{
 "cells": [
  {
   "cell_type": "raw",
   "id": "7d9ef05c",
   "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": "4a2b0d98",
   "metadata": {},
   "source": [
    "# Sparse Inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8eea702",
   "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": "f3685580",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f05b12b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:56:55.099352Z",
     "iopub.status.busy": "2022-07-27T13:56:55.098887Z",
     "iopub.status.idle": "2022-07-27T13:56:57.817811Z",
     "shell.execute_reply": "2022-07-27T13:56:57.817107Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting memory_profiler\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Downloading memory_profiler-0.60.0.tar.gz (38 kB)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Preparing metadata (setup.py) ... \u001b[?25l-"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: psutil in /home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages (from memory_profiler) (5.9.1)\r\n",
      "Building wheels for collected packages: memory_profiler\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Building wheel for memory_profiler (setup.py) ... \u001b[?25l-"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b \b\\"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b \bdone\r\n",
      "\u001b[?25h  Created wheel for memory_profiler: filename=memory_profiler-0.60.0-py3-none-any.whl size=31267 sha256=0765a2d00950163b8bdc91ec344d3120bcc9a17e9c0d4bf0e947ff2740e15f29\r\n",
      "  Stored in directory: /home/runner/.cache/pip/wheels/01/ca/8b/b518dd2aef69635ad6fcab87069c9c52f355a2e9c5d4c02da9\r\n",
      "Successfully built memory_profiler\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-0.60.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install memory_profiler\n",
    "%load_ext memory_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c915f896",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:56:57.823872Z",
     "iopub.status.busy": "2022-07-27T13:56:57.822674Z",
     "iopub.status.idle": "2022-07-27T13:56:59.500437Z",
     "shell.execute_reply": "2022-07-27T13:56:59.499785Z"
    }
   },
   "outputs": [],
   "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": "f3ee23f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:56:59.505858Z",
     "iopub.status.busy": "2022-07-27T13:56:59.504549Z",
     "iopub.status.idle": "2022-07-27T13:56:59.524062Z",
     "shell.execute_reply": "2022-07-27T13:56:59.523441Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import scikeras\n",
    "except ImportError:\n",
    "    !python -m pip install scikeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "faf130e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:56:59.530291Z",
     "iopub.status.busy": "2022-07-27T13:56:59.528594Z",
     "iopub.status.idle": "2022-07-27T13:56:59.843487Z",
     "shell.execute_reply": "2022-07-27T13:56:59.842834Z"
    }
   },
   "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",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a16e80a2",
   "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": "218766b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:56:59.849146Z",
     "iopub.status.busy": "2022-07-27T13:56:59.847546Z",
     "iopub.status.idle": "2022-07-27T13:56:59.853325Z",
     "shell.execute_reply": "2022-07-27T13:56:59.852838Z"
    }
   },
   "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": "b68360b6",
   "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": "f66985a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:56:59.857463Z",
     "iopub.status.busy": "2022-07-27T13:56:59.856443Z",
     "iopub.status.idle": "2022-07-27T13:56:59.861484Z",
     "shell.execute_reply": "2022-07-27T13:56:59.861017Z"
    }
   },
   "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": "2452a493",
   "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=False` to force a dense matrix as the output and another that uses `sparse=True` (the default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c4b52e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:56:59.865535Z",
     "iopub.status.busy": "2022-07-27T13:56:59.864511Z",
     "iopub.status.idle": "2022-07-27T13:56:59.869600Z",
     "shell.execute_reply": "2022-07-27T13:56:59.869135Z"
    }
   },
   "outputs": [],
   "source": [
    "dense_pipeline = Pipeline(\n",
    "    [\n",
    "        (\"encoder\", OneHotEncoder(sparse=False)),\n",
    "        (\"model\", KerasRegressor(get_clf, loss=\"mse\", epochs=5, verbose=False))\n",
    "    ]\n",
    ")\n",
    "\n",
    "sparse_pipeline = Pipeline(\n",
    "    [\n",
    "        (\"encoder\", OneHotEncoder(sparse=True)),\n",
    "        (\"model\", KerasRegressor(get_clf, loss=\"mse\", epochs=5, verbose=False))\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0742dab",
   "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": "d949fc0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:56:59.873619Z",
     "iopub.status.busy": "2022-07-27T13:56:59.872604Z",
     "iopub.status.idle": "2022-07-27T13:58:16.077906Z",
     "shell.execute_reply": "2022-07-27T13:58:16.077061Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 3469.38 MiB, increment: 3089.32 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit dense_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97320f3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:58:16.084788Z",
     "iopub.status.busy": "2022-07-27T13:58:16.083201Z",
     "iopub.status.idle": "2022-07-27T13:58:36.918778Z",
     "shell.execute_reply": "2022-07-27T13:58:36.917788Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_1/dense_2/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_1/dense_2/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_1/dense_2/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 659.04 MiB, increment: 34.25 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit sparse_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fcf20fe",
   "metadata": {},
   "source": [
    "You should see at least 100x more memory consumption **increment** in the dense pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37328415",
   "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": "561b3d47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T13:58:36.923687Z",
     "iopub.status.busy": "2022-07-27T13:58:36.923290Z",
     "iopub.status.idle": "2022-07-27T14:08:10.668211Z",
     "shell.execute_reply": "2022-07-27T14:08:10.666605Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1min 12s ± 7.28 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": "d70fc955",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T14:08:10.691546Z",
     "iopub.status.busy": "2022-07-27T14:08:10.690797Z",
     "iopub.status.idle": "2022-07-27T14:09:59.844889Z",
     "shell.execute_reply": "2022-07-27T14:09:59.844218Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_10/dense_20/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_10/dense_20/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_10/dense_20/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_11/dense_22/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_11/dense_22/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_11/dense_22/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_12/dense_24/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_12/dense_24/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_12/dense_24/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_13/dense_26/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_13/dense_26/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_13/dense_26/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_14/dense_28/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_14/dense_28/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_14/dense_28/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_15/dense_30/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_15/dense_30/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_15/dense_30/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_16/dense_32/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_16/dense_32/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_16/dense_32/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_17/dense_34/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_17/dense_34/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_17/dense_34/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.7 s ± 754 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sparse_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dafe2955",
   "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": "2e1e4ada",
   "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": "557e74c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T14:09:59.852466Z",
     "iopub.status.busy": "2022-07-27T14:09:59.851952Z",
     "iopub.status.idle": "2022-07-27T14:09:59.856240Z",
     "shell.execute_reply": "2022-07-27T14:09:59.855580Z"
    }
   },
   "outputs": [],
   "source": [
    "sparse_pipline_uint8 = Pipeline(\n",
    "    [\n",
    "        (\"encoder\", OneHotEncoder(sparse=True, dtype=np.uint8)),\n",
    "        (\"model\", KerasRegressor(get_clf, loss=\"mse\", epochs=5, verbose=False))\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d97c29c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-27T14:09:59.860183Z",
     "iopub.status.busy": "2022-07-27T14:09:59.859646Z",
     "iopub.status.idle": "2022-07-27T14:10:16.048731Z",
     "shell.execute_reply": "2022-07-27T14:10:16.047630Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradient_tape/sequential_18/dense_36/embedding_lookup_sparse/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradient_tape/sequential_18/dense_36/embedding_lookup_sparse/Reshape:0\", shape=(None, 100), dtype=float32), dense_shape=Tensor(\"gradient_tape/sequential_18/dense_36/embedding_lookup_sparse/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 1201.53 MiB, increment: 1.71 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit sparse_pipline_uint8.fit(X, y)"
   ]
  }
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