hawkes_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tick\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tick.base import TimeFunction\n",
    "import tick.plot\n",
    "from tick.hawkes import SimuInhomogeneousPoisson\n",
    "from tick.hawkes import HawkesExpKern\n",
    "from tick.plot import plot_hawkes_kernels\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['u1.txt', 'u10.txt', 'u11.txt', 'u12.txt', 'u13.txt', 'u14.txt', 'u15.txt', 'u16.txt', 'u17.txt', 'u18.txt', 'u19.txt', 'u2.txt', 'u20.txt', 'u21.txt', 'u22.txt', 'u23.txt', 'u24.txt', 'u3.txt', 'u4.txt', 'u5.txt', 'u6.txt', 'u7.txt', 'u8.txt', 'u9.txt']\n",
      "/home/aditya/BTP/Datasets/RTGen/u1.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u10.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u11.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u12.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u13.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u14.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u15.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u16.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u17.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u18.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u19.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u2.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u20.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u21.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u22.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u23.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u24.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u3.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u4.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u5.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u6.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u7.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u8.txt\n",
      "/home/aditya/BTP/Datasets/RTGen/u9.txt\n"
     ]
    }
   ],
   "source": [
    "df_arr = []\n",
    "\n",
    "# for root,dir_names,f_names in os.walk(os.path.join(os.getcwd(),'Datasets','RTGen')):\n",
    "f_names = sorted(os.listdir(os.path.join(os.getcwd(),'Datasets','RTGen')))\n",
    "print(f_names)\n",
    "\n",
    "for name in sorted(f_names):\n",
    "#     print(name)\n",
    "    # root_spl=root.split('/')\n",
    "    path=os.path.join(os.path.join(os.getcwd(),'Datasets','RTGen'),name)\n",
    "    print(path)\n",
    "#         filepath = os.path.join(\"data\",\"u1.txt\")\n",
    "\n",
    "    df = pd.read_csv(path,sep=\"\\t\",engine='python')\n",
    "    \n",
    "    df1 = df[df['retweeted_tweet'].notnull()]  #only retweet timestamps\n",
    "    df_arr.append(df1)\n",
    "#     print(df1)\n",
    "\n",
    "#         df2 = df[df['retweeted_tweet'].isnull()]\n",
    "#         print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeling = {'u1.txt':'HP',\n",
    "            'u10.txt':'SU',\n",
    "            'u11.txt':'SU',\n",
    "            'u12.txt':'SU',\n",
    "            'u13.txt':'HP',\n",
    "            'u14.txt':'SU',\n",
    "            'u15.txt':'SU',\n",
    "            'u16.txt':'SU',\n",
    "            'u17.txt':'SU',\n",
    "            'u18.txt':'SU',\n",
    "            'u19.txt':'SU',\n",
    "            'u2.txt':'MP',\n",
    "            'u20.txt':'SU',\n",
    "            'u21.txt':'SU',\n",
    "            'u22.txt':'SU',\n",
    "            'u23.txt':'SU',\n",
    "            'u24.txt':'HP',\n",
    "            'u3.txt':'HP',\n",
    "            'u4.txt':'MP',\n",
    "            'u5.txt':'MP',\n",
    "            'u6.txt':'LP',\n",
    "            'u7.txt':'MP',\n",
    "            'u8.txt':'LP',\n",
    "            'u9.txt':'HP'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df_arr[0]['retweeted_tweet_timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {}\n",
    "for i, name in enumerate(f_names):\n",
    "    \n",
    "#     print(type(df_arr[i]['retweeted_tweet_timestamp']))\n",
    "    last = pd.to_datetime(sorted(df_arr[i]['retweeted_tweet_timestamp'].tolist())[-1], unit = 's')\n",
    "    print(last)\n",
    "#     print(len(set(df_arr[i]['retweeted_tweet_timestamp'])))\n",
    "    filtered_timestamps = []\n",
    "    for x in df_arr[i]['retweeted_tweet_timestamp']:\n",
    "        dtTime = pd.to_datetime(x,unit = 's')\n",
    "        timeDelta = last - dtTime\n",
    "#         print(timeDelta.days)\n",
    "        if timeDelta.days <= 7:  #only selecting timestamps of the last week before collection ends\n",
    "#             print(dtTime)\n",
    "            filtered_timestamps.append(x)\n",
    "#     filtered_timestamps = sorted(set(filtered_timestamps))\n",
    "    filtered_timestamps = sorted(filtered_timestamps)\n",
    "    print(len(filtered_timestamps))\n",
    "    if labeling[name] == 'HP' or labeling[name] == 'MP' or labeling[name] == 'LP':\n",
    "        mapping[(name.split('.')[0],'genuine')] = filtered_timestamps\n",
    "    else:\n",
    "        mapping[(name.split('.')[0],'fake')] = filtered_timestamps\n",
    "#     print(len(df_arr[i]['retweeted_tweet_timestamp']))\n",
    "\n",
    "print(mapping.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('user_label_to_timestamps.pickle','wb') as f:\n",
    "    pickle.dump(mapping,f)\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('u1', 'genuine')  :  9379\n",
      "('u10', 'fake')  :  5\n",
      "('u11', 'fake')  :  228\n",
      "('u12', 'fake')  :  28\n",
      "('u13', 'genuine')  :  16023\n",
      "('u14', 'fake')  :  7301\n",
      "('u15', 'fake')  :  10418\n",
      "('u16', 'fake')  :  212\n",
      "('u17', 'fake')  :  546\n",
      "('u18', 'fake')  :  16540\n",
      "('u19', 'fake')  :  30180\n",
      "('u2', 'genuine')  :  776\n",
      "('u20', 'fake')  :  8806\n",
      "('u21', 'fake')  :  16\n",
      "('u22', 'fake')  :  53430\n",
      "('u23', 'fake')  :  996010\n",
      "('u24', 'genuine')  :  31621\n",
      "('u3', 'genuine')  :  2654\n",
      "('u4', 'genuine')  :  86\n",
      "('u5', 'genuine')  :  2299\n",
      "('u6', 'genuine')  :  358\n",
      "('u7', 'genuine')  :  1605\n",
      "('u8', 'genuine')  :  8\n",
      "('u9', 'genuine')  :  1206\n"
     ]
    }
   ],
   "source": [
    "for key in mapping.keys():\n",
    "#     mapping[key] = sorted(set(mapping[key]))\n",
    "    print(key, \" : \", len(mapping[key]))\n",
    "#     for x in mapping[key]:\n",
    "#         print(pd.to_datetime(x,unit = 's'))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r\"user_label_to_timestamps.pickle\", \"rb\") as input_file:\n",
    "    data = pickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "y = []\n",
    "for key in data.keys():\n",
    "    if key[1] == 'genuine':\n",
    "        y.append(1)\n",
    "    else:\n",
    "        y.append(0)\n",
    "    X.append(sorted(list(set(data[key]))))  # we remove duplicate timestamps here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(suppress=True)\n",
    "np.set_printoptions(threshold=np.nan)\n",
    "\n",
    "with open('input2.csv','w') as f:\n",
    "    f.write('sequence,target\\n')\n",
    "    for i, x in enumerate(X):\n",
    "        a = ' '.join([str(int(i)) for i in list(x)]) + ',' + str(y[i]) + '\\n'\n",
    "        f.write(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "# X_train=np.array(X_train)\n",
    "# X_test=np.array(X_test)\n",
    "# y_train=np.array(y_train)\n",
    "# y_test=np.array(y_test)\n",
    "\n",
    "# print(X_train.shape,y_train.shape)\n",
    "# print(type(X_train[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#TODO: try with differences in timestamps\n",
    "\n",
    "from keras.layers import Dense, Dropout, LSTM, Embedding\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "input_file = 'input2.csv'\n",
    "max_len = 10000\n",
    "\n",
    "def load_data(test_split = 0.2):\n",
    "    print ('Loading data...')\n",
    "    df = pd.read_csv(input_file)\n",
    "    df['sequence'] = df['sequence'].apply(lambda x: [int(e) for e in x.split()])\n",
    "    df = df.reindex(np.random.permutation(df.index))\n",
    "\n",
    "    train_size = int(len(df) * (1 - test_split))\n",
    "\n",
    "    X_train = df['sequence'].values[:train_size]\n",
    "    y_train = np.array(df['target'].values[:train_size])\n",
    "    X_test = np.array(df['sequence'].values[train_size:])\n",
    "    y_test = np.array(df['target'].values[train_size:])\n",
    "\n",
    "    return pad_sequences(X_train, maxlen=max_len,padding='post'), y_train, pad_sequences(X_test,maxlen=max_len,padding='post'), y_test\n",
    "\n",
    "\n",
    "def create_model(input_shape):\n",
    "    print ('Creating model...')\n",
    "    model = Sequential()\n",
    "#     model.add(Embedding(input_dim = 188, output_dim = 50, input_length = input_length))\n",
    "    model.add(LSTM(32, input_shape=input_shape[1:3], activation='sigmoid', recurrent_activation='hard_sigmoid', return_sequences=True))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(LSTM(32, activation='sigmoid', recurrent_activation='hard_sigmoid'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    print ('Compiling...')\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='rmsprop',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_LSTM(X_train, y_train, X_test, y_test):\n",
    "              \n",
    "    X_train = X_train.reshape(X_train.shape[0],X_train.shape[1],1)\n",
    "    X_test = X_test.reshape(X_test.shape[0],X_test.shape[1],1)\n",
    "\n",
    "    print(X_train.shape)\n",
    "    print(X_test.shape)\n",
    "\n",
    "    input_shape = X_train.shape\n",
    "    # print(input_shape)\n",
    "    # print(y_train.shape)\n",
    "\n",
    "    model = create_model(input_shape)\n",
    "\n",
    "    print ('Fitting model...')\n",
    "    hist = model.fit(X_train, y_train, batch_size=64, epochs=10, validation_split = 0.3, verbose = 1)\n",
    "\n",
    "    score, acc = model.evaluate(X_test, y_test, batch_size=1)\n",
    "    print('Test score:', score)\n",
    "    print('Test accuracy:', acc)\n",
    "\n",
    "    print(model.summary())\n",
    "\n",
    "    print(model.predict_classes(X_test, verbose=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      "(19, 10000, 1)\n",
      "(5, 10000, 1)\n",
      "Creating model...\n",
      "Compiling...\n",
      "Fitting model...\n",
      "Train on 13 samples, validate on 6 samples\n",
      "Epoch 1/10\n",
      "13/13 [==============================] - 10s 802ms/step - loss: 0.8337 - acc: 0.4615 - val_loss: 0.7985 - val_acc: 0.3333\n",
      "Epoch 2/10\n",
      "13/13 [==============================] - 7s 568ms/step - loss: 0.6562 - acc: 0.6154 - val_loss: 0.7800 - val_acc: 0.3333\n",
      "Epoch 3/10\n",
      "13/13 [==============================] - 8s 593ms/step - loss: 0.7916 - acc: 0.3846 - val_loss: 0.7581 - val_acc: 0.3333\n",
      "Epoch 4/10\n",
      "13/13 [==============================] - 8s 605ms/step - loss: 0.7580 - acc: 0.4615 - val_loss: 0.7510 - val_acc: 0.3333\n",
      "Epoch 5/10\n",
      "13/13 [==============================] - 8s 612ms/step - loss: 0.8037 - acc: 0.3077 - val_loss: 0.7386 - val_acc: 0.3333\n",
      "Epoch 6/10\n",
      "13/13 [==============================] - 8s 621ms/step - loss: 0.9014 - acc: 0.2308 - val_loss: 0.7284 - val_acc: 0.3333\n",
      "Epoch 7/10\n",
      "13/13 [==============================] - 8s 625ms/step - loss: 0.7600 - acc: 0.3846 - val_loss: 0.7201 - val_acc: 0.3333\n",
      "Epoch 8/10\n",
      "13/13 [==============================] - 8s 631ms/step - loss: 0.7955 - acc: 0.5385 - val_loss: 0.7190 - val_acc: 0.3333\n",
      "Epoch 9/10\n",
