predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meta Learning\n",
    "\n",
    "NumerAi\n",
    "\n",
    "Tanmay Singh\n",
    "2021569\n",
    "CSAI\n",
    "Class of '25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gc\n",
    "import time\n",
    "import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "import cloudpickle as cp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchsummary import summary\n",
    "from torch.utils.data import DataLoader, Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from tqdm.auto import tqdm\n",
    "from datetime import datetime\n",
    "from numerapi import NumerAPI\n",
    "from scipy.stats import pearsonr\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import *\n",
    "from sklearn.metrics import *\n",
    "from sklearn.ensemble import *\n",
    "from sklearn.linear_model import *\n",
    "from sklearn.decomposition import *\n",
    "from sklearn.preprocessing import *\n",
    "from sklearn.neural_network import *\n",
    "from sklearn.model_selection import *\n",
    "from sklearn.cluster._kmeans import KMeans\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to compute NumerAi Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_frequency(predictions):\n",
    "    unique, counts = np.unique(predictions, return_counts=True)\n",
    "    label_frequencies = dict(zip(unique, counts))\n",
    "    print(\"Label frequencies:\", label_frequencies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a Feature Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-06 15:23:55,664 INFO numerapi.utils: target file already exists\n",
      "2024-11-06 15:23:55,665 INFO numerapi.utils: download complete\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature_sets 17\n",
      "targets 37\n"
     ]
    }
   ],
   "source": [
    "feature_metadata = json.load(open(f\"../data/features.json\"))\n",
    "\n",
    "for metadata in feature_metadata:\n",
    "  print(metadata, len(feature_metadata[metadata]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_sets = feature_metadata[\"feature_sets\"]\n",
    "for feature_set in [\"small\", \"medium\", \"all\"]:\n",
    "  print(feature_set, len(feature_sets[feature_set]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_sets = feature_metadata[\"feature_sets\"]\n",
    "feature_sets.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature_set in feature_sets:\n",
    "  print(f'Feature Set: {feature_set:<25}', f'Size: {len(feature_sets[feature_set])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the Saved Experts & the Meta-Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "with open('../saved_models/numerai_expert1.pkl', 'rb') as f:\n",
    "    expert1 = pickle.load(f)\n",
    "print(\"Model loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "with open('../saved_models/numerai_expert2.pkl', 'rb') as f:\n",
    "    expert2 = pickle.load(f)\n",
    "print(\"Model loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "with open('../saved_models/numerai_expert3.pkl', 'rb') as f:\n",
    "    expert3 = pickle.load(f)\n",
    "print(\"Model loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "with open('../saved_models/numerai_expert4.pkl', 'rb') as f:\n",
    "    expert4 = pickle.load(f)\n",
    "print(\"Model loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "with open('../saved_models/numerai_expert5.pkl', 'rb') as f:\n",
    "    expert5 = pickle.load(f)\n",
    "print(\"Model loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "with open('../saved_models/numerai_expert6.pkl', 'rb') as f:\n",
    "    expert6 = pickle.load(f)\n",
    "print(\"Model loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meta Model loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "with open('../saved_models/numerai_meta_model.pkl', 'rb') as f:\n",
    "    meta_model = pickle.load(f)\n",
    "print(\"Meta Model loaded successfully!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the Live Data Set, with a 'medium' feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-06 15:24:02,971 INFO numerapi.utils: target file already exists\n",
      "2024-11-06 15:24:02,973 INFO numerapi.utils: starting download\n",
      "v5.0/live.parquet: 15.6MB [00:00, 23.5MB/s]                   \n"
     ]
    }
   ],
   "source": [
    "feature_set = feature_sets[\"medium\"]\n",
    "\n",
    "live_data = pd.read_parquet(f\"../data/live.parquet\")\n",
    "features = [f for f in live_data.columns if \"feature\" in f]\n",
    "live = live_data[[\"era\"] + feature_set]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing the Live Set (in the same manner as the Training & Validation Sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "live.rename(columns=lambda x: f'feature {feature_set.index(x)}' if x in feature_set else x, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6231 rows × 706 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 era  feature 0  feature 1  feature 2  feature 3  feature 4  \\\n",
       "id                                                                            \n",
       "n0039880040fb023   X          4          0          3          0          2   \n",
       "n005b7bee7ec0816   X          3          1          4          3          2   \n",
       "n00693f63f9be56b   X          2          3          2          0          3   \n",
       "n00b8288605a5c6d   X          2          3          0          2          0   \n",
       "n00b97850c393025   X          4          4          2          4          3   \n",
