test-uxml-best-trial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.sparse as sp\n",
    "from scipy.sparse import csc_matrix as csc\n",
    "import pandas as pd\n",
    "pd.options.display.float_format = '{:,.6f}'.format\n",
    "from sklearn.metrics import (\n",
    "    mean_squared_error,\n",
    "    mean_absolute_error,\n",
    "    r2_score,\n",
    "    explained_variance_score,\n",
    "    roc_auc_score,\n",
    "    log_loss,\n",
    "    confusion_matrix\n",
    ")\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "FONT_SIZE = 24\n",
    "plt.rcParams['figure.figsize'] = (20,8)\n",
    "plt.rcParams['font.size'] = FONT_SIZE\n",
    "plt.rcParams['legend.fontsize'] = FONT_SIZE\n",
    "plt.rcParams['xtick.labelsize'] = FONT_SIZE\n",
    "plt.rcParams['ytick.labelsize'] = FONT_SIZE\n",
    "%config InlineBackend.figure_format ='retina'\n",
    "import itertools\n",
    "from constants import (SEED, EPSILON, EVENT_THRESHOLD, DEFAULT_K, DEFAULT_THRESHOLD,  UX_CONSTANTS, LOG_DIR, \n",
    "                       DATA_DIR, TEST_DATA_PATH, DATA_OCT, DATA_NOV, USECOLS, USER, ITEM, RATING, PREDICTION)\n",
    "from utilities.ms_evaluation import (rmse, auc, logloss, precision_at_k, recall_at_k, ndcg_at_k, map_at_k, mae, rsquared, exp_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NAME = r'BasicMatrixFactorization'\n",
    "NAME = r'AdamUXML-b' \n",
    "Y_HAT_PATH = DATA_DIR+r'/'+NAME+r'-y_hat.npz'\n",
    "TEST_RESULTS_PATH = LOG_DIR+'\\\\'+NAME+'\\\\test-results.csv'\n",
    "SKL = \"(calculated using sklearn.metrics on non-zero values of sparse matrices)\"\n",
    "SPA = \"(calculated using CSC sparse matrix operations)\"\n",
    "MSE = \"(calculated using the Microsoft Evaluation method)\"\n",
    "ux_constants = pd.Series(pd.read_csv(UX_CONSTANTS, index_col=0, squeeze=True, header=None), dtype='float32')\n",
    "POSITIVE_ABOVE = ux_constants['positive_above']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the matrices: (177592, 44780)\n",
      "Number of non-zero values:\n",
      "Y:  552,255\n",
      "Ŷ:  552,255\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "log = pd.Series(dtype='float64')\n",
    "y_hat = sp.load_npz(Y_HAT_PATH) \n",
    "y = sp.load_npz(TEST_DATA_PATH)\n",
    "assert y_hat.shape == y.shape, 'The shape of Y and Y_hat must match, otherwise they are not comparable.'\n",
    "print(f\"Shape of the matrices: {y.shape}\")\n",
    "print(\"Number of non-zero values:\")\n",
    "print(f\"Y: {y.nnz:8,}\")\n",
    "print(f\"Ŷ: {y_hat.nnz:8,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sparse_to_dense(matrix):\n",
    "    matrix = matrix.todok()\n",
    "    dense = []\n",
    "    rows,cols = matrix.nonzero()\n",
    "    for row,col in zip(rows,cols):\n",
    "        dense.append([row,col,matrix[row,col]])\n",
    "    return dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_true = pd.DataFrame(data=sparse_to_dense(y),columns=[USER, ITEM, RATING])\n",
    "df_pred = pd.DataFrame(data=sparse_to_dense(y_hat),columns=[USER, ITEM, PREDICTION])\n",
    "# Usually, the CSC is used when there are more rows than columns. (If there are more columns, use CSR instead.)\n",
    "y_hat = y_hat.tocsc()\n",
    "y = y.tocsc()\n",
    "y_nz = np.array(y[y.nonzero()]).reshape(-1)\n",
    "y_hat_nz = np.array(y_hat[y_hat.nonzero()]).reshape(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Square Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error: 0.07672638064616889 (calculated using CSC sparse matrix operations)\n",
