{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Main Idea \n",
    "> Anomalies are more susceptible to isolation (hence have short path lengths) under random partitioning,\n",
    "\n",
    "### Method\n",
    "\n",
    "> In this example, partitions are generated by randomly selecting an attribute and then randomly selecting a split value between the maximum and minimum values of\n",
    "the selected attribute.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# default plot settings\n",
    "plt.rcParams['figure.dpi'] = 150\n",
    "plt.rcParams['figure.figsize'] = [5, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = np.random.normal(loc= 0, size= (100,2))\n",
    "X2 = np.random.normal(loc= 4, size= (100,2))\n",
    "X3 = np.random.normal(loc= -5, size= (100,2))\n",
    "\n",
    "A = np.array([[9,-6]])\n",
    "\n",
    "data = np.concatenate((X1,X2,X3, A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a4f1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[:,0], data[:,1],'o')\n",
    "plt.grid()\n",
    "plt.xlim([-10, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.012564</td>\n",
       "      <td>0.297794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.179264</td>\n",
       "      <td>0.253776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.306310</td>\n",
       "      <td>0.256859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000864</td>\n",
       "      <td>-1.508026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.346579</td>\n",
       "      <td>-0.179718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1\n",
       "0 -0.012564  0.297794\n",
       "1  0.179264  0.253776\n",
       "2 -0.306310  0.256859\n",
       "3  1.000864 -1.508026\n",
       "4  0.346579 -0.179718"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select random feature\n",
    "f = np.random.choice(df.shape[1])\n",
    "feature = df.loc[:,f]\n",
    "# get min and max value of selected feature\n",
    "mini, maxi = feature.min() , feature.max()\n",
    "# generate a cut-value between min and max\n",
    "cut = np.random.uniform(low=mini, high=maxi)\n",
    "# find isolated instance\n",
    "isolated_index = -1\n",
    "smaller, greater = feature[feature < cut], feature[feature >= cut]\n",
    "if len(smaller) == 1: isolated_index = smaller.index[0]\n",
    "if len(greater) == 1: isolated_index = greater.index[0]\n",
    "if isolated_index!= -1:\n",
    "    isolated = list( df.loc[isolated_index])\n",
    "    print(\"cutoff point: \",cut,\" at feature \", f, \"\\ngives  \", isolated_index, \"th data \", isolated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recursive_partition(df, depth = 2, level = 0, info = \"start\"):\n",
    "    \n",
    "    # Base Case \n",
    "    if level == depth: return -1\n",
    "    \n",
    "    print(\"\\n\\n\", info); print(\"At level: \", level)\n",
    "    \n",
    "    # select random feature\n",
    "    f = np.random.choice(df.shape[1])\n",
    "    feature = df.loc[:,f]\n",
    "    # get min and max value of selected feature\n",
    "    mini, maxi = feature.min() , feature.max()\n",
    "    # generate a cut-value between min and max\n",
    "    cut = np.random.uniform(low=mini, high=maxi)\n",
    "    \n",
    "    print(\"\\n\\tcutoff point: \",cut,\" at feature \", f)\n",
    "    #print(df)\n",
    "    # find isolated instance\n",
    "    smaller, greater = feature[feature < cut], feature[feature >= cut]\n",
    "    if len(smaller) == 1: return smaller.index[0]\n",
    "    if len(greater) == 1: return greater.index[0]\n",
    "    \n",
    "    # go to small left child\n",
    "    isolated_index = recursive_partition(df[df[f] < cut ], depth, level = level +1, info = \"small\")\n",
    "    if isolated_index != -1: return isolated_index\n",
    "    \n",
    "    # go to big right child\n",
    "    isolated_index  = recursive_partition(df[df[f] >= cut ], depth, level = level +1, info = \"big\")\n",
    "    if isolated_index != -1: return isolated_index\n",
    "    \n",
    "    return -1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " start\n",
      "At level:  0\n",
      "\n",
      "\tcutoff point:  6.0249995922613575  at feature  1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso = recursive_partition(df, depth = 10)\n",
    "iso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b883400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[:,0], data[:,1],'o')\n",
