basic_submission.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "id": "tEd1A1tFBOKs"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/crunchdao/adialab-notebooks/blob/main/basic_submission.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aWkyd7l6wBse",
    "tags": []
   },
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QIGffwE2wBsi"
   },
   "source": [
    "# Set up your crunch workspace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wPwpdl83wBsi"
   },
   "source": [
    "#### STEP 1\n",
    "Run this cell to install the crunch library in your workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fctOAv3CwBsi",
    "outputId": "41de4317-bfb4-469d-c7df-b7172d38515b",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip3 install crunch-cli --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kaVIsoyowBsl"
   },
   "source": [
    "#### STEP 2\n",
    "Import the crunch package and instantiate it to be able to access its functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tHV_tK7EwBsm",
    "outputId": "e4eb51be-18cc-4277-8ba4-27f7f5aa9794",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import crunch\n",
    "crunch = crunch.load_notebook(__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GhDWzOWDBOKw"
   },
   "source": [
    "#### STEP 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4C7QjMkqBOKw",
    "outputId": "05da2d5d-a94c-4c77-89e2-71de6fd6a7ec",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# go to your submit page and copy paste your setup command to access the data\n",
    "# https://adialab.crunchdao.com/submit\n",
    "!crunch --notebook setup resonant-poincare --token 1d5jXddVQA2XLv7uFU9ukQm66L4jFisBXUs4UbeKIojeu0wXDcnyuAg9AChPyBZf\n",
    "%cd resonant-poincare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fJqTMeLqwBsm"
   },
   "source": [
    "# ADIA Lab Market Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VlAzMPaIwBsm"
   },
   "source": [
    "## A code competition\n",
    "\n",
    "This competition is divided in two phases.\n",
    "\n",
    "Submission phase - 12 weeks\n",
    "\n",
    "Out-of-Sample phase - 12 weeks\n",
    "\n",
    "During the first phase, participants will submit Python notebooks or Python scripts which build their best possible model on the data proposed by the organizers. In the second phase, also called [Out-of-Sample](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) (OOS) phase, the participant's code will be automatically executed by the platform on unseen data. During this phase, the participants won't be able to modify their code.\n",
    "\n",
    "There are two main reasons for this:\n",
    "\n",
    "- Participants won't be able to game or cheat their scores.\n",
    "\n",
    "- [Overfitting](https://deliverypdf.ssrn.com/delivery.php?ID=634087103098022017102089127026118070055022030067038035066070070118003108076075122073107013020035005031116084117030102014013119017036066065011126115081078006004108029033051020066006092025091103065117104075029100098011096065096065079019015002101078070&EXT=pdf&INDEX=TRUE) of the training data will lead to very bad performance OOS.\n",
    "\n",
    "To ensure reproducibility of your work, you will need to follow certain coding guidelines to participate in the competition. These guidelines will also allow our scoring system to run your code in the cloud during the OOS period without any issues.\n",
    "\n",
    "In this competition, CrunchDAO acts as a third party intermediary and will never communicate this code to the organizers.\n",
    "\n",
    "## The Coding Guideline\n",
    "\n",
    "Your submission needs at least to provide three components: imports, `train()`, and `infer()`.\n",
    "\n",
    "1. **imports**: As with any script, if your solution contains dependencies on external packages make sure to import them. The system will automatically install your dependancies. Make sure that you are using only packages that are whitelisted [here](https://adialab.crunchdao.io/submit?tab=libraries).\n",
    "\n",
    "2. **`train()`**: In the training phase the users will build the model and train it such that it can perform inferences on the testing data. The model must be saved in the `resources` directory.\n",
    "\n",
    "4. **`infer()`**: In the inference function the model trained in the previous step will be loaded and used to perform inferences on a data sample matching the characteristic of the training test.\n",
    "\n",
    "## Scoring on the public leaderboard\n",
    "\n",
