LOFandKNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read mds data from file\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import IsolationForest\n",
    "import pandas as pd\n",
    "from sklearn import manifold\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2dims = pd.read_csv(\"reduceDims/mds2dims_X_train.csv\",\n",
    "                            index_col=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load finish\n"
     ]
    }
   ],
   "source": [
    "print(\"load finish\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove index column\n",
    "X_train_2dims.drop(X_train_2dims.columns[0], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dim1</th>\n",
       "      <th>dim2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.969745</td>\n",
       "      <td>7.240796</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.449684</td>\n",
       "      <td>0.982294</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dim1      dim2  label\n",
       "0  5.969745  7.240796      0\n",
       "1  5.449684  0.982294      0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_2dims[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = list(X_train_2dims[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = []\n",
    "for i in y:\n",
    "    if i == 0:\n",
    "        y_true.append(1)\n",
    "    elif i == 1:\n",
    "        y_true.append(-1)\n",
    "    else:\n",
    "        print(\"error.....\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2dims.drop(X_train_2dims.columns[-1], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dim1</th>\n",
       "      <th>dim2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.969745</td>\n",
       "      <td>7.240796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.449684</td>\n",
       "      <td>0.982294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dim1      dim2\n",
       "0  5.969745  7.240796\n",
       "1  5.449684  0.982294"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_2dims[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2dims[:1000].to_csv(\"./reduceDims/mds2dims_X_train_1000samples.csv\", header=None, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_2dims = pd.read_csv(\"reduceDims/mds2dims_X_test.csv\", index_col=False, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>324.248009</td>\n",
       "      <td>-389.177698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>193.976694</td>\n",
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       "            0           1\n",
       "0  324.248009 -389.177698\n",
       "1  193.976694  267.734475"
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_2dims[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lof 的论文的具体算法实现\n",
    "# https://github.com/damjankuznar/pylof\n",
    "#!/usr/bin/python\n",
    "# -*- coding: utf8 -*-\n",
    "\"\"\"\n",
    "lof\n",
    "~~~~~~~~~~~~\n",
    "\n",
    "This module implements the Local Outlier Factor algorithm.\n",
    "\n",
    ":copyright: (c) 2013 by Damjan Kužnar.\n",
    ":license: GNU GPL v2, see LICENSE for more details.\n",
    "\n",
    "\"\"\"\n",
    "from __future__ import division\n",
    "import warnings\n",
    "\n",
    "def distance_euclidean(instance1, instance2):\n",
    "    \"\"\"Computes the distance between two instances. Instances should be tuples of equal length.\n",
    "    Returns: Euclidean distance\n",
    "    Signature: ((attr_1_1, attr_1_2, ...), (attr_2_1, attr_2_2, ...)) -> float\"\"\"\n",
    "    def detect_value_type(attribute):\n",
    "        \"\"\"Detects the value type (number or non-number).\n",
    "        Returns: (value type, value casted as detected type)\n",
    "        Signature: value -> (str or float type, str or float value)\"\"\"\n",
    "        from numbers import Number\n",
    "        attribute_type = None\n",
    "        if isinstance(attribute, Number):\n",
    "            attribute_type = float\n",
    "            attribute = float(attribute)\n",
    "        else:\n",
