machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Connexion au serveur sur lequel la base est hébergée.\n",
    "!pip install pymongo \n",
    "import pymongo\n",
    "import urllib\n",
    "from pprint import pprint\n",
    "db_uri = \"mongodb://admin_if29:passwordIF29%23@13.38.0.254:27017/?authMechanism=DEFAULT\"\n",
    "client = pymongo.MongoClient(db_uri)\n",
    "print(client.list_database_names())\n",
    "base_db=client.small_tweets_database\n",
    "collec_co=base_db.small_tweets_grouped_by_user\n",
    "\n",
    "pprint(collec_co.find_one({}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Analyse', 'admin', 'config', 'local', 'small_tweets_database', 'test']\n",
      "dataset shape:  (267467, 8)\n",
      "Summary information on the dataset\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 267467 entries, 0 to 267466\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   follow_popularity         267467 non-null  float64\n",
      " 1   group_popularity          267467 non-null  float64\n",
      " 2   hashtags_freq             267467 non-null  float64\n",
      " 3   mentions_freq             267467 non-null  float64\n",
      " 4   url_freq                  267467 non-null  float64\n",
      " 5   date_of_creation_account  267467 non-null  float64\n",
      " 6   tweets_frequency          267467 non-null  float64\n",
      " 7   label                     267467 non-null  int64  \n",
      "dtypes: float64(7), int64(1)\n",
      "memory usage: 16.3 MB\n",
      "display NA values in each colums\n",
      "display NULL values in each colums\n",
      "remove line with NA\n",
      "(160480, 8)\n",
      "(106987, 8)\n",
      "(53493, 8)\n",
      "(53494, 8)\n",
      " Train dataset       : (160480, 8)\n",
      " Test dataset       : (53493, 8)\n",
      " Validation dataset : (53494, 8)\n",
      "predicted values: \n",
      "Accuracy: 0.9997382851445663\n",
      "Accuracy: 0.9997009010356301\n",
      "Accuracy: 0.9997943656179313\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1260x1260 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pymongo\n",
    "import matplotlib.pyplot as plt     #for plotting data and cerating different charts\n",
    "import numpy as np                  #for math and array\n",
    "import pandas as pd                 #data for the data\n",
    "import seaborn as sns               #for plotting\n",
    "\n",
    "#GET DATA FROM MONOGODB\n",
    "db_uri = \"mongodb://admin_if29:passwordIF29%23@13.38.0.254:27017/?authMechanism=DEFAULT\"\n",
    "client = pymongo.MongoClient(db_uri)\n",
    "print(client.list_database_names())\n",
    "base_db=client.small_tweets_database\n",
    "collec_co=base_db.small_tweets_final_with_labels_wo_verifiedBadge\n",
    "#convert entire collection to Pandas dataframe\n",
    "datas=pd.DataFrame(list(collec_co.find()))\n",
    "datas= datas.drop(columns='_id')\n",
    "\n",
    "print('dataset shape: ',datas.shape)\n",
    "print('Summary information on the dataset')\n",
    "datas.info()\n",
    "\n",
    "#CLEANING THE DATA\n",
    "print(\"display NA values in each colums\")\n",
    "datas.isna().sum(axis=0)\n",
    "print(\"display NULL values in each colums\")\n",
    "datas.isnull().sum()\n",
    "\n",
    "print(\"remove line with NA\")\n",
    "datas= datas.dropna()\n",
    "datas = datas\n",
    "\n",
    "datas= datas.sample(frac=1)     #Shuffle the datas to not be sorted\n",
    "\n",
    "#SPLIT THE DTATA INTO TRAIN / TEST SPLITS\n",
    "#Split the datas into 60% train and 40% test\n",
    "train_dataset, temp_test_dataset = train_test_split(datas, test_size=0.4)\n",
    "print(train_dataset.shape)\n",
    "print(temp_test_dataset.shape)\n",
    "\n",
    "#Split the test_dataset to 50% test and validation\n",
    "test_dataset, valid_dataset = train_test_split(temp_test_dataset, test_size=0.5)\n",
    "print(test_dataset.shape)\n",
    "print(valid_dataset.shape)\n",
    "\n",
    "#Resume the split's dataset\n",
    "print(f\" Train dataset       : {train_dataset.shape}\")\n",
    "print(f\" Test dataset       : {test_dataset.shape}\")\n",
    "print(f\" Validation dataset : {valid_dataset.shape}\")\n",
    "\n",
    "# Plot the relationship between each two variables to spot anything incorrect.\n",
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"label\")\n",
    "sns.pairplot(train_stats[train_stats.columns], diag_kind=\"kde\") # or diag_kind\n",
    "\n",
    "# Statistics on the train dataset to make sure it is in a good shape.\n",
    "# (Can display the same stat for test and validate)\n",
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"label\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats\n",
    "\n",