      "13/13 [==============================] - 8s 621ms/step - loss: 0.7715 - acc: 0.3846 - val_loss: 0.7207 - val_acc: 0.3333\n",
      "Epoch 10/10\n",
      "13/13 [==============================] - 8s 647ms/step - loss: 0.6698 - acc: 0.6923 - val_loss: 0.7090 - val_acc: 0.3333\n",
      "5/5 [==============================] - 5s 1s/step\n",
      "Test score: 0.6496855616569519\n",
      "Test accuracy: 1.0\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 10000, 32)         4352      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 10000, 32)         0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 32)                8320      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 12,705\n",
      "Trainable params: 12,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "5/5 [==============================] - 1s 232ms/step\n",
      "[[0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]]\n"
     ]
    }
   ],
   "source": [
    "# X_train, y_train, X_test, y_test = load_data(0.5) #70-30 train split\n",
    "X_train, y_train, X_test, y_test = load_data() # 80-20 train split\n",
    "\n",
    "test_LSTM(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1394511859]\n",
      " [1394516231]\n",
      " [1394616554]\n",
      " ...\n",
      " [         0]\n",
      " [         0]\n",
      " [         0]]\n",
      "[1 0 1 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(X_train[1])\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.38479573e+09 1.38480523e+09 1.38481489e+09 ... 1.38542900e+09\n",
      " 1.38544588e+09 1.38544588e+09]\n"
     ]
    }
   ],
   "source": [
    "x = [np.array(item) for item in X]\n",
    "\n",
    "print(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tick.hawkes import SimuHawkesExpKernels, HawkesExpKern\n",
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd\n",
    "from tick.plot import plot_hawkes_kernels\n",
    "\n",
    "\n",
    "decays = 0.01\n",
    "\n",
    "input_file = 'input2.csv'\n",
    "\n",
    "def load_timestamps(test_split = 0.2):\n",
    "    print ('Loading data...')\n",
    "    df = pd.read_csv(input_file)\n",
    "    df['sequence'] = df['sequence'].apply(lambda x: [float(e) for e in x.split()])\n",
    "    df = df.reindex(np.random.permutation(df.index))\n",
    "\n",
    "    train_size = int(len(df) * (1 - test_split))\n",
    "\n",
    "    X_train = df['sequence'].values[:train_size]\n",
    "    y_train = np.array(df['target'].values[:train_size])\n",
    "    X_test = np.array(df['sequence'].values[train_size:])\n",
    "    y_test = np.array(df['target'].values[train_size:])\n",
    "\n",
    "    return X_train, y_train, X_test, y_test\n",
    "\n",
    "\n",
    "def simulate():\n",
    "    n_nodes = 1\n",
    "    baseline = [0.1]\n",
    "    adjacency = [[0.1]]\n",
    "\n",
    "    end_time = 10000\n",
    "    # max_jumps=1000;\n",
    "    a_sim = SimuHawkesExpKernels(adjacency, decays, baseline=baseline, end_time=end_time, verbose=True)\n",
    "    a_sim.track_intensity(0.01)\n",
    "\n",
    "    a_sim.simulate()\n",
    "    # print(a_sim.timestamps)\n",
    "\n",
    "    # print('Tracked intensity: ', a_sim.tracked_intensity)\n",
    "\n",
    "    with open('sample_timestamps.txt', 'w') as f:\n",
    "        f.write(str(list(a_sim.timestamps[0])))\n",
    "\n",
    "def learn(X_train, y_train):\n",
    "    \n",
    "    timestamps_genuine = []\n",
    "    timestamps_fake = []\n",
    "    \n",
    "    count_genuine = 0\n",
    "    count_fake = 0\n",
    "    \n",
    "    for i,x in enumerate(X_train):\n",
    "        if y_train[i] == 0:\n",
    "            timestamps_fake.append(x) \n",
    "            count_fake+=1\n",
    "        else:\n",
    "            timestamps_genuine.append(x) \n",
    "            count_genuine+=1\n",
    "            \n",
    "    print(\"No. of training samples: \", len(X_train))\n",
    "    print(\"No. of genuine users: \", count_genuine)\n",
    "    print(\"No. of fake users: \", count_fake)\n",
    "    \n",
    "    timestamps_genuine = [item for sublist in timestamps_genuine for item in sublist] \n",
    "    #flattening out list of lists,merge all the timestamps into one sequence\n",
    "    timestamps_fake = [item for sublist in timestamps_fake for item in sublist]\n",
    "    timestamps_genuine = sorted(timestamps_genuine)\n",
    "    timestamps_fake = sorted(timestamps_fake)\n",
    "    \n",
    "    gofit = 'least-squares'\n",
    "    penalty = 'l2'\n",
    "    C = 1e3\n",
    "    solver = 'bfgs'\n",
    "    step = None\n",
    "    tol = 1e-05\n",
    "    max_iter = 100\n",
    "    verbose = False\n",
    "    print_every = 10\n",
    "    random_state = None\n",
    "    elastic_net_ratio = 0.95\n",
    "\n",
    "    genuine_kernel = HawkesExpKern(decays, gofit=gofit, penalty=penalty, C=C, solver=solver, step=step, tol=tol,\n",
    "                             max_iter=max_iter, verbose=verbose, print_every=print_every,\n",
    "                             # elastic_net_ratio=elastic_net_ratio,\n",
    "                             random_state=random_state)\n",
    "\n",
    "    timestamps_genuine = np.array(timestamps_genuine)\n",
    "\n",
    "    # print(timestamps)\n",
    "\n",
    "    timestamps_genuine_list = []\n",
    "    timestamps_genuine_list.append(timestamps_genuine)\n",
    "\n",
    "    genuine_kernel.fit(timestamps_genuine_list)\n",
    "    \n",
    "    print('For genuine retweet timestamps:')\n",
    "\n",
    "#     print(\"No of users: \", genuine_kernel.n_nodes)\n",
    "    print(\"Estimated mu: \", genuine_kernel.baseline)\n",
    "    print(\"Estimated alpha:\", genuine_kernel.adjacency)\n",
    "    print(\"Estimated coeffs: \", genuine_kernel.coeffs)\n",
    "    \n",
    "    fake_kernel = HawkesExpKern(decays, gofit=gofit, penalty=penalty, C=C, solver=solver, step=step, tol=tol,\n",
    "                             max_iter=max_iter, verbose=verbose, print_every=print_every,\n",
    "                             # elastic_net_ratio=elastic_net_ratio,\n",
    "                             random_state=random_state)\n",
    "\n",
    "    timestamps_fake = np.array(timestamps_fake)\n",
    "\n",
    "    # print(timestamps)\n",
    "\n",
    "    timestamps_fake_list = []\n",
    "    timestamps_fake_list.append(timestamps_fake)\n",
    "\n",
    "    fake_kernel.fit(timestamps_fake_list)\n",
    "    \n",
    "    print('For fake retweet timestamps:')\n",
    "\n",
    "#     print(\"No of users: \", fake_kernel.n_nodes)\n",
    "    print(\"Estimated mu: \", fake_kernel.baseline)\n",
    "    print(\"Estimated alpha:\", fake_kernel.adjacency)\n",
    "    print(\"Estimated coeffs: \", fake_kernel.coeffs)\n",
    "    \n",
    "    print('Simulating for genuine and fake and comparing with results...')\n",
    "    \n",
    "    hawkes_exp_kernels_genuine = SimuHawkesExpKernels(adjacency=genuine_kernel.adjacency, \n",
    "                                              decays=decays, \n",
    "                                              baseline=genuine_kernel.baseline,\n",
    "                                              verbose=False)\n",
    "    \n",
    "    genuine_kernel.plot_estimated_intensity(timestamps_genuine_list, t_min = min(timestamps_genuine))\n",
    "    \n",
    "    hawkes_exp_kernels_fake = SimuHawkesExpKernels(adjacency=fake_kernel.adjacency, \n",
    "                                              decays=decays, \n",
    "                                              baseline=fake_kernel.baseline, \n",
    "                                              verbose=False)\n",
    "    fake_kernel.plot_estimated_intensity(timestamps_fake_list, t_min = min(timestamps_fake))\n",
    "    \n",
    "    return genuine_kernel, fake_kernel\n",
    "    \n",
    "    \n",
    "def predict(X_test, y_test, genuine_kernel, fake_kernel):\n",
    "    \n",
    "    preds = []\n",
    "    \n",
    "    for list_ in X_test:\n",
    "        \n",
    "        list_ = [np.array(list_)]\n",
    "#         print(list_)\n",
    "        \n",
    "        likelihood_genuine = genuine_kernel.score(list_)\n",
    "\n",
    "#         print('Likelihood for genuine: ', likelihood_genuine)\n",
    "\n",
    "#         print('Negative Log likelihood for genuine: ', -np.log(likelihood))\n",
    "    \n",
    "        likelihood_fake = fake_kernel.score(list_)\n",
    "\n",
    "#         print('Likelihood for fake: ', likelihood_fake)\n",
    "\n",
    "        if likelihood_fake > likelihood_genuine:\n",
    "            preds.append(0)\n",
    "        else:\n",
    "            preds.append(1)\n",
    "            \n",
    "    print(\"No. of test samples: \", len(X_test))\n",
    "    \n",
    "    print(preds)\n",
    "        \n",
    "    print(\"Test accuracy: \", accuracy_score(y_test, preds))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from random import randint\n",
    "from scipy.optimize import minimize, check_grad\n",
    "import ast, math\n",
    "\n",
    "\n",
    "np.set_printoptions(suppress=True)\n",
    "np.set_printoptions(threshold=np.nan)\n",
    "\n",
    "omega = 1000\n",
    "\n",
    "class hawkes_process_classifier:\n",
    "\n",
    "    def neg_log_likelihood(self, param, *args):\n",
    "        # print time_stamps\n",
    "        # time_stamps=time_stamps[1]\n",
    "        if args[0] == 'mu':\n",
    "            mu = param\n",
    "            print(\"Mu: \", mu)\n",
    "\n",
    "            alpha = args[1]\n",
    "            beta = args[2]\n",
    "            time_stamps = args[3]\n",
    "        elif args[0] == 'alpha':\n",
    "\n",
    "            alpha = param\n",
    "            print(\"Alpha: \", alpha)\n",
    "\n",
    "            mu = args[1]\n",
    "            beta = args[2]\n",
    "            time_stamps = args[3]\n",
    "\n",
    "        elif args[0] == 'beta':\n",
    "\n",
    "            beta = param\n",
    "            print(\"Beta: \", beta)\n",
    "\n",
    "            mu = args[1]\n",
    "            alpha = args[2]\n",
    "            time_stamps = args[3]\n",
    "\n",
    "        elif args[0] == 'all':\n",
    "            mu = param[0]\n",
    "            alpha = param[1]\n",
    "            beta = param[2]\n",
    "\n",
    "            time_stamps = args[1]\n",
    "\n",
    "        # time_diff = time_stamps[-1] - time_stamps\n",
    "        # time_exp = np.exp(-beta * time_diff) - 1\n",
    "        # first_sum = (alpha * 1.0 / max(1e-10, beta) )* np.sum(time_exp)\n",
    "\n",
    "        # print time_stamps\n",
    "\n",
    "        R = np.zeros(time_stamps.shape)\n",
    "\n",
    "        for i in range(1, len(time_stamps)):\n",
    "            R[i] = np.exp(-beta * (time_stamps[i] - time_stamps[i - 1])) * (1 + R[i - 1])\n",
    "\n",
    "        temp = np.sum(np.log(mu + alpha * R))\n",
    "\n",
    "        temp1 = (alpha * 1.0 / max(1e-10, beta)) * np.sum(np.exp(-beta*(time_stamps[-1]-time_stamps)) -1)\n",
    "\n",
    "        ll = (-mu * time_stamps[-1]) + temp + temp1\n",
    "\n",
    "        # print(\"Negative likelihood: \", -ll)\n",
    "\n",
    "        return -ll + omega * self.regularization_l2(mu, alpha, beta)\n",
    "\n",
    "\n",
    "    def regularization_l2(self, mu, alpha, beta):\n",
    "\n",
    "        return math.pow(mu, 2) + math.pow(alpha, 2) + math.pow(beta, 2)\n",
    "\n",
    "    def neg_log_likelihood_wrapper(self, param, *args):\n",
    "\n",
    "        p = np.exp(param)\n",
    "\n",
    "        ll = self.neg_log_likelihood(p, *args)\n",
    "\n",
    "        print('Negative Likelihood :', ll)\n",
    "\n",
    "        return ll\n",
    "\n",
    "    def gradient_mu(self, param, *args):\n",
    "\n",
    "        # mu = np.exp(param)\n",