       "...               ..        ...        ...        ...        ...        ...   \n",
       "nffceeac42d236e9   X          0          0          1          0          3   \n",
       "nffcf2396cb13040   X          4          4          0          4          3   \n",
       "nffcfec6bec2fa94   X          1          0          1          3          3   \n",
       "nffd7089792a1c2a   X          3          2          1          4          1   \n",
       "nfffc9c17ff955e7   X          1          3          1          3          4   \n",
       "\n",
       "                  feature 5  feature 6  feature 7  feature 8  ...  \\\n",
       "id                                                            ...   \n",
       "n0039880040fb023          0          1          1          0  ...   \n",
       "n005b7bee7ec0816          2          0          0          3  ...   \n",
       "n00693f63f9be56b          2          2          0          2  ...   \n",
       "n00b8288605a5c6d          0          4          2          1  ...   \n",
       "n00b97850c393025          2          1          4          3  ...   \n",
       "...                     ...        ...        ...        ...  ...   \n",
       "nffceeac42d236e9          4          3          1          0  ...   \n",
       "nffcf2396cb13040          1          3          3          4  ...   \n",
       "nffcfec6bec2fa94          4          3          3          0  ...   \n",
       "nffd7089792a1c2a          2          0          4          4  ...   \n",
       "nfffc9c17ff955e7          3          0          4          3  ...   \n",
       "\n",
       "                  feature 695  feature 696  feature 697  feature 698  \\\n",
       "id                                                                     \n",
       "n0039880040fb023            3            1            3            3   \n",
       "n005b7bee7ec0816            3            1            2            4   \n",
       "n00693f63f9be56b            0            3            2            2   \n",
       "n00b8288605a5c6d            2            1            3            2   \n",
       "n00b97850c393025            2            3            2            4   \n",
       "...                       ...          ...          ...          ...   \n",
       "nffceeac42d236e9            3            4            2            2   \n",
       "nffcf2396cb13040            3            4            3            1   \n",
       "nffcfec6bec2fa94            3            4            2            2   \n",
       "nffd7089792a1c2a            1            0            0            1   \n",
       "nfffc9c17ff955e7            3            0            1            4   \n",
       "\n",
       "                  feature 699  feature 700  feature 701  feature 702  \\\n",
       "id                                                                     \n",
       "n0039880040fb023            1            3            2            4   \n",
       "n005b7bee7ec0816            4            1            1            2   \n",
       "n00693f63f9be56b            3            4            2            2   \n",
       "n00b8288605a5c6d            1            4            2            0   \n",
       "n00b97850c393025            2            1            0            4   \n",
       "...                       ...          ...          ...          ...   \n",
       "nffceeac42d236e9            2            2            0            2   \n",
       "nffcf2396cb13040            4            1            0            4   \n",
       "nffcfec6bec2fa94            1            2            0            2   \n",
       "nffd7089792a1c2a            1            1            4            0   \n",
       "nfffc9c17ff955e7            4            1            3            4   \n",
       "\n",
       "                  feature 703  feature 704  \n",
       "id                                          \n",
       "n0039880040fb023            0            4  \n",
       "n005b7bee7ec0816            1            1  \n",
       "n00693f63f9be56b            3            2  \n",
       "n00b8288605a5c6d            3            0  \n",
       "n00b97850c393025            3            4  \n",
       "...                       ...          ...  \n",
       "nffceeac42d236e9            1            2  \n",
       "nffcf2396cb13040            4            2  \n",
       "nffcfec6bec2fa94            0            2  \n",
       "nffd7089792a1c2a            2            1  \n",
       "nfffc9c17ff955e7            4            3  \n",
       "\n",
       "[6231 rows x 706 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "live"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting a Numeric Value to the 'era' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "live['era'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>era</th>\n",
       "      <th>feature 0</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6231 rows × 706 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  era  feature 0  feature 1  feature 2  feature 3  feature 4  \\\n",
       "id                                                                             \n",
       "n0039880040fb023    1          4          0          3          0          2   \n",
       "n005b7bee7ec0816    1          3          1          4          3          2   \n",
       "n00693f63f9be56b    1          2          3          2          0          3   \n",
       "n00b8288605a5c6d    1          2          3          0          2          0   \n",