    "train_labels = train_dataset.pop('label')\n",
    "test_labels = test_dataset.pop('label')\n",
    "valid_labels = valid_dataset.pop('label')\n",
    "\n",
    "#DATA NORMALISATION / SCALING\n",
    "#Subtract the mean of the training data and divide\n",
    "# by the standard deviation of the training data.\n",
    "\n",
    "# define a function to normalize the data set.\n",
    "def norm(x):\n",
    "    return (x - train_stats['mean']) / train_stats['std']\n",
    "\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)\n",
    "normed_valid_dataset = norm(valid_dataset)\n",
    "\n",
    "# show a sample of the data after normalized\n",
    "normed_train_data.head(10)\n",
    "\n",
    "#TRAIN THE MODEL\n",
    "#Create a svm Classifier\n",
    "model = svm.SVC(C = 1, # reg paramater\n",
    "                kernel='linear', #kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}, default=’rbf’\n",
    ") # Linear Kernel\n",
    "\n",
    "#Train the model using the training sets\n",
    "model.fit(normed_train_data, train_labels)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = model.predict(normed_test_data)\n",
    "\n",
    "example_batch = normed_test_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "print('predicted values: ')\n",
    "example_result\n",
    "\n",
    "#See how the training went\n",
    "from sklearn import metrics\n",
    "y_pred = model.predict(normed_train_data)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(train_labels, y_pred))\n",
    "\n",
    "y_pred = model.predict(normed_valid_dataset)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(valid_labels, y_pred))\n",
    "\n",
    "y_pred = model.predict(normed_test_data)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(test_labels, y_pred))\n",
    "\n",
    "ax= plt.subplot()\n",
    "predict_results = model.predict(normed_test_data)\n",
    "\n",
    "cm = confusion_matrix(predict_results, predict_results)\n",
    "\n",
    "sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');\n",
    "ax.set_title('Confusion Matrix');\n",
    "# ax.xaxis.set_ticklabels(['Positive', 'Negative']); ax.yaxis.set_ticklabels(['Positive', 'Negative']);\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Protocol not known:  Relax &amp; Recap\\n\\nNightLife 24/7 Podcast {@NightLife24_7} S3-E83: FWB (Part 1)\\n\\nhttps",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12396/3145684836.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;31m#print(np.size(tweets))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tweets'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_id'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    205\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    206\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 207\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    208\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    209\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, encoding_errors, lines, chunksize, compression, nrows, storage_options)\u001b[0m\n\u001b[0;32m    588\u001b[0m         \u001b[0mconvert_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 590\u001b[1;33m     json_reader = JsonReader(\n\u001b[0m\u001b[0;32m    591\u001b[0m         \u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m         \u001b[0morient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath_or_buffer, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression, nrows, storage_options, encoding_errors)\u001b[0m\n\u001b[0;32m    673\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nrows can only be passed if lines=True\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    674\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 675\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_data_from_filepath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    676\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_get_data_from_filepath\u001b[1;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[0;32m    710\u001b[0m             \u001b[1;32mor\u001b[0m \u001b[0mfile_exists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    711\u001b[0m         ):\n\u001b[1;32m--> 712\u001b[1;33m             self.handles = get_handle(\n\u001b[0m\u001b[0;32m    713\u001b[0m                 \u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m                 \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    607\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[1;31m# open URLs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 609\u001b[1;33m     ioargs = _get_filepath_or_buffer(\n\u001b[0m\u001b[0;32m    610\u001b[0m         \u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    611\u001b[0m         \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m    356\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 358\u001b[1;33m             file_obj = fsspec.open(\n\u001b[0m\u001b[0;32m    359\u001b[0m                 \u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfsspec_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstorage_options\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m             ).open()\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\fsspec\\core.