    "        mu = param\n",
    "\n",
    "        alpha = args[1]\n",
    "        beta = args[2]\n",
    "        time_stamps = args[3]\n",
    "\n",
    "        r = np.zeros(time_stamps.shape)\n",
    "\n",
    "        # print time_stamps.shape\n",
    "\n",
    "        # for i in range(1, len(time_stamps)):\n",
    "        #     r[i] = np.exp(-beta * (time_stamps[i] - time_stamps[i - 1])) * (1 + r[i - 1])\n",
    "        #\n",
    "        # # first_sum = np.sum(1.0/(mu + alpha * r))\n",
    "        #\n",
    "        # first_sum = 0\n",
    "        # for elt in r:\n",
    "        #     first_sum=first_sum+(1.0/max(1e-10,(mu+alpha*elt)))\n",
    "        #\n",
    "        # del_mu = (-time_stamps[-1] + first_sum)\n",
    "\n",
    "        A = np.zeros(time_stamps.shape)\n",
    "\n",
    "        for i in range(1, len(time_stamps)):\n",
    "            A[i] = np.exp(-beta * (time_stamps[i] - time_stamps[i - 1])) * (1 + A[i - 1])\n",
    "\n",
    "        temp = (mu + alpha * A)\n",
    "        del_mu = (-time_stamps[-1] + np.sum(1.0 / temp))\n",
    "\n",
    "        return -del_mu + 2*omega*mu\n",
    "\n",
    "    def grad_mu(self, param, *args):\n",
    "\n",
    "        mu = np.exp(param)\n",
    "\n",
    "        gr_mu = self.gradient_mu(mu, *args)\n",
    "        gr_mu = gr_mu * mu\n",
    "\n",
    "        print(\"Negative of gradient_mu: \", gr_mu)\n",
    "\n",
    "        return gr_mu\n",
    "\n",
    "    def gradient_alpha(self, param, *args):\n",
    "\n",
    "        # alpha = np.exp(param)\n",
    "        alpha = param\n",
    "\n",
    "        mu = args[1]\n",
    "        beta = args[2]\n",
    "        time_stamps = args[3]\n",
    "\n",
    "        # time_diff = time_stamps[-1] - time_stamps\n",
    "        # time_exp = np.exp(-beta * time_diff) - 1\n",
    "        # first_sum = (alpha * 1.0 / max(1e-10, beta)) * np.sum(time_exp)\n",
    "        #\n",
    "        # r = np.zeros(time_stamps.shape)\n",
    "        #\n",
    "        # # print time_stamps.shape\n",
    "        #\n",
    "        # for i in range(1, len(time_stamps)):\n",
    "        #     r[i] = np.exp(-beta * (time_stamps[i] - time_stamps[i - 1])) * (1 + r[i - 1])\n",
    "        #\n",
    "        # second_sum = 0\n",
    "        # for elt in r:\n",
    "        #     second_sum = second_sum + ( elt/ max(1e-10,(mu + alpha * elt)) )\n",
    "        #\n",
    "        # del_alpha = (second_sum + ((1.0/beta) * first_sum))\n",
    "\n",
    "        A = np.zeros(time_stamps.shape)\n",
    "\n",
    "        for i in range(1, len(time_stamps)):\n",
    "            A[i] = np.exp(-beta * (time_stamps[i] - time_stamps[i - 1])) * (1 + A[i - 1])\n",
    "\n",
    "        temp = (mu + alpha * A)\n",
    "\n",
    "        del_alpha = np.sum(A / temp) + math.pow(beta, -1) * np.sum(np.exp(-beta * (time_stamps[-1] - time_stamps)) - 1)\n",
    "\n",
    "        return -del_alpha + 2*omega*math.pow(alpha,1)\n",
    "\n",
    "    def grad_alpha(self, param, *args):\n",
    "\n",
    "        alpha = np.exp(param)\n",
    "\n",
    "        gr_alpha = self.gradient_alpha(alpha, *args)\n",
    "        gr_alpha = gr_alpha * alpha\n",
    "\n",
    "        print(\"Negative of gradient_alpha: \", gr_alpha)\n",
    "\n",
    "        return gr_alpha\n",
    "\n",
    "    def gradient_beta(self, param, *args):\n",
    "\n",
    "        beta = param\n",
    "        mu = args[1]\n",
    "        alpha = args[2]\n",
    "        time_stamps = args[3]\n",
    "\n",
    "        # time_diff = time_stamps[-1] - time_stamps\n",
    "        # time_exp = np.exp(-beta * time_diff) - 1\n",
    "\n",
    "        # a = np.zeros(time_stamps.shape)\n",
    "        # b = np.zeros(time_stamps.shape)\n",
    "        #\n",
    "        # for i in range(1, len(time_stamps)):\n",
    "        #     a[i] = np.exp(-beta * (time_stamps[i] - time_stamps[i - 1])) * (1 + a[i - 1])\n",
    "        #\n",
    "        # for i in range(1, len(time_stamps)):\n",
    "        #\n",
    "        #     for j in range(1,i-1):\n",
    "        #\n",
    "        #         b[i]=b[i]+(time_stamps[i]-time_stamps[j])*(np.exp(-beta*(time_stamps[i]-time_stamps[j])))\n",
    "        #\n",
    "        #\n",
    "        # first_sum=0\n",
    "        # second_sum=0\n",
    "        #\n",
    "        # for i in range(len(time_stamps)):\n",
    "        #\n",
    "        #     first_sum=first_sum+(alpha*b[i]/max((mu+alpha*a[i]),1e-10))\n",
    "        #\n",
    "        #     second_sum=second_sum+ (1.0/beta)*(time_stamps[-1]-time_stamps[i])*np.exp(-beta*(time_stamps[-1]-time_stamps[i]))+(1.0/(beta*beta))*np.exp(-beta*(time_stamps[-1]-time_stamps[i]))\n",
    "\n",
    "        # del_beta = (-first_sum-alpha*second_sum)\n",
    "\n",
    "        A = np.zeros(time_stamps.shape)\n",
    "\n",
    "        for i in range(1, len(time_stamps)):\n",
    "            A[i] = np.exp(-beta * (time_stamps[i] - time_stamps[i - 1])) * (1 + A[i - 1])\n",
    "\n",
    "        temp = (mu + alpha * A)\n",
    "\n",
    "        B = np.zeros(time_stamps.shape)\n",
    "\n",
    "        for i in range(1, len(time_stamps)):\n",
    "            tmp = time_stamps[i] - time_stamps[:i]\n",
    "            tmp1 = np.exp(-beta * tmp)\n",
    "            B[i] = np.dot(tmp, tmp1)\n",
    "\n",
    "        B_temp = alpha * B\n",
    "        temp2 = B_temp / temp\n",
    "\n",
    "        first_term = math.pow(beta, -1) * (time_stamps[-1] - time_stamps) * np.exp(\n",
    "            -beta * (time_stamps[-1] - time_stamps))\n",
    "        second_term = math.pow(beta, -2) * np.exp(-beta * (time_stamps[-1] - time_stamps))\n",
    "\n",
    "        del_beta = -np.sum(alpha * (first_term + second_term)) - np.sum(temp2)\n",
    "\n",
    "        return -del_beta + 2*omega*beta\n",
    "\n",
    "    def grad_beta(self, param, *args):\n",
    "\n",
    "        beta = np.exp(param)\n",
    "\n",
    "        gr_beta = self.gradient_beta(beta, *args)\n",
    "        gr_beta = gr_beta * beta\n",
    "\n",
    "        print(\"Negative of gradient_beta: \", gr_beta)\n",
    "\n",
    "        return gr_beta\n",
    "\n",
    "    def gradient_combined(self, param, *args):\n",
    "\n",
    "        mu = param[0]\n",
    "        alpha = param[1]\n",
    "        beta = param[2]\n",
    "\n",
    "        time_stamps = args[1]\n",
    "\n",
    "        # for del_mu\n",
    "\n",
    "        A = np.zeros(time_stamps.shape)\n",
    "\n",
    "        for i in range(1, len(time_stamps)):\n",
    "            A[i] = np.exp(-beta*(time_stamps[i]-time_stamps[i-1]))*(1+A[i-1])\n",
    "\n",
    "        temp = (mu + alpha*A)\n",
    "        del_mu = (-time_stamps[-1] + np.sum(1.0/temp))\n",
    "\n",
    "        # for del_alpha\n",
    "\n",
    "        del_alpha = np.sum(A/temp) + math.pow(beta,-1) * np.sum(np.exp(-beta*(time_stamps[-1]-time_stamps))-1)\n",
    "\n",
    "        # for del_beta\n",
    "\n",
    "        B = np.zeros(time_stamps.shape)\n",
    "\n",
    "        for i in range(1, len(time_stamps)):\n",
    "            tmp = time_stamps[i] - time_stamps[:i]\n",
    "            tmp1 = np.exp(-beta * tmp)\n",
    "            B[i] = np.dot(tmp, tmp1)\n",
    "\n",
    "        B_temp = alpha*B\n",
    "        temp2 = B_temp/temp\n",
    "\n",
    "        first_term = math.pow(beta,-1) * (time_stamps[-1]-time_stamps) * np.exp(-beta*(time_stamps[-1]-time_stamps))\n",
    "        second_term = math.pow(beta,-2) * np.exp(-beta*(time_stamps[-1]-time_stamps))\n",
    "\n",
    "        del_beta = -np.sum(alpha*(first_term+second_term)) - np.sum(temp2)\n",
    "\n",
    "        return np.array([-del_mu, -del_alpha, -del_beta])\n",
    "\n",
    "    def grad_combined(self, param, *args):\n",
    "\n",
    "        p = np.exp(param)\n",
    "        grad = np.zeros(len(p))\n",
    "\n",
    "        gr = self.gradient_combined(p, *args)\n",
    "        for i in range(len(gr)):\n",
    "            grad[i] = gr[i] * p[i]\n",
    "\n",
    "        print(\"Negative of gradients: \", grad)\n",
    "\n",
    "        return grad\n",
    "\n",
    "    def check_grad_mu(self, time_stamps):\n",
    "        beta = 0.01\n",
    "        alpha = 0.1\n",
    "\n",
    "        args = ('mu', alpha, beta, time_stamps)\n",
    "        # res = check_grad(self.neg_log_likelihood, self.gradient_mu, [], args = args)\n",
    "        #\n",
    "        for i in range(100):\n",
    "            mu = i/10\n",
    "\n",
    "            print(\"Mu: \", mu, \"Err: \", check_grad(self.neg_log_likelihood, self.grad_mu, np.array([np.log(mu)]), *args))\n",
    "\n",
    "    def check_grad_alpha(self, time_stamps):\n",
    "        beta = 0.01\n",
    "        mu = 0.1\n",
    "\n",
    "        args = ('alpha', mu, beta, time_stamps)\n",
    "        # res = check_grad(self.neg_log_likelihood, self.gradient_mu, [], args = args)\n",
    "        #\n",
    "        for i in range(100):\n",
    "            alpha = i / 10\n",
    "\n",
    "            print(\"Alpha: \", alpha, \"Err: \", check_grad(self.neg_log_likelihood, self.grad_alpha, np.array([alpha]), *args))\n",
    "\n",
    "\n",
    "    def estimate_params(self, time_stamps):\n",
    "\n",
    "        res1 = res2 = res3 = res4 = []\n",
    "\n",
    "        ###################################################\n",
    "\n",
    "        mu0 = 0.1\n",
    "        beta0 = 0.1\n",
    "        alpha0 = 0.1\n",
    "\n",
    "        x = [mu0]\n",
    "\n",
    "        args = ('mu', alpha0, beta0, time_stamps)\n",
    "\n",
    "        n_ll = lambda x: self.neg_log_likelihood(x, *args)\n",
    "        n_grad = lambda x: self.gradient_mu(x, *args)\n",
    "\n",
    "        cons = ({'type': 'ineq', 'fun': lambda x: np.array([x[0] - 1e-5])})\n",
    "\n",
    "        print(\"Estimating mu...\")\n",
    "        res1 = minimize(\n",
    "                        self.neg_log_likelihood_wrapper,\n",
    "                        # n_ll,\n",
    "                        x,\n",
    "                        args=args,\n",
    "                        # method='SLSQP',\n",
    "                        method = 'BFGS',\n",
    "                        # bounds=((1e-5, None)),\n",
    "                        # constraints=cons,\n",
    "                        options={'disp': False, 'maxiter': 200},\n",
    "                        jac = self.grad_mu\n",
    "                        )\n",
    "        #\n",
    "        #\n",
    "        # ##########################################################\n",
    "        #\n",
    "        alpha0 = 0.1\n",
    "        beta0 = 0.1\n",
    "        mu0 = 0.1\n",
    "\n",
    "        x = [alpha0]\n",
    "\n",
    "        args = ('alpha', mu0, beta0, time_stamps)\n",
    "\n",
    "        n_ll = lambda x: self.neg_log_likelihood(x, *args)\n",
    "        n_grad = lambda x: self.gradient_alpha(x, *args)\n",
    "\n",
    "        cons = ({'type': 'ineq', 'fun': lambda x: np.array([beta0 - alpha0 - 1e-5])})\n",
    "\n",
    "        print(\"Estimating alpha...\")\n",
    "        res2 = minimize(\n",
    "                        self.neg_log_likelihood_wrapper,\n",
    "                        # n_ll,\n",
    "                        x,\n",
    "                        args=args,\n",
    "                        # method='SLSQP',\n",
    "                        method = 'BFGS',\n",
    "                        # bounds=((1e-5, None)),\n",
    "                        # constraints=cons,\n",
    "                        options={'disp': False, 'maxiter': 200},\n",
    "                        jac=self.grad_alpha\n",
    "                    )\n",
    "        #\n",
    "        # ##########################################################\n",
    "\n",
