kaggle/iris/Simple analysis of Iris dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a6f5e4ba-5d3b-47bf-8fef-0400af6e7d93",
    "_execution_state": "idle",
    "_uuid": "3c30ccab5dba1f70dcfd09ca58abda68be0dfdfb"
   },
   "source": [
    "# Intros\n",
    "This is a starter kernel ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "ca7f4cf5-a2e2-464c-bfc8-57a766dcb7d7",
    "_execution_state": "idle",
    "_uuid": "6f70489c23175d9374d9e14ac1fa72092f65da6f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# analytics libraries installed listed in the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "#from subprocess import check_output\n",
    "#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "6069a15c-95ce-4f7c-b3d2-daa8cf6f72d6",
    "_execution_state": "idle",
    "_uuid": "a48cde050b68c980f0e79a9f94b37a435af6ce04",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "#matplotlib.style.use('ggplot')\n",
    "import pylab\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.core.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ad66bd60-be2e-43d1-9b6b-3fc51311618f",
    "_execution_state": "idle",
    "_uuid": "e88f72bb9560508453edda607f86abbc31389c93"
   },
   "source": [
    "# Data samples and traits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "e5b1e4a3-b5f6-4027-be9a-d68cfb89415b",
    "_execution_state": "idle",
    "_uuid": "f35d8dfcece95e8363a0df70262722b1974da274",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      0   1            5.1           3.5            1.4           0.2   \n",
       "1      1   2            4.9           3.0            1.4           0.2   \n",
       "2      2   3            4.7           3.2            1.3           0.2   \n",
       "3      3   4            4.6           3.1            1.5           0.2   \n",
       "4      4   5            5.0           3.6            1.4           0.2   \n",
       "\n",
       "       Species  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../input/Iris.csv\", header = 0)\n",
    "#reset index\n",
    "data = data.reset_index()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "01bf7bd5-5512-4da1-88f2-986d0d246405",
    "_execution_state": "idle",
    "_uuid": "05502ca33fc73e5bac0a26385d4f476f9953f444",
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Types of species: ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n",
      "\n",
      "Dataset length: 150\n",
      "\n",
      "Sepal length range: [4.3, 7.9]\n",
      "Sepal width range:  [2.0, 7.9]\n",
      "Petal length range: [1.0, 6.9]\n",
      "Petal width range:  [0.1, 2.5]\n",
      "\n",
      "Sepal length variance:\t 0.681122\n",
      "Sepal width variance: \t 0.186751\n",
      "Petal length variance:\t 3.092425\n",
      "Petal width variance: \t 0.578532\n",
      "\n",
      "Sepal length stddev:\t 0.825301\n",
      "Sepal width stddev: \t 0.432147\n",
      "Petal length stddev:\t 1.758529\n",
      "Petal width stddev: \t 0.760613\n",
      "\n",
      "Data describe\n",
      "---\n",
      "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
      "count     150.000000    150.000000     150.000000    150.000000\n",
      "mean        5.843333      3.054000       3.758667      1.198667\n",
      "std         0.828066      0.433594       1.764420      0.763161\n",
      "min         4.300000      2.000000       1.000000      0.100000\n",
      "25%         5.100000      2.800000       1.600000      0.300000\n",
      "50%         5.800000      3.000000       4.350000      1.300000\n",
      "75%         6.400000      3.300000       5.100000      1.800000\n",
      "max         7.900000      4.400000       6.900000      2.500000\n"
     ]
    }
   ],
   "source": [
    "species_list = list(data[\"Species\"].unique())\n",
    "print(\"Types of species: %s\\n\" % species_list)\n",
    "\n",
    "print(\"Dataset length: %i\\n\" % len(data))\n",
    "\n",
    "print(\"Sepal length range: [%s, %s]\" % (min(data[\"SepalLengthCm\"]), max(data[\"SepalLengthCm\"])))\n",
    "print(\"Sepal width range:  [%s, %s]\" % (min(data[\"SepalWidthCm\"]), max(data[\"SepalLengthCm\"])))\n",