    "            attribute_type = str\n",
    "            attribute = str(attribute)\n",
    "        return attribute_type, attribute\n",
    "    # check if instances are of same length\n",
    "    if len(instance1) != len(instance2):\n",
    "        raise AttributeError(\"Instances have different number of arguments.\")\n",
    "    # init differences vector\n",
    "    differences = [0] * len(instance1)\n",
    "    # compute difference for each attribute and store it to differences vector\n",
    "    for i, (attr1, attr2) in enumerate(zip(instance1, instance2)):\n",
    "        type1, attr1 = detect_value_type(attr1)\n",
    "        type2, attr2 = detect_value_type(attr2)\n",
    "        # raise error is attributes are not of same data type.\n",
    "        if type1 != type2:\n",
    "            raise AttributeError(\"Instances have different data types.\")\n",
    "        if type1 is float:\n",
    "            # compute difference for float\n",
    "            differences[i] = attr1 - attr2\n",
    "        else:\n",
    "            # compute difference for string\n",
    "            if attr1 == attr2:\n",
    "                differences[i] = 0\n",
    "            else:\n",
    "                differences[i] = 1\n",
    "    # compute RMSE (root mean squared error)\n",
    "    rmse = (sum(map(lambda x: x**2, differences)) / len(differences))**0.5\n",
    "    return rmse\n",
    "\n",
    "class LOF:\n",
    "    \"\"\"Helper class for performing LOF computations and instances normalization.\"\"\"\n",
    "    def __init__(self, instances, normalize=True, distance_function=distance_euclidean):\n",
    "        self.instances = instances\n",
    "        self.normalize = normalize\n",
    "        self.distance_function = distance_function\n",
    "        if normalize:\n",
    "            self.normalize_instances()\n",
    "\n",
    "    def compute_instance_attribute_bounds(self):\n",
    "        min_values = [float(\"inf\")] * len(self.instances[0]) #n.ones(len(self.instances[0])) * n.inf\n",
    "        max_values = [float(\"-inf\")] * len(self.instances[0]) #n.ones(len(self.instances[0])) * -1 * n.inf\n",
    "        for instance in self.instances:\n",
    "            min_values = tuple(map(lambda x,y: min(x,y), min_values,instance)) #n.minimum(min_values, instance)\n",
    "            max_values = tuple(map(lambda x,y: max(x,y), max_values,instance)) #n.maximum(max_values, instance)\n",
    "\n",
    "        diff = [dim_max - dim_min for dim_max, dim_min in zip(max_values, min_values)]\n",
    "        if not all(diff):\n",
    "            problematic_dimensions = \", \".join(str(i+1) for i, v in enumerate(diff) if v == 0)\n",
    "            warnings.warn(\"No data variation in dimensions: %s. You should check your data or disable normalization.\" % problematic_dimensions)\n",
    "\n",
    "        self.max_attribute_values = max_values\n",
    "        self.min_attribute_values = min_values\n",
    "\n",
    "    def normalize_instances(self):\n",
    "        \"\"\"Normalizes the instances and stores the infromation for rescaling new instances.\"\"\"\n",
    "        if not hasattr(self, \"max_attribute_values\"):\n",
    "            self.compute_instance_attribute_bounds()\n",
    "        new_instances = []\n",
    "        for instance in self.instances:\n",
    "            new_instances.append(self.normalize_instance(instance)) # (instance - min_values) / (max_values - min_values)\n",
    "        self.instances = new_instances\n",
    "\n",
    "    def normalize_instance(self, instance):\n",
    "        return tuple(map(lambda value,max,min: (value-min)/(max-min) if max-min > 0 else 0,\n",
    "                         instance, self.max_attribute_values, self.min_attribute_values))\n",
    "\n",
    "    def local_outlier_factor(self, min_pts, instance):\n",
    "        \"\"\"The (local) outlier factor of instance captures the degree to which we call instance an outlier.\n",
    "        min_pts is a parameter that is specifying a minimum number of instances to consider for computing LOF value.\n",