       "n00b97850c393025    1          4          4          2          4          3   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "nffceeac42d236e9    1          0          0          1          0          3   \n",
       "nffcf2396cb13040    1          4          4          0          4          3   \n",
       "nffcfec6bec2fa94    1          1          0          1          3          3   \n",
       "nffd7089792a1c2a    1          3          2          1          4          1   \n",
       "nfffc9c17ff955e7    1          1          3          1          3          4   \n",
       "\n",
       "                  feature 5  feature 6  feature 7  feature 8  ...  \\\n",
       "id                                                            ...   \n",
       "n0039880040fb023          0          1          1          0  ...   \n",
       "n005b7bee7ec0816          2          0          0          3  ...   \n",
       "n00693f63f9be56b          2          2          0          2  ...   \n",
       "n00b8288605a5c6d          0          4          2          1  ...   \n",
       "n00b97850c393025          2          1          4          3  ...   \n",
       "...                     ...        ...        ...        ...  ...   \n",
       "nffceeac42d236e9          4          3          1          0  ...   \n",
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       "nffcfec6bec2fa94          4          3          3          0  ...   \n",
       "nffd7089792a1c2a          2          0          4          4  ...   \n",
       "nfffc9c17ff955e7          3          0          4          3  ...   \n",
       "\n",
       "                  feature 695  feature 696  feature 697  feature 698  \\\n",
       "id                                                                     \n",
       "n0039880040fb023            3            1            3            3   \n",
       "n005b7bee7ec0816            3            1            2            4   \n",
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       "...                       ...          ...          ...          ...   \n",
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       "nffd7089792a1c2a            1            0            0            1   \n",
       "nfffc9c17ff955e7            3            0            1            4   \n",
       "\n",
       "                  feature 699  feature 700  feature 701  feature 702  \\\n",
       "id                                                                     \n",
       "n0039880040fb023            1            3            2            4   \n",
       "n005b7bee7ec0816            4            1            1            2   \n",
       "n00693f63f9be56b            3            4            2            2   \n",
       "n00b8288605a5c6d            1            4            2            0   \n",
       "n00b97850c393025            2            1            0            4   \n",
       "...                       ...          ...          ...          ...   \n",
       "nffceeac42d236e9            2            2            0            2   \n",
       "nffcf2396cb13040            4            1            0            4   \n",
       "nffcfec6bec2fa94            1            2            0            2   \n",
       "nffd7089792a1c2a            1            1            4            0   \n",
       "nfffc9c17ff955e7            4            1            3            4   \n",
       "\n",
       "                  feature 703  feature 704  \n",
       "id                                          \n",
       "n0039880040fb023            0            4  \n",
       "n005b7bee7ec0816            1            1  \n",
       "n00693f63f9be56b            3            2  \n",
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       "...                       ...          ...  \n",
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       "nffd7089792a1c2a            2            1  \n",
       "nfffc9c17ff955e7            4            3  \n",
       "\n",
       "[6231 rows x 706 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "live"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating Predictions from Experts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXPERT-1 (XGBOOST CLASSIFIER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "expert1_live_pred = expert1.predict(live)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expert1_live_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXPERT-2 (RANDOM FOREST CLASSIFIER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=4)]: Done 1000 out of 1000 | elapsed:    0.8s finished\n"
     ]
    }
   ],
   "source": [
    "expert2_live_pred = expert2.predict(live)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 1., 2., ..., 2., 2., 2.], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expert2_live_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXPERT-3 (ADABOOST CLASSIFIER with DECISION TREE CLASSIFIER as BASE ESTIMATOR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "expert3_live_pred = expert3.predict(live)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 1., 2., ..., 2., 2., 2.], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expert3_live_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXPERT-4 (LOGISTIC REGRESSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "expert4_live_pred = expert4.predict(live)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 0., 2., ..., 1., 2., 2.], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expert4_live_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXPERT-5 (CATBOOST CLASSIFIER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "expert5_live_pred = expert5.predict(live)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if expert5_live_pred.ndim > 1:\n",
