prediction_on_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47d2536a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-18 06:01:24.860115: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-11-18 06:01:25.445375: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2022-11-18 06:01:27.579496: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/t/miniconda3/envs/tf/lib/\n",
      "2022-11-18 06:01:27.579805: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/t/miniconda3/envs/tf/lib/\n",
      "2022-11-18 06:01:27.579823: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import precision_recall_fscore_support , accuracy_score\n",
    "from preprocess import text_cleaning_apos, cont_to_exp, text_cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8083e1d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_results(y_true, y_pred, model_name:str=None):\n",
    "  \"\"\"\n",
    "  Calculates model accuracy, precision, recall and f1 score of a binary classification model.\n",
    "  Args:\n",
    "      y_true: true labels in the form of a 1D array\n",
    "      y_pred: predicted labels in the form of a 1D array\n",
    "  Returns a dictionary of accuracy, precision, recall, f1-score.\n",
    "  \"\"\"\n",
    "  # Calculate model accuracy\n",
    "  model_accuracy = accuracy_score(y_true, y_pred) * 100\n",
    "  # Calculate model precision, recall and f1 score using \"weighted average\n",
    "  model_precision, model_recall, model_f1, _ = precision_recall_fscore_support(y_true, y_pred, average=\"weighted\")\n",
    "  model_results = {\"model\":model_name,\n",
    "                   \"accuracy\": model_accuracy,\n",
    "                  \"precision\": model_precision,\n",
    "                  \"recall\": model_recall,\n",
    "                  \"f1\": model_f1}\n",
    "  return model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "45448574",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b42d045",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-18 06:01:49.846084: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:49.906501: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:49.907311: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:49.909261: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-11-18 06:01:49.911434: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:49.912231: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:49.912968: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:51.193108: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:51.194417: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:51.195024: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-11-18 06:01:51.195504: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2630 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5\n",
      "2022-11-18 06:01:54.290664: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:01:56.261442: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:01:57.304018: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:01:57.466567: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:01:59.910681: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:01:59.927050: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:01:59.960769: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:01.589884: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:01.636984: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:02.199737: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:02.786371: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:04.450852: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:04.507078: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:06.673050: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:06.882196: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:06.903762: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:07.051377: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:07.067112: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:07.159425: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:07.176403: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:08.178842: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:08.248465: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:08.274762: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:08.550650: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:08.567320: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:08.585372: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:08.601389: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-18 06:02:09.274237: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:09.290093: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:09.582480: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:10.011530: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:10.153434: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:10.169886: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:10.617112: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:10.636353: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:10.801454: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:10.816895: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:10.945914: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:11.056511: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:11.074780: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:11.613839: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:11.911531: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:11.943464: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:12.212602: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:12.460122: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:12.493543: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:12.806735: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:12.856271: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:12.881881: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.204214: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.225100: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.242883: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.381257: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.400526: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.502135: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.530289: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.546966: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.799887: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.817358: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.940459: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:13.957655: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:15.375368: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:15.391716: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:15.421497: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:15.439125: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:15.458555: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:15.475864: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-18 06:02:15.632185: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:15.677295: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:15.697884: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:16.682945: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:16.701397: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.127979: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.147278: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.169027: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.186515: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.257153: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.275355: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.318220: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.625210: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:17.641144: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:18.188755: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:18.342728: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond/while' has 13 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:18.358783: W tensorflow/core/common_runtime/graph_constructor.cc:805] Node 'cond' has 5 outputs but the _output_shapes attribute specifies shapes for 46 outputs. Output shapes may be inaccurate.\n",
      "2022-11-18 06:02:18.684104: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 19240800 exceeds 10% of free system memory.\n",