      "Mean Square Error: 0.07672636210918427 (calculated using sklearn.metrics on non-zero values of sparse matrices)\n",
      "Note: The smaller the better.\n"
     ]
    }
   ],
   "source": [
    "mse_spa = csc.sum(csc.power(y_hat-y,2))/y.nnz\n",
    "mse_skl = mean_squared_error(y_nz,y_hat_nz)\n",
    "print(f\"Mean Square Error: {mse_spa} {SPA}\")\n",
    "print(f\"Mean Square Error: {mse_skl} {SKL}\")\n",
    "print('Note: The smaller the better.')\n",
    "log[\"mse\"]=mse_spa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root Mean Square Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error: 0.27699527188414047 (calculated using CSC sparse matrix operations)\n",
      "Root Mean Square Error: 0.2769952416419983 (calculated using sklearn.metrics on non-zero values of sparse matrices)\n",
      "Root Mean Square Error: 0.27699527097945054 (calculated using the Microsoft Evaluation method)\n",
      "Note: The smaller the better.\n"
     ]
    }
   ],
   "source": [
    "rmse_spa = np.sqrt(mse_spa)\n",
    "rmse_skl = np.sqrt(mse_skl)\n",
    "rmse_mse = rmse(df_true,df_pred)\n",
    "print(f\"Root Mean Square Error: {rmse_spa} {SPA}\")\n",
    "print(f\"Root Mean Square Error: {rmse_skl} {SKL}\")\n",
    "print(f\"Root Mean Square Error: {rmse_mse} {MSE}\")\n",
    "print('Note: The smaller the better.')\n",
    "log[\"rmse\"]=rmse_spa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Absolute Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.18501841314247947 (calculated using CSC sparse matrix operations)\n",
      "Mean Absolute Error: 0.1850184053182602 (calculated using sklearn.metrics on non-zero values of sparse matrices)\n",
      "Mean Absolute Error: 0.18501840147529566 (calculated using the Microsoft Evaluation method)\n",
      "Note: The smaller the better.\n"
     ]
    }
   ],
   "source": [
    "mae_spa = csc.sum(abs(y_hat-y))/y.nnz\n",
    "mae_skl = mean_absolute_error(y_nz,y_hat_nz)\n",
    "mae_mse = mae(df_true,df_pred)\n",
    "print(f\"Mean Absolute Error: {mae_spa} {SPA}\")\n",
    "print(f\"Mean Absolute Error: {mae_skl} {SKL}\")\n",
    "print(f\"Mean Absolute Error: {mae_mse} {MSE}\")\n",
    "print('Note: The smaller the better.')\n",
    "log[\"mae\"]=mae_spa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R²"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination (R²): -0.0861978709640232 (calculated using sklearn.metrics on non-zero values of sparse matrices)\n",
      "Coefficient of determination (R²): -0.08619780771711616 (calculated using the Microsoft Evaluation method)\n",
      "Note: The closer to 1 the better.\n"
     ]
    }
   ],
   "source": [
    "r2_skl = r2_score(y_nz,y_hat_nz)\n",
    "r2_mse = rsquared(df_true,df_pred)\n",
    "print(f\"Coefficient of determination (R\\u00B2): {r2_skl} {SKL}\")\n",
    "print(f\"Coefficient of determination (R\\u00B2): {r2_mse} {MSE}\")\n",
    "print(\"Note: The closer to 1 the better.\")\n",
    "log[\"r-squared\"]=r2_skl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explained variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance: -0.08619308471679688 (calculated using sklearn.metrics on non-zero values of sparse matrices)\n",
      "Explained variance: -0.08619313615628599 (calculated using the Microsoft Evaluation method)\n",
      "Note: The closer to 1 the better.\n"
     ]
    }
   ],
   "source": [
    "exp_var_skl = explained_variance_score(y_nz,y_hat_nz)\n",
    "exp_var_mse = exp_var(df_true,df_pred)\n",