    "print(\"Petal length range: [%s, %s]\" % (min(data[\"PetalLengthCm\"]), max(data[\"PetalLengthCm\"])))\n",
    "print(\"Petal width range:  [%s, %s]\\n\" % (min(data[\"PetalWidthCm\"]), max(data[\"PetalWidthCm\"])))\n",
    "\n",
    "print(\"Sepal length variance:\\t %f\" % np.var(data[\"SepalLengthCm\"]))\n",
    "print(\"Sepal width variance: \\t %f\" % np.var(data[\"SepalWidthCm\"]))\n",
    "print(\"Petal length variance:\\t %f\" % np.var(data[\"PetalLengthCm\"]))\n",
    "print(\"Petal width variance: \\t %f\\n\" % np.var(data[\"PetalWidthCm\"]))\n",
    "\n",
    "print(\"Sepal length stddev:\\t %f\" % np.std(data[\"SepalLengthCm\"]))\n",
    "print(\"Sepal width stddev: \\t %f\" % np.std(data[\"SepalWidthCm\"]))\n",
    "print(\"Petal length stddev:\\t %f\" % np.std(data[\"PetalLengthCm\"]))\n",
    "print(\"Petal width stddev: \\t %f\\n\" % np.std(data[\"PetalWidthCm\"]))\n",
    "\n",
    "print(\"Data describe\\n---\")\n",
    "print(data[data.columns[2:]].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b8b7811a-c99b-493d-9946-bfc6393a6c73",
    "_execution_state": "idle",
    "_uuid": "4de1b52a2776df8ea3353dfef5cd91f5d1140902"
   },
   "source": [
    "**3 types of species**  \n",
    "Relatively small dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d5185617-f8a8-4686-91bd-394b46a8379d",
    "_execution_state": "idle",
    "_uuid": "468d441e72208d5407523dda4ef4e223b545e70b"
   },
   "source": [
    "# Data analysis - distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "e72ff2b6-92b1-432c-b301-599d23337095",
    "_execution_state": "idle",
    "_uuid": "22143ae614690f31fae96e7c878e285df7775d24",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f11618b1c50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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WxN3dXatWrdKmTZv05z//WZs2bVJ8fLxcXV0VHh6umTNnyt/f3zqvxo0bW08oKMmvjxUr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11618b1c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "\n",
    "# data.hist calls data.plot\n",
    "# pandas.DataFrame.plot() returns a matplotlib axis\n",
    "data.hist(\n",
    "    column=[\"SepalLengthCm\", \"SepalWidthCm\", \"PetalLengthCm\", \"PetalWidthCm\", \"Species\"],\n",
    "    figsize=(10, 10)\n",
    "    #,sharey=True, sharex=True\n",
    ")\n",
    "pylab.suptitle(\"Analyzing distribution for the series\", fontsize=\"xx-large\")\n",
    "\n",
    "#alternative\n",
    "#plt.subplot(2,3,1)  # if using subplot\n",
    "#data.hist(...)\n",
    "#plt.title('your title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "910f1539-a717-4a98-84e1-fb29751e34e0",
    "_execution_state": "idle",
    "_uuid": "45d0c70923b059636ab72d07de33ede2504f08e4"
   },
   "source": [
    "*At first sight*, **Petal length** and **petal width** seem to **diverge** from the **normal distribution**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "2a1a95a9-982c-4282-9459-1bc08e5bc741",
    "_execution_state": "idle",
    "_uuid": "b65763cdd3b0fd656e742bab169dd11fba4e1820",
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SepalLengthCm has a p-value of 0.056824\n",
      "SepalWidthCm has a p-value of 0.167241\n",
      "PetalLengthCm has a p-value of 0.000000 - distribution is not normal\n",
      "PetalWidthCm has a p-value of 0.000000 - distribution is not normal\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "#print(\"Sepal length variance:\\t %f\" % np.var(data[\"SepalLengthCm\"]))\n",
    "#print(\"Sepal width variance: \\t %f\" % np.var(data[\"SepalWidthCm\"]))\n",
    "#print(\"Petal length variance:\\t %f\" % np.var(data[\"PetalLengthCm\"]))\n",
    "#print(\"Petal width variance: \\t %f\\n\" % np.var(data[\"PetalWidthCm\"]))\n",
    "\n",
    "for param in [\"SepalLengthCm\", \"SepalWidthCm\", \"PetalLengthCm\", \"PetalWidthCm\"]:\n",
    "    z, pval = stats.normaltest(data[param])\n",
    "    #print(z)\n",
    "    if(pval < 0.055):\n",
    "        print(\"%s has a p-value of %f - distribution is not normal\" % (param, pval))\n",
    "    else:\n",