    "To ensure the reliability of the public leaderboard, you don't have access to all of the testing data on which you will be scored.\n",
    "The `X_test` data downloaded in your workspace is composed of **only** 5 dates to test locally that your code actually runs when submitted. Notice that `y_test` is not available for those 5 dates.\n",
    "Once you have uploaded your solution the system will run your code on a larger test set of approximately 30 dates, and compute the score.\n",
    "\n",
    "When submitting, you are left to decide how frequently to retrain your model, considering the limit of 5 hours of resources / week / user allowed to predict the 30 dates of the private test set.\n",
    "\n",
    "\n",
    "## Scoring on the out-of-sample phase\n",
    "\n",
    "During the out-of-sample phase, after the submission period ends, the system will call your code 3 times every week on live datapoint.\n",
    "\n",
    "The mean spearman score after 12 weeks of OOS will determine the winners of the tournament.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SwxiUFGAwBsq"
   },
   "source": [
    "# Construction of a basic submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0o1AvFK_wBso"
   },
   "source": [
    "### Submission process\n",
    "\n",
    "1- Make sure to put all your code in the code interface inside your Notebook. The system will parse these functions to execute it in the cloud. You can work outside of the code interface but to be able to submit you will need to fill in the submission function with the code you want to submit\n",
    "\n",
    "2- Once satisfied with your work. Download this notebook ( file -> Download -> Download.ipynb )\n",
    "\n",
    "3- Then upload this Notebook on https://adialab.crunchdao.com/submit\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S5jTqHSpBOKy",
    "tags": []
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This is a basic example of what you need to do to participate to the tournament.\n",
    "The code will not have access to the internet (or any socket related operation).\n",
    "\"\"\"\n",
    "\n",
    "# Imports\n",
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "import typing\n",
    "import joblib\n",
    "from pathlib import Path\n",
    "\n",
    "\n",
    "def train(X_train: pd.DataFrame, y_train: pd.DataFrame,\n",
    "          model_directory_path: str = \"resources\") -> None:\n",
    "    \"\"\"\n",
    "    Do your model training here.\n",
    "    At each retrain this function will have to save an updated version of\n",
    "    the model under the model_directiory_path, as in the example below.\n",
    "    Note: You can use other serialization methods than joblib.dump(), as\n",
    "    long as it matches what reads the model in infer().\n",
    "    \n",
    "    Args:\n",
    "        X_train, y_train: the data to train the model.\n",
    "        model_directory_path: the path to save your updated model\n",
    "    \n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "\n",
    "    # basic xgboost regressor\n",
    "    model = xgb.XGBRegressor(\n",
    "        objective='reg:squarederror',\n",
    "        max_depth=4,\n",
    "        learning_rate=0.1,\n",
    "        n_estimators=2,\n",
    "        n_jobs=-1,\n",
    "        colsample_bytree=0.05\n",
    "    )\n",
    "\n",
    "    # training the model\n",
    "    print(\"training...\")\n",
    "    model.fit(X_train.iloc[:,2:], y_train.iloc[:,2:])\n",
    "\n",
    "    # make sure that the train function correctly save the trained model\n",
    "    # in the model_directory_path\n",
    "    model_pathname = Path(model_directory_path) / \"model.joblib\"\n",
    "    print(f\"Saving model in {model_pathname}\")\n",
    "    joblib.dump(model, model_pathname)\n",
    "\n",
    "\n",
    "def infer(X_test: pd.DataFrame,\n",
    "          model_directory_path: str = \"resources\") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Do your inference here.\n",
    "    This function will load the model saved at the previous iteration and use\n",
    "    it to produce your inference on the current date.\n",
    "    It is mandatory to send your inferences with the ids so the system\n",
    "    can match it correctly.\n",
    "    \n",
    "    Args:\n",
    "        model_directory_path: the path to the directory to the directory in wich we will be saving your updated model.\n",
    "        X_test: the independant  variables of the current date passed to your model.\n",
    "\n",
    "    Returns:\n",
    "        A dataframe (date, id, value) with the inferences of your model for the current date.\n",