    "        mu0 = 0.1\n",
    "        alpha0 = 0.1\n",
    "        beta0 = 0.01\n",
    "\n",
    "        x =  [beta0]\n",
    "\n",
    "        args = ('beta', mu0, alpha0, time_stamps)\n",
    "\n",
    "        n_ll = lambda x: self.neg_log_likelihood(x, *args)\n",
    "        n_grad = lambda x: self.gradient_beta(x, *args)\n",
    "\n",
    "        cons = ({'type': 'ineq', 'fun': lambda x: np.array([beta0 - alpha0 - 1e-5])})\n",
    "\n",
    "        print(\"Estimating beta...\")\n",
    "\n",
    "        res3 = minimize(\n",
    "                        # n_ll,\n",
    "                        self.neg_log_likelihood_wrapper,\n",
    "                        x,\n",
    "                        args=args,\n",
    "                        # method='SLSQP',\n",
    "                        method='BFGS',\n",
    "                        # bounds=((1e-5, None)),\n",
    "                        # constraints=cons,\n",
    "                        options={'disp': False, 'maxiter': 200},\n",
    "                        jac= self.grad_beta\n",
    "                        )\n",
    "\n",
    "        #########################################################\n",
    "\n",
    "        mu0 = 0.1\n",
    "        alpha0 = 0.1\n",
    "        beta0 = 0.01\n",
    "\n",
    "        x = [mu0, alpha0, beta0]\n",
    "\n",
    "        args = ('all', time_stamps)\n",
    "\n",
    "        n_ll = lambda x: self.neg_log_likelihood(x, *args)\n",
    "        n_grad = lambda x: self.gradient_combined(x, *args)\n",
    "\n",
    "        cons = ({'type': 'ineq', 'fun': lambda x: np.array([beta0 - alpha0 - 1e-5])})\n",
    "\n",
    "        print(\"Estimating params...\")\n",
    "\n",
    "        # res4 = minimize(self.neg_log_likelihood_wrapper,\n",
    "        #                 x,\n",
    "        #                 method='BFGS',\n",
    "        #                 args = args,\n",
    "        #                 options={'disp': False, 'maxiter': 200},\n",
    "        #                 jac = self.grad_combined\n",
    "        #                 )\n",
    "\n",
    "\n",
    "        # res4 = minimize(\n",
    "        #                 n_ll,\n",
    "        #                 x,\n",
    "        #                 method='SLSQP',\n",
    "        #                 # args=args,\n",
    "        #                 bounds=((1e-5, None), (1e-5, None), (1e-5, None)),\n",
    "        #                 constraints=cons,\n",
    "        #                 options={'disp': False, 'maxiter': 200},\n",
    "        #                 jac = n_grad\n",
    "        #                 )\n",
    "\n",
    "\n",
    "        return res1, res2, res3, res4\n",
    "\n",
    "    def main(self):\n",
    "\n",
    "\n",
    "        with open('sample_timestamps.txt', 'r') as f:\n",
    "            test_timestamps = ast.literal_eval(f.read())\n",
    "\n",
    "        test_timestamps = np.array(test_timestamps)\n",
    "\n",
    "        # self.check_grad_alpha(test_timestamps)\n",
    "        # self.check_grad_mu(test_timestamps)\n",
    "\n",
    "        res1, res2, res3, res4 = self.estimate_params(test_timestamps)\n",
    "\n",
    "        print(res1)\n",
    "\n",
    "        print(\"\\n\\n\", res2)\n",
    "\n",
    "        print(\"\\n\\n\", res3)\n",
    "\n",
    "        print(\"\\n\\n\", res4)\n",
    "\n",
    "        print(\"\\n\\nEstimated mu: \", res1.x)\n",
    "        print(\"Estimated alpha: \", res2.x)\n",
    "        print(\"Estimated beta: \", res3.x)\n",
    "        # print(\"Estimated all: \", res4.x)\n",
    "\n",
    "        print(\"\\n\\nEstimated mu: \", np.exp(res1.x))\n",
    "        print(\"Estimated alpha: \", np.exp(res2.x))\n",
    "        print(\"Estimated beta: \", np.exp(res3.x))\n",
    "        # print(\"Estimated all: \", np.exp(res4.x))\n",
    "\n",
    "        # print(\"Estimated all: \", res4.x)\n",
    "\n",
    "\n",
    "\n",
    "    def fit(self):\n",
    "        pass\n",
    "\n",
    "    def predict(self):\n",
    "        pass\n",
    "\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    hawkes_process_classifier().main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      "No. of training samples:  16\n",
      "No. of genuine users:  6\n",
      "No. of fake users:  10\n",
      "For genuine retweet timestamps:\n",
      "Estimated mu:  [7.16519814e-07]\n",
      "Estimated alpha: [[0.52637872]]\n",
      "Estimated coeffs:  [7.16519814e-07 5.26378719e-01]\n",
      "For fake retweet timestamps:\n",
      "Estimated mu:  [4.11363742e-07]\n",
      "Estimated alpha: [[0.72715354]]\n",
      "Estimated coeffs:  [4.11363742e-07 7.27153538e-01]\n",
      "Simulating for genuine and fake and comparing with results...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c544b2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c5447c940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of test samples:  8\n",
      "[1, 1, 1, 1, 1, 1, 1, 1]\n",
      "Test accuracy:  0.625\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train, X_test, y_test = load_timestamps(0.3)\n",
    "\n",
    "genuine_kernel, fake_kernel = learn(X_train, y_train)\n",
    "\n",
    "predict(X_test, y_test, genuine_kernel, fake_kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "def load_retweets_delete_fb():\n",
    "    dataset_location = os.path.join(os.getcwd(), 'Datasets', 'collected_data')\n",
    "    with open(os.path.join(dataset_location,'labeled_users_deletefb.pickle'),'rb') as f:\n",
    "        delete_fb = pickle.load(f)\n",
    "    return delete_fb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# experimental weighted 'hybrid' model of both timestamps (predicted by Hawkes) and a different \n",
    "# classification model for the other retweet features\n",
    "\n",
    "delete_fb = load_retweets_delete_fb()\n",
    "\n",
    "for key in delete_fb.keys():\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import time\n",
    "import pickle\n",
    "\n",
    "def get_timestamps_ASONAM(path, stringname):\n",
    "    allFiles = os.listdir(path)\n",
    "    data = {}\n",
    "    count = 0\n",
    "    for file in allFiles:\n",
    "        count+=1\n",
    "        print(count)\n",
    "        with open(os.path.join(path, file),'r') as fr:\n",
    "            retweeted_timestamps = []\n",
    "            content = fr.read()\n",
    "            useralltweets = json.loads(content)\n",
    "            userid = -1\n",
    "            if len(useralltweets) > 0:\n",
    "                t = json.loads(useralltweets[0])\n",
    "                userid = t['id']\n",
    "#                 print(userid)\n",
    "            for tweet in useralltweets:\n",
    "                tweet = json.loads(tweet)\n",
    "                if 'retweeted_status' in tweet.keys():\n",
    "                    timestamp = tweet['created_at']\n",
    "                    dt = datetime.strptime(timestamp, \"%a %b %d %X %z %Y\").timetuple()\n",
    "    #                 print(dt)\n",
    "                    timestamp = time.mktime(dt)\n",
    "    #                 print(timestamp)\n",
    "                    retweeted_timestamps.append(timestamp)\n",
    "#             print(len(retweeted_timestamps))\n",
    "            if userid != -1 and len(retweeted_timestamps) != 0:\n",
    "                data[userid] = retweeted_timestamps\n",
    "    with open(stringname + '.pickle','wb') as f:\n",
    "        pickle.dump(data,f)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_fake = os.path.join(os.getcwd(), 'Datasets', 'ASONAM_18', 'tweets_fake')\n",
    "pickle_name = 'ASONAM_user_timestamps_fake'\n",
    "get_timestamps_ASONAM(path_fake, pickle_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_genuine = os.path.join(os.getcwd(), 'Datasets', 'ASONAM_18', 'tweets_genuine')\n",
    "pickle_name = 'ASONAM_user_timestamps_genuine'\n",
    "get_timestamps_ASONAM(path_genuine, pickle_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1305\n",
      "1305\n",
      "874 431\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "with open(r\"ASONAM_user_timestamps_fake.pickle\", \"rb\") as input_file:\n",
    "    fake_users = pickle.load(input_file)\n",
    "    \n",
    "with open(r\"ASONAM_user_timestamps_genuine.pickle\", \"rb\") as input_file:\n",
    "    genuine_users = pickle.load(input_file)\n",
    "\n",
    "fake_labels = [0]*len(fake_users.keys())\n",
    "# print(fake_labels)\n",
    "genuine_labels = [1]*len(genuine_users.keys())\n",
    "\n",
    "y = fake_labels+genuine_labels\n",
    "print(len(y))\n",
    "X = []\n",
    "for key in fake_users.keys():\n",
    "    X.append(fake_users[key])\n",
    "for key in genuine_users.keys():\n",
    "    X.append(genuine_users[key])\n",
    "print(len(X))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "print(len(X_train), len(X_test))\n",
    "\n",
    "# print(y_train)\n",
    "# print(X_train[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(874, 10000) (431, 10000) (874,) (431,)\n",
      "(874, 5000) (431, 5000) (874,) (431,)\n",
      "(874, 5000, 1)\n",
      "(431, 5000, 1)\n",
      "Creating model...\n",
      "Compiling...\n",
      "Fitting model...\n",
      "Train on 611 samples, validate on 263 samples\n",
      "Epoch 1/10\n",
      "611/611 [==============================] - 43s 70ms/step - loss: 0.7250 - acc: 0.5057 - val_loss: 0.6910 - val_acc: 0.5741\n",
      "Epoch 2/10\n",
      "611/611 [==============================] - 43s 70ms/step - loss: 0.7082 - acc: 0.5139 - val_loss: 0.6844 - val_acc: 0.5741\n",
      "Epoch 3/10\n",
      "611/611 [==============================] - 42s 68ms/step - loss: 0.7118 - acc: 0.5155 - val_loss: 0.6846 - val_acc: 0.5741\n",
      "Epoch 4/10\n",
      "611/611 [==============================] - 42s 69ms/step - loss: 0.7041 - acc: 0.5319 - val_loss: 0.6853 - val_acc: 0.5741\n",
      "Epoch 5/10\n",
      "611/611 [==============================] - 42s 68ms/step - loss: 0.7041 - acc: 0.5205 - val_loss: 0.6837 - val_acc: 0.5741\n",
      "Epoch 6/10\n",
      "611/611 [==============================] - 41s 68ms/step - loss: 0.7150 - acc: 0.5221 - val_loss: 0.6838 - val_acc: 0.5741\n",
      "Epoch 7/10\n",
      "611/611 [==============================] - 42s 68ms/step - loss: 0.7329 - acc: 0.4550 - val_loss: 0.6857 - val_acc: 0.5741\n",
      "Epoch 8/10\n",
      "611/611 [==============================] - 42s 68ms/step - loss: 0.7123 - acc: 0.5123 - val_loss: 0.6842 - val_acc: 0.5741\n",
      "Epoch 9/10\n",
      "611/611 [==============================] - 42s 68ms/step - loss: 0.7204 - acc: 0.4795 - val_loss: 0.6855 - val_acc: 0.5741\n",
      "Epoch 10/10\n",
      "611/611 [==============================] - 42s 69ms/step - loss: 0.7139 - acc: 0.4943 - val_loss: 0.6859 - val_acc: 0.5741\n",
      "431/431 [==============================] - 207s 480ms/step\n",
      "Test score: 0.693466407378699\n",
      "Test accuracy: 0.5127610208816705\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_7 (LSTM)                (None, 5000, 32)          4352      \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 5000, 32)          0         \n",
      "_________________________________________________________________\n",
      "lstm_8 (LSTM)                (None, 32)                8320      \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 12,705\n",
      "Trainable params: 12,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "431/431 [==============================] - 8s 19ms/step\n",
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     ]
    }
   ],
   "source": [
    "# for LSTM model\n",
    "\n",
    "max_len = 5000\n",
    "\n",
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "X_train = pad_sequences(X_train, maxlen=max_len,padding='post')\n",