    "    expert5_live_pred = expert5_live_pred.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expert5_live_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXPERT-6 (HISTOGRAM-BASED GRADIENT BOOST CLASSIFIER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "expert6_live_pred = expert6.predict(live)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 4., 2., ..., 2., 2., 2.], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expert6_live_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating Predictions from Meta-Model (LIGHTGBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_live_x = np.column_stack((expert1_live_pred, expert2_live_pred, expert3_live_pred, expert4_live_pred, expert5_live_pred, expert6_live_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 2., 3., 2., 3., 2.],\n",
       "       [1., 1., 1., 0., 3., 4.],\n",
       "       [2., 2., 2., 2., 2., 2.],\n",
       "       ...,\n",
       "       [2., 2., 2., 1., 3., 2.],\n",
       "       [2., 2., 2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2., 2., 2.]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_live_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_live_predictions = meta_model.predict(meta_live_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.40410961, 1.45875632, 2.07233549, ..., 2.19886853, 2.07233549,\n",
       "       2.07233549])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_live_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [0.5, 1.5, 2.5, 3.5]\n",
    "\n",
    "live_predictions = np.digitize(meta_live_predictions, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 2, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "live_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label frequencies: {1: 1152, 2: 3773, 3: 1306}\n"
     ]
    }
   ],
   "source": [
    "label_frequency(live_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the Classes back into Numeric Form for Submission to NumerAi Platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a Label Map (Both forward & reverse Mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_dict = {0.0: 0, 0.25: 1, 0.5: 2, 0.75: 3, 1.0: 4}\n",
    "reverse_labels_dict = {v: k for k, v in labels_dict.items()}\n",
    "og_live_predictions = np.vectorize(reverse_labels_dict.get)(live_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5 , 0.25, 0.5 , ..., 0.5 , 0.5 , 0.5 ])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_live_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label frequencies: {0.25: 1152, 0.5: 3773, 0.75: 1306}\n"
     ]
    }
   ],
   "source": [
    "label_frequency(og_live_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preparing Data into Submission Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n0039880040fb023</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n005b7bee7ec0816</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00693f63f9be56b</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00b8288605a5c6d</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00b97850c393025</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffceeac42d236e9</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffcf2396cb13040</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffcfec6bec2fa94</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd7089792a1c2a</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfffc9c17ff955e7</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6231 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prediction\n",
       "id                          \n",
       "n0039880040fb023        0.50\n",
       "n005b7bee7ec0816        0.25\n",
       "n00693f63f9be56b        0.50\n",
       "n00b8288605a5c6d        0.25\n",
       "n00b97850c393025        0.50\n",
       "...                      ...\n",
       "nffceeac42d236e9        0.50\n",
       "nffcf2396cb13040        0.50\n",
       "nffcfec6bec2fa94        0.50\n",
       "nffd7089792a1c2a        0.50\n",
       "nfffc9c17ff955e7        0.50\n",
       "\n",
       "[6231 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.Series(og_live_predictions, index=live.index).to_frame(\"prediction\")\n",
    "\n",
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the Live Predictions into Pickle & CSV formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions saved to 'live_submission.pkl' using cloudpickle.\n"
     ]
    }
   ],
   "source": [
    "predictions_dir = \"../predictions/\"\n",
    "os.makedirs(predictions_dir, exist_ok=True)\n",
    "\n",
    "current_date = datetime.now().strftime(\"%d-%m-%Y\")\n",
    "prediction_file_path = os.path.join(predictions_dir, f\"{current_date}_predictions.csv\")\n",
    "\n",
    "# with open(prediction_file_path, \"wb\") as f:\n",
    "#     f.write(cloudpickle.dumps(submission))\n",
    "\n",
    "# print(f\"Predictions saved to '{prediction_file_path}' using cloudpickle.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions saved to 'live_submission.csv' with ID and Prediction columns.\n"
     ]
    }
   ],
   "source": [
    "submission = submission.reset_index()\n",
    "submission = submission.rename(columns={'index': 'id'})\n",
    "submission.to_csv(prediction_file_path, index=False)\n",
    "\n",
    "print(f\"Predictions saved to '{prediction_file_path}'\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
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}