    "        print(\"%s has a p-value of %f\" % (param, pval))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6271a290-54c2-4bd7-9763-aecb75032a4d",
    "_execution_state": "idle",
    "_uuid": "7d859ecf6fa2e5a0633a6e569cfc6147eaa34246"
   },
   "source": [
    "Hypothesis has been confirmed. Why ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "856e3750-62bf-4e85-9652-7abd2ab9d794",
    "_execution_state": "idle",
    "_uuid": "32067a7d4196d17e1404d48a0a6bd48bc01c1ce7"
   },
   "source": [
    "# Data analysis - correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "78ed2093-c178-41fa-8df9-1a5ee015efcf",
    "_execution_state": "idle",
    "_uuid": "e78c7509a8fd3baf980a25e169e1bffa3345e44d",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>Analyzing the <a href=\"https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\">Pearson correlation coefficient</a></h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "SepalLengthCm       1.000000           NaN            NaN           NaN\n",
       "SepalWidthCm       -0.109369      1.000000            NaN           NaN\n",
       "PetalLengthCm       0.871754     -0.420516       1.000000           NaN\n",
       "PetalWidthCm        0.817954     -0.356544       0.962757           1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1157665630>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Z8zlEX19frF27VmdCb7Vajfj4ePTq1UvycEREJCMWgvG3JkBvC3Hu3LmIi4vDoEGD4O7u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\nIhEREQAWRCIiIgDA/wOm8K7/JdvMCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11611fee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "display(HTML('<h1>Analyzing the ' +\n",
    "             '<a href=\"https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\">' +\n",
    "             'Pearson correlation coefficient</a></h1>'))\n",
    "\n",
    "# data without the indexes\n",
    "dt = data[data.columns[2:]]\n",
    "\n",
    "# method : {‘pearson’, ‘kendall’, ‘spearman’}\n",
    "corr = dt.corr(method=\"pearson\") #returns a dataframe, so it can be reused\n",
    "\n",
    "# eliminate upper triangle for readability\n",
    "bool_upper_matrix = np.tril(np.ones(corr.shape)).astype(np.bool)\n",
    "corr = corr.where(bool_upper_matrix)\n",
    "display(corr)\n",
    "# alternate method: http://seaborn.pydata.org/examples/many_pairwise_correlations.html\n",
    "\n",
    "# seaborn matrix here\n",
    "#sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "#            square=True, ax=ax)\n",
    "sns.heatmap(corr, cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7206ccec-7e23-4681-ab69-3f102f8a576e",
    "_execution_state": "idle",
    "_uuid": "d54fbabffdf61e3512c057dfcd6543951affdf2b"
   },
   "source": [
    "### Interpretation\n",
    "Diagonal values and upper triangle are ignored (melted the upper triangle through **np.tril** and **df.where**).  \n",
    "Naturally, we find:\n",
    "\n",
    "+ a **high positive correlation** between PetalWidth and PetalLength (**0.96**)\n",
    "+ a **high positive correlation** between PetalLength and SepalLength (**0.87**)\n",
    "+ a **high positive correlation** between PetalWidth and SepalLength (**0.81**)\n",
    "\n",
    "As such, we observe correlations between these main attributes: **PetalWidth**, **PetalLength** and **SepalLength**.\n",
    "\n",
    "### Theory\n",
    "PCC is:\n",
    "\n",
    "+ 1 is total positive linear correlation\n",
    "+ 0 is no linear correlation\n",
    "+ −1 is total negative linear correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c513faa6-420b-4bc8-9017-81fcd8a1f3f6",
    "_execution_state": "idle",
    "_uuid": "bec14f8a2636626e30c248e6ed8ad5b745b855ab"
   },
   "source": [
    "## Check correlation in 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "03d297c2-a5de-45c1-960e-f19a110f0b1f",
    "_execution_state": "idle",
    "_uuid": "b06778271d6f823c2e006e6671f4094ddec287f7",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "6bbe9c82-b2d5-40a5-b55c-87bc7b70feb7",
    "_execution_state": "idle",