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(urlpath, mode, compression, encoding, errors, protocol, newline, **kwargs)\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mOpenFile\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    451\u001b[0m     \"\"\"\n\u001b[1;32m--> 452\u001b[1;33m     return open_files(\n\u001b[0m\u001b[0;32m    453\u001b[0m         \u001b[0murlpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0murlpath\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m         \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\fsspec\\core.py\u001b[0m in \u001b[0;36mopen_files\u001b[1;34m(urlpath, mode, compression, encoding, errors, name_function, num, protocol, newline, auto_mkdir, expand, **kwargs)\u001b[0m\n\u001b[0;32m    284\u001b[0m     \u001b[0mbe\u001b[0m \u001b[0mused\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0ma\u001b[0m \u001b[0msingle\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m     \"\"\"\n\u001b[1;32m--> 286\u001b[1;33m     fs, fs_token, paths = get_fs_token_paths(\n\u001b[0m\u001b[0;32m    287\u001b[0m         \u001b[0murlpath\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m         \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\fsspec\\core.py\u001b[0m in \u001b[0;36mget_fs_token_paths\u001b[1;34m(urlpath, mode, num, name_function, storage_options, protocol, expand)\u001b[0m\n\u001b[0;32m    592\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    593\u001b[0m         \u001b[0murlpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murlpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 594\u001b[1;33m     \u001b[0mchain\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_un_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murlpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstorage_options\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    595\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    596\u001b[0m         \u001b[0minkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\fsspec\\core.py\u001b[0m in \u001b[0;36m_un_chain\u001b[1;34m(path, kwargs)\u001b[0m\n\u001b[0;32m    316\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_un_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 318\u001b[1;33m         \u001b[0mbits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_un_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    319\u001b[0m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    320\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mpbit\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mbits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\fsspec\\core.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    316\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_un_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 318\u001b[1;33m         \u001b[0mbits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_un_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    319\u001b[0m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    320\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mpbit\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mbits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\fsspec\\core.py\u001b[0m in \u001b[0;36m_un_chain\u001b[1;34m(path, kwargs)\u001b[0m\n\u001b[0;32m    341\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mbit\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplit_protocol\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m\"file\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 343\u001b[1;33m         \u001b[0mcls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_filesystem_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    344\u001b[0m         \u001b[0mextra_kwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_kwargs_from_urls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    345\u001b[0m         \u001b[0mkws\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\louis\\anaconda3\\lib\\site-packages\\fsspec\\registry.py\u001b[0m in \u001b[0;36mget_filesystem_class\u001b[1;34m(protocol)\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mregistry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mknown_implementations\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Protocol not known: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m         \u001b[0mbit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mknown_implementations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Protocol not known:  Relax &amp; Recap\\n\\nNightLife 24/7 Podcast {@NightLife24_7} S3-E83: FWB (Part 1)\\n\\nhttps"
