analysis/12_fragsys_ML_test_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TESTING KNN, AND ANN MODELS FOR THE RSA CLUSTER LABEL PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we select the best ANN model out of 500 models (50 randomly initiliased models from 10 different seeds) and test this model, as well as the KNN on the blind set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTING NECESSARY PACKAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-22 10:25:30.552725: 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:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "from fragsys_ml import *\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.proportion import proportion_confint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### READING INPUT DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_dir = \"./../\"\n",
    "results_dir = os.path.join(main_dir, \"results\")\n",
    "figs_dir = os.path.join(main_dir, \"figs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsa_cluster_dict = load_pickle(os.path.join(results_dir, \"rsa_cluster_dict.pkl\"))\n",
    "cluster_membership = load_pickle(os.path.join(results_dir, \"rsa_cluster_membership.pkl\"))\n",
    "rsa_profs = load_pickle(os.path.join(results_dir, \"rsa_profs.pkl\"))\n",
    "ud_mat = pd.read_pickle(os.path.join(results_dir, \"UD_df.pkl\"))\n",
    "vector_df = pd.read_pickle(os.path.join(results_dir, \"rsa_vectors.pkl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H0Y4R8_0_BS0</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O43809_0_BS0</th>\n",
       "      <td>0.600</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O43809_0_BS1</th>\n",
       "      <td>0.325</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0      1      2      3      4      5      6      7      8   \\\n",
       "H0Y4R8_0_BS0  0.150  0.000  0.000  0.167  0.167  0.333  0.000  0.167  0.167   \n",
       "O43809_0_BS0  0.600  0.375  0.250  0.083  0.167  0.042  0.042  0.000  0.042   \n",
       "O43809_0_BS1  0.325  0.231  0.077  0.231  0.077  0.077  0.077  0.077  0.154   \n",
       "\n",
       "               9    10  \n",
       "H0Y4R8_0_BS0  0.0  0.0  \n",
       "O43809_0_BS0  0.0  0.0  \n",
       "O43809_0_BS1  0.0  0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(293, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CREATING LABEL DATAFRAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_df = pd.DataFrame.from_dict(rsa_cluster_dict, orient = \"index\", columns = [\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_df = label_df - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.16\n",
       "1    0.43\n",
       "2    0.31\n",
       "3    0.10\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(label_df.label.value_counts().sort_index()/len(label_df),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(293, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vector_df.sort_index() # here, X is still a dataframe. Thus, we maintain binding site IDs\n",
    "y = label_df.sort_index()    # here, y is still a list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA SPLIT IN MODEL (10/11) (USED FOR TRAINING) AND BLIND (USED FOR FINAL TESTING) (1/11) STRATIFYING BY CLUSTER LABEL, SO THEY ARE BALANCED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_model, X_blind, y_model, y_blind = train_test_split(X, y, test_size = 0.091, random_state = 12345, shuffle = True, stratify = y) ### X_blind and y_blind will be the blind test at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X.index) == list(y.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dump_pickle(y_blind.index.tolist(), os.path.join(results_dir, \"x_blind.pkl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_model_df, X_blind_df, y_model_df, y_blind_df = train_test_split(X, y, test_size = 0.091, random_state = 12345, shuffle = True, stratify = y) ### X_blind and y_blind will be the blind test at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(266, 11)\n",
      "(27, 11)\n",