    "X_test = pad_sequences(X_test,maxlen=max_len,padding='post')\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "test_LSTM(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of training samples:  874\n",
      "No. of genuine users:  480\n",
      "No. of fake users:  394\n",
      "For genuine retweet timestamps:\n",
      "Estimated mu:  [9.71295141e-05]\n",
      "Estimated alpha: [[0.49279461]]\n",
      "Estimated coeffs:  [9.71295141e-05 4.92794606e-01]\n",
      "For fake retweet timestamps:\n",
      "Estimated mu:  [2.36528697e-05]\n",
      "Estimated alpha: [[0.79240656]]\n",
      "Estimated coeffs:  [2.36528697e-05 7.92406558e-01]\n",
      "Simulating for genuine and fake and comparing with results...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c53fb4898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hBgAA6AFDwyP6yJ0bNDI6Jpc0Mjqmj9y5QUPDI2V37QjffmK33vPXD5bdjdQImAEAAHrAJ76yURNNddsmDro+8ZWNJfUo2bef2F12F1IjYAYAAOgBz+2faKsd6REwAwAAAAkImAEAAIAEBMwAAABAAgJmAACAHjBncKCtdqRHwAwAANADLjhzdlvtSI+AGQAAoAc88PiuttqRHgEzAABAD9g5OtZWO9IjYAYAAOgBgzP722pHegTMAAAAPcC9vXakR8AMAADQA0bHxttqR3oEzAAAAD2gz6ytdqRHwAwAANADJmJyL+LakR4BMwAAQA+o48IlQ8MjZXchFQJmAACAHlDHhUtuuGdT2V1IhYAZAACgB9Rx4ZI9e+sxITFVwGxmF5nZZjPbYmZLI26/xsweM7NHzeybZnZq6LYrzeyHwb8r8+w8AAAAGkZiFiiJa0d6LQNmM+uT9HlJb5d0lqQrzOysps2GJS1093Ml3SXps8F9j5V0naTXSTpf0nVmNiu/7gMAAECqZ5WM6vbscGlGmM+XtMXdt7r7fkm3SbokvIG7P+Due4Nf10o6Kfh5saT73X23u++RdL+ki/LpOgAAACbVsUpGdXt2uDQB8xxJ20O/7wja4rxf0tfaua+ZXWVm68xs3a5d1c2zAQAAqKo6jjBXuYJH2PQU20Tt5cgTAjN7r6SFkt7czn3d/UZJN0rSwoUL63KyAQAAUBntjjAPDY9o5erN2jk6phMHB7Rk8Rm6dEHSmGj+qlzBIyzNCPMOSSeHfj9J0s7mjczsrZI+Ielid9/Xzn0BAACQzayZ/anbh4ZHdO3dGzUyOiZXY2LgtXdv7Hpd5CpX8AhLEzA/LGm+mc0zsxmSLpe0KryBmS2Q9EU1guWnQjetlvQ2M5sVTPZ7W9AGAACAHMWlKke1r1y9WWPjE4e1jY1PaOXqzQX0LF5dKni0TMlw9wNmdrUagW6fpJvcfZOZLZe0zt1XSVop6RhJd1ojT2abu1/s7rvN7FNqBN2StNzddxfyTAAAAKawZ8aiaxpHtVOCrj1pcpjl7vdJuq+p7ZOhn9+acN+bJN3UaQcBAADQ2uDM/siFQAYjUjJM0RPSqjs9sFys9AcAANAD2knJiKuwQOWFaATMAAAAPaCdlAy0J1VKBgAAVVaF8lhA2dpJyaiKuqSAEDADAGptaHhES+7coPGDjYvJI6NjWnLnBkkiaMaU0k5KxkD/NI2NH4xs76a6pICQkgEAqLXrV206FCxPGj/oun7VppJ6BJRjNCb1Iqo9KlhOap/qGGEGANRaO0FCu0j1QJ30mUWu6je5NHb49Yz2EDADABCBVA/UTdLS2EPDI1py1waNT1QrCaIuOcykZAAAEIFUD9TNnMGB2PYb7tlUuWBZIocZAIBaKzLVAyjCBWfOjm2Pqp6B9AiYAQAAesADj+9qqx3pETADAAD0gLjJfEzyy46AGQAAoAfELVBS5YVL6oKAGQAAoAe0s3AJ2kPADABABIupdxXXDpSNiarFIWAGACACo3Wom76Ys7m4dqRHwAwAQIRpMTFGXDtQtqSFS5ANK/0BABDhYEyMEdeO7lo2tFG3PrRdE+7qM9MVrztZn770nLK7Vao5gwMaiaiIEdeO9BhhBgAAtbJsaKNuXrvt0MjphLtuXrtNy4Y2ltyzciUtXIJsCJgBAJU0NDyiRSvWaN7Se7VoxRoNDY+U3SVUxC1rt7XVPlWwcElxSMkAAFTO0PCIrr17o8bGJyRJI6NjuvbuxujhpQvmdKUPM/unae/4wch2lCsuK2aqZ8skLVxiYv9kQcAMAKiclas3HwqWJ42NT2jl6s1dC5hnTO+LDJhnTO/ryt+vA/KIq2VwZr/27D2yhFxcO9JLdZpsZheZ2WYz22JmSyNuf5OZfc/MDpjZZU23TZjZ+uDfqrw6DgDoXXETlLo5cYmatsnII66epFKIlJbLpmXAbGZ9kj4v6e2SzpJ0hZmd1bTZNkm/I+lLEQ8x5u7nBf8uzthfAAC6gpq2yeqaR9zLufHPxJzMPTM2nrq0HK/uaGlSMs6XtMXdt0qSmd0m6RJJj01u4O5PBrcdee0KAIAaoqZtsjLziPvMIo9Dq5OZMnLjh4ZHtHL1Zu0cHdOJgwNasviMwv5WUkrG6N7xVMeGV3e0NCkZcyRtD/2+I2hL60Vmts7M1prZpVEbmNlVwTbrdu1iJicAoHyzZva31Y72ZBnp7fRkJik3vgiTAfrI6JhcLwToRY1q72t6bpPc2wuESas5UpqAOep0rZ39foq7L5T025I+Z2anH/Fg7je6+0J3Xzh7NrUCAQDlY2ns4mQNJDtdhTGpikQRuhmgDw2PRE5SleJTNeLc8lC102rKkCZg3iHp5NDvJ0namfYPuPvO4P+tkr4laUEb/QMAoBRM+itO1kCy01UYB2OuDsS1Z9XNyatJ+67d58dJ4ZHSBMwPS5pvZvPMbIakyyWlqnZhZrPM7Kjg5+MlLVIo9xkAgKpi0l9xkgLJIifidfuqQTdfQ0mj5JSUy67lpD93P2BmV0taLalP0k3uvsnMlkta5+6rzOy1kr4iaZakd5rZDe5+tqRXSvpiMBlwmqQV7k7ADADomk4nXTHprxwjo2NacucGSfET8VotKhN3zJOqSBShm6+hEwcHulp2capJtXCJu98n6b6mtk+Gfn5YjVSN5vt9RxIVzAEApchSFaHTSgy9KGqBkiKNH3Rdv2pT7DFKWlQm6ZgnVZGouwvOnK2bK17Sr85Y3xNAW3q5hil6T5ZcWUaYG+IWKClaUq54Un75R+7YEHvMe3ki5wOP51tljM/3w7E0NoDUhoZHtOSuDRqfaHy7jIyOacldyZdOgTJlmXTFCHNDEcHxNElZFm6IOzZS/AlNUo5vUSkZ3ZR3OkZ4ZB6MMANoww33bDoULE8an3DdcM+mknqEXhUXkrYbqmaZdMUIc3HSBMtJh6iTYzBzRl/Xq2R0UxEncs0j9VMZATOA1OJmWjMDG3nLaxW5LEEvVTLKlXSIOjkGz+2f6OmUDE7kikVKBgCgcmbFTM5qd5W9LI/DCHNxZvSZ9k8k78ekY9TpMYjLfa5ySkbaKi9xr3XkgxFmAKnldZkcaCWvkcAsj8PS2MVpTu2KknSM8j4GM2f05fp4eWlnRUTO44rFCDOA1PK6TB6l01q56E151cvNslpfL1++L1uaXZh0rPflnFu7d381c3WTqrw0fz5WeZS8FzDCDCC1okbc2hlFwdRw4uBAW+1xsuQhszR2uZIm4kXVYM6iqudASVVe5i69V6dfe5+WDb1QYxrFYYQZQGpFjbi1M4qCqWHmjOjxnLj2OFkn/VFWrjy9MJI/0D9NYxHB/UB/utdxUvk86fCa2L2wv6qMgBlAakWNuMXVR02qm4re9sOnnmurPc6cmOWC56QYqe71SX9lpkGZWo/q9kKKQVSwnNTeLO1r7ea125hLUjBSMgCkVlSZrbwuvwPNLjhzdlvtYb086a/sNKg0YWA3UwzSnECVoZ3XGikZxSJgBpBaUSNuWYIaIEnccsFplhGOm1iW94SzMmRZMryVvEY6uzmQX9XPmnb2QY9c+KgsAmYAqU2L+SaMa08rS1CD3jT/hKPbao+TZWnsuIlleU84K0OW/dJKXnFbNydXVvWzpp20lF5IYakyAmYAqR2M+SaMa0+LHGY027s/JliNaY/Dan3R4p5+XXZL3mkxeZwoFKGdtDRS2IpFwAygdOQwo1leI6BZ0oh6eaGeuteYzrufVT2BaidVpKppJb2CgBlAakUFEOQw52doeESLVqzRvKX3atGKNbWtZZ3XyHDcZK40k7yKXKinLiZr/FZN3ukaVa180k6qSFXTSnoFATOA1IoKIMhhzkfZlQ/ylNcE0ywnY1M9nWPZ0MZDNX6rJu9jUNXKJ+2kpZHCViwCZgClI4c5H0VWPui2vILVr274SVvtYb1eh7mVWyoaLEv5H4OqHtJ2SsVRVq5YBMwASkcOcz6KrHzQbXkFq1kW2+nlOswzY1aam2xfNrSx0qkneR+DqlaYoKxcdRAwA0itqEvU5DDno5dSCKoQrNZpYly7ueszpvcltt/yUHVHl6X8j0FVR2fbCeS7WYZvKiJgBpDa60+b1VZ7WuQw56OXUgiej1kcJK69CEUtBZ+3oeERLblrw2G560vu2pAYNMcFYpPtVX/J5H0Mqvp8ucpWHakCZjO7yMw2m9kWM1sacfubzOx7ZnbAzC5ruu1KM/th8O/KvDoOoPuefDr60n5ce1ppc5h7pQJEUbJUhKiasZjFQeLai1CXEfsb7tmk8YnDI77xCdcN92yKvU/ciGpVR1qb5X0MykjJSPP5xVW26pjeagMz65P0eUkXStoh6WEzW+Xuj4U22ybpdyR9tOm+x0q6TtJCNSbSPxLcd08+3QfQTUXlyJ44OBD5GOHRlckKEJOT2iYrQEjSpQvmZPr7VTM0PKKVqzdr5+iYThwc0JLFZ6R6jhecOTuyqgFfup2py4j9nr3RwV5cu1SNEfws8j4GRZ0omOKrCF1796OSlPhe5ypbdaQZYT5f0hZ33+ru+yXdJumS8Abu/qS7Pyqp+dR/saT73X13ECTfL+miHPoNoIekyWHupQoQSbKUhiO1JV9VyKMuShVG8LPI+xgUdaKQFNaPjR9s+V6nUlB1pAmY50jaHvp9R9CWRqr7mtlVZrbOzNbt2sUHOzDV3PtodImvcPtUKT2X5cRgquyjbqnLpL9eXpEwTt7HoKwThVbvdXKYqyNNwBz1nkv7Uk11X3e/0d0XuvvC2bO5dAhU1bSYb+C49rSSLilP5irXPecyrSxB71TZR93SamJcVXSyoFDdg+wiJl5WZV5EOD2NdKrqSBMw75B0cuj3kyTtTPn4We4LoGKOmh79kRHXnofJy5TPPh/9BVmXnMu0stSkrsuIaBoxZYJj24tQlxOQTiYn1n3Z7yImXlZlZczwc5sq6VRVXYI9LM1Hz8OS5pvZPDObIelySatSPv5qSW8zs1lmNkvS24I2ADX0fMxly7j2tAYHkgOQsfEJxf2JuuRcppWlJnVdyqClcSDmsMa1F6EuJyB1mZyYp6KeWxXmRYSf21RJp7r1oe2tNypZy4DZ3Q9IulqNQPf7ku5w901mttzMLpYkM3utme2Q9FuSvmhmm4L77pb0KTWC7oclLQ/aANRQUSNu1198dua0jl6RZeJeXcqgpVGFEdC6pGR0cty7cbWoyDKQRb6m2636k/fzDD+3ql3NKEodTu5alpWTJHe/T9J9TW2fDP38sBrpFlH3vUnSTRn6CKAiihxx6zPTwQ4eqBcqFoRlyWGeiiONRRqc2R+ZX1+1IKaT474/Zqg+rr1dRZeBLPo1PTQ8kqqfRTzP8HObKm/dOgyYsNIfgNSKGnFbuXqzxg929s1w1stfnOlvV02WHOZeLoNWhrqkZHRy3IsewS+6DGTRV03S5jK3ep6d9DL83OqYTtWJIufB5KX6PQRQGUWlZGTJ01u7tX7rICVdws2Sw1yXAK8u6pKSsS9m4mtcezfEpTXklZNb9AhzXqUcO+ll+LnVMZ2qE3WYi0LADCC1ogKyLLVG65Zu0Gphkiw5zHUJ8OqiLlUy9sYEG3HtrRRZJaLdfRd3ctmNqyZpcpmzXBGKM81eOAZ1+3zrVB1ODAiYAaRWVBWGLLVG65D7FtbqEi51mKtjqo7Y33DPpsIeu919t+TODYedXC65c4OGhkcqcwziPrvmHjegRSvWdPSYB1368O3rtWxoo+ZMkYVL6nBikGrSHwBIjVGAqA+2rKMDWWqN9tUsYG4VEM+c0afn9h95KX3mjL6Wjz1VA7yi1GXEfmb/tMjR5JkdFq2OW0goD8+Mjbc1gt08t2H8oOv6VZu6dgwWrVijnaNjOnFwQEsWn3HERL64z67vPLE7Uz64S7pl7Ta98fRj267aUUd1mGfBCDOA1IqowjA0PJLpC6EGqW+HaTUKvDciWE5qD6tLgFcXdRmxnzE9+mQqrl3q7gIwYQP90w5VkejU6Nh4145BXOrUpLgT4DSfiK3O9V31nKPRiTqc1DPCDCC1OYMDkcFtp5cNJ/N5p5JWo8CdVi8YGh7RtJgrAGnzKYeGR3T9qk2HUmxmzezXde9PrthdAAAW90lEQVQ8O5cyYHVUlxH7TlKlyjrRHDtwMJf9V8YxmEydCr8fToz5TEwjzVOoQ6pCHupwUs8IM4DUslRwiBKVz9uuo1OkKlRJ3BfDaHCpupNFKCZPPOK+XNMcn6HhEV1z+/rDgqw9e8e15K4NpS8VXJa6jNjXacGaPOI/U3nl1ppHlLPMv8ALqnbVJgoBM4DUslRwiJJHbl4ni52UKWm099q7N+r4Y6K/OE6bPTP2fq1OPNIcn+tXbVLUoOP4hJe+VHBZiqiAUISptmCNq7P6xnlonkuQZf4FXlCHlyoBM4DUslRwiJLHCFgd6neGzT0uPtgaG5/QT3++P/K2rbv2xt6v1f5Pc3ySRuzyqp1bN3lfUSnKVFywpqz4qnlC7lR9b+StDgu0EDADSC3vSVC9OgKW5MGtuzu6X9K+arX/s46IVm1EtVvyvqJSlLrkWveiqfreyFsV04eaETADSC3vFcXy+JCsUw7z0PCIOlwBPPESdKvAKOuIaNVGVLsl7ysqRalLrnWvGRoe0e7n9pXdjZ5Qh8ETAmYAqeW9olgeH5J1ymHOkguc9CxbBUZZR0SrNqLaLXUpK1eXfvaSyYm2dUsJq6o6pA8RMAMozVTLYS5qZLLVZeGR0bFMlS6qNqLaLWlTHeKWb+4WUjK6L48KP3hBHV6rBMwAUhsciBnJimlvJa/LcGUGK+3Iku+YdHKRJmUiatGFtKZqnmaaVIeh4REtuatp+eYul+IjJaN7Jt+FU2H1vW5i0h+AnvKOV7+8rfZWpuU0zyMcrFxzx/rKBs1ZcoGTysp9+ZEdLe8/uehCJ8rIYY47P+jG3KDJEeO407nwCcQN92zS+ETT8s0Trhvu2VRgD+P7k6Zdqlfuf5XUYCAUBSFgBpBa3lUDOp0A1+oxP373o/k/cA6y5AInlZVLm5bSaWpFGTnMZaUZTOamJo0ghk8g9uyNHhnbs3e8aydunZS/6+/j6x9oB0tjA0gtLoio2uXJTichFi1LLnAe6SudTgIrI4e5L2aZ7zzLT81beq9OHBzQksVnHFruOE1uatoTiCV3bZAkXbpgjoaGR7Ry9WbtHB3TiYMDuuDM2Xrg8V2Hfg/3oV2dnMiSrgG0h4AZQGrdCGJ62YmDAx2fXOSxjzuNucvIYe7G6nWTaTzX3r1RUiOwTXN8JidRtkpxCadmNCoqTBy6/81rtx32eOE+tKuT8neDM/tjR8cBHIlrMgBSyzuIKSqPsqrxe1E5zGl1OrGm1+swj41P6MO3N3Lf0+bVf+j29amC6z17x1ONWmfJMe+krFwdqhJUVVXnSKBYBMwAUst7Cd6iaiifcMyMQh43q6JymNPqdJR6KtRhdkkfuXNDIXn1aa8qdHr1IW7hoGefjz9BIiWjc9ev6t6ETlRHqpQMM7tI0v+U1Cfpb9x9RdPtR0n6R0mvkfS0pHe7+5NmNlfS9yVNnjavdfcP5NN1AN2W90Ssomoo//Tn+wt5XElH5KK2k3tadg7zhPth/X/pQL/MpNEWl+anSh3miSKi5TZ0emEkLmd//GDj9Rr1+iQlo3N1KIGG/LUcYTazPkmfl/R2SWdJusLMzmra7P2S9rj7KyT9maQ/Cd32hLufF/wjWAZqbKrXew1XUAjnv6a9RFtUHea0qS0mHdb/0bFx7dk73rJU1lStw9xtLmnB8m/kesk/Ls2DlAygPWlSMs6XtMXdt7r7fkm3SbqkaZtLJP1D8PNdkn7VrKpZhAA61Um91yR1qwUblYvaTu5pllzg1582K/a2tCXCXOpodbK5x3U3YG4nYOy1fNI9e8czLTLTLO7qwFQ5yQXykuZTdo6k7aHfdwRtkdu4+wFJz0g6LrhtnpkNm9n/MbNfjvoDZnaVma0zs3W7dvV+rhxQV53Ue01SVC3YlxxVTCCetaxellzgtVv3xAZRRQc/a7fuyeVx0i4h3c7kt04nylVZmpOwZUMbdfq192nu0nsTt4ub+DezZierQNnSfFtFjRQ3X8yJ2+Ynkk5x9wWSrpH0JTN7yREbut/o7gvdfeHs2b09Gxuos7wXLikqF/Df97U/ippGXFpE2sl0WXOYP3z7es2NCDaLTpnII3+6nXSWdvZTr+ZXJ52ELRvaqJvXbkt1XJ6PuaLw3P5i3iNAr0oTMO+QdHLo95Mk7YzbxsymS3qppN3uvs/dn5Ykd39E0hOSfiFrpwGUo5N6r70ka1m9rIHt5F9pDjbrUPYtLp0lquJA0n562YsPr4DS6WIsVZd0EnbrQ9tjb2tW1MRaYKpJEzA/LGm+mc0zsxmSLpe0qmmbVZKuDH6+TNIad3czmx1MGpSZnSZpvqSt+XQdQLd1Uu+1itKmBjSbExPIxbU3yzMXOBxsfnXDT3J73KLEnVSNjh2+hPTQ8Ih2P7cv9nF+9uzhVyV6tdJD0klYnou3AEinZVk5dz9gZldLWq1GWbmb3H2TmS2XtM7dV0n6W0n/ZGZbJO1WI6iWpDdJWm5mByRNSPqAu+8u4okAKF7eZeVMR+Z3FW0yNSC86lraVdYuOHP2YSu0hdvTyCsXeNJksFlGmavJ8nQjo2OHVoCck1BmL2mVw5WrNx9aPrpxbOJHRSfctWjFmkNl/aaC5lKGndy/02W3gW6p+us0VR1md79P0n1NbZ8M/fy8pN+KuN+XJX05Yx8BVESWsnLNX/pzjxvoerAsJVe6aPVhnTWHu4iRwTImvTWfdEw+r6STj7iTjcn7zVt6r6bFLL0etX34/142NDyiJXdt0PjEC/u4XWle20DZPnLH+kq/TlMFzACyy7LgRVXEjRK2GvWKGtUtK9jJUukiaw53X8qAsB0jo2M6avo07TvQvVzVpKWe404+Wp1UuEg1aDY0PKIP374+84nl5AlJXT93MDVMVPztz9LYQBdkXfCiKjotK5cUYBVl2dDGyPZpMXOp4trDstahPm32zFTbtaubwbLU+gQh6uRjKowG5+1DOQTLkyY/d665Y70WLP9GTo8KTB0EzEAXZF3woio6TUkoI1i69aHtkZP74lY/TrMqctY61Ft37U21XdW1m0dbtxPDXnbQe3eiJFAkAmagC7IueFEVnaYkpK1TnKcJ98hR/SyqmMPcLeGgt90ydjfcc2TpOACoEwJmoAuyLnhRFZ2WlSsrUIwa1c8ijxzmugqnEKU5QZi79F5d+KffksSIJoD6I2AGuiDrghdV0WlZuToEioMDrWtJZ61DXVQOczeEU4jSniD88KnnDgXNAFBnBMxAF2Rd8KIqOi0rV8aJQdwkvv6YT713vPrlLR8zax3quucwTwbK7eQw//Cp52L3OQDUBR9jQBdknSxWFZ1WiSjjxKAvJmCOWxMjTZpB3IlB82p1zSYnH9btikKzyePc7uu2y0U8ACB3BMxAF2SdLFYVnQb+ZZwYJCwWFylNmkHSiUFcmcBwScG6+7dnxrRg+TdiFyCJU+/TBAAgYI4VVY4K6FSvVMn46oafRLbf8tC2xPdIHU4M0uQhJwX+cWUCy6hBXZQJSpIBmKJY6S9C1Kpkccu9AmnErfDW7mS4slcLHI1JSXBX4nsk7SSxMqXJlkhTb3rRijWHHZ86PHcAQDJGmCP0yiITqI48qmRUfbXAsfEJfeSODZH9aXehizK0mrgopQv8m4/PzBl9OfQOAFAmAuYIWWutojxVTaXJo0pGFU7kWg2ITy4W0rzflyw+o8Be5WOaWcvXTbuB/9j4hPbu7410DACYygiYI8R+KRpLvFZZlUdg86iSUYU86DQD4nW9GjPhfuh1s+Su6JHyTiYvMuENAOqPgDlC3Jeiu2K/SFG+KozAxsmjSkadVgtsvhpThWPQjvEJj1zOuQ6TFwEA+SNgjpD0pTg+4bX78p8qqpxKk0ff6rRaYPNVmiocg3ZFVYOoW1UTAEA+qJIRodWXex2//MM6qbRQdnWGVoaGRzQtphJFq7zTvJ7bsqGNuvWh7ZpwV5+Zrnjdyfr0pecc6kNUsJXUt+Z+zZrZH1vSa9GKNZH97vS5Zd0nI6ONer3XvfPsRoOplrkJc5feq1kz+3XdO8+u1OsdANBd5hUbnVq4cKGvW7eu1D4sWrEmcSTJJP3Zu8+r3BdomiCnuWSeJA309+kzv3FO7PPp5D7dFNW/sPe+/pRDgWuzZUMbdcvabYfFcp08t2VDG2MXc5gzOKC5xw3o20/sju1b87G74MzZ+vIjI23V752MSecEx15S5H5pFQAmHe8P3b4+dX+kF5anPlitjxkAQAU9ueLXu/43zewRd1/YcjsC5iMlBT+TqhQwSumD2riTgTmDA/r20l+JfOxO7tNNrU5wBgf6tf66tx3RPjQ8og/fvj5y4LPPTAfdU4+uzlt6b+IAatwA62Rw23zssg7IDvT36UX902JHpJNev0nHm5QEAEBRqhwwk5IRIc3EnsnJZFUJmJMmvIX72EkubZVzg6XW/RgdG9fQ8MgRx2rl6s2xQelkakfaRWtaBbdxt+8cHYs8dllPY8fGJxJHp8OTIa9ftenQgiRJaR8EywCAqSrVpD8zu8jMNpvZFjNbGnH7UWZ2e3D7Q2Y2N3TbtUH7ZjNbnF/Xi5M2EKxSAJE2qI3LmU3Kpe3kPt2Uph9REzXTHue4Shvhms+deulAf2knHiOjY1py54bDVu9j2WMAAI7UMiXDzPok/UDShZJ2SHpY0hXu/lhomz+QdK67f8DMLpf0Lnd/t5mdJelWSedLOlHS/5b0C+4eO/RVdkpGmnSMbpjZ3ziX2Tt+sOW2L3vxDP305/sjbxvon6bffM1JhyajmRqLT4RzSvv7TO9+7cl