     ]
    }
   ],
   "source": [
    "from sklearn import cluster\n",
    "import pymongo\n",
    "import json\n",
    "import matplotlib.pyplot as plt     \n",
    "import numpy as np                  \n",
    "import pandas as pd                 \n",
    "from sklearn import preprocessing\n",
    "from sklearn.datasets import load_iris\n",
    "from bson.json_util import dumps\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "db_uri = \"mongodb://admin_if29:passwordIF29%23@13.38.0.254:27017/?authMechanism=DEFAULT\"\n",
    "client = pymongo.MongoClient(db_uri)\n",
    "#print(client.list_database_names())\n",
    "base_db=client.small_tweets_database\n",
    "collec_co=base_db.small_tweets_final_with_text\n",
    "\n",
    "cursor = collec_co.find()\n",
    "list_cur = list(cursor)\n",
    "json_data = dumps(list_cur)\n",
    "users = json.loads(json_data)\n",
    "#print(np.size(tweets))\n",
    "\n",
    "df = pd.read_json(json_data)\n",
    "df.drop('tweets', inplace=True, axis=1)\n",
    "df.drop('_id', inplace=True, axis=1)\n",
    "df.drop('verified_badge', inplace=True, axis=1)\n",
    "\n",
    "print(df.head())\n",
    "users = df.to_numpy()\n",
    "\n",
    "\n",
    "users_prep=preprocessing.scale(users)\n",
    "\n",
    "kmeans = cluster.KMeans(n_clusters=2)\n",
    "kmeans.fit(users_prep)\n",
    "#print(kmeans.inertia_)\n",
    "number_of_1 = [i for i in kmeans.labels_ if i == 1]\n",
    "number_of_0 = [i for i in kmeans.labels_ if i == 0]\n",
    "\n",
    "size_array = len(number_of_1)+len(number_of_0)\n",
    "\n",
    "print(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "715697084016541696\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# for i in range(len(df)):\n",
    "#     if(type(df['date_of_creation_account'][i]) == dict):\n",
    "#         print(df['_id'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (0.11.2)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from seaborn) (1.4.1)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from seaborn) (1.8.0)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from seaborn) (1.22.3)\n",
      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from seaborn) (3.5.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from matplotlib>=2.2->seaborn) (3.0.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from matplotlib>=2.2->seaborn) (1.4.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from matplotlib>=2.2->seaborn) (9.0.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from matplotlib>=2.2->seaborn) (4.31.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from matplotlib>=2.2->seaborn) (21.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pandas>=0.23->seaborn) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\alexa\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\Alexa\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "!pip install seaborn\n",
    "import seaborn as sns\n",
    " \n",
    "# generating correlation heatmap\n",
    "sns.heatmap(df.corr(), annot = True)\n",
    " \n",
    "# posting correlation heatmap to output console\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0         1\n",
      "10 -0.006612  0.615860\n",
      "11  0.857316 -0.951475\n",
      "12  0.057674  1.773004\n",
      "13  0.684026  1.089764\n",
      "14 -0.128365  0.188692\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(2)\n",
    "pca.fit(users_prep)\n",
    " \n",
    "pca_data = pd.DataFrame(pca.transform(users_prep))\n",
    " \n",
    "print(pca_data[10:15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors as mcolors\n",
    "import math\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "new_pca_data = pca_data\n",
    "cluster_colors = ('blue', 'red')\n",
    "#We insert label column in new_PCA_data if necessary\n",
    "if(not 'label' in new_pca_data.columns):\n",
    "      new_pca_data.insert(2,\"label\",kmeans.labels_)\n",
    "      \n",
    "#We zoom on the graph by selecting only certain scales\n",
    "new_pca_data = new_pca_data[new_pca_data[0] < 50]\n",
    "new_pca_data = new_pca_data[new_pca_data[1] < 15]\n",
    "ax.scatter(new_pca_data[0], new_pca_data[1],\n",
    "           c = list(map(lambda label : cluster_colors[label],new_pca_data['label'])))\n",