      "(266, 1)\n",
      "(27, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_model_df.shape)\n",
    "print(X_blind_df.shape)\n",
    "print(y_model_df.shape)\n",
    "print(y_blind_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.16\n",
       "1    0.43\n",
       "2    0.31\n",
       "3    0.10\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(y_model.label.value_counts().sort_index()/len(y_model),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.15\n",
       "1    0.44\n",
       "2    0.30\n",
       "3    0.11\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(y_blind.label.value_counts().sort_index()/len(y_blind),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANALYSIS OF DIFFERENT SEEDS AND BEST MODEL SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "wd = os.path.join(main_dir, \"ANN_results/diff_seeds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "seeds = os.listdir(wd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['99914', '12303', '27520', '63945', '277', '49757', '51094', '3716', '56724', '79619']\n"
     ]
    }
   ],
   "source": [
    "print(seeds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_acc_rows = []\n",
    "weird_runs = []\n",
    "for seed in seeds:\n",
    "\n",
    "    seed_dir = os.path.join(wd, str(seed))\n",
    "    hist_dir = os.path.join(seed_dir, \"hists\")\n",
    "    hist_files = os.listdir(hist_dir)\n",
    "    for hist in hist_files:\n",
    "        \n",
    "        hist_df = pd.read_csv(os.path.join(hist_dir, hist))\n",
    "        data = hist.split(\"_\")\n",
    "        n = int(data[1])\n",
    "        f_epoch = int(data[4])\n",
    "        hist_df[\"seed\"] = seed\n",
    "        hist_df[\"n\"] = n\n",
    "        if f_epoch == -100:\n",
    "            weird_runs.append(os.path.join(hist_dir, hist))\n",
    "            f_epoch = 299\n",
    "\n",
    "        acc = hist_df.loc[f_epoch,\"val_accuracy\"]\n",
    "        best_acc_rows.append(hist_df.loc[f_epoch, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_models = pd.DataFrame(best_acc_rows)\n",
    "best_models[\"epoch\"] = best_models.index\n",
    "best_models.seed = best_models.seed.astype(int)\n",
    "best_models.n = best_models.n.astype(int)\n",
    "best_models.epoch = best_models.epoch.astype(int)\n",
    "best_models = best_models.sort_values([\"seed\", \"n\"])\n",
    "best_models.index = list(range(len(best_models)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COMPARISON BETWEEN DIFFERENT SEED MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models.epoch.median() # median of epoch in which validation accuracy is best, weights of the model come from this epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(best_models.query('epoch < 20'))/len(best_models)*100 # no models stop training before epoch 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of final training epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(best_models.epoch, stat = \"proportion\")\n",
    "plt.xlim(-5, 170)\n",
    "plt.ylim(0,0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "      <th>seed</th>\n",
       "      <th>n</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.207761</td>\n",
       "      <td>0.952830</td>\n",
       "      <td>0.283437</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>277</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.198641</td>\n",
       "      <td>0.943396</td>\n",
       "      <td>0.279275</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>277</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.215212</td>\n",
       "      <td>0.948113</td>\n",
       "      <td>0.279875</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>277</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy  seed  n  epoch\n",
       "0  0.207761  0.952830  0.283437      0.962963   277  0     53\n",
       "1  0.198641  0.943396  0.279275      0.962963   277  1     55\n",