    "if iso != -1: plt.plot(data[iso,0], data[iso,1],'o')\n",
    "plt.grid()\n",
    "plt.xlim([-8, 8])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a =[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.append((4,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(4, 5)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default plot settings\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "\n",
    "class myIsolation:\n",
    "    def __init__(self, data):\n",
    "        self.path = []\n",
    "        self.data = data\n",
    "        df = pd.DataFrame(data)\n",
    "        self.bool2D = (df.shape[1] == 2)\n",
    "        \n",
    "        self.iso = self.recursive_partition(df, depth = int(np.log2(len(data))))\n",
    "        \n",
    "        \n",
    "    def recursive_partition(self, df, depth = 2, level = 0, info = \"start\"):\n",
    "    \n",
    "        # Base Case \n",
    "        if level == depth: return -1\n",
    "\n",
    "        #### print(\"\\n\\n\", info); print(\"At level: \", level)\n",
    "\n",
    "        # select random feature\n",
    "        f = np.random.choice(df.shape[1])\n",
    "        feature = df.loc[:,f]\n",
    "        # get min and max value of selected feature\n",
    "        mini, maxi = feature.min() , feature.max()\n",
    "        # generate a cut-value between min and max\n",
    "        cut = np.random.uniform(low=mini, high=maxi)\n",
    "        \n",
    "        ######################################################## 2d draw \n",
    "        if self.bool2D:\n",
    "            other_feature = df.loc[:,df.shape[1]-f-1]\n",
    "            mini, maxi = other_feature.min() , other_feature.max()\n",
    "\n",
    "        #### print(\"\\n\\tcutoff point: \",cut,\" at feature \", f)\n",
    "        self.path.append({'level':level, \n",
    "                          'info': info, \n",
    "                          'cut' : cut, \n",
    "                          'feature' : f, \n",
    "                          'mini' :mini, \n",
    "                          'maxi' :maxi})\n",
    "        ######################################################## 2d draw \n",
    "\n",
    "\n",
    "        # find isolated instance\n",
    "        smaller, greater = feature[feature < cut], feature[feature >= cut]\n",
    "        if len(smaller) == 1: return smaller.index[0]\n",
    "        if len(greater) == 1: return greater.index[0]\n",
    "\n",
    "        # go to small left child\n",
    "        isolated_index = self.recursive_partition(df[df[f] < cut ], depth, level = level +1, info = \"small\")\n",
    "        if isolated_index != -1: return isolated_index\n",
    "\n",
    "        # go to big right child\n",
    "        isolated_index  = self.recursive_partition(df[df[f] >= cut ], depth, level = level +1, info = \"big\")\n",
    "        if isolated_index != -1: return isolated_index\n",
    "\n",
    "        return -1\n",
    "    \n",
    "    def score(self):\n",
    "        if self.iso != -1: return len(self.path)\n",
    "    \n",
    "    def drawCuts(self, fig, ax):\n",
    "        for i in range(len(self.path)):\n",
    "            f, v, mi, ma = self.path[i]['feature'], self.path[i]['cut'], self.path[i]['mini'],self.path[i]['maxi']\n",
    "            x = np.linspace(mi, ma, 100); y = x * 0 + v; print(f, v)\n",
    "            if f == 0: plt.plot(y,x, color = 'r', linestyle ='--' )\n",
    "            else: plt.plot(x,y, color = 'r', linestyle ='--' )\n",
    "        \n",
    "    def display(self):\n",
    "        fig, ax = plt.subplots()\n",
    "        plt.plot(self.data[:,0], self.data[:,1],'o')\n",
    "        \n",
    "        if self.iso != -1: plt.plot(self.data[self.iso,0], self.data[self.iso,1],'o')\n",
    "            \n",
    "        if self.bool2D: self.drawCuts(fig, ax)\n",
    "            \n",
    "        print(self.iso) \n",
    "        plt.title(\"score\" + str(self.score()))\n",
    "        plt.grid(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 5.933870615524162\n",
      "1 5.540043535038084\n",
      "0 -3.876448840286511\n",
      "1 -5.6505819810294895\n",
      "0 -4.082377153859769\n",
      "0 -4.666303320634922\n",
      "1 -6.754035516268094\n",
      "219\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8b9908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219 7\n"
     ]
    }
   ],
   "source": [
    "a = myIsolation(data)\n",
    "a.display()\n",
