test_sklearn_knneighbours_(3_brand).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas\n",
    "from math import sqrt\n",
    "from brand_lists import *\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.set_option('display.max_rows', 15)\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# scikit-learn modules\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics \n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "#caricamento dei due dataframe (True Gold Standard & USERS-TAGS table)\n",
    "user_tags_values = pd.read_csv('final_results/users_tags_values.csv')\n",
    "\n",
    "#SELEZIONE DEGLI UTENTI DA ANALIZZARE CON IL KNN (SCEGLIERE UNO DEI TRE)\n",
    "#gold_standard = pd.read_excel('final_results/true_gold_standard.xlsx')       #SOLO UTENTI FOLLOWERS DELLE PAGINE UFFICIALI\n",
    "gold_standard = pd.read_excel('final_results/sentiment_gold_standard.xlsx')    #SOLO UTENTI ETICHETTATI CON LA SENTIMENT ANALYSIS\n",
    "#gold_standard = pd.read_excel('final_results/strong_gold_standard.xlsx')      #UTENTI INTERSEZIONE TRA FOLLOWERS E ETICHETTATI CON LA SENTIMENT ANALYSIS\n",
    "\n",
    "\n",
    "#lista dei tags in ordine del dataframe \"user_tags_values\"\n",
    "all_tags_in_order = user_tags_values.drop(columns='User').columns.to_list()\n",
    "\n",
    "#Lista degli utenti del gold standard\n",
    "users_list = np.unique(gold_standard['User'].to_list())\n",
    "\n",
    "#Raggruppamento dei due dataframe in dizionari per facilitare la ricerca degli utenti/etichette interessati\n",
    "dict_1 = dict(tuple(gold_standard.groupby('Brand')))  #raggruppa gli utenti del gold standard per Brand\n",
    "dict_2 = dict(tuple(user_tags_values.groupby('User')))     #raggruppa gli utenti di USERS-TAGS table per VETTORI\n",
    "dict_3 = dict(tuple(gold_standard.groupby('User')))   #raggruppa gli utenti del gold standard per User\n",
    "\n",
    "#Restituisce il vettore di un utente\n",
    "def get_user_vector(user):\n",
    "    user_values = dict_2.get(user)                     #riga dei valori di sentiment\n",
    "    vector = []\n",
    "    for tag in all_brands_tags:                     #per ogni tag\n",
    "        vector.append(user_values[tag].values[0])   #copia il valore letto   \n",
    "    return np.array(vector)\n",
    "\n",
    "#Restituisce la/le classe/i di un utente\n",
    "def get_user_class(user):\n",
    "    return dict_3.get(user)['Brand'].to_list()\n",
    "\n",
    "#crea un dataframe di vettori etichettati\n",
    "def make_df():\n",
    "    cols = all_tags_in_order.copy()\n",
    "    cols.append('label')\n",
    "    tmp = pd.DataFrame(columns=cols)\n",
    "    for user in users_list:\n",
    "        v = get_user_vector(user)\n",
    "        for label in get_user_class(user):\n",
    "            row = v.tolist()\n",
    "            row.append(label)\n",
    "            tmp.loc[len(tmp)] = row\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>clothing</th>\n",
       "      <th>computer</th>\n",
       "      <th>fashion</th>\n",
       "      <th>home</th>\n",
       "      <th>italian</th>\n",
       "      <th>office</th>\n",
       "      <th>pasta</th>\n",
       "      <th>sporty</th>\n",
       "      <th>technology</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     clothing  computer  fashion  home  italian  office  pasta  sporty  \\\n",
       "2         1.0       0.5      1.0   0.5      0.5     0.5    0.5     0.5   \n",
       "5         0.5       0.5      1.0   1.0      1.0     0.5    1.0     0.5   \n",
       "9         0.5       0.5      0.5   1.0      0.5     0.5    1.0     0.5   \n",
       "13        0.5       0.5      0.5   1.0      1.0     0.5    0.5     0.5   \n",
       "20        1.0       0.5      0.5   1.0      0.5     1.0    0.5     1.0   \n",
       "..        ...       ...      ...   ...      ...     ...    ...     ...   \n",
       "318       0.5       0.5      0.5   0.5      0.5     1.0    0.5     0.5   \n",
       "319       0.5       0.5      0.5   1.0      0.5     0.5    1.0     0.5   \n",
       "323       1.0       1.0      0.0   1.0      0.5     0.5    0.5     0.5   \n",
       "325       0.0       0.5      1.0   1.0      1.0     0.0    0.5     1.0   \n",
       "328       0.5       0.5      0.5   1.0      1.0     0.5    0.5     1.0   \n",
       "\n",
       "     technology      label  \n",
       "2           0.5     adidas  \n",
       "5           1.0    barilla  \n",
       "9           0.5    barilla  \n",
       "13          0.5    barilla  \n",
       "20          0.5     adidas  \n",
       "..          ...        ...  \n",
       "318         1.0  microsoft  \n",
       "319         0.5    barilla  \n",
       "323         1.0  microsoft  \n",
       "325         0.5    barilla  \n",
       "328         0.5    barilla  \n",
       "\n",
       "[101 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DATAFRAME CON SOLO I BRAND: MICROSOFT - BARILLA - ADIDAS\n",
    "#sono inclusi i soli tag di questi tre brand\n",
    "\n",
    "df = make_df()\n",
    "brand = ['alfaromeo','samsung','lavazza','armani','ferrero','delonghi','amarelli']\n",
    "tag = ['baby','car','chocolate',\n",
    "            'coffee','household appliances','licorice',\n",
    "            'scent']\n",
    "\n",
    "for b in brand:\n",
    "    df = df.drop(df[df['label'] == b].index)\n",
    "for t in tag:\n",