       "2  0.215212  0.948113  0.279875      0.962963   277  2     53"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_models.to_pickle(os.path.join(results_dir, \"ML_seed_models_df.pkl\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VALIDATION LOSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"seed\", y = \"val_loss\", data = best_models)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VALIDATION ACCURACY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"seed\", y = \"val_accuracy\", data = best_models)\n",
    "plt.ylim(0.86,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ALL TRAINING METRICS ACROSS SEEDS (N = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"variable\", y = \"value\", data = pd.melt(best_models.loc[:,['loss', 'val_loss', 'accuracy', 'val_accuracy']]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SELECTING BEST MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "      <th>seed</th>\n",
       "      <th>n</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>0.15766</td>\n",
       "      <td>0.957547</td>\n",
       "      <td>0.244619</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>79619</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loss  accuracy  val_loss  val_accuracy   seed  n  epoch\n",
       "402  0.15766  0.957547  0.244619      0.962963  79619  2     49"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models.query('seed == 79619 & n == 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "      <th>seed</th>\n",
       "      <th>n</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>0.157660</td>\n",
       "      <td>0.957547</td>\n",
       "      <td>0.244619</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>79619</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>0.180022</td>\n",
       "      <td>0.962264</td>\n",
       "      <td>0.246044</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>79619</td>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>0.165029</td>\n",
       "      <td>0.966981</td>\n",
       "      <td>0.249912</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>79619</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss  accuracy  val_loss  val_accuracy   seed   n  epoch\n",
       "402  0.157660  0.957547  0.244619      0.962963  79619   2     49\n",
       "443  0.180022  0.962264  0.246044      0.962963  79619  43     46\n",
       "427  0.165029  0.966981  0.249912      0.962963  79619  27     48"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models.query('val_accuracy > 0.95 & val_loss < 0.25').sort_values(by=[\"val_accuracy\", \"val_loss\", \"accuracy\", \"loss\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model chosen is the following: seed = 79619, n = 2, epoch = 49, val_accuracy = 0.96. This decision was made based on the models' validation accuracy, validation loss, and training accuracy, and loss in that order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ff = pd.read_csv(\"./../ANN_results/diff_seeds/79619/hists/79619_2_hist_epoch_49_train_acc_0.96.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BEST MODEL TRAINING VISUALISATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc(ff, 49)\n",
    "plt.show()\n",
    "plot_loss(ff, 49)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PREDICTING ON BLIND TEST SET WITH ANN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final ANN model presents an accuracy of 96.3%, with "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.963\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_path = \"./../ANN_results/diff_seeds/79619/models/79619_2_model_epoch_49_train_acc_0.96.h5\"\n",
    "final_model = keras.models.load_model(model_path)\n",
    "final_preds = final_model.predict(x = X_blind, batch_size = 27, verbose = 0)\n",
    "rounded_predictions = np.argmax(final_preds, axis = -1)\n",
    "cm = confusion_matrix(y_true = y_blind, y_pred = rounded_predictions, normalize = 'true')\n",
    "cm_display = ConfusionMatrixDisplay(cm).plot()\n",
    "blind_acc = sum(rounded_predictions == np.array(y_blind.label))/len(rounded_predictions)\n",