64PFdbZ8EHD2jT3v3TxyWuvCev37wiHzd977+FC089VgtuXO9mp9SO881i3CaA7m1AABUS5VTMtIEzG+QdL27Lw5+v1aS3P0zoW1WB9s8aGbTJf2bpNmSloa3DW8X9/fKDJirEix3m0nqmyYdyBivHjV9ml7+0qP05NPVGXkHAAD1UOWAOU1KxhxJ20O/7wjaIrdx9wOSnpF0XMr7VsatD21vvVEPcmUPliVp34GDBMsAAKDnpAmYo5YRax6WjtsmzX1lZleZ2TozW7drV3kraVVxAQgAAACUK03AvEPSyaHfT5K0M26bICXjpZJ2p7yv3P1Gd1/o7gtnz45elrobqrjEMAAAAMqVJmB+WNJ8M5tnZjMkXS5pVdM2qyRdGfx8maQ13kiOXiXp8qCKxjxJ8yV9N5+u5++K153ceqMe1D/N1N+X/WRhoL9P8084OoceAQAAVEfLgDnISb5a0mpJ35d0h7tvMrPlZnZxsNnfSjrOzLZIukYvTPbbJOkOSY9J+rqkP0yqkFG2T196jt77+lPK7oakRuWIyeoRrbzsxTMic19MjeoU7339KYdGz/vMtOj0YzVncECmxmIUK3/r1Vp52as1p80ycaZGlYzJx/nMb5yj+695ixadfuwR27739afoc+8+T1FPqZ3nmkV4H02zxn4DAADlK2PCXztY6Q8AAABTUp5VMgAAAIApi4AZAAAASEDADAAAACQgYAYAAAASEDADAAAACQiYAQAAgASVKytnZrsk/biDux4v6Wc5dwfdxTHsDRzH3sBxrD+OYW/gOBbrVHdvucx05QLmTpnZujR19FBdHMPewHHsDRzH+uMY9gaOYzWQkgEAAAAkIGAGAAAAEvRSwHxj2R1AZhzD3sBx7A0cx/rjGPYGjmMF9EwOMwAAAFCEXhphBgAAAHJHwAwAAAAkqHzAbGY3mdlTZvavMbe/x8weDf59x8xeHbrtSTPbaGbrzWxd93qNZimO4yXBMVxvZuvM7JdCt11pZj8M/l3ZvV4jLOMxnAja15vZqu71Gs1aHcfQdq8NjttloTbeixWQ8RjyXqyIFJ+pbzGzZ0LH65Oh2y4ys81mtsXMlnav11NX5XOYzexNkp6V9I/u/qqI298o6fvuvsfM3i7pend/XXDbk5IWujsFv0uW4jgeI+k5d3czO1fSHe5+ppkdK2mdpIWSXNIjkl7j7nu62H2o82MY3Pasux/T3R4jSqvjGGzTJ+l+Sc9Lusnd7+K9WB2dHsOgnfdiRaT4TH2LpI+6+zua2vsk/UDShZJ2SHpY0hXu/ljhnZ7CKj/C7O7/V9LuhNu/E/rAXivppK50DG1JcRyf9RfO3o5W4wtZkhZLut/ddwfH+X5JFxXaWUTKcAxRIa2OY+CDkr4s6alQG+/FishwDFEhKY9jlPMlbXH3re6+X9Jtki7JtXM4QuUD5ja9X9LXQr+7pG+Y2SNmdlVJfUJKZvYuM3tc0r2S/lPQPEfS9tBmO4I2VFDMMZSkFwVpGmvN7NKSuocUzGyOpHdJ+kLTTbwXayLhGEq8F+vmDWa2wcy+ZmZnB228F0swvewO5MXMLlAjYP6lUPMid99pZidIut/MHg/O6FBB7v4VSV8JLlN9StJbJVnUpl3tGFKLOYaSdErwXjxN0hoz2+juT5TWUST5nKSPufuE2WFvP96L9RF3DCXei3XyPUmnuvuzZvZrkoYkzRfvxVL0xAhzkC/5N5IucfenJ9vdfWfw/1OSvqLGZQxUXHBSc7qZHa/GmfPJoZtPkrSzlI4htaZjGH4vbpX0LUkLyusdWlgo6bZgDshlkv4yGInkvVgfcceQ92KNuPu/u/uzwc/3Serne7E8tQ+YzewUSXdLep+7/yDUfrSZvXjyZ0lvk5Q4oxjlMbNXWDAUYma/KGmGpKclrZb0NjObZWaz1DiOq8vrKeLEHcPg2B0VtB8vaZEkJqdUlLvPc/e57j5X0l2S/sDdh8R7sTbijiHvxXoxs/8Q+kw9X42Y7Wk1JvnNN7N5ZjZD0uWSqHhSsMqnZJjZrZLeIul4M9sh6TpJ/ZLk7l+Q9ElJx6lxBi1JB9x9oaSXqXFpWGo8zy+5+9e7/gQgKdVx/E1J/9HMxiWNSXp3MIFst5l9So0PCEla7u6dTJJARp0eQzN7paQvmtlBNT7wVzCbuzwpjmMkd+e9WBGdHkNJvBcrJMVxvEzS75vZATU+Uy8PvhcPmNnVapyw9qlRBWVTCU9hSql8WTkAAACgTLVPyQAAAACKRMAMAAAAJCBgBgAAABIQMAMAAAAJCJgBAABQK2Z2k5k9ZWYtSwab2alm9k0ze9TMvmVmJ7X79wiYAQAAUDd/L+milNv+D0n/6O7nSlou6TPt/jECZgCoOTP7XTP7L2X3AwC6JVhR9rBa8GZ2upl93cweMbP/Z2ZnBjedJembwc8PSLqk3b9HwAwA9fdZSReW3QkAKNmNkj7o7q+R9FFJfxm0b1BjcS1JepekF5vZce08cOVX+gMAxDOzV0g6XtLasvsCAGUxs2MkvVHSncEqz5J0VPD/RyX9hZn9jqT/K2lE0oG2Hp+V/gCgnsxsSNGXFj/t7n/U7f4AQDeZ2VxJX3X3V5nZSyRtdveXt7jPMZIed/e2Jv4xwgwA9XWjpD5J75D0+5KeDdofLK1HAFACd/93M/uRmf2Wu99pjWHmc919g5kdL2m3ux+UdK2km9p9fEaYAaDGzOyfJb3B3U8ouy8A0C1mdqukt6iRkvZTSddJWiPpryS9XFK/pNvcfbmZXaZGZQxXIyXjD919X1t/j4AZAOrLzLZLeszdF5fdFwDoVVTJAICaCi4zniTpe2X3BQB6GQEzANTXa4L/CZgBoEAEzABQXwuC/wmYAaBABMwAUF+nBf9vK7UXANDjKCsHAPW1Nfj/z83sQUkTkr7kzOYGgFxRJQMAasrMZkr6gqS3q1FaaZu7n1purwCg9xAwAwAAAAnIYQYAAAASEDADAAAACQiYAQAAgAQEzAAAAEACAmYAAAAgAQEzAAAAkICAGQAAAEhAwAwAAAAkIGAGAAAAEvx/iN4zkjjWrWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c52b63390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of test samples:  431\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "Test accuracy:  0.5614849187935035\n"
     ]
    }
   ],
   "source": [
    "genuine_kernel, fake_kernel = learn(X_train, y_train)\n",
    "\n",
    "predict(X_test, y_test, genuine_kernel, fake_kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import pickle\n",
    "\n",
    "dataset_location = os.path.join(os.getcwd(), 'Datasets', 'collected_data')\n",
    "df = pd.read_csv(os.path.join(dataset_location, 'user_timelines_final.csv'))\n",
    "\n",
    "df = df[df['Fake/Genuine User'].notnull()]\n",
    "\n",
    "# print(df)\n",
    "\n",
    "df.describe()\n",
    "\n",
    "labels = df['Fake/Genuine User'].tolist()\n",
    "screennames = df['screen name'].tolist()\n",
    "\n",
    "# print(labels)\n",
    "# print(screennames)\n",
    "# print(df1)\n",
    "\n",
    "with open('user_to_tweets_info.json','r') as f:\n",
    "    user_to_tweets_info = json.load(f)\n",
    "    \n",
    "# print(user_to_tweets_info)\n",
    "\n",
    "labeling = {}\n",
    "\n",
    "with open('tweets_#deletefacebook_readable.json') as f:\n",
    "    \n",
    "    ctr = 1\n",
    "    for line in f:\n",
    "\n",
    "        try:\n",
    "            # if ctr==10:\n",
    "            #   break\n",
    "            tweet = json.loads(line)\n",
    "#             print(tweet)\n",
    "            user_name= tweet['user']['screen_name']\n",
    "            if user_name in screennames:\n",
    "#                 print(user_name)\n",
    "                ctr+=1\n",
    "#                 print(ctr)\n",
    "                idx = screennames.index(user_name)\n",
    "                user_id= tweet['user']['id']\n",
    "#                 print(user_name)\n",
    "                if (user_id, labels[idx]) in labeling.keys():\n",
    "                    labeling[(user_id, labels[idx])].append(tweet)\n",
    "                else:\n",
    "                    labeling[(user_id, labels[idx])] = [tweet]\n",
    "                \n",
    "        except Exception as e:\n",
    "#             print(e)\n",
    "            continue\n",
    "# print(labeling)\n",
    "genuine = 0\n",
    "fake = 0\n",
    "for key in labeling.keys():\n",
    "    print(key, \" -> \", len(labeling[key]))\n",
    "    if key[1] == 'fake':\n",
    "        fake+=1\n",
    "    else:\n",
    "        genuine+=1\n",
    "print(\"No. of users: \", len(labeling.keys()))\n",
    "print(\"No. of fake users: \",fake)\n",
    "print(\"No. of genuine users: \",genuine)\n",
    "with open(\"labeled_users_deletefb.pickle\",\"wb\") as pickle_out:\n",
    "    pickle.dump(labeling, pickle_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No. of users:  754\n",
    "No. of fake users:  118\n",
    "No. of genuine users:  636"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# calculate retweet threads of the users under consideration, for ndsync purposes\n",
    "\n",
    "import pickle\n",
    "import os, json\n",
    "\n",
    "dataset_location = os.path.join(os.getcwd(), 'Datasets', 'collected_data')\n",
    "\n",
    "with open(os.path.join(dataset_location, \"labeled_users_deletefb.pickle\"),\"rb\") as pickle_in:\n",
    "    labeled_users_deletefb = pickle.load(pickle_in)\n",
    "    \n",
    "labeling = list(labeled_users_deletefb.keys())\n",
    "\n",
    "retweet_threads = {}\n",
    "\n",
    "for x in labeling:\n",
    "    retweet_threads[x[0]] = {} # this is going to be a dict of dictionaries (each dictionary: retweet thread, key: tweet id)\n",
    "\n",
    "with open('tweets_#deletefacebook_readable.json') as f:\n",
    "    \n",
    "    ctr = 1\n",
    "    for line in f:\n",
    "\n",
    "        try:\n",
    "            # if ctr==10:\n",
    "            #   break\n",
    "            tweet = json.loads(line)\n",
    "#             print(tweet)\n",
    "            user_id = tweet['user']['id']\n",
    "            if user_id in retweet_threads.keys(): # user to be considered\n",
    "#                 print(user_id)\n",
    "                tweet_id = tweet['id']\n",
    "                if tweet_id not in retweet_threads[user_id].keys() and 'retweeted_status' not in tweet.keys():\n",
    "                    retweet_threads[user_id][tweet_id] = []\n",
    "        except Exception as e:\n",
    "#             print(e)\n",
    "            continue\n",
    "    \n",
    "    print(len(retweet_threads))\n",
    "    \n",
    "    for key in retweet_threads.keys():\n",
    "        print(key , \" - > \", len(retweet_threads[key].keys())) #no. of original tweets by each user\n",
    "    \n",
    "    for line in f:\n",
    "        try:\n",
    "            # if ctr==10:\n",
    "            #   break\n",
    "            tweet = json.loads(line)\n",
    "#             print(tweet)\n",
    "            if 'retweeted_status' in tweet.keys():\n",
    "                user_id_retweet = tweet['retweeted_status']['user']['id'] #original tweet's user\n",
    "                tweet_id_retweet = tweet['retweeted_status']['id'] # original tweet's id\n",
    "                if user_id_retweet in retweet_threads.keys(): #if original tweet's user is a reqd user\n",
    "                    print('Hello') # doesn't reach here even once\n",
    "                    #it is a retweet whose original tweet belongs to one of the users under inspection\n",
    "                    if tweet_id_retweet in retweet_threads[user_id_retweet].keys():\n",
    "                        retweet_threads[user_id_retweet][tweet_id_retweet].append(tweet)\n",
    "                    else:\n",
    "                        retweet_threads[user_id_retweet][tweet_id_retweet] = [tweet]\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            continue\n",
    "    \n",
    "print(retweet_threads)\n",
    "    \n",
    "with open(\"retweet_threads.pickle\",\"wb\") as pickle_out:\n",
    "    pickle.dump(retweet_threads, pickle_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# different approach to identifying retweet threads:\n",
    "# 1. for every user, compare with every other user\n",
    "# 2. If they have retweeted the same tweet, add both tweets to a thread\n",
    "# 3. Label the thread fake if either of the users is fake\n",
    "\n",
    "import pickle, json\n",
    "import os\n",
    "\n",
    "dataset_location = os.path.join(os.getcwd(), 'Datasets', 'collected_data')\n",
    "\n",
    "with open(os.path.join(dataset_location, \"labeled_users_deletefb.pickle\"),\"rb\") as pickle_in:\n",
    "    labeled_users_deletefb = pickle.load(pickle_in)\n",
    "    \n",
    "retweet_threads = {}\n",
    "labels = {}\n",
    "\n",
    "    \n",
    "for key_i in labeled_users_deletefb.keys():\n",
    "    user_id_i, label_i = key_i[0], key_i[1]\n",
    "    tweets1 = labeled_users_deletefb[key_i]\n",
    "    for tweet_i in tweets1:\n",
    "        for key_j in labeled_users_deletefb.keys():\n",
    "            user_id_j, label_j = key_j[0], key_j[1]\n",
    "            tweets2 = labeled_users_deletefb[key_j]\n",
    "            for tweet_j in tweets2:\n",
    "                if tweet_i != tweet_j and 'retweeted_status' in tweet_i.keys() and 'retweeted_status' in tweet_j.keys() and tweet_i['retweeted_status']['id'] == tweet_j['retweeted_status']['id']:\n",
    "                    user_id = tweet_i['retweeted_status']['user']['id']\n",
    "                    tweet_id = tweet_i['retweeted_status']['id']\n",
    "                    if user_id in retweet_threads.keys():\n",
    "                        if tweet_id not in retweet_threads[user_id].keys():\n",
    "                            retweet_threads[user_id][tweet_id] = [tweet_i, tweet_j]\n",
    "                        else:\n",
    "                            retweet_threads[user_id][tweet_id].append(tweet_i)\n",
    "                            retweet_threads[user_id][tweet_id].append(tweet_j)\n",
    "                    else:\n",
    "                        retweet_threads[user_id] = {}\n",
    "                        retweet_threads[user_id][tweet_id] = [tweet_i, tweet_j]\n",
    "                    \n",
    "                    if key_i[1] == 'fake' or key_j[1] == 'fake':\n",
    "                        labels[user_id] = 'fake'\n",
    "                    else:\n",
    "                        labels[user_id] = 'genuine'\n",
    "print(len(retweet_threads))\n",
    "\n",
    "for key in retweet_threads.keys():\n",
    "    print(key)\n",
    "    for key2 in retweet_threads[key].keys():\n",
    "        retweet_threads[key][key2] = list(set(retweet_threads[key][key2]))\n",