      "2022-11-18 06:02:18.731268: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 19240800 exceeds 10% of free system memory.\n",
      "2022-11-18 06:02:18.776354: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 19240800 exceeds 10% of free system memory.\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model('./saved_model/fake_news_classifier.tf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0319f469",
   "metadata": {},
   "outputs": [],
   "source": [
    "# validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "47b0215a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xval = tf.data.Dataset.load('./saved_dataset/test_dataset.data')\n",
    "yval = np.load('./saved_dataset/YTEST.data.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1172ace6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "253/253 [==============================] - 42s 164ms/step\n"
     ]
    }
   ],
   "source": [
    "valpred = model.predict(xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d6c53ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "valres = calculate_results(y_pred=tf.squeeze(tf.round(valpred)), y_true = yval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6aad4f1e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': None,\n",
       " 'accuracy': 98.91142998515586,\n",
       " 'precision': 0.9891869415132419,\n",
       " 'recall': 0.9891142998515586,\n",
       " 'f1': 0.9891157397533423}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "cc431cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bf2e80e6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>Daniel Victor</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Truth Broadcast Network</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                              title  \\\n",
       "0  20800  Specter of Trump Loosens Tongues, if Not Purse...   \n",
       "1  20801  Russian warships ready to strike terrorists ne...   \n",
       "2  20802  #NoDAPL: Native American Leaders Vow to Stay A...   \n",
       "3  20803  Tim Tebow Will Attempt Another Comeback, This ...   \n",
       "4  20804                    Keiser Report: Meme Wars (E995)   \n",
       "\n",
       "                    author                                               text  \n",
       "0         David Streitfeld  PALO ALTO, Calif.  —   After years of scorning...  \n",
       "1                      NaN  Russian warships ready to strike terrorists ne...  \n",
       "2            Common Dreams  Videos #NoDAPL: Native American Leaders Vow to...  \n",
       "3            Daniel Victor  If at first you don’t succeed, try a different...  \n",
       "4  Truth Broadcast Network  42 mins ago 1 Views 0 Comments 0 Likes 'For th...  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv('./dataset/test.csv')\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e55a24bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "116b5bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.62 s, sys: 31.8 ms, total: 7.66 s\n",
      "Wall time: 7.71 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "test_data['text'] = test_data['text'].apply(text_cleaning_apos)\n",
    "test_data['title'] = test_data['title'].apply(text_cleaning_apos)\n",
    "\n",
    "test_data['text'] = test_data['text'].apply(cont_to_exp)\n",
    "test_data['title'] = test_data['title'].apply(cont_to_exp)\n",
    "\n",
    "test_data['title'] = test_data['title'].apply(text_cleaning) \n",
    "test_data['text'] = test_data['text'].apply(text_cleaning) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2e37db5c",
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                              title  \\\n",
       "0  20800  specter of trump loosens tongues if not purse ...   \n",
       "1  20801  russian warships ready to strike terrorists ne...   \n",
       "2  20802   nodapl native american leaders vow to stay al...   \n",
       "3  20803  tim tebow will attempt another comeback this t...   \n",
       "4  20804                           keiser report meme wars    \n",
       "\n",
       "                    author                                               text  \n",
       "0         David Streitfeld  palo alto calif after years of scorning the po...  \n",
       "1                      NaN  russian warships ready to strike terrorists ne...  \n",
       "2            Common Dreams  videos nodapl native american leaders vow to s...  \n",
       "3            Daniel Victor  if at first you do not succeed try different s...  \n",
       "4  Truth Broadcast Network   mins ago views comments likes for the first t...  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf73e7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make tf.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "54a4f775",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_title = tf.data.Dataset.from_tensor_slices(test_data['title'].values)\n",
    "# test_text = tf.data.Dataset.from_tensor_slices(test_data['text'].values)\n",
    "\n",
    "# # test_dataset = tf.data.Dataset.zip(test_title,test_text)\n",
    "# test_dataset = tf.data.Dataset.from_tensor_slices((test_data['title'].values, test_data['text'].values))\n",
    "# BATCH_SIZE = 8\n",
    "# test_dataset = test_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "516096f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices([test_data['title'].values, test_data['text'].values])\n",
    "dataset = dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ea8506de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b7ead726",
   "metadata": {},
   "outputs": [],
   "source": [
    "jj=[test_data['title'].values,test_data['text'].values]\n",
    "jj_data = tf.data.Dataset.from_tensor_slices(jj).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b0eb67e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "163/163 [==============================] - 72s 437ms/step\n",
      "CPU times: user 1min 14s, sys: 439 ms, total: 1min 14s\n",
      "Wall time: 1min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pred = tf.squeeze(tf.round(model.predict(x=jj)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0469eba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(5,), dtype=float32, numpy=array([0., 1., 1., 0., 1.], dtype=float32)>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "91473ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      id  label\n",
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       "2  20802      0\n",
       "3  20803      1\n",
       "4  20804      1"
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     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ysub = pd.read_csv('./dataset/submit.csv')\n",
    "ysub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c4e7058c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ysub['pred'] = tf.cast(pred, tf.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "83e508ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = calculate_results(y_pred=pred, y_true = ysub.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "21c2d6e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': None,\n",
       " 'accuracy': 63.63461538461539,\n",
       " 'precision': 0.641532856991091,\n",
       " 'recall': 0.6363461538461539,\n",
       " 'f1': 0.6372640183526227}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "deaa0120",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>the bangladeshi traffic jam that never ends th...</td>\n",
       "      <td>Jody Rosen</td>\n",
       "      <td>of all the dysfunctions that plague the world ...</td>\n",
       "      <td>25995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5196</th>\n",
       "      <td>25996</td>\n",
       "      <td>john kasich signs one abortion bill in ohio bu...</td>\n",
       "      <td>Sheryl Gay Stolberg</td>\n",
       "      <td>washington gov john kasich of ohio on tuesday ...</td>\n",
       "      <td>25996</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5197</th>\n",
       "      <td>25997</td>\n",
       "      <td>california today what exactly is in your sushi...</td>\n",
       "      <td>Mike McPhate</td>\n",
       "      <td>good morning want to get california today by e...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5198</th>\n",
       "      <td>25998</td>\n",