    "    df = df.drop(t, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CREAZIONE DEI DATI PER L'ANALIDI DEL KNN\n",
    "\n",
    "features = ['clothing','computer','fashion',\n",
    "            'home', 'italian', 'office',\n",
    "            'pasta','technology','sporty']\n",
    "\n",
    "\n",
    "#VARIABILE x = I VETTORI\n",
    "#VARIABILE y = LE ETICHETTE \n",
    "x = df[features]\n",
    "y = df['label']\n",
    "\n",
    "# Splitting del dataset in training e test (80% training - 20% set)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ESECUZIONE DEL KNN PER DIVERSI K\n",
    "#Mostra il tasso di errore\n",
    "\n",
    "error_rate = []\n",
    "k_min = 1\n",
    "k_max = 51\n",
    "for i in range(k_min,k_max):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x_train,y_train)\n",
    "    pred_i = knn.predict(x_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(range(k_min,k_max),error_rate,color='blue', linestyle='dashed', marker='o', markerfacecolor='red', markersize=5)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.xticks(range(k_min,k_max))\n",
    "plt.ylabel('Error Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report (K = 5 )\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      adidas       0.80      0.67      0.73         6\n",
      "     barilla       0.89      0.89      0.89         9\n",
      "   microsoft       0.86      1.00      0.92         6\n",
      "\n",
      "    accuracy                           0.86        21\n",
      "   macro avg       0.85      0.85      0.85        21\n",
      "weighted avg       0.85      0.86      0.85        21\n",
      "\n",
      "KNN Classification Accuracy of the model (K = 5 ): 85.71%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ESECUZIONE DEK KNN\n",
    "\n",
    "#valore di K \n",
    "K = 5\n",
    "\n",
    "# Fitting KNN Classifier to the Training set\n",
    "model = KNeighborsClassifier(n_neighbors = K, metric = 'euclidean', p = 2)\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# Predicting the results\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "# Classification Report\n",
    "print(\"\\nClassification Report (K =\",K,\")\")\n",
    "report = classification_report(y_test, y_pred,zero_division=1)\n",
    "print(report)\n",
    "\n",
    "# Accuracy of the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('KNN Classification Accuracy of the model (K =',K,'): {:.2f}%'.format(accuracy*100))\n",
    "\n",
    "#Confusion matrix\n",
    "confusionmatrix = confusion_matrix(y_test, y_pred)\n",
    "fig, ax = plt.subplots()\n",
    "sns.heatmap(confusionmatrix, annot=True, linewidth=3, cmap='viridis')\n",
    "plt.xlabel(\"Confusion Matrix\", fontsize=18, labelpad=20)\n",
    "ax.xaxis.tick_top()\n",
    "plt.ylabel(\"True\", fontsize=14, rotation=0, labelpad=30)\n",
    "plt.yticks(rotation=0)\n",
    "plt.title(\"Predicted\", fontsize=14, pad=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>label</th>\n",
       "      <th>Predicted-label (K=5)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>282</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>292</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>308</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>225</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>224</td>\n",
       "      <td>adidas</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>150</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>190</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>196</td>\n",
       "      <td>adidas</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>163</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "      <td>adidas</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>adidas</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>178</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>133</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>250</td>\n",
       "      <td>barilla</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67</td>\n",
       "      <td>adidas</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>243</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>188</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>39</td>\n",
       "      <td>adidas</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index      label Predicted-label (K=5)\n",
       "0     282    barilla               barilla\n",
       "1     292  microsoft             microsoft\n",
       "2     308    barilla               barilla\n",
       "3     102    barilla               barilla\n",
       "4     225    barilla               barilla\n",
       "5     224     adidas               barilla\n",
       "6      22  microsoft             microsoft\n",
       "7      68  microsoft             microsoft\n",
       "8     150    barilla               barilla\n",
       "9     190  microsoft             microsoft\n",
       "10    196     adidas                adidas\n",
       "11    163  microsoft             microsoft\n",
       "12     44     adidas                adidas\n",
       "13      2     adidas                adidas\n",
       "14    178  microsoft             microsoft\n",
       "15    133    barilla               barilla\n",
       "16    250    barilla                adidas\n",
       "17     67     adidas             microsoft\n",
       "18    243    barilla               barilla\n",
       "19    188    barilla               barilla\n",