    "_uuid": "4a4ca0e892d8656360de04d5d4c3fbad8ca6ae46",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JrwXBI498H48//qj574MHD+Cpp54x//2BD7wHw8MjpqPAb//272FoaDiw/XED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t0N03ROxuH9fh8EgUcmk0UuZ20F2TsLl0ag/X6fjTmhdwt3s1C1UnBKMiPj3NU4QgEO60kFe7aO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\nCAQCgUCIWv4fyMik4HFwJSQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1160ffb780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "X = [data[\"PetalWidthCm\"], data[\"PetalLengthCm\"]]\n",
    "n = 100\n",
    "ax.scatter(data[\"PetalWidthCm\"], data[\"PetalLengthCm\"], data[\"SepalLengthCm\"])\n",
    "\n",
    "\n",
    "ax.set_xlabel('PetalWidthCm')\n",
    "ax.set_ylabel('PetalLengthCm')\n",
    "ax.set_zlabel('SepalLengthCm')\n",
    "\n",
    "plt.tight_layout(pad=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "cc394e3f-0472-4e59-8b66-d318785efce2",
    "_execution_state": "idle",
    "_uuid": "ecd708f7c4617e2b4b72841efe865d23655caa23",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "1df34c2d-914a-4f72-a7b4-64001a94e899",
    "_execution_state": "idle",
    "_uuid": "5dcca6d50f7b48ad3f48c57944c6a322c5d49e24",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1154cd3a20>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7vD+o8eBjjRoSuTWDOnfujCVLlth6LURERHaLp5SJiIhEYFXhXrp0CRMmTMBTTz2Fffv2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1160ffb2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "image/png": 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DELgAABiAwAUAwAAELgAABiBwAQAwgKk6jaZOnar169ervLxcDz/8sHr27OnqcQEAUK9U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\n4AIAYAACFwAAAxC4AAAYgMAFAMAA/x/7zQXze+cR2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1160ffb390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "image/png": 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dMTEx2LNnzyONOXr0aGi1WjRq1AhNmzaFyWSCSqXC+PHjOa6Lxi0oKEB4eDiU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\nehS//fYbzp8/D7PZjOeee86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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1157470828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "image/png": 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b7JLsymOTjZkv3Cbpsrm6fWqzvTbZFfetr/m74jh1dfzr9d/51T4OsutyQzg/tS3ma/qV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\nQSkDAGAQlDIAAAbx/wAWycFftgdaqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f115520d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "data[data.columns[2:3]].plot.bar() #x=data[\"Index\"], y=data[\"PetalLengthCm\"]\n",
    "data[data.columns[3:4]].plot.bar()\n",
    "data[data.columns[4:5]].plot.bar()\n",
    "data[data.columns[5:6]].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "378a78b6-224b-4b7e-9714-7dd29fc84b3c",
    "_execution_state": "idle",
    "_uuid": "a57887f38f280899b6a7c96242a1b99b256e751f"
   },
   "source": [
    "# Data analysis - clusterization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "aa8fa6de-4da6-4bbd-813b-e9b4922d6ea8",
    "_execution_state": "idle",
    "_uuid": "d56d440435f5db56f1a3e11b7a6ca259f269401b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "74420f78-b8ee-48b1-8fd2-638b81dc47ee",
    "_execution_state": "idle",
    "_uuid": "04b4b18aa1363a030993ac6338d7fc42f61ee6ca",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>Scatterplots for the correlating pairs</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "image/png": 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XRlx8VkTn6GgTkbV/3KPK6npJUkNNvdZu3BPx+Vpi6MhepGzAAQCxxbC2wUyZ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\nAAxDcQYAwDD/HzaC+lISCc+7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f115402aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "image/png": 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0ikUGOTEiH2bkxIh8mJETM9cceffv31+TJ0/WRx99pNraWs2aNUvvvPOOUlNT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\nAOAyFG8AAFyG4g0AgMv8P/+yfWkv4DvPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f114d6557f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "image/png": 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HcVfzaodt2O0c5/w0i3yaFe4vrMUvXrx4sfHfJik5OVm5ubmKi4tTamqq3njj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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f114d5ced30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "#pd.scatter_matrix(dt, alpha = 0.3, figsize = (14,8), diagonal = 'kde');\n",