    "print(a.iso, len(a.path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = {}\n",
    "for i in range(10000):\n",
    "    a = myIsolation(data)\n",
    "    isolated,score = a.iso, a.score()\n",
    "    del(a)\n",
    "    if counter.get(isolated) == None: counter[isolated] = list()\n",
    "    counter[isolated].append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 112 artists>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104e1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = list(counter.keys())\n",
    "val = [np.mean(counter[k]) for k in counter.keys()]\n",
    "plt.bar(idx, val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1109345c0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1103ec390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = list(counter.keys())\n",
    "val = [np.mean(counter[k]) for k in counter.keys()]\n",
    "plt.bar(idx, val)\n",
    "\n",
    "mu, sigma = np.mean(val), np.std(val)\n",
    "\n",
    "x = np.linspace(0, data.shape[0], 100); \n",
    "y = x * 0 + mu; \n",
    "plt.plot(x,y, color = 'r', linestyle ='--' ,lw = 3)\n",
    "\n",
    "y = x * 0 + mu - sigma; \n",
    "plt.plot(x,y, color = 'r', linestyle ='--' ,lw = 2)\n",
    "\n",
    "y = x * 0 + mu - 2 * sigma; \n",
    "plt.plot(x,y, color = 'r', linestyle ='--' ,lw = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import heapq\n",
    "\n",
    "counter_len = {k:np.mean(counter[k]) for k in counter.keys()}\n",
    "# Gettings best 5 lines             \n",
    "anomaly = heapq.nsmallest(10, counter, key=counter_len.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[114, 300, 186, 0, 76, 157, 33, 11, 55, 68]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(114, 1.1781818181818182),\n",
       " (300, 1.923874053407732),\n",
       " (186, 2.0),\n",
       " (0, 2.0),\n",
       " (76, 2.0),\n",
       " (157, 2.0),\n",
       " (33, 2.0),\n",
       " (11, 2.0),\n",
       " (55, 2.0),\n",
       " (68, 2.0)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(a, counter_len[a]) for a in anomaly]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110434978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# default plot settings\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['figure.figsize'] = [9, 6]\n",
    "\n",
    "x = np.concatenate((np.random.normal(loc=-2, scale=.5,size=500), \n",
    "                    np.random.normal(loc=2, scale=.5, size=500)))\n",
    "plt.hist(x, normed=True)\n",
    "plt.xlim([-5, 5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110aa65c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "isolation_forest = IsolationForest(n_estimators=100)\n",
    "isolation_forest.fit(x.reshape(-1, 1))\n",
    "xx = np.linspace(-6, 6, 100).reshape(-1,1)\n",
    "anomaly_score = isolation_forest.decision_function(xx)\n",
    "outlier = isolation_forest.predict(xx)\n",
    "\n",
    "plt.plot(xx, anomaly_score, label='anomaly score')\n",
    "plt.fill_between(xx.flatten(), np.min(anomaly_score), np.max(anomaly_score), \n",
    "                 where=outlier==-1, color='r', \n",
    "                 alpha=.4, label='outlier region')\n",
    "plt.legend()\n",
    "plt.ylabel('anomaly score')\n",
    "plt.xlabel('x')\n",
    "plt.xlim([-5, 5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### decision_function(X)\n",
    "> The anomaly score of the input samples. The lower, the more abnormal. Negative scores represent outliers, positive scores represent inliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look data in google trends\n",
    " - [darbe](https://trends.google.com.tr/trends/explore?date=today%205-y&geo=TR&q=darbe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 262 entries, Hafta to 2018-10-28\n",
      "Data columns (total 1 columns):\n",
      "Kategori: Tüm kategoriler    262 non-null object\n",
      "dtypes: object(1)\n",
      "memory usage: 4.1+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "darbe = pd.read_csv('darbe.csv')\n",
    "darbe.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kategori: Tüm kategoriler</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hafta</th>\n",
       "      <td>darbe: (Türkiye)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Kategori: Tüm kategoriler\n",
       "Hafta          darbe: (Türkiye)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darbe.iloc[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kategori: Tüm kategoriler</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-11-10</th>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Kategori: Tüm kategoriler\n",