       "20     39     adidas                adidas"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.set_option('display.max_rows', 30)\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "col_name = 'Predicted-label (K='+str(K)+\")\"\n",
    "predictions = pd.DataFrame({'label': y_test, col_name : y_pred}).reset_index()\n",
    "predictions.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>index</th>\n",
       "      <th>User</th>\n",
       "      <th>label</th>\n",
       "      <th>Predicted-label (K=5)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>21Phylk</td>\n",
       "      <td>adidas</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>CLHNews</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>ColU_CF</td>\n",
       "      <td>adidas</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Davey_Mitchell</td>\n",
       "      <td>adidas</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>FirstMediaTeam</td>\n",
       "      <td>adidas</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68</td>\n",
       "      <td>FirstMediaTeam</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>102</td>\n",
       "      <td>JPGirl3</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>133</td>\n",
       "      <td>MarkPerozziello</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>150</td>\n",
       "      <td>MuseoArpaSalvi</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>163</td>\n",
       "      <td>PrecisionProco</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>178</td>\n",
       "      <td>SheridanDesign</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>188</td>\n",
       "      <td>TheChilipepperL</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>190</td>\n",
       "      <td>TheCorbino</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>196</td>\n",
       "      <td>Travelooney</td>\n",
       "      <td>adidas</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>224</td>\n",
       "      <td>cutelilpuppytv</td>\n",
       "      <td>adidas</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>225</td>\n",
       "      <td>cutelilpuppytv</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>243</td>\n",
       "      <td>espressofied</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>250</td>\n",
       "      <td>fondpirelli</td>\n",
       "      <td>barilla</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>282</td>\n",
       "      <td>leviedellaperla</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>292</td>\n",
       "      <td>mollyrperk</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>308</td>\n",
       "      <td>saverio825</td>\n",
       "      <td>barilla</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index             User      label Predicted-label (K=5)\n",
       "0       2          21Phylk     adidas                adidas\n",
       "1      22          CLHNews  microsoft             microsoft\n",
       "2      39          ColU_CF     adidas                adidas\n",
       "3      44   Davey_Mitchell     adidas                adidas\n",
       "4      67   FirstMediaTeam     adidas             microsoft\n",
       "5      68   FirstMediaTeam  microsoft             microsoft\n",
       "6     102          JPGirl3    barilla               barilla\n",
       "7     133  MarkPerozziello    barilla               barilla\n",
       "8     150   MuseoArpaSalvi    barilla               barilla\n",
       "9     163   PrecisionProco  microsoft             microsoft\n",
       "10    178   SheridanDesign  microsoft             microsoft\n",
       "11    188  TheChilipepperL    barilla               barilla\n",
       "12    190       TheCorbino  microsoft             microsoft\n",
       "13    196      Travelooney     adidas                adidas\n",
       "14    224   cutelilpuppytv     adidas               barilla\n",
       "15    225   cutelilpuppytv    barilla               barilla\n",
       "16    243     espressofied    barilla               barilla\n",
       "17    250      fondpirelli    barilla                adidas\n",
       "18    282  leviedellaperla    barilla               barilla\n",
       "19    292       mollyrperk  microsoft             microsoft\n",
       "20    308       saverio825    barilla               barilla"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TABELLA DI COMPARAZIONE\n",
    "\n",
    "compare_df = df\n",
    "compare_df = compare_df.reset_index()\n",
    "user_df = gold_standard['User'].reset_index()\n",
    "compare_df = compare_df.merge(user_df, on=[\"index\"])\n",
    "compare_df = compare_df.merge(predictions, on=[\"index\",\"label\"])\n",
    "\n",
    "predict_col_name = compare_df.iloc[:,-1:].columns[0]\n",
    "compare_df = compare_df[['index','User',\n",
    "                        'clothing','computer','fashion',\n",
    "                        'home', 'italian', 'office',\n",
    "                        'pasta','technology','sporty',\n",
    "                        'label',predict_col_name]]\n",
    "\n",
    "clean_compare_df = compare_df[['index','User','label',predict_col_name]]\n",
    "\n",
    "#SALVA I RISULTATI\n",
    "#clean_compare_df.to_excel('final_results/sklearn_knn_predictions_compatc.xlsx',index=False)\n",
    "#compare_df.to_excel('final_results/sklearn_knn_predictions.xlsx',index=False)\n",
    "\n",
    "clean_compare_df"
   ]
  }
 ],
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