    "print(f\"Explained variance: {exp_var_skl} {SKL}\")\n",
    "print(f\"Explained variance: {exp_var_mse} {MSE}\")\n",
    "print(\"Note: The closer to 1 the better.\")\n",
    "log[\"exp_var\"]=exp_var_skl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arear Under Curve (AUC) - integral area under the receiver operating characteristic curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For AUC and logloss we need binary ratings and predictions in the [0,1] range\n",
    "df_true_bin = df_true.copy()\n",
    "df_true_bin[RATING] = df_true_bin[RATING].apply(lambda x: 1 if x > POSITIVE_ABOVE else 0)\n",
    "df_pred_bin = df_pred.copy()\n",
    "df_pred_bin[PREDICTION] = minmax_scale(df_pred_bin[PREDICTION].astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arear Under Curve (AUC): 0.7996968784653418 (calculated using the Microsoft Evaluation method)\n",
      "Note: The closer to 1 the better. 0.5 indicates an uninformative classifier\n"
     ]
    }
   ],
   "source": [
    "auc_mse = auc(df_true_bin,df_pred_bin)\n",
    "print(f\"Arear Under Curve (AUC): {auc_mse} {MSE}\")\n",
    "print(\"Note: The closer to 1 the better. 0.5 indicates an uninformative classifier\")\n",
    "log[\"auc\"]=auc_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic loss (logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic loss (logloss): 0.6117154434272443 (calculated using the Microsoft Evaluation method)\n",
      "Note: The closer to 0 the better.\n"
     ]
    }
   ],
   "source": [
    "logloss_mse = logloss(df_true_bin,df_pred_bin)\n",
    "print(f\"Logistic loss (logloss): {logloss_mse} {MSE}\")\n",
    "print(\"Note: The closer to 0 the better.\")\n",
    "log[\"logloss\"]=logloss_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 507,
       "width": 499
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_mat = confusion_matrix(\n",
    "    [1 if x > POSITIVE_ABOVE  else 0 for x in y_nz],\n",
    "    [1 if x > POSITIVE_ABOVE  else 0 for x in y_hat_nz],  \n",
    "    labels=[1,0])\n",
    "\n",
    "conf_mat_p = np.round(conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis],4)*100\n",
    "log[\"true_positive\"]=f\"{conf_mat_p[0,0]:.2f}%\"\n",
    "log[\"false_negative\"]=f\"{conf_mat_p[0,1]:.2f}%\"\n",
    "log[\"false_positive\"]=f\"{conf_mat_p[1,0]:.2f}%\"\n",
    "log[\"true_negative\"]=f\"{conf_mat_p[1,1]:.2f}%\"\n",
    "\n",
    "precision = conf_mat[0,0]/(conf_mat[0,0]+conf_mat[1,0])\n",
    "recall = conf_mat[0,0]/(conf_mat[0,0]+conf_mat[0,1])\n",
    "log[\"precision\"]=precision\n",
    "log[\"recall\"]=recall\n",
    "log[\"F1\"] = (2*precision*recall)/(precision+recall)\n",
    "\n",
    "descriptions = np.array([[\"True Positive\", \"False Negative\"], [\"False Positive\", \"True Negatives\"]])\n",
    "colors = np.array([[\"green\", \"red\"], [\"red\", \"green\"]])\n",
    "\n",
    "plt.imshow([[0,0],[0,0]], interpolation='nearest', cmap=plt.cm.Greys)\n",
    "for i, j in itertools.product(range(conf_mat_p.shape[0]), range(conf_mat_p.shape[1])): \n",
    "    plt.text(j, i,\n",
    "             format(conf_mat[i, j], \",\")+'\\n'+\n",
    "             format(conf_mat_p[i, j], \".2f\")+'%\\n'+\n",
    "             descriptions[i, j], horizontalalignment=\"center\", verticalalignment=\"center\",\n",
    "             color=colors[i,j])\n",
    "plt.axhline(y=0.5, xmin=0, xmax=1, color=\"black\", linewidth=0.75)\n",
    "plt.axvline(x=0.5, ymin=0, ymax=1, color=\"black\", linewidth=0.75)\n",
    "plt.ylabel('True')\n",