    "#sns.pairplot(dt)\n",
    "display(HTML('<h1>Scatterplots for the correlating pairs</h1>'))\n",
    "\n",
    "dt.plot(kind='scatter', x='PetalWidthCm', y='PetalLengthCm');\n",
    "dt.plot(kind='scatter', x='PetalLengthCm', y='SepalLengthCm');\n",
    "dt.plot(kind='scatter', x='PetalWidthCm', y='SepalLengthCm');\n",
    "\n",
    "# --- linear regreesion visualization\n",
    "\n",
    "# TODO: random selection method from sklearn\n",
    "#top_corr_x_train = data[\"PetalWidthCm\"][0:75]\n",
    "#top_corr_y_train = data[\"PetalLengthCm\"][0:75]\n",
    "#top_corr_x_test = data[\"PetalWidthCm\"][75:]\n",
    "#top_corr_y_test = data[\"PetalLengthCm\"][75:]\n",
    "#\n",
    "#regr = linear_model.LinearRegression()\n",
    "#\n",
    "#regr.fit(top_corr_x_train, top_corr_y_train)\n",
    "#\n",
    "## The coefficients\n",
    "##print('Coefficients: \\n', regr.coef_)\n",
    "## The mean squared error\n",
    "#print(\"Mean squared error: %.2f\"\n",
    "#      % np.mean((regr.predict(top_corr_x_test) - top_corr_y_test) ** 2))\n",
    "## Explained variance score: 1 is perfect prediction\n",
    "#print('Variance score: %.2f' % regr.score(top_corr_x_test, top_corr_y_test))\n",
    "#\n",
    "#plt.plot(top_corr_x_test, regr.predict(top_corr_x_test), color='blue',\n",
    "#         linewidth=3)\n",
    "#\n",
    "#prediction = regr.predict(top_corr_x_test)\n",
    "##prediction = prediction[:]\n",
    "#print(prediction)\n",
    "#print(\"Length: \" + len(top_corr_x_test))\n",
    "#\n",
    "#plt.xticks(())\n",
    "#plt.yticks(())\n",
    "#\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "3caa9d83-8775-4a16-807e-f135c3b97e1f",
    "_execution_state": "idle",
    "_uuid": "3d063fcaeb14335410ee22a7c6d8d10208d28a07",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import neighbors, datasets\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "import math\n",
    "import random\n",
    "from numpy.random import permutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "720a6957-db79-4bf3-9c03-e40836b86522",
    "_execution_state": "idle",
    "_uuid": "0cbf23f0a026cf5edbe535c80695f48347573664",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_spl = data[data.columns[2:6]]\n",
    "\n",
    "random_indices = permutation(data_spl.index)\n",
    "# Set a cutoff for how many items we want in the test set (in this case 1/3 of the items)\n",
    "test_cutoff = math.floor(len(data_spl)/3)\n",
    "# Generate the test set by taking the first 1/3 of the randomly shuffled indices.\n",
    "test = data_spl.loc[random_indices[1:test_cutoff]]\n",
    "# Generate the train set with the rest of the data.\n",
    "train = data_spl.loc[random_indices[test_cutoff:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "c6c81b37-f81f-460f-98d6-2ee1ae4fa709",
    "_execution_state": "idle",
    "_uuid": "ee622b1c3ae670cc81892f61d31a73854098355c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#knn\n",
    "def predictKNN(train,labels,test, n_neighbors = 2):\n",
    "    print(\"start knn\")\n",
    "    knn = neighbors.KNeighborsClassifier()\n",
    "    knn.fit(train, labels) \n",
    "    probabilities = knn.predict_proba(test)\n",
    "    predictions = knn.predict(test)\n",
    "    bestScores = probabilities.max(axis=1)\n",
    "    print(\"done with knn\")\n",
    "    return predictions, bestScores\n",
    "\n",
    "\n",
    "data_sk = np.array(data)\n",
    "#print(data_sk)\n",
    "\n",
    "# import some data to play with\n",
    "#eiris = datasets.load_iris()\n",
    "#print(data[\"PetalWidthCm\"].shape)\n",
    "#print(len(data[\"PetalLengthCm\"]))\n",
    "\n",
    "#display(dt[\"PetalWidthCm\"].head())\n",
    "\n",
    "X = [data[\"PetalWidthCm\"], data[\"PetalLengthCm\"]]\n",