       "2013-11-10                        <1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darbe.iloc[2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(262, 1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darbe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals = list(darbe.to_dict()['Kategori: Tüm kategoriler'].values())[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1',\n",
       " '<1']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals = [np.random.randn() if v == '<1' else int(v) for v in vals]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11615f2b0>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b583320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(vals, 'r--o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.41954023, 0.05172414, 0.01532567, 0.00191571, 0.00191571,\n",
       "        0.        , 0.00191571, 0.        , 0.        , 0.00191571,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.00191571,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.00191571, 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.00191571]),\n",
       " array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,\n",
       "         22.,  24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,\n",
       "         44.,  46.,  48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,\n",
       "         66.,  68.,  70.,  72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,\n",
       "         88.,  90.,  92.,  94.,  96.,  98., 100.]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bb612e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(vals, normed=True, bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b3b1160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "isolation_forest = IsolationForest(n_estimators=100)\n",
    "isolation_forest.fit(np.array(vals).reshape(-1, 1))\n",
    "xx = np.linspace(0, 100, 200).reshape(-1,1)\n",
    "anomaly_score = isolation_forest.decision_function(xx)\n",
    "outlier = isolation_forest.predict(xx)\n",
    "\n",
    "plt.plot(xx, anomaly_score, label='anomaly score')\n",
    "plt.fill_between(xx.flatten(), np.min(anomaly_score), np.max(anomaly_score), \n",
    "                 where=outlier==-1, color='r', \n",
    "                 alpha=.4, label='outlier region')\n",
    "plt.legend()\n",
    "plt.ylabel('anomaly score')\n",
    "plt.xlabel('x')\n",
    "plt.xlim([-5, 105])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b8db3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xx, anomaly_score, label='anomaly score')\n",
    "plt.fill_between(xx.flatten(), np.min(anomaly_score), np.max(anomaly_score), \n",
    "                 where=outlier==-1, color='r', \n",
    "                 alpha=.4, label='outlier region')\n",
    "plt.legend()\n",
    "plt.ylabel('anomaly score')\n",
    "plt.xlabel('x')\n",
    "plt.xlim([-5, 5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.percentile?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x1a1c0d40f0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1a1c1f5a58>,\n",
       "  <matplotlib.lines.Line2D at 0x1a1c280198>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1a1c31db00>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1a1c31d390>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x1a1c1ca6d8>,\n",
       "  <matplotlib.lines.Line2D at 0x1a1c1ca780>]}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c1832e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1, Q3 = np.percentile(vals, 25), np.percentile(vals, 75)\n",
    "Q1, Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = Q3 - Q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.5, 2.5)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallest, greatest = Q1 - 1.5 * IQR, Q3 + 1.5 * IQR\n",
    "smallest, greatest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal = [v for v in vals if (v > smallest) and (v < greatest)]\n",
    "len(normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anormal = [v for v in vals if (v < smallest) or (v > greatest)]\n",
    "len(anormal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For more\n",
    " - [anomaly-detection](https://www.kaggle.com/pavansanagapati/anomaly-detection-credit-card-fraud-analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}