (\"model\", KerasRegressor(get_clf, loss=\"mse\", epochs=5, verbose=False))\n",
    "    ]\n",
    ")\n",
    "\n",
    "sparse_pipeline = Pipeline(\n",
    "    [\n",
    "        (\"encoder\", OneHotEncoder(sparse_output=True)),\n",
    "        (\"model\", KerasRegressor(get_clf, loss=\"mse\", epochs=5, verbose=False))\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965196b1",
   "metadata": {},
   "source": [
    "## Benchmark\n",
    "\n",
    "Our benchmark will be to just train each one of these pipelines and measure peak memory consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73c7fd35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:12.441900Z",
     "iopub.status.busy": "2024-12-12T21:42:12.441230Z",
     "iopub.status.idle": "2024-12-12T21:42:41.548574Z",
     "shell.execute_reply": "2024-12-12T21:42:41.547891Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 5082.25 MiB, increment: 4528.79 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit dense_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39f57be3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:42:41.550574Z",
     "iopub.status.busy": "2024-12-12T21:42:41.550358Z",
     "iopub.status.idle": "2024-12-12T21:43:14.112006Z",
     "shell.execute_reply": "2024-12-12T21:43:14.111111Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 1125.83 MiB, increment: 54.78 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit sparse_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672955b4",
   "metadata": {},
   "source": [
    "You should see at least 100x more memory consumption **increment** in the dense pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaed13ea",
   "metadata": {},
   "source": [
    "### Runtime\n",
    "\n",
    "Using sparse inputs can have a drastic impact on memory usage, but it often (not always) hurts overall runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1835a31c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:43:14.116475Z",
     "iopub.status.busy": "2024-12-12T21:43:14.115911Z",
     "iopub.status.idle": "2024-12-12T21:47:33.818792Z",
     "shell.execute_reply": "2024-12-12T21:47:33.818028Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.9 s ± 5.66 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit dense_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6df623f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:47:33.821494Z",
     "iopub.status.busy": "2024-12-12T21:47:33.821194Z",
     "iopub.status.idle": "2024-12-12T21:50:32.885942Z",
     "shell.execute_reply": "2024-12-12T21:50:32.885060Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 s ± 1.16 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sparse_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "becc3ee1",
   "metadata": {},
   "source": [
    "## Tensorflow Datasets\n",
    "\n",
    "Tensorflow provides a whole suite of functionality around the [Dataset].\n",
    "Datasets are lazily evaluated, can be sparse and minimize the transformations required to feed data into the model.\n",
    "They are _a lot_ more performant and efficient at scale than using numpy datastructures, even sparse ones.\n",
    "\n",
    "SciKeras does not (and cannot) support Datasets directly because Scikit-Learn itself does not support them and SciKeras' outwards API is Scikit-Learn's API.\n",
    "You may want to explore breaking out of SciKeras and just using TensorFlow/Keras directly to see if Datasets can have a large impact for your use case.\n",
    "\n",
    "[Dataset]: https://www.tensorflow.org/api_docs/python/tf/data/Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55dff39",
   "metadata": {},
   "source": [
    "## Bonus: dtypes\n",
    "\n",
    "You might be able to save even more memory by changing the output dtype of `OneHotEncoder`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98e05550",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:50:32.888002Z",
     "iopub.status.busy": "2024-12-12T21:50:32.887557Z",
     "iopub.status.idle": "2024-12-12T21:50:32.890652Z",
     "shell.execute_reply": "2024-12-12T21:50:32.890210Z"
    }
   },
   "outputs": [],
   "source": [
    "sparse_pipline_uint8 = Pipeline(\n",
    "    [\n",
    "        (\"encoder\", OneHotEncoder(sparse_output=True, dtype=np.uint8)),\n",
    "        (\"model\", KerasRegressor(get_clf, loss=\"mse\", epochs=5, verbose=False))\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1a3ee26c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-12T21:50:32.892454Z",
     "iopub.status.busy": "2024-12-12T21:50:32.892098Z",
     "iopub.status.idle": "2024-12-12T21:50:55.974868Z",
     "shell.execute_reply": "2024-12-12T21:50:55.974253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 1317.72 MiB, increment: 108.48 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit sparse_pipline_uint8.fit(X, y)"
   ]
  }
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