    "print(round(blind_acc, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "AMI = sklearn.metrics.adjusted_mutual_info_score(y_blind.label.tolist(), list(rounded_predictions), average_method='arithmetic') # range = [0, 1] (1 is best)\n",
    "\n",
    "ARI = sklearn.metrics.adjusted_rand_score(y_blind.label.tolist(), list(rounded_predictions)) # range = [-0.5, 1] (1 is best)\n",
    "\n",
    "NMI = sklearn.metrics.normalized_mutual_info_score(y_blind.label.tolist(), list(rounded_predictions)) # range = [0, 1] (1 is best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.963\n",
      "0.909\n",
      "0.932\n",
      "0.924\n"
     ]
    }
   ],
   "source": [
    "print(round(blind_acc, 3))\n",
    "\n",
    "print(round(AMI, 3))\n",
    "\n",
    "print(round(ARI, 3))\n",
    "\n",
    "print(round(NMI, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating ARI, and AMI 95% CI for ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ARI_CI(labels_true, labels_pred, alpha = 0.05):\n",
    "    \"\"\"\n",
    "    Grabbed it from https://github.com/benbo/AdjustedRandCI/tree/master, and edited as I could not make it work,\n",
    "    with the original code from:\n",
    "    Steinley, Douglas, Michael J. Brusco, and Lawrence Hubert. \"The variance of the adjusted Rand index.\" Psychological methods 21.2 (2016): 261.\n",
    "    \"\"\"\n",
    "    table = sklearn.metrics.cluster.contingency_matrix(labels_true, labels_pred).astype(float)\n",
    "\n",
    "    n = np.sum(table)\n",
    "    rm = np.sum(table, axis=0)\n",
    "    cm = np.sum(table, axis=1)\n",
    "    tsq = table**2\n",
    "    rmsq = rm**2\n",
    "    cmsq = cm**2\n",
    "    nsq = n**2\n",
    "\n",
    "    a = (np.sum(tsq) - n) / 2\n",
    "    b = (np.sum(rmsq) - np.sum(tsq)) / 2\n",
    "    c = (np.sum(cmsq) - np.sum(tsq)) / 2\n",
    "    d = (np.sum(tsq) + nsq - np.sum(rmsq) - np.sum(cmsq)) / 2\n",
    "    nc2 = comb(n, 2)\n",
    "\n",
    "    ari = (nc2 * (a + d) - ((a + b) * (a + c) + (c + d) * (b + d))) / (nc2**2 - ((a + b) * (a + c) + (c + d) * (b + d)))\n",
    "\n",
    "    tcub = table**3\n",
    "    rmcub = rm**3\n",
    "    cmcub = cm**3\n",
    "\n",
    "    e = 2 * np.sum(rmsq) - n * (n + 1)\n",
    "    f = 2 * np.sum(cmsq) - n * (n + 1)\n",
    "    g = 4 * np.sum(rmcub) - 4 * (n + 1) * np.sum(rmsq) + n * (n + 1)**2\n",
    "    h = n * (n - 1)\n",
    "    i = 4 * np.sum(cmcub) - 4 * (n + 1) * np.sum(cmsq) + n * (n + 1)**2\n",
    "\n",
    "    vad = (1 / 16) * (2 * n * (n - 1) - ((e * f) / (n * (n - 1)))**2 + (4 * (g - h) * (i - h)) / (n * (n - 1) * (n - 2)) + ((e**2 - 4 * g + 2 * h) * (f**2 - 4 * i + 2 * h)) / (n * (n - 1) * (n - 2) * (n - 3)))\n",
    "\n",
    "    vari = (nc2**2 * vad) / (nc2**2 - ((a + b) * (a + c) + (c + d) * (b + d)))**2\n",
    "\n",
    "    r1, c1 = table.shape\n",
    "    t = table\n",
    "    t1 = np.zeros((r1, c1))\n",
    "    t2 = np.zeros((r1, c1))\n",
    "\n",
    "    for i in range(r1):\n",
    "        for j in range(c1):\n",
    "            t1[i, j] = t[i, j] * (2 * t[i, j] - (rm[j] + cm[i]))**2\n",
    "            t2[i, j] = t[i, j] * (2 * t[i, j] - (rm[j] + cm[i]))\n",
    "\n",
    "    vargam = ((2 / n)**4) * (np.sum(t1) - (1 / n) * (np.sum(t2))**2)\n",
    "    varrand = (1 / 16) * ((n * (n - 1))**2) * vargam\n",
    "    vari_multi = (nc2**2 * varrand) / (nc2**2 - ((a + b) * (a + c) + (c + d) * (b + d)))**2\n",
    "\n",
    "    ari_std = np.sqrt(vari_multi)\n",
    "\n",
    "    sum_comb_c = sum(comb(n_c, 2) for n_c in np.ravel(table.sum(axis=1)))\n",
    "    sum_comb_k = sum(comb(n_k, 2) for n_k in np.ravel(table.sum(axis=0)))\n",
    "    sum_comb = sum(comb(n_ij, 2) for n_ij in np.ravel(table))\n",
    "\n",
    "    prod_comb = (sum_comb_c * sum_comb_k) / nc2\n",
    "    mean_comb = (sum_comb_k + sum_comb_c) / 2.0\n",
    "\n",
    "    #compute ari\n",
    "    ari = (sum_comb - prod_comb) / (mean_comb - prod_comb)\n",
    "    #compute CI\n",