    "plt.xlabel('Predicted')\n",
    "# plt.title(\"Confusion Matrix\")\n",
    "plt.xticks([0,1], [1,0])\n",
    "plt.yticks([0,1], [1,0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision @ K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision @ 10: 0.34191752705560396 (calculated using the Microsoft Evaluation method)\n",
      "Note: The closer to 1 the better.\n"
     ]
    }
   ],
   "source": [
    "precision_at_k_mse = precision_at_k(df_true,df_pred)\n",
    "print(f\"Precision @ {DEFAULT_K}: {precision_at_k_mse} {MSE}\")\n",
    "print(\"Note: The closer to 1 the better.\")\n",
    "log[f\"precision-at-{DEFAULT_K}\"]=precision_at_k_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recall @ K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall @ 10: 0.9668412602660089 (calculated using the Microsoft Evaluation method)\n",
      "Note: The closer to 1 the better.\n"
     ]
    }
   ],
   "source": [
    "recall_at_k_mse = recall_at_k(df_true,df_pred)\n",
    "print(f\"Recall @ {DEFAULT_K}: {recall_at_k_mse} {MSE}\")\n",
    "print(\"Note: The closer to 1 the better.\")\n",
    "log[f\"recall-at-{DEFAULT_K}\"]=recall_at_k_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### normalized Discounted Cumulative Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalized Discounted Cumulative Gain (nDCG@10): 1.0 (calculated using the Microsoft Evaluation method)\n",
      "Note: The closer to 1 the better.\n"
     ]
    }
   ],
   "source": [
    "ndcg_mse = ndcg_at_k(df_true,df_pred)\n",
    "print(f\"normalized Discounted Cumulative Gain (nDCG@{DEFAULT_K}): {ndcg_mse} {MSE}\")\n",
    "print(\"Note: The closer to 1 the better.\")\n",
    "log[f\"ndcg-at-{DEFAULT_K}\"]=ndcg_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mAP (mean Average Precision) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean Average Precision (mAP@10): 0.9668412602660089 (calculated using the Microsoft Evaluation method)\n",
      "Note: The closer to 1 the better.\n"
     ]
    }
   ],
   "source": [
    "map_mse = map_at_k(df_true,df_pred)\n",
    "print(f\"mean Average Precision (mAP@{DEFAULT_K}): {map_mse} {MSE}\")\n",
    "print(\"Note: The closer to 1 the better.\")\n",
    "log[f\"map-at-{DEFAULT_K}\"]=map_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mse                0.076726\n",
       "rmse               0.276995\n",
       "mae                0.185018\n",
       "r-squared         -0.086198\n",
       "exp_var           -0.086193\n",
       "auc                0.799697\n",
       "logloss            0.611715\n",
       "true_positive        30.53%\n",
       "false_negative       69.47%\n",
       "false_positive        4.04%\n",
       "true_negative        95.96%\n",
       "precision          0.572273\n",
       "recall             0.305264\n",
       "F1                 0.398147\n",
       "precision-at-10    0.341918\n",
       "recall-at-10       0.966841\n",
       "ndcg-at-10         1.000000\n",
       "map-at-10          0.966841\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 181.81 seconds\n"
     ]
    }
   ],
   "source": [
    "print(f\"Elapsed time: {time.time()-start_time:.2f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results saved to: C:\\TensorLogs\\AdamUXML-b\\test-results.csv\n"
     ]
    }
   ],
   "source": [
    "log.to_csv(TEST_RESULTS_PATH, index = True, header=False)\n",
    "print(f\"Results saved to: {TEST_RESULTS_PATH}\")"
   ]
  }
 ],
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