    "        Returns: local outlier factor\n",
    "        Signature: (int, (attr1, attr2, ...), ((attr_1_1, ...),(attr_2_1, ...), ...)) -> float\"\"\"\n",
    "        if self.normalize:\n",
    "            instance = self.normalize_instance(instance)\n",
    "        return local_outlier_factor(min_pts, instance, self.instances, distance_function=self.distance_function)\n",
    "\n",
    "def k_distance(k, instance, instances, distance_function=distance_euclidean):\n",
    "    #TODO: implement caching\n",
    "    \"\"\"Computes the k-distance of instance as defined in paper. It also gatheres the set of k-distance neighbours.\n",
    "    Returns: (k-distance, k-distance neighbours)\n",
    "    Signature: (int, (attr1, attr2, ...), ((attr_1_1, ...),(attr_2_1, ...), ...)) -> (float, ((attr_j_1, ...),(attr_k_1, ...), ...))\"\"\"\n",
    "    distances = {}\n",
    "    for instance2 in instances:\n",
    "        distance_value = distance_function(instance, instance2)\n",
    "        if distance_value in distances:\n",
    "            distances[distance_value].append(instance2)\n",
    "        else:\n",
    "            distances[distance_value] = [instance2]\n",
    "    distances = sorted(distances.items())\n",
    "    neighbours = []\n",
    "    [neighbours.extend(n[1]) for n in distances[:k]]\n",
    "    k_distance_value = distances[k - 1][0] if len(distances) >= k else distances[-1][0]\n",
    "    return k_distance_value, neighbours\n",
    "\n",
    "def reachability_distance(k, instance1, instance2, instances, distance_function=distance_euclidean):\n",
    "    \"\"\"The reachability distance of instance1 with respect to instance2.\n",
    "    Returns: reachability distance\n",
    "    Signature: (int, (attr_1_1, ...),(attr_2_1, ...)) -> float\"\"\"\n",
    "    (k_distance_value, neighbours) = k_distance(k, instance2, instances, distance_function=distance_function)\n",
    "    return max([k_distance_value, distance_function(instance1, instance2)])\n",
    "\n",
    "def local_reachability_density(min_pts, instance, instances, **kwargs):\n",
    "    \"\"\"Local reachability density of instance is the inverse of the average reachability\n",
    "    distance based on the min_pts-nearest neighbors of instance.\n",
    "    Returns: local reachability density\n",
    "    Signature: (int, (attr1, attr2, ...), ((attr_1_1, ...),(attr_2_1, ...), ...)) -> float\"\"\"\n",
    "    (k_distance_value, neighbours) = k_distance(min_pts, instance, instances, **kwargs)\n",
    "    reachability_distances_array = [0]*len(neighbours) #n.zeros(len(neighbours))\n",
    "    for i, neighbour in enumerate(neighbours):\n",
    "        reachability_distances_array[i] = reachability_distance(min_pts, instance, neighbour, instances, **kwargs)\n",
    "    if not any(reachability_distances_array):\n",
    "        warnings.warn(\"Instance %s (could be normalized) is identical to all the neighbors. Setting local reachability density to inf.\" % repr(instance))\n",
    "        return float(\"inf\")\n",
    "    else:\n",
    "        return len(neighbours) / sum(reachability_distances_array)\n",
    "\n",
    "def local_outlier_factor(min_pts, instance, instances, **kwargs):\n",
    "    \"\"\"The (local) outlier factor of instance captures the degree to which we call instance an outlier.\n",
    "    min_pts is a parameter that is specifying a minimum number of instances to consider for computing LOF value.\n",
    "    Returns: local outlier factor\n",
    "    Signature: (int, (attr1, attr2, ...), ((attr_1_1, ...),(attr_2_1, ...), ...)) -> float\"\"\"\n",
    "    (k_distance_value, neighbours) = k_distance(min_pts, instance, instances, **kwargs)\n",
    "    instance_lrd = local_reachability_density(min_pts, instance, instances, **kwargs)\n",
    "    lrd_ratios_array = [0]* len(neighbours)\n",
    "    for i, neighbour in enumerate(neighbours):\n",
    "        instances_without_instance = set(instances)\n",
    "        instances_without_instance.discard(neighbour)\n",