       "      <td>us marines to be deployed to russian border i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>previous next us marines to be deployed to ru...</td>\n",
       "      <td>25998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5199</th>\n",
       "      <td>25999</td>\n",
       "      <td>awkward sex onscreen and off the new york times</td>\n",
       "      <td>Teddy Wayne</td>\n",
       "      <td>perhaps you have seen the new tv series whose ...</td>\n",
       "      <td>25999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          I                                                     \\\n",
       "         id                                              title   \n",
       "0     20800  specter of trump loosens tongues if not purse ...   \n",
       "1     20801  russian warships ready to strike terrorists ne...   \n",
       "2     20802   nodapl native american leaders vow to stay al...   \n",
       "3     20803  tim tebow will attempt another comeback this t...   \n",
       "4     20804                           keiser report meme wars    \n",
       "...     ...                                                ...   \n",
       "5195  25995  the bangladeshi traffic jam that never ends th...   \n",
       "5196  25996  john kasich signs one abortion bill in ohio bu...   \n",
       "5197  25997  california today what exactly is in your sushi...   \n",
       "5198  25998   us marines to be deployed to russian border i...   \n",
       "5199  25999    awkward sex onscreen and off the new york times   \n",
       "\n",
       "                               \\\n",
       "                       author   \n",
       "0            David Streitfeld   \n",
       "1                         NaN   \n",
       "2               Common Dreams   \n",
       "3               Daniel Victor   \n",
       "4     Truth Broadcast Network   \n",
       "...                       ...   \n",
       "5195               Jody Rosen   \n",
       "5196      Sheryl Gay Stolberg   \n",
       "5197             Mike McPhate   \n",
       "5198                      NaN   \n",
       "5199              Teddy Wayne   \n",
       "\n",
       "                                                             d             \n",
       "                                                   text     id label pred  \n",
       "0     palo alto calif after years of scorning the po...  20800     0    0  \n",
       "1     russian warships ready to strike terrorists ne...  20801     1    1  \n",
       "2     videos nodapl native american leaders vow to s...  20802     0    1  \n",
       "3     if at first you do not succeed try different s...  20803     1    0  \n",
       "4      mins ago views comments likes for the first t...  20804     1    1  \n",
       "...                                                 ...    ...   ...  ...  \n",
       "5195  of all the dysfunctions that plague the world ...  25995     0    0  \n",
       "5196  washington gov john kasich of ohio on tuesday ...  25996     1    0  \n",
       "5197  good morning want to get california today by e...  25997     0    0  \n",
       "5198   previous next us marines to be deployed to ru...  25998     1    1  \n",
       "5199  perhaps you have seen the new tv series whose ...  25999     0    0  \n",
       "\n",
       "[5200 rows x 7 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_test = pd.concat((test_data,ysub),keys='Id' ,axis=1)\n",
    "full_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "c64fc789",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'pred'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [119]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfull_test\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpred\u001b[49m\u001b[38;5;241m.\u001b[39mvalues\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/generic.py:5902\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5895\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   5896\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[1;32m   5897\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[1;32m   5898\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[1;32m   5899\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m   5900\u001b[0m ):\n\u001b[1;32m   5901\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[0;32m-> 5902\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'pred'"
     ]
    }
   ],
   "source": [
    "full_test.pred.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1c47d152",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'pred'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'pred'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[0;32mIn [118]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ress \u001b[38;5;241m=\u001b[39m calculate_results(y_pred\u001b[38;5;241m=\u001b[39m\u001b[43mfull_test\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpred\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalues, y_true \u001b[38;5;241m=\u001b[39m full_test[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:3803\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 3803\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_multilevel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3804\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   3805\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:3854\u001b[0m, in \u001b[0;36mDataFrame._getitem_multilevel\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3852\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_multilevel\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m   3853\u001b[0m     \u001b[38;5;66;03m# self.columns is a MultiIndex\u001b[39;00m\n\u001b[0;32m-> 3854\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3855\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, (\u001b[38;5;28mslice\u001b[39m, np\u001b[38;5;241m.\u001b[39mndarray)):\n\u001b[1;32m   3856\u001b[0m         new_columns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns[loc]\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/multi.py:2916\u001b[0m, in \u001b[0;36mMultiIndex.get_loc\u001b[0;34m(self, key, method)\u001b[0m\n\u001b[1;32m   2913\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m mask\n\u001b[1;32m   2915\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m-> 2916\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_level_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2917\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _maybe_to_slice(loc)\n\u001b[1;32m   2919\u001b[0m keylen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(key)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/multi.py:3263\u001b[0m, in \u001b[0;36mMultiIndex._get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m   3259\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mslice\u001b[39m(i, j, step)\n\u001b[1;32m   3261\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3263\u001b[0m     idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_loc_single_level_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlevel_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3265\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m level \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lexsort_depth \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   3266\u001b[0m         \u001b[38;5;66;03m# Desired level is not sorted\u001b[39;00m\n\u001b[1;32m   3267\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(idx, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m   3268\u001b[0m             \u001b[38;5;66;03m# test_get_loc_partial_timestamp_multiindex\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/multi.py:2849\u001b[0m, in \u001b[0;36mMultiIndex._get_loc_single_level_index\u001b[0;34m(self, level_index, key)\u001b[0m\n\u001b[1;32m   2847\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   2848\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2849\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlevel_index\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3810\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'pred'"
     ]
    }
   ],
   "source": [
    "ress = calculate_results(y_pred=full_test['pred'].values, y_true = full_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f96189ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "name": "tf"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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