{
 "cells": [
  {
   "cell_type": "raw",
   "id": "a0ee9ce0",
   "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": "7f8bd19b",
   "metadata": {},
   "source": [
    "# Sparse Inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc65de6c",
   "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": "a9d3707c",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d903105d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:09:37.235799Z",
     "iopub.status.busy": "2023-06-28T16:09:37.235318Z",
     "iopub.status.idle": "2023-06-28T16:09:39.522421Z",
     "shell.execute_reply": "2023-06-28T16:09:39.521711Z"
    }
   },
   "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 (31 kB)\r\n",
      "Requirement already satisfied: psutil in /home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages (from memory_profiler) (5.9.5)\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": "fb62f9e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:09:39.528114Z",
     "iopub.status.busy": "2023-06-28T16:09:39.526658Z",
     "iopub.status.idle": "2023-06-28T16:09:43.417707Z",
     "shell.execute_reply": "2023-06-28T16:09:43.416516Z"
    }
   },
   "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": "c4fa2a17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:09:43.423248Z",
     "iopub.status.busy": "2023-06-28T16:09:43.421579Z",
     "iopub.status.idle": "2023-06-28T16:09:43.429749Z",
     "shell.execute_reply": "2023-06-28T16:09:43.429179Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import scikeras\n",
    "except ImportError:\n",
    "    !python -m pip install scikeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1c6c65f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:09:43.434102Z",
     "iopub.status.busy": "2023-06-28T16:09:43.432951Z",
     "iopub.status.idle": "2023-06-28T16:09:44.410594Z",
     "shell.execute_reply": "2023-06-28T16:09:44.409874Z"
    }
   },
   "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": "303c705c",
   "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": "23fda7a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:09:44.415752Z",
     "iopub.status.busy": "2023-06-28T16:09:44.414495Z",
     "iopub.status.idle": "2023-06-28T16:09:44.420241Z",
     "shell.execute_reply": "2023-06-28T16:09:44.419682Z"
    }
   },
   "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": "38aa2b72",
   "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": "e680d7c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:09:44.424662Z",
     "iopub.status.busy": "2023-06-28T16:09:44.423525Z",
     "iopub.status.idle": "2023-06-28T16:09:44.429331Z",
     "shell.execute_reply": "2023-06-28T16:09:44.428730Z"
    }
   },
   "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": "ac72911c",
   "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": "1a724d93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:09:44.433030Z",
     "iopub.status.busy": "2023-06-28T16:09:44.432376Z",
     "iopub.status.idle": "2023-06-28T16:09:44.438643Z",
     "shell.execute_reply": "2023-06-28T16:09:44.437856Z"
    }
   },
   "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": "ec3652d7",
   "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": "65063e19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:09:44.442334Z",
     "iopub.status.busy": "2023-06-28T16:09:44.442068Z",
     "iopub.status.idle": "2023-06-28T16:10:36.663139Z",
     "shell.execute_reply": "2023-06-28T16:10:36.661827Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 3550.33 MiB, increment: 3127.02 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit dense_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8566e064",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:10:36.667356Z",
     "iopub.status.busy": "2023-06-28T16:10:36.667090Z",
     "iopub.status.idle": "2023-06-28T16:10:58.753761Z",
     "shell.execute_reply": "2023-06-28T16:10:58.752975Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 682.77 MiB, increment: 55.90 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit sparse_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44679fcd",
   "metadata": {},
   "source": [
    "You should see at least 100x more memory consumption **increment** in the dense pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6fca80",
   "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": "ec2ffeb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:10:58.759139Z",
     "iopub.status.busy": "2023-06-28T16:10:58.757932Z",
     "iopub.status.idle": "2023-06-28T16:18:39.772997Z",
     "shell.execute_reply": "2023-06-28T16:18:39.772213Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.6 s ± 2.69 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": "c5fd1efc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:18:39.779515Z",
     "iopub.status.busy": "2023-06-28T16:18:39.777155Z",
     "iopub.status.idle": "2023-06-28T16:21:15.928502Z",
     "shell.execute_reply": "2023-06-28T16:21:15.927868Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 s ± 1.72 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sparse_pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696b4576",
   "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": "d8e14e9d",
   "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": "e2dc972b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:21:15.933572Z",
     "iopub.status.busy": "2023-06-28T16:21:15.933100Z",
     "iopub.status.idle": "2023-06-28T16:21:15.936964Z",
     "shell.execute_reply": "2023-06-28T16:21:15.936402Z"
    }
   },
   "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": "a24c9cba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-28T16:21:15.939921Z",
     "iopub.status.busy": "2023-06-28T16:21:15.939498Z",
     "iopub.status.idle": "2023-06-28T16:21:34.326673Z",
     "shell.execute_reply": "2023-06-28T16:21:34.325767Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/scikeras/scikeras/.venv/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 960.73 MiB, increment: 30.37 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit sparse_pipline_uint8.fit(X, y)"
   ]
  }
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