    "        neighbour_lrd = local_reachability_density(min_pts, neighbour, instances_without_instance, **kwargs)\n",
    "        lrd_ratios_array[i] = neighbour_lrd / instance_lrd\n",
    "    return sum(lrd_ratios_array) / len(neighbours)\n",
    "\n",
    "def outliers(k, instances, **kwargs):\n",
    "    \"\"\"Simple procedure to identify outliers in the dataset.\"\"\"\n",
    "    instances_value_backup = instances\n",
    "    outliers = []\n",
    "    for i, instance in enumerate(instances_value_backup):\n",
    "        instances = list(instances_value_backup)\n",
    "        instances.remove(instance)\n",
    "        l = LOF(instances, **kwargs)\n",
    "        value = l.local_outlier_factor(k, instance)\n",
    "        if value > 1:\n",
    "            outliers.append({\"lof\": value, \"instance\": instance, \"index\": i})\n",
    "    outliers.sort(key=lambda o: o[\"lof\"], reverse=True)\n",
    "    return outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lof = LOF(X_train_2dims[:1000].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_value_list = list()\n",
    "# y_pred = list()\n",
    "for i in range(100):\n",
    "    instance = tuple(X_test_2dims.iloc[i])\n",
    "#     print(instance)\n",
    "    value = lof.local_outlier_factor(5, instance)\n",
    "    p_value_list.append(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[154.68874088857166,\n",
       " 1519.7343790621583,\n",
       " 1261.2567529520602,\n",
       " 622.5766379436541,\n",
       " 822.904289259944,\n",
       " 1624.4128568725193,\n",
       " 3156.7879531416547,\n",
       " 1350.4676026344293,\n",
       " 61.240135409706696,\n",
       " 1222.5417593257437,\n",
       " 333.9165109899988,\n",
       " 2395.173135327111,\n",
       " 30.98745572182721,\n",
       " 443.215807923379,\n",
       " 1184.4973605290088,\n",
       " 108.6933843950147,\n",
       " 796.5830527182145,\n",
       " 758.1629788251428,\n",
       " 1099.880422528952,\n",
       " 118.02224355084127]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value_list[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p_value_list)\n",
    "plt.ylabel('value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "\"\"\"\n",
    "The LOF algorithm is an unsupervised outlier detection method\n",
    "\"\"\"\n",
    "def local_outlier_factor_AD(X):\n",
    "    clf = LocalOutlierFactor(n_neighbors=20)\n",
    "    y_pred = clf.fit_predict(X)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = local_outlier_factor_AD(X_train_2dims[:1000].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(y_pred).count(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\yi\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\ranking.py:563: UndefinedMetricWarning: No negative samples in y_true, false positive value should be meaningless\n",
      "  UndefinedMetricWarning)\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_true[:1000], y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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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]\n"
     ]
    }
   ],
   "source": [
    "print(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9, 1. ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print(p_value_dict)\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# ---------------------------------------------------------------------\n",
    "def plotOutputKNN(points, sccolors):\n",
    "    plt.figure(1)\n",
    "    plt.scatter(points[:, 0], points[:, 1], c=sccolors)\n",
    "    plt.title(\"Scatterplot data. Red points are anomalous.\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def writeOutputKNN(stime, anomList, stringG):\n",
    "    print(stringG, \"kNN has taken %s seconds:\" % (time.time() - stime))\n",
    "    print(\"The following points are recongnized as anomalies:\")\n",
    "    for p in anomList:\n",
    "        print(\"(x, y) equal to:\", p[0], p[1])\n",
    "\n",
    "\n",
    "class fileHandling(object):\n",