    "\n",
    "  \n",
    "''' str_labels = list(map(lambda label:'% s' % label, kmeans.labels_))\n",
    "  \n",
    "# list(map(lambda data1, data2, str_label:\n",
    "#         ax.text(data1, data2, s = str_label, size = 16.5,\n",
    "#         zorder = 20, color = 'k'), pca_data[0], pca_data[1], str_labels))''' \n",
    "  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1 - Add labels for each individual in the dataframe. \n",
    "#2 - Convert this in Json, push to mongoDB \n",
    "\n",
    "labels_array = kmeans.labels_\n",
    "#iterate through dataframe, add each cells of labels_array in a new column \"label\" : \n",
    "\n",
    "new_df = df\n",
    "\n",
    "if(not 'label' in new_df.columns):\n",
    "    new_df.insert(9, \"label\", labels_array, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ATTENTION LE BLOC SUIVANT PERMET D'IMPORTER LES DONNEES DANS MONGODB "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# collec_co=base_db.small_tweets_final_with_labels_wo_verifiedBadge\n",
    "# collec_co.insert_many(new_df.to_dict('records'))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean values for group label = 0, size =  151179 \n",
      "\n",
      "follow_popularity_new_df_0 =  0.013922822121327106\n",
      "group_popularity_new_df_0 =  0.028251251923524023\n",
      "hashtags_freq_new_df_0 =  1.847021280657356\n",
      "mentions_freq_new_df_0 =  1.2882208806892927\n",
      "url_freq_new_df_0 =  0.058713986074023956\n",
      "date_of_creation_account_new_df_0 =  0.5207207514165384\n",
      "tweets_frequency_new_df_0 =  6.728998735083572e-06\n",
      "levenshtein_distance_new_df_0 =  0.17330667741160566\n",
      "tweet_length_new_df_0 =  131.15385583996058\n",
      "\n",
      "\n",
      "\n",
      "*************************************************************************************\n",
      "\n",
      "\n",
      "\n",
      "mean values for group label = 1, size =  116288 \n",
      "\n",
      "follow_popularity_new_df_1 =  0.04045561588937194\n",
      "group_popularity_new_df_1 =  0.055521899504851556\n",
      "hashtags_freq_new_df_1 =  1.1788649364764818\n",
      "mentions_freq_new_df_1 =  0.4078225228771666\n",
      "url_freq_new_df_1 =  0.5379318257295491\n",
      "date_of_creation_account_new_df_1 =  2.791153616930045\n",
      "tweets_frequency_new_df_1 =  2.9540549044112856e-05\n",
      "levenshtein_distance_new_df_1 =  0.08392420047209918\n",
      "tweet_length_new_df_1 =  87.05158203637347\n"
     ]
    }
   ],
   "source": [
    "#Display mean values of each class from k_means : \n",
    "\n",
    "new_df_0 = new_df[new_df['label'] == 0]\n",
    "new_df_1 = new_df[new_df['label'] == 1]\n",
    "new_df_0_size = np.shape(new_df_0)[0]\n",
    "new_df_1_size = np.shape(new_df_1)[0]\n",
    "\n",
    "print('mean values for group label = 0, size = ',new_df_0_size,'\\n')\n",
    "\n",
    "print('follow_popularity_new_df_0 = ', np.mean(new_df_0['follow_popularity']))\n",
    "print('group_popularity_new_df_0 = ', np.mean(new_df_0['group_popularity']))\n",
    "print('hashtags_freq_new_df_0 = ', np.mean(new_df_0['hashtags_freq']))\n",
    "print('mentions_freq_new_df_0 = ', np.mean(new_df_0['mentions_freq']))\n",
    "print('url_freq_new_df_0 = ', np.mean(new_df_0['url_freq']))\n",
    "print('date_of_creation_account_new_df_0 = ', np.mean(new_df_0['date_of_creation_account']))\n",
    "print('tweets_frequency_new_df_0 = ', np.mean(new_df_0['tweets_frequency']))\n",
    "print('levenshtein_distance_new_df_0 = ', np.mean(new_df_0['levenshtein_distance']))\n",
    "print('tweet_length_new_df_0 = ', np.mean(new_df_0['tweet_length']))\n",
    "\n",
    "print('\\n\\n')\n",
    "print('*************************************************************************************')\n",
    "print('\\n\\n')\n",
    "print('mean values for group label = 1, size = ',new_df_1_size,'\\n')\n",
    "print('follow_popularity_new_df_1 = ', np.mean(new_df_1['follow_popularity']))\n",
    "print('group_popularity_new_df_1 = ', np.mean(new_df_1['group_popularity']))\n",
    "print('hashtags_freq_new_df_1 = ', np.mean(new_df_1['hashtags_freq']))\n",
    "print('mentions_freq_new_df_1 = ', np.mean(new_df_1['mentions_freq']))\n",
    "print('url_freq_new_df_1 = ', np.mean(new_df_1['url_freq']))\n",
    "print('date_of_creation_account_new_df_1 = ', np.mean(new_df_1['date_of_creation_account']))\n",
    "print('tweets_frequency_new_df_1 = ', np.mean(new_df_1['tweets_frequency']))\n",
    "print('levenshtein_distance_new_df_1 = ', np.mean(new_df_1['levenshtein_distance']))\n",
    "print('tweet_length_new_df_1 = ', np.mean(new_df_1['tweet_length']))"
   ]
  }
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