    "    qnrm = norm.ppf(1.0-alpha/2.0)\n",
    "    lowerci = ari - qnrm*ari_std\n",
    "    upperci = ari + qnrm*ari_std\n",
    "    if upperci > 1.0:\n",
    "        upperci = 1.0\n",
    "    \n",
    "    return ari, lowerci, upperci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_resampling(labels_true, labels_pred, n_resamples=10000):\n",
    "    ari_scores = []\n",
    "    ami_scores = []\n",
    "    nmi_scores = []\n",
    "    for _ in range(n_resamples):\n",
    "        # Resample with replacement\n",
    "        indices = np.random.choice(range(len(labels_true)), size=len(labels_true), replace=True)\n",
    "        resampled_true = labels_true[indices]\n",
    "        resampled_pred = labels_pred[indices]\n",
    "\n",
    "        # Calculate ARI and AMI for resampled data\n",
    "        ari_scores.append(sklearn.metrics.adjusted_rand_score(resampled_true, resampled_pred))\n",
    "        ami_scores.append(sklearn.metrics.adjusted_mutual_info_score(resampled_true, resampled_pred))\n",
    "        nmi_scores.append(sklearn.metrics.normalized_mutual_info_score(resampled_true, resampled_pred))\n",
    "\n",
    "    return ari_scores, ami_scores, nmi_scores\n",
    "\n",
    "labels_true = y_blind.label\n",
    "labels_pred = rounded_predictions\n",
    "\n",
    "# Calculate the bootstrap samples\n",
    "ari_bootstrap, ami_bootstrap, nmi_bootstrap = bootstrap_resampling(labels_true, labels_pred)\n",
    "\n",
    "# Calculate confidence intervals\n",
    "ari_lower, ari_upper = np.percentile(ari_bootstrap, [2.5, 97.5])\n",
    "ami_lower, ami_upper = np.percentile(ami_bootstrap, [2.5, 97.5])\n",
    "nmi_lower, nmi_upper = np.percentile(nmi_bootstrap, [2.5, 97.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARI = 0.93. 95% CI = [0.81, 1.0]\n"
     ]
    }
   ],
   "source": [
    "ari, ari_low, ari_upp = get_ARI_CI(labels_true, labels_pred)\n",
    "print(\"ARI = {}. 95% CI = [{}, {}]\".format(round(ari, 2), round(ari_low, 2), round(ari_upp, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.77 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.81 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.84 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(round(ari_lower, 2), round(ari_upper, 2))\n",
    "plt.title(\"Distribution of bootstrapped ARI\")\n",
    "sns.histplot(ari_bootstrap, stat=\"proportion\", bins = 25)\n",
    "plt.axvline(x = ari_lower, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.axvline(x = ari_upper, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.xlabel(\"Adjusted Rand Index (ARI)\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(round(ami_lower, 2), round(ami_upper, 2))\n",
    "plt.title(\"Distribution of bootstrapped AMI\")\n",
    "sns.histplot(ami_bootstrap, stat=\"proportion\", bins = 25, color = \"firebrick\")\n",
    "plt.axvline(x = ami_lower, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.axvline(x = ami_upper, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.xlabel(\"Adjusted Mutual Information (AMI)\")\n",
    "plt.show()\n",
    "\n",
    "print(round(nmi_lower, 2), round(nmi_upper, 2))\n",
    "plt.title(\"Distribution of bootstrapped NMI\")\n",
    "sns.histplot(nmi_bootstrap, stat=\"proportion\", bins = 25, color = \"forestgreen\")\n",
    "plt.axvline(x = nmi_lower, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.axvline(x = nmi_upper, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.xlabel(\"Normalised Mutual Information (NMI)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confidence interval of ANN accuracry (proportion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96\n",
      "0.82 0.99\n"
     ]
    }
   ],
   "source": [
    "lw, up = proportion_confint(count = 26,    # Number of \"successes\"\n",
    "                   nobs = 27,    # Number of trials\n",
    "                   alpha = (1 - 0.95),\n",
    "                  method = \"wilson\")\n",
    "print(round(blind_acc, 2))\n",