    "    \"\"\"\n",
    "\n",
    "    # loading the model saved by the train function at previous iteration\n",
    "    model = joblib.load(Path(model_directory_path) / \"model.joblib\")\n",
    "    \n",
    "    # creating the predicted label dataframe with correct dates and ids\n",
    "    y_test_predicted = X_test[[\"date\", \"id\"]].copy()\n",
    "    y_test_predicted[\"value\"] = model.predict(X_test.iloc[:, 2:])\n",
    "\n",
    "    return y_test_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LyStcF3dRY5O"
   },
   "source": [
    "\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P7e3xWnpJrzb"
   },
   "source": [
    "# Testing your model locally\n",
    "\n",
    "This last part of the notebook is not necessary for the submission but only for you, to analyze your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mQSnWoL6KWD2",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import spearmanr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4INH-g0KBOKy",
    "outputId": "28cae60a-1a65-4c28-b85c-1aace653421c",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Getting the data\n",
    "X_train, y_train, X_test = crunch.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0drhNihqL4lV"
   },
   "source": [
    "To have a preliminary assessment of our solution above we need to create train and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def temporal_train_test_split(X_train_loc, y_train_loc, test_size=0.2):\n",
    "    unique_dates = X_train_loc.date.unique()\n",
    "    split_date = unique_dates[int(len(unique_dates)*(1-test_size))]\n",
    "    X_train_local = X_train_loc[X_train_loc['date'] <= split_date]\n",
    "    X_test_local = X_train_loc[X_train_loc['date'] > split_date]\n",
    "    \n",
    "    y_train_local = y_train_loc[y_train_loc['date'] <= split_date]\n",
    "    y_test_local = y_train_loc[y_train_loc['date'] > split_date]\n",
    "    \n",
    "    return X_train_local, X_test_local, y_train_local, y_test_local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D1SlGcI0IKw-",
    "outputId": "07a8a23e-8069-430a-b372-1ffb9cfeb42d",
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(\"Splitting (X_train, y_train) in X_train_local, X_test_local, y_train_local, y_test_local\")\n",
    "X_train_local, X_test_local, y_train_local, y_test_local = temporal_train_test_split(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    test_size=0.2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D1SlGcI0IKw-",
    "outputId": "07a8a23e-8069-430a-b372-1ffb9cfeb42d",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Training. It may require a few minutes.\n",
    "train(X_train_local, y_train_local)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g9KlcFddJ1nh",
    "outputId": "61b31c41-bcc7-48e1-b39d-f9650cc8628f"
   },
   "outputs": [],
   "source": [
    "print(\"Inference\")\n",
    "y_test_local_pred = infer(X_test_local, model_directory_path=\"resources\")\n",
    "score = spearmanr(y_test_local[\"y\"], y_test_local_pred[\"value\"])[0] * 100\n",
    "print(f\"Spearman's correlation {score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6XA4DXZtBOKz"
   },
   "source": [
    "# Testing your submission **locally**\n",
    "\n",
    "This function of the crunch package will run your code locally, in the same way in which the function is called in the cloud (ie: one date at a time). If it runs without problem, it is highly likely that there won't be problems when executing it on the CrunchDAO's system, on the cloud.\n",
    "\n",
    "You can setup the a retraining frequency as you which. A train frequency of 2 means that the system will retrain your model every two dates.\n",
    "\n",
    "`force_first_train=True` means that your model will be trained on the first date of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x-6qSxTNXJ5p"
   },
   "outputs": [],
   "source": [
    "print(\"Remove unused data to release memory\")\n",
    "del X_train, y_train, X_test, X_train_local, X_test_local, y_train_local, y_test_local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "jio87DOxBOKz",
    "outputId": "d6c54785-a843-47d1-a8ae-9ce36fe66c09"
   },
   "outputs": [],
   "source": [
    "crunch.test(force_first_train=True, train_frequency=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JcxaEabsZrXm"
   },
   "source": [
    "# Now remember to download and then submit this notebook to https://adialab.crunchdao.com/submit"
   ]
  }
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