    "        print(key2, ' -> ', len(retweet_threads[key][key2]))\n",
    "\n",
    "retweet_threads_new = {}\n",
    "for key in retweet_threads.keys():\n",
    "    retweet_threads_new[(key, labels[key])] = retweet_threads[key]\n",
    "    \n",
    "with open(\"retweet_threads_new.pickle\",\"wb\") as pickle_out:\n",
    "    pickle.dump(retweet_threads, pickle_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24787367, 'genuine')\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'dict'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-20e1bc792dda>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mnew_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mretweet_threads_new\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretweet_threads_new\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0mnew_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' -> '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'dict'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "new_ = {}\n",
    "for key in retweet_threads_new.keys():\n",
    "    print(key)\n",
    "    new_[key] = {}\n",
    "    for key2 in retweet_threads_new[key].keys():\n",
    "        a = list(set(retweet_threads_new[key][key2]))\n",
    "        new_[key][key2] = a\n",
    "        print(key2, ' -> ', len(new_[key][key2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os\n",
    "\n",
    "dataset_location = os.path.join(os.getcwd(), 'Datasets', 'collected_data')\n",
    "\n",
    "with open(os.path.join(dataset_location, \"labeled_users_deletefb.pickle\"),\"rb\") as pickle_in:\n",
    "    labeled_users_deletefb = pickle.load(pickle_in)\n",
    "    \n",
    "retweet_threads = \n",
    "\n",
    "    \n",
    "    \n",
    "for key in labeled_users_deletefb.keys():\n",
    "    user_id, label = key[0], key[1]\n",
    "    tweets = labeled_users_deletefb[key] #array of tweets (not necessarily rts) associated with user\n",
    "    \n",
    "    retweetThread = [tweet for tweet in tweets if 'retweet_status' in tweet.keys()] \n",
    "    ###################### NOOOOOOOOO\n",
    "    \n",
    "    \n",
    "    retweet_creation_times = []\n",
    "        try:\n",
    "            for rts in retweetThread:\n",
    "\n",
    "                retweet_creation_times.append(rts._json['created_at'])\n",
    "\n",
    "\n",
    "            print(retweet_creation_times)\n",
    "            retweet_creation_times.reverse()\n",
    "\n",
    "            # Get first retweet\n",
    "            first_retweet_creation_time = time.strptime(retweet_creation_times[0], \"%a %b %d %H:%M:%S +0000 %Y\")\n",
    "            \n",
    "            # Get last retweet\n",
    "            last_retweet_creation_time = time.strptime(retweet_creation_times[-1], \"%a %b %d %H:%M:%S +0000 %Y\")\n",
    "\n",
    "            # Calculate features\n",
    "            feature_retweets = orig_tweetDetails['retweet_count']\n",
    "            feature_response_time = (time.mktime(first_retweet_creation_time) - time.mktime(orig_tweet_creation_time))\n",
    "            \n",
    "            lifespan = (time.mktime(last_retweet_creation_time) - time.mktime(first_retweet_creation_time))\n",
    "\n",
    "            retweets_inter_arrival_times = [time.mktime(time.strptime(j, \"%a %b %d %H:%M:%S +0000 %Y\"))-time.mktime(time.strptime(i, \"%a %b %d %H:%M:%S +0000 %Y\")) for i, j in zip(retweet_creation_times[:-1], retweet_creation_times[1:])]\n",
    "            q_75 = int(0.75 * len(retweets_inter_arrival_times))\n",
    "            q_25 = int(0.25* len(retweets_inter_arrival_times))\n",
    "            q_50 = int(0.50* len(retweets_inter_arrival_times))\n",
    "\n",
    "            arr_mad = robust.mad(retweets_inter_arrival_times)\n",
    "            iqr = np.subtract(*np.percentile(retweets_inter_arrival_times, [q_75, q_25]))\n",
    "\n",
    "            rt_q3_reponse_time = (time.mktime(time.strptime(retweet_creation_times[q_75], \"%a %b %d %H:%M:%S +0000 %Y\")) - time.mktime(orig_tweet_creation_time))\n",
    "            rt_q2_reponse_time = (time.mktime(time.strptime(retweet_creation_times[q_50], \"%a %b %d %H:%M:%S +0000 %Y\")) - time.mktime(orig_tweet_creation_time))\n",
    "\n",
    "            features_ndsync = [files[:-5], feature_retweets, feature_response_time, lifespan, rt_q3_reponse_time, rt_q2_reponse_time, arr_mad, iqr]\n",
    "\n",
    "            if arr_mad != 0.0:\n",
    "                with open(\"a_final-features-genuine.csv\", \"a+\") as f:\n",
    "                    writer = csv.writer(f)\n",
    "                    writer.writerow(features_ndsync)\n",
    "                    \n",
    "\n",
    "        except Exception,e:\n",
    "            print str(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[array([1.37762593e+09, 1.37762818e+09, 1.37763220e+09, ...,\n",
      "       1.38553609e+09, 1.38553614e+09, 1.38554068e+09])]]\n"
     ]
    }
   ],
   "source": [
    "timestamps = df1['timestamp'].values\n",
    "# print(timestamps[:30])\n",
    "timestamps2 = df2['timestamp'].values\n",
    "# print(timestamps2[:30])\n",
    "\n",
    "timestamps = [float(x) for x in timestamps]\n",
    "timestamps1 = [[np.array(timestamps)]]\n",
    "timestamps2 = list(np.array(timestamps2))\n",
    "\n",
    "print(timestamps1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f00b04fd748>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00a9ddc860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,5))\n",
    "y = [1]*len(timestamps[:30])\n",
    "plt.plot(timestamps[:30], y[:30], 'ro')\n",
    "y2 = [2]*len(timestamps2[:30])\n",
    "plt.plot(timestamps2[:30], y2[:30], 'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decays = 0.01\n",
    "\n",
    "\n",
    "timestamps1 = x\n",
    "\n",
    "learner = HawkesExpKern(decays, penalty='l2', C=0.01)\n",
    "learner.fit(timestamps1)\n",
    "\n",
    "# est = learner.estimated_intensity(timestamps1,intensity_track_step=1)\n",
    "\n",
    "# print(est)\n",
    "\n",
    "# k = learner.get_kernel_values(0,0,np.array(timestamps))\n",
    "# print(list(k))\n",
    "\n",
    "print(learner.)\n",
    "\n",
    "plot_hawkes_kernels(learner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tick.base.timefunc.TimeFunction'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89fd27b908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89fd27b908>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_time = 30\n",
    "\n",
    "T = np.arange((run_time * 0.9) * 5, dtype=float) / 5\n",
    "Y = np.maximum(15 * np.sin(T) * (np.divide(np.ones_like(T),\n",
    "                                           np.sqrt(T + 1) + 0.1 * T)), 0.001)\n",
    "\n",
    "tf = TimeFunction((T, Y), dt=0.01)\n",
    "\n",
    "print(type(tf))\n",
    "# We define a 1 dimensional inhomogeneous Poisson process with the\n",
    "# intensity function seen above\n",
    "in_poi = SimuInhomogeneousPoisson([tf], end_time=run_time, verbose=False)\n",
    "\n",
    "\n",
    "# We activate intensity tracking and launch simulation\n",
    "in_poi.track_intensity(0.1)\n",
    "in_poi.simulate()\n",
    "\n",
    "# We plot the resulting inhomogeneous Poisson process with its\n",
    "# intensity and its ticks over time\n",
    "tick.plot.plot_point_process(in_poi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89f9df1a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89f9df1a20>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tick.plot import plot_hawkes_kernels\n",
    "from tick.hawkes import SimuHawkesExpKernels, SimuHawkesMulti, HawkesExpKern\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "end_time = 1000\n",
    "n_realizations = 10\n",
    "\n",
    "decays = [[4., 1.], [2., 2.]]\n",
    "baseline = [0.12, 0.07]\n",
    "adjacency = [[.3, 0.], [.6, .21]]\n",
    "\n",
    "hawkes_exp_kernels = SimuHawkesExpKernels(\n",
    "    adjacency=adjacency, decays=decays, baseline=baseline,\n",
    "    end_time=end_time, verbose=False, seed=1039)\n",
    "\n",
    "multi = SimuHawkesMulti(hawkes_exp_kernels, n_simulations=n_realizations)\n",
    "\n",
    "multi.end_time = [(i + 1) / 10 * end_time for i in range(n_realizations)]\n",
    "multi.simulate()\n",
    "\n",
    "learner = HawkesExpKern(decays, penalty='l1', C=10)\n",
    "learner.fit(multi.timestamps)\n",
    "\n",
    "plot_hawkes_kernels(learner, hawkes=hawkes_exp_kernels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Launching simulation using SimuHawkesExpKernels...\n",
      "Done simulating using SimuHawkesExpKernels in 2.31e-03 seconds.\n",
      "[236.16868879 347.88637262 358.21379421 361.97902432 366.27765209\n",
      " 399.46655993 412.01356806 416.10443512 486.67896265 581.07961706\n",
      " 596.99420798 597.98640996 599.00109959]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92e95bc8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define an exponential kernel for my hawkes process\n",
    "# for simulation : Use SimuHawkesExpKernels \n",
    "import tick\n",
    "from tick.hawkes import SimuHawkesExpKernels\n",
    "import matplotlib.pyplot as plt\n",
    "from tick.plot import plot_point_process\n",
    "\n",
    "n_nodes=1;\n",
    "baseline=[0.01];\n",
    "adjacency=[[0.9]];\n",
    "decays= 0.01\n",
    "end_time=600;\n",
    "# max_jumps=1000;\n",
    "\n",
    "a_sim=SimuHawkesExpKernels(adjacency, decays, baseline=baseline, end_time=end_time, verbose=True)\n",
    "a_sim.track_intensity(0.1)\n",
    "\n",
    "a_sim.simulate()\n",
    "print (a_sim.timestamps[0])\n",
    "\n",
    "\n",
    "# print (a_sim.simulation_time)\n",
    "# print (a_sim.n_total_jumps)\n",
    "# print (a_sim.tracked_intensity)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n",
    "plot_point_process(a_sim, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[array([1.15548319e+00, 5.00557993e+00, 1.22625256e+01, ...,\n",
      "       1.29504795e+04, 1.29818797e+04, 1.29948022e+04]), array([1.37803383e+00, 1.64713184e+00, 2.63211712e+01, ...,\n",
      "       1.29923062e+04, 1.29928273e+04, 1.29948340e+04])], [array([7.21966896e+00, 1.78829838e+01, 3.16234234e+01, ...,\n",
      "       2.59947898e+04, 2.59954825e+04, 2.59995330e+04]), array([9.54036700e+00, 1.35858744e+01, 1.79852944e+01, ...,\n",
      "       2.59930850e+04, 2.59956402e+04, 2.59960070e+04])], [array([2.02771776e+01, 4.04243146e+01, 8.55550523e+01, ...,\n",
      "       3.89810214e+04, 3.89811988e+04, 3.89840623e+04]), array([3.45953295e-01, 9.16114499e+00, 2.09900505e+01, ...,\n",
      "       3.89798956e+04, 3.89837939e+04, 3.89841336e+04])], [array([1.20665398e+01, 1.51740264e+01, 1.54488199e+01, ...,\n",
      "       5.19692688e+04, 5.19804782e+04, 5.19823785e+04]), array([1.45207936e+01, 1.52977024e+01, 2.10412214e+01, ...,\n",
      "       5.19481964e+04, 5.19593831e+04, 5.19830856e+04])], [array([5.90551236e+00, 1.16574850e+01, 3.17783978e+01, ...,\n",
      "       6.49978630e+04, 6.49978938e+04, 6.49979554e+04]), array([5.95998191e+00, 2.53489068e+01, 2.66257975e+01, ...,\n",
      "       6.49981965e+04, 6.49984835e+04, 6.49985959e+04])], [array([1.29586108e+01, 2.88132762e+01, 2.88724940e+01, ...,\n",
      "       7.79894855e+04, 7.79895160e+04, 7.79927837e+04]), array([3.08162104e+00, 1.74867078e+01, 2.43066202e+01, ...,\n",
      "       7.79902206e+04, 7.79907906e+04, 7.79928032e+04])], [array([1.15997014e+01, 1.62943321e+01, 2.01282931e+01, ...,\n",
      "       9.09963935e+04, 9.09990553e+04, 9.09996412e+04]), array([1.66645092e+00, 2.96601610e+01, 3.32933954e+01, ...,\n",
      "       9.09919795e+04, 9.09966275e+04, 9.09966492e+04])], [array([9.76365004e+00, 1.25875003e+01, 1.56528084e+01, ...,\n",
      "       1.03991514e+05, 1.03992721e+05, 1.03993211e+05]), array([9.81728454e+00, 1.26828416e+01, 1.29944265e+01, ...,\n",