    "    \"\"\"\n",
    "    Basic Object Properties for file handling.\n",
    "    :return: inputData: class variable with the 2D inputdata\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "#         runDir = os.getcwd()\n",
    "        \n",
    "#         with open('eggs.csv', 'wb') as csvfile:\n",
    "#             spamwriter = csv.writer(csvfile, delimiter=' ',\n",
    "#                             quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
    "#         with open('./reduceDims/mds2dims_X_train_1000samples.csv', 'r') as f:\n",
    "#             reader = csv.reader(f)\n",
    "# #         fn = os.path.join(runDir, './reduceDims/mds2dims_X_train_1000samples.csv')\n",
    "# #         ifile = open(fn)\n",
    "\n",
    "# #         reader = csv.reader(ifile)\n",
    "        inData = []\n",
    "        reader = pd.read_csv(\"./reduceDims/mds2dims_X_train_1000samples.csv\")\n",
    "        for rowindex in range(reader.shape[0]):\n",
    "            x = float(reader.iloc[rowindex][0])\n",
    "            y = float(reader.iloc[rowindex][0])\n",
    "            rowIn = [x, y]\n",
    "            inData.append(rowIn)\n",
    "        self.inputData = np.array(inData)\n",
    "        del inData # is probably quite overkill ..\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class kNN(fileHandling):\n",
    "    '''\n",
    "\n",
    "    '''\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "\n",
    "        :rtype: object\n",
    "        \"\"\"\n",
    "        super(kNN,self).__init__()\n",
    "\n",
    "\n",
    "    def normalizeAndTransInput2D(self, data2handle):\n",
    "        \"\"\"\n",
    "        Normalize and translate 2D input such that max. distance in x- and y- direction\n",
    "        both equal 1 and left bottom corner starts at (x,y) = (0,0).\n",
    "        :param: data to normalize\n",
    "        \"\"\"\n",
    "\n",
    "        dd = np.array(data2handle)\n",
    "        self.xMax = np.absolute(np.max(data2handle[:, 0])) + np.absolute(np.min(data2handle[:, 0]))\n",
    "        dd[:, 0] = data2handle[:, 0] / self.xMax\n",
    "\n",
    "        self.yMax = np.absolute(np.max(data2handle[:, 1])) + np.absolute(np.min(data2handle[:, 1]))\n",
    "        dd[:, 1] = data2handle[:, 1] / self.yMax\n",
    "\n",
    "        cor = [np.absolute(np.min(dd[:, 0])), np.absolute(np.min(dd[:, 1]))]\n",
    "        self.cor = np.array(cor)\n",
    "\n",
    "        self.inputNormT = dd + self.cor\n",
    "        del dd\n",
    "\n",
    "    def distanceEuclidean2D(self, p1, p2):\n",
    "        \"\"\"\n",
    "        Compute the Euclidean distance between 2 points.\n",
    "        :param p1: point one (x, y)\n",
    "        :param p2: point two (x,y)\n",
    "        :return: Euclidean distance between 2 points\n",
    "        \"\"\"\n",
    "        #\n",
    "        # TODO: check if this function should be in the object or out.\n",
    "        #\n",
    "        dist = np.sqrt(np.power(np.absolute(p1[0] - p2[0]), 2.) +\n",
    "                       np.power(np.absolute(p1[1] - p2[1]), 2.) )\n",
    "        return dist\n",
    "\n",
    "\n",
    "    def transformPointBack2D(self, pcoord):\n",
    "        \"\"\"\n",
    "        Transform point back to the 'original' space:\n",
    "        1- translate\n",
    "        2- multiply with normalizer\n",
    "        :param: pcoord: point to be transformed\n",
    "        :return: tpoint: transformed point\n",
    "        \"\"\"\n",
    "\n",
    "        tpoint = pcoord - self.cor\n",
    "        tpoint[0] = tpoint[0] * self.xMax\n",
    "        tpoint[1] = tpoint[1] * self.yMax\n",
    "        return tpoint\n",
    "\n",
    "\n",
    "    def divide2samples(self, numSamples):\n",
    "        \"\"\"\n",
    "        Devide the input data into numSamples of different samples. The\n",
    "        :param numSamples: amount of requested different samples\n",
    "        :return: inputDataSamples\n",
    "        \"\"\"\n",
    "        inputRand = self.inputNormT\n",