    "print(round(lw, 2), round(up,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ANN_pred_df = pd.DataFrame(np.around(final_preds,2))\n",
    "ANN_pred_df[\"pred\"] = rounded_predictions\n",
    "ANN_pred_df[\"real\"] = y_blind.label.tolist()\n",
    "ANN_pred_df.index = y_blind_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>pred</th>\n",
       "      <th>real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q32ZE1_0_BS3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0    1     2     3  pred  real\n",
       "Q32ZE1_0_BS3  0.0  0.0  0.67  0.32     2     3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ANN_pred_df[ANN_pred_df.pred != ANN_pred_df.real]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q32ZE1_0_BS3']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ANN_pred_df[ANN_pred_df.pred != ANN_pred_df.real].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "dump_pickle(ANN_pred_df[ANN_pred_df.pred != ANN_pred_df.real].index.tolist(), os.path.join(results_dir, \"x_wrong_ANN.pkl\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confidence in ANN blind set predictions (N = 27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_good_df1, conf_good_df2 = get_confidences_df_blind(final_preds, rounded_predictions, y_blind.label)\n",
    "\n",
    "plot_conf_acc_cov(conf_good_df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PREDICTING ON BLIND TEST SET WITH KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trying different number of K nearest neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "knn_acc_dict = {}\n",
    "for i in range(1,25):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(ud_mat.loc[X_model.index, X_model.index], y_model)\n",
    "    #Predict the response for test dataset\n",
    "    y_pred_KNN = knn.predict(ud_mat.loc[X_blind.index, X_model.index])\n",
    "    acc_pred = metrics.accuracy_score(y_blind, y_pred_KNN)\n",
    "    blind_acc_KNN = sum(y_pred_KNN == y_blind.label)/len(y_pred_KNN)\n",
    "    knn_acc_dict[i] = round(blind_acc_KNN, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(knn_acc_dict.keys(), knn_acc_dict.values())\n",
    "plt.ylim(0.75, 1.02)\n",
    "plt.xlabel(\"K (number of nearest neighbours)\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 266)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ud_mat.loc[X_model.index,X_model.index].shape # used for training: square distance matrix of 266 training binding sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 266)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ud_mat.loc[X_blind.index, X_model.index].shape # used for testing: distance between the 27 test sites to the 266 sites used for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cluster/gjb_lab/2394007/miniconda/envs/deep_learning/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "knn.fit(ud_mat.loc[X_model.index, X_model.index], y_model)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred_KNN = knn.predict(ud_mat.loc[X_blind.index, X_model.index])\n",
    "\n",
    "acc_pred = metrics.accuracy_score(y_blind, y_pred_KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_KNN = confusion_matrix(y_true = y_blind, y_pred = y_pred_KNN, normalize = 'true')\n",
    "cm_display_KNN = ConfusionMatrixDisplay(cm_KNN).plot()\n",
    "blind_acc_KNN = sum(y_pred_KNN == y_blind.label)/len(y_pred_KNN)\n",
    "print(round(blind_acc_KNN, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "AMI_KNN = sklearn.metrics.adjusted_mutual_info_score(y_blind.label.tolist(), y_pred_KNN, average_method='arithmetic') # range = [0, 1] (1 is best)\n",
    "\n",
    "ARI_KNN = sklearn.metrics.adjusted_rand_score(y_blind.label.tolist(), y_pred_KNN) # range = [-0.5, 1] (1 is best)\n",
    "\n",
    "NMI_KNN = sklearn.metrics.normalized_mutual_info_score(y_blind.label.tolist(), y_pred_KNN) # range = [0, 1] (1 is best)\n",
    "\n",
    "print(round(blind_acc_KNN, 3))\n",
    "\n",
    "print(round(AMI_KNN, 3))\n",
    "\n",
    "print(round(ARI_KNN, 3))\n",
    "\n",