      "       1.03983014e+05, 1.03992738e+05, 1.03993247e+05])], [array([3.09026195e+01, 3.37603315e+01, 3.87645591e+01, ...,\n",
      "       1.16993745e+05, 1.16994899e+05, 1.16996045e+05]), array([7.72400964e-01, 2.18868470e+01, 2.28854810e+01, ...,\n",
      "       1.16995259e+05, 1.16996279e+05, 1.16996376e+05])], [array([8.78771746e+00, 1.39960432e+01, 1.46037219e+01, ...,\n",
      "       1.29967945e+05, 1.29990222e+05, 1.29997648e+05]), array([9.01194742e+00, 1.40168707e+01, 1.72861561e+01, ...,\n",
      "       1.29967990e+05, 1.29978947e+05, 1.29997656e+05])]]\n",
      "Launching the solver SVRG...\n",
      "  n_iter  |    obj    |  rel_obj \n",
      "        0 | -3.00e+00 |  6.16e-02\n",
      "        5 | -3.00e+00 |  9.65e-14\n",
      "Done solving using SVRG in 0.5934481620788574 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tick.hawkes.inference.hawkes_expkern_fixeddecay.HawkesExpKern at 0x7f92e91f3a20>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tick.plot import plot_hawkes_kernels\n",
    "from tick.hawkes import SimuHawkesExpKernels, SimuHawkesMulti, HawkesExpKern\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "end_time = 1.3e+05\n",
    "n_realizations = 10\n",
    "\n",
    "decays = 3.\n",
    "baseline = [0.12, 0.07]\n",
    "adjacency = [[.3, 0.], [.6, .21]]\n",
    "\n",
    "hawkes_exp_kernels = SimuHawkesExpKernels(\n",
    "    adjacency=adjacency, decays=decays, baseline=baseline,\n",
    "    end_time=end_time, verbose=False, seed=1039)\n",
    "\n",
    "multi = SimuHawkesMulti(hawkes_exp_kernels, n_simulations=n_realizations)\n",
    "\n",
    "multi.end_time = [(i + 1) / 10 * end_time for i in range(n_realizations)]\n",
    "multi.simulate()\n",
    "\n",
    "learner = HawkesExpKern(decays, penalty='l1', C=10, gofit='likelihood', \n",
    "                        verbose=True, tol=1e-11, solver='svrg', step=1e-3)\n",
    "print(multi.timestamps)\n",
    "learner.fit(multi.timestamps, start=0.1)\n",
    "\n",
    "# plot_hawkes_kernels(learner, hawkes=hawkes_exp_kernels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d67519f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "user_to_timestamps={}\n",
    "\n",
    "normalizer=10e9\n",
    "\n",
    "\n",
    "for root,dir_names,f_names in os.walk('data'):\n",
    "\n",
    "\tfor name in f_names:\n",
    "\t\t# print name\n",
    "\t\tdata=[]\n",
    "\t\t# root_spl=root.split('/')\n",
    "\t\tpath=os.path.join(root,name)\n",
    "\t\twith open(path) as f:\n",
    "\n",
    "\t\t\tfor line in f:\n",
    "\t\t\t\tline=line[:-1]\n",
    "\n",
    "\t\t\t\tif line.split('\\t')[2]!='timestamp':\n",
    "\t\t\t\t\t\n",
    "\t\t\t\t\ttime_stamp=int(line.split('\\t')[2])\n",
    "\t\t\t\t\tdata.append(time_stamp)\n",
    "\n",
    "\t\tuser_to_timestamps[name]=data\n",
    "\n",
    "\t\t\t\t# exit()\n",
    "\n",
    "# print user_to_timestamps['u11.txt']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "ctr=1\n",
    "\n",
    "num_points=100\n",
    "\n",
    "for user in user_to_timestamps:\n",
    "\tif user=='u11.txt':\n",
    "\t\t# pass\n",
    "\t\tfx=[ctr for i in range(len(user_to_timestamps[user][:num_points]))]\n",
    "\t\tplt.plot(user_to_timestamps[user][:num_points],fx,'o',markersize=1)\n",
    "\t\tctr+=1\n",
    "\n",
    "plt.plot(user_to_timestamps['u11.txt'][:num_points],fx,'ro',markersize=1)\n",
    "plt.show()\n",
    "# print X[-1]\n",
    "\n",
    "with open('user_to_timestamps.pickle','wb') as f:\n",
    "\tpickle.dump(user_to_timestamps['u11.txt'][:num_points],f)\n",
    "\tf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "1377600177 1383952009\n",
      "[0.01503065 0.01503068 0.01509754 0.015165   0.015165   0.015165\n",
      " 0.06056614 0.0661479  0.06621375 0.06628079 0.06976864 0.07066697\n",
      " 0.07353155 0.07550302 0.07550309 0.07556685 0.07557302 0.07670912\n",
      " 0.07818588 0.07838122 0.08010366 0.08016851 0.08016854 0.08023122\n",
      " 0.08023126 0.08023129 0.08048628 0.08054892 0.08069063 0.08132178\n",
      " 0.08166544 0.08318974 0.08428012 0.0843435  0.08441173 0.08441193\n",
      " 0.08448099 0.08448103 0.08448525 0.08448678 0.08601208 0.08660807\n",
      " 0.08730546 0.08941328 0.08947586 0.09022694 0.09069509 0.09341448\n",
      " 0.09347723 0.09354096 0.09609668 0.09774031 0.09807477 0.09824376\n",
      " 0.09951892 0.10547103 0.10547107 0.1055188  0.10553476 0.1055348\n",
      " 0.10705491 0.10805832 0.10856035 0.11439173 0.11452009 0.11468535\n",
      " 0.11771641 0.12640557 0.12640888 0.12793968 0.13731916 0.13731923\n",
      " 0.13738345 0.13738351 0.13738355 0.13738355 0.15327549 0.15333692\n",
      " 0.15333702 0.15333713 0.15627825 0.15694724 0.15780383 0.15910241\n",
      " 0.15931903 0.17148158 0.17148161 0.17154618 0.22131897]\n",
      "1\n",
      "[169.94804624]\n",
      "[[5.44591064]]\n",
      "0.2213189672169086\n",
      "0.015030645243780946\n",
      "--------------------------------------------------\n",
      "Launching simulation using SimuHawkesExpKernels...\n",
      "Done simulating using SimuHawkesExpKernels in 1.71e-04 seconds.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aditya/anaconda3/lib/python3.6/site-packages/tick/base/learner/learner_optim.py:391: RuntimeWarning: Penalty \"l2\" has no elastic_net_ratio attribute\n",
      "  self.penalty, RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Launching simulation using SimuHawkesExpKernels...\n",
      "Done simulating using SimuHawkesExpKernels in 6.95e-02 seconds.\n",
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e33205860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tick.hawkes import HawkesExpKern\n",
    "import numpy as np\n",
    "from tick.hawkes import SimuHawkesExpKernels\n",
    "import matplotlib.pyplot as plt\n",
    "from tick.plot import plot_point_process\n",
    "import pickle\n",
    "\n",
    "def normalize(v):\n",
    "    norm = np.linalg.norm(v)\n",
    "    if norm == 0: \n",
    "        return v\n",
    "    return v / norm\n",
    "\n",
    "\n",
    "with open(\"user_to_timestamps.pickle\",'rb') as f:\n",
    "\tu11_timestamps= pickle.load(f)\n",
    "\tf.close()\n",
    "\n",
    "min_t = min(u11_timestamps)\n",
    "max_t = max(u11_timestamps)\n",
    "\n",
    "print(len(u11_timestamps))\n",
    "print(min_t, max_t)\n",
    "u11_timestamps = sorted([(t-min_t)/max_t for t in u11_timestamps])\n",
    "u11_timestamps = normalize(u11_timestamps)\n",
    "threshold = 1e-03\n",
    "u11_timestamps = u11_timestamps[u11_timestamps>threshold]\n",
    "print(u11_timestamps)\n",
    "# fx=[1 for i in range(len(u11_timestamps))]\n",
    "# plt.plot(u11_timestamps,fx,'ro',markersize=1)\n",
    "plt.show()\n",
    "\n",
    "decays=0.5\n",
    "gofit='least-squares'\n",
    "penalty='l2'\n",
    "C=1e3\n",
    "solver='gd'\n",
    "step=None\n",
    "tol=1e-05\n",
    "max_iter=1000;\n",
    "verbose=False\n",
    "print_every=10\n",
    "random_state=None\n",
    "elastic_net_ratio=0.95\n",
    "\n",
    "a_kernel=HawkesExpKern(decays, gofit=gofit, penalty=penalty, C=C, solver=solver, step=step, tol=tol, max_iter=max_iter, verbose=verbose, print_every=print_every, elastic_net_ratio=elastic_net_ratio, random_state=random_state)\n",
    "\n",
    "\n",
    "u11_timestamps=1.0*np.array(u11_timestamps)\n",
    "\n",
    "list_of_timestamps=[]\n",
    "\n",
    "list_of_timestamps.append(u11_timestamps)\n",
    "\n",
    "min_t = min(u11_timestamps)\n",
    "a_kernel.fit(list_of_timestamps, start=min_t)\n",
    "\n",
    "print(a_kernel.n_nodes)\n",
    "print(a_kernel.baseline)\n",
    "print(a_kernel.adjacency)\n",
    "# print(a_kernel.coeffs)\n",
    "\n",
    "end_time=max(u11_timestamps);\n",
    "print(end_time)\n",
    "start_time=min(u11_timestamps)\n",
    "print(start_time)\n",
    "a_sim=SimuHawkesExpKernels(a_kernel.adjacency, decays, baseline=a_kernel.baseline, end_time=end_time, force_simulation=True, verbose=True)\n",
    "a_sim.track_intensity(0.1)\n",
    "\n",
    "a_sim.simulate()\n",
    "# print (a_sim.timestamps)\n",
    "# print (a_sim.simulation_time)\n",
    "# print (a_sim.n_total_jumps)\n",
    "# print (a_sim.tracked_intensity)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(20, 10))\n",
    "plot_point_process(a_sim, ax=ax)\n",
    "# plt.show()\n",
    "\n",
    "n_nodes=1;\n",
    "baseline=[0.01];\n",
    "adjacency=[[0.1]];\n",
    "decays= 0.1\n",
    "\n",
    "a_sim=SimuHawkesExpKernels(adjacency, decays, baseline=baseline, end_time=end_time, verbose=True)\n",
    "a_sim.track_intensity(0.1)\n",
    "\n",
    "a_sim.simulate()\n",
    "print (len(a_sim.timestamps[0]))\n",
    "plot_point_process(a_sim, ax=ax)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Launching simulation using SimuHawkesExpKernels...\n",
      "Done simulating using SimuHawkesExpKernels in 8.05e-03 seconds.\n",
      "140\n",
      "--------------------------------------------------\n",
      "Launching simulation using SimuHawkesExpKernels...\n",
      "Done simulating using SimuHawkesExpKernels in 7.64e-03 seconds.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aditya/anaconda3/lib/python3.6/site-packages/tick/base/learner/learner_optim.py:391: RuntimeWarning: Penalty \"l2\" has no elastic_net_ratio attribute\n",
      "  self.penalty, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e2b853128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define an exponential kernel for my hawkes process\n",
    "# for simulation : Use SimuHawkesExpKernels \n",
    "import tick\n",
    "from tick.hawkes import SimuHawkesExpKernels\n",
    "import matplotlib.pyplot as plt\n",
    "from tick.plot import plot_point_process\n",
    "from tick.hawkes import HawkesExpKern\n",
    "\n",
    "\n",
    "\n",
    "n_nodes=1;\n",
    "baseline=[0.01];\n",
    "adjacency=[[0.1]];\n",
    "decays= 0.1\n",
    "end_time=1.38553473e+04;\n",
    "# max_jumps=1000;\n",
    "\n",
    "a_sim=SimuHawkesExpKernels(adjacency, decays, baseline=baseline, end_time=end_time, verbose=True)\n",
    "a_sim.track_intensity(0.1)\n",
    "\n",
    "a_sim.simulate()\n",
    "print (len(a_sim.timestamps[0]))\n",
    "# print (a_sim.simulation_time)\n",
    "# print (a_sim.n_total_jumps)\n",
    "# print (a_sim.tracked_intensity)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(20, 10))\n",
    "plot_point_process(a_sim, ax=ax)\n",
    "# plt.show()\n",
    "\n",
    "decays=0.1\n",
    "\n",
    "gofit='least-squares'\n",
    "penalty='l2'\n",
    "C=1e3\n",
    "solver='gd'\n",
    "step=None\n",
    "tol=1e-04\n",
    "max_iter=1000;\n",
    "verbose=False\n",
    "print_every=10\n",
    "random_state=None\n",
    "elastic_net_ratio=0.9\n",
    "\n",
    "a_kernel=HawkesExpKern(decays, gofit=gofit, penalty=penalty, C=C, solver=solver, step=step, tol=tol, max_iter=max_iter, verbose=verbose, print_every=print_every, elastic_net_ratio=elastic_net_ratio, random_state=random_state)\n",
    "\n",
    "# list_of_timestamps.append(b)\n",
    "\n",
    "a_kernel.fit(a_sim.timestamps)\n",
    "\n",
    "\n",
    "# end_time=1.38553473e+04;\n",
    "\n",
    "\n",
    "test_sim=SimuHawkesExpKernels(a_kernel.adjacency, decays, baseline=a_kernel.baseline, end_time=end_time, verbose=True)\n",
    "test_sim.track_intensity(0.1)\n",
    "\n",
    "test_sim.simulate()\n",
    "# print (a_sim.timestamps)\n",
    "# print (a_sim.simulation_time)\n",
    "# print (a_sim.n_total_jumps)\n",
    "# print (a_sim.tracked_intensity)\n",
    "\n",
    "# fig, ax = plt.subplots(test_sim.n_nodes, 1, figsize=(20, 10))\n",
    "plot_point_process(test_sim, ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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