    "        np.random.shuffle(inputRand)\n",
    "        self.inputDataSamples = np.array_split(inputRand, numSamples)\n",
    "        self.inputDataSamples = np.array(self.inputDataSamples)\n",
    "\n",
    "\n",
    "    def distanceKNN(self, pCheck, pTrain, k):\n",
    "        \"\"\"\n",
    "        Find the distance to the k Nearest Neighbours.\n",
    "        Method utilizes:\n",
    "         -/ distanceEuclidean2D.\n",
    "        :param pCheck: point to check\n",
    "        :param pTrain: dataset to compare with\n",
    "        :param k: number of nearest neighbours\n",
    "        :return: distKNN: array with distances to the k Nearest Neighbours\n",
    "        \"\"\"\n",
    "        dList = []\n",
    "        for p in pTrain:\n",
    "            dList.append(self.distanceEuclidean2D(pCheck, p))\n",
    "        dList = np.array(dList)\n",
    "        dList = np.sort(dList)\n",
    "        if (dList[0] == 0):\n",
    "            distKNN = dList[1:k+1]\n",
    "        else:\n",
    "            distKNN = dList[:k]\n",
    "        del dList\n",
    "        return distKNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------------------------------------------------\n",
    "def kNNstraightforward(k, d):\n",
    "    \"\"\"\n",
    "    Straighforward application of KNN algorithm to identify the anomalous points.\n",
    "    :param k: value of k for the k nearest neighbour\n",
    "    :param d: threshold value for distance to consider a point anomalous (d is specified in normalized space)\n",
    "    :return: the anomalous points, a score list and the input data\n",
    "    \"\"\"\n",
    "\n",
    "    # 0. initiate instance of kNN classe\n",
    "    anomKNN = kNN()\n",
    "\n",
    "    # 1. normalize and translate 2D input data\n",
    "    anomKNN.normalizeAndTransInput2D(anomKNN.inputData)\n",
    "\n",
    "    # 2.0 find the averaged distance to the k Nearest Neighbours\n",
    "    # 2.1 find the anomalies: when averaged distance is larger and d\n",
    "    # 2.2 make an anomaly score\n",
    "    # TODO: decide whether loop below should be in kNNfunctions - and clean up accordingly\n",
    "    sList = []\n",
    "    anomalyList = []\n",
    "    for tp in anomKNN.inputNormT:\n",
    "        dist2neighbours = anomKNN.distanceKNN(tp, anomKNN.inputNormT, k)\n",
    "        sList.append(np.mean(dist2neighbours))\n",
    "        if (np.mean(dist2neighbours) > d):\n",
    "\n",
    "            # transform back to normal space to present results\n",
    "            tpBack = anomKNN.transformPointBack2D(tp)\n",
    "            anomalyList.append(tpBack)\n",
    "\n",
    "    anomalyList = np.array(anomalyList)\n",
    "    sList = np.array(sList)\n",
    "\n",
    "    # 3. use the averaged distance as score in a scatter plot\n",
    "    scorecolors = ['red' if value > d else 'blue' for value in sList]\n",
    "\n",
    "    return anomKNN.inputData, scorecolors, anomalyList\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Straightforward kNN has taken 23.133944034576416 seconds:\n",
      "The following points are recongnized as anomalies:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "inputD, scoreC,  anomalyList = kNNstraightforward(10, 10)\n",
    "\n",
    "# 1.2 Present results\n",
    "writeOutputKNN(start_time, anomalyList, 'Straightforward')\n",
    "plotOutputKNN(inputD, scoreC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomalyList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.44968364,   5.44968364],\n",
       "       [  6.04953984,   6.04953984],\n",
       "       [  7.20450616,   7.20450616],\n",
       "       ...,\n",
       "       [-15.08805674, -15.08805674],\n",
       "       [ -5.29549963,  -5.29549963],\n",
       "       [  3.72405219,   3.72405219]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scoreC\n",
    "inputD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we should normalize the data before I input the X_train ??\n"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}