    "y = [\"PetalWidthCm\", \"PetalLengthCm\"] #[\"PetalWidthCm\", \"PetalLengthCm\"]\n",
    "\n",
    "X = [np.array(data[\"PetalWidthCm\"]), np.array(data[\"PetalLengthCm\"])]\n",
    "\n",
    "#data.columns = range(data.shape[1])\n",
    "X = np.array(data[data.columns[2:4]])#.astype(np.float)\n",
    "#X = data.columns[2:6]\n",
    "#print(X)\n",
    "Y = np.array(data[data.columns[0:1]]).ravel() #.T\n",
    "#print(y.shape)\n",
    "\n",
    "# h = .02  # step size in the mesh\n",
    "\n",
    "# # Create color maps\n",
    "# cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "# cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "# for weights in ['uniform', 'distance']:\n",
    "#     # Plot the decision boundary. For that, we will assign a color to each\n",
    "#     # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "#     x_min = min(X[0]) - 1 #X[0].min() - 1 #min(X[0]) - 1\n",
    "#     x_max = max(X[0]) + 1\n",
    "#     y_min = min(X[1]) - 1\n",
    "#     y_max = max(X[1]) + 1\n",
    "#     xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "#                          np.arange(y_min, y_max, h))\n",
    "#     #test = np.c_[xx.ravel(), yy.ravel()]\n",
    "    \n",
    "    \n",
    "#     #clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n",
    "#     #clf.fit(X, y)\n",
    "#     Z, scores = predictKNN(X,y,test)\n",
    "#     #Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "#     # Put the result into a color plot\n",
    "#     Z = Z.reshape(xx.shape)\n",
    "#     plt.figure()\n",
    "#     plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "   \n",
    "#     # Plot also the training points\n",
    "#     plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
    "#     plt.xlim(xx.min(), xx.max())\n",
    "#     plt.ylim(yy.min(), yy.max())\n",
    "#     plt.title(\"3-Class classification (k = %i, weights = '%s')\"\n",
    "#               % (n_neighbors, weights))\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "7e28ab5f7d2a1b4b07e92857d29fd498f3827358",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import some data to play with\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2] # we only take the first two features. \n",
    "Y = iris.target\n",
    "# print(X)\n",
    "# print(Y)\n",
    "# print(np.bincount(Y, minlength=np.size(Y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "8f32e7b3-a353-4896-bf74-0ff2b65fae7f",
    "_execution_state": "idle",
    "_uuid": "94b0180ddcb2560dd7572d633e7a230e721bc697",
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'neighbors' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-fb37a2f17b30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m.02\u001b[0m \u001b[1;31m# step size in the mesh\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mknn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mneighbors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[1;31m# we create an instance of Neighbours Classifier and fit the data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'neighbors' is not defined"
     ]
    }
   ],
   "source": [
    "h = .02 # step size in the mesh\n",
    "\n",
    "knn=neighbors.KNeighborsClassifier()\n",
    "\n",
    "# we create an instance of Neighbours Classifier and fit the data.\n",
    "knn.fit(X, Y)\n",
    "\n",
    "# Plot the decision boundary. For that, we will asign a color to each\n",
    "# point in the mesh [x_min, m_max]x[y_min, y_max].\n",
    "x_min, x_max = X[:,0].min() - .5, X[:,0].max() + .5\n",
    "y_min, y_max = X[:,1].min() - .5, X[:,1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(4, 3))\n",
    "plt.set_cmap(plt.cm.Paired)\n",
    "plt.pcolormesh(xx, yy, Z)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X[:,0], X[:,1],c=Y )\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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    "_execution_state": "idle",
    "_uuid": "172eb1f37f20527cd061c45ce7cac503316621c3",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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