    "print(round(NMI_KNN, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CI of ARI and AMI for KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_true_KNN = y_blind.label\n",
    "labels_pred_KNN = y_pred_KNN\n",
    "\n",
    "# Calculate the bootstrap samples\n",
    "ari_bootstrap_KNN, ami_bootstrap_KNN = bootstrap_resampling(labels_true_KNN, labels_pred_KNN)\n",
    "\n",
    "# Calculate confidence intervals\n",
    "ari_lower_KNN, ari_upper_KNN = np.percentile(ari_bootstrap_KNN, [2.5, 97.5])\n",
    "ami_lower_KNN, ami_upper_KNN = np.percentile(ami_bootstrap_KNN, [2.5, 97.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(round(ari_lower_KNN, 2), round(ari_upper_KNN, 2))\n",
    "plt.title(\"Distribution of bootstrapped ARI for KNN predictions\")\n",
    "sns.histplot(ari_bootstrap_KNN, stat=\"proportion\", bins = 25)\n",
    "plt.axvline(x = ari_lower_KNN, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.axvline(x = ari_upper_KNN, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.xlabel(\"Adjusted Rand Index (ARI)\")\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"Distribution of bootstrapped AMI  for KNN predictions\")\n",
    "\n",
    "print(round(ami_lower_KNN, 2), round(ami_upper_KNN, 2))\n",
    "sns.histplot(ami_bootstrap_KNN, stat=\"proportion\", bins = 25, color = \"firebrick\")\n",
    "plt.axvline(x = ami_lower_KNN, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.axvline(x = ami_upper_KNN, c = \"k\", linestyle = \"--\", linewidth = 3)\n",
    "plt.xlabel(\"Adjusted Mutual Information (AMI)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 0, 1, 2, 2, 2, 3, 2, 2, 0, 1, 1, 0, 1, 1, 3, 1, 2, 0, 1, 3, 2, 2]\n",
      "[1, 1, 1, 1, 1, 0, 1, 2, 2, 2, 3, 2, 2, 0, 1, 1, 0, 1, 1, 3, 1, 2, 0, 1, 3, 2, 2]\n"
     ]
    }
   ],
   "source": [
    "print(labels_true_KNN.tolist())\n",
    "print(list(labels_pred_KNN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARI = 1.0. 95% CI = [1.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "ari_KNN, ari_low_KNN, ari_upp_KNN = get_ARI_CI(labels_true_KNN, labels_pred_KNN)\n",
    "print(\"ARI = {}. 95% CI = [{}, {}]\".format(round(ari_KNN, 2), round(ari_low_KNN, 2), round(ari_upp_KNN, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 1.0 1.0\n"
     ]
    }
   ],
   "source": [
    "print(ari_KNN, ari_low_KNN, ari_upp_KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.88 1.0\n"
     ]
    }
   ],
   "source": [
    "lw, up = proportion_confint(count = 27,    # Number of \"successes\"\n",
    "                   nobs = 27,    # Number of trials\n",
    "                   alpha = (1 - 0.95),\n",
    "                  method = \"wilson\")\n",
    "print(round(blind_acc_KNN, 2))\n",
    "print(round(lw, 2), round(up,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_pred_df = pd.DataFrame(list(zip(y_pred_KNN, y_blind.label.tolist())), columns = [\"pred\", \"real\"])\n",
    "KNN_pred_df.index = y_blind_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>pred</th>\n",
       "      <th>real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [pred, real]\n",
       "Index: []"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_pred_df[KNN_pred_df.pred != KNN_pred_df.real]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_pred_df[KNN_pred_df.pred != KNN_pred_df.real].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dump_pickle(KNN_pred_df[KNN_pred_df.pred != KNN_pred_df.real].index.tolist(), os.path.join(results_dir, \"x_wrong_KNN.pkl\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONCLUSIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of the ANN, and KNN models trained from fragsys are 26/27 = 0.96, 95% CI = [0.82, 0.99] for ANN, and 27/27 = 1.0, 95% CI = [0.88, 1.0] for KNN. The 95% CI for the two accuracies, overlap, so we can conclude there is no significant difference between the performance of the two models."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:deep_learning]",
   "language": "python",
   "name": "conda-env-deep_learning-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}