demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"AIM: Select appropriate sensors/columns/features from multidimensional time series data\"\n",
    "@author: Ajay Arunachalam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import operator, statistics\n",
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "from msda.msda import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import time-series data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The raw sensor data includes 6 sensors logging data every 2 minutes ##\n",
    "## The columns include\n",
    "Date, Time, Light, Temp Air, Temp Soil, Soil Moisture, Soil Moisture, Soil Moisture\n",
    "'''\n",
    "The code includes:-\n",
    "1) Time series analysis\n",
    "2) The variation of each sensor column wrt time (increasing, decreasing, equal)\n",
    "3) How each column values varies wrt other column, and the maximum variation ratio between each column wrt other column\n",
    "4) Relationship establishment with trend array to identify most appropriate sensor.\n",
    "5) User can select window length and then check average value and standard deviation accross each window for each sensor column\n",
    "6) It provides count of growth/decay value for each sensor column values above or below a threshold value\n",
    "7) Feature Engineering \n",
    "    a) Features involving trend of values across various aggregation windows: change and rate of change in average,\n",
    "    std. deviation across window\n",
    "    b) Ratio of changes, growth rate with std. deviation\n",
    "    c) Change over time\n",
    "    d) Rate of change over time\n",
    "    e) Growth or decay\n",
    "    f) Rate of growth or decay\n",
    "    g) Count of values above or below a threshold value \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/Data1.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Light</th>\n",
       "      <th>Temp_Air</th>\n",
       "      <th>Temp_Soil</th>\n",
       "      <th>Soil_Moisture_1</th>\n",
       "      <th>Soil_Moisture_2</th>\n",
       "      <th>Soil_Moisture_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>08:40</td>\n",
       "      <td>64.0</td>\n",
       "      <td>23.1875</td>\n",
       "      <td>20.5625</td>\n",
       "      <td>756.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>1002.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>08:42</td>\n",
       "      <td>64.0</td>\n",
       "      <td>23.1250</td>\n",
       "      <td>20.5625</td>\n",
       "      <td>755.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>08:44</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>20.5625</td>\n",
       "      <td>758.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>1001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>08:46</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.9375</td>\n",
       "      <td>20.5625</td>\n",
       "      <td>756.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>08:48</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.8750</td>\n",
       "      <td>20.5625</td>\n",
       "      <td>756.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>1003.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Time  Light  Temp_Air  Temp_Soil  Soil_Moisture_1  \\\n",
       "0  22-05-2019  08:40   64.0   23.1875    20.5625            756.0   \n",
       "1  22-05-2019  08:42   64.0   23.1250    20.5625            755.0   \n",
       "2  22-05-2019  08:44   66.0   23.0000    20.5625            758.0   \n",
       "3  22-05-2019  08:46   71.0   22.9375    20.5625            756.0   \n",
       "4  22-05-2019  08:48   71.0   22.8750    20.5625            756.0   \n",
       "\n",
       "   Soil_Moisture_2  Soil_Moisture_3  \n",
       "0            780.0           1002.0  \n",
       "1            782.0           1002.0  \n",
       "2            782.0           1001.0  \n",
       "3            782.0           1002.0  \n",
       "4            783.0           1003.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Light</th>\n",
       "      <th>Temp_Air</th>\n",
       "      <th>Temp_Soil</th>\n",
       "      <th>Soil_Moisture_1</th>\n",
       "      <th>Soil_Moisture_2</th>\n",
       "      <th>Soil_Moisture_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>15:32</td>\n",
       "      <td>69.0</td>\n",
       "      <td>22.000</td>\n",
       "      <td>20.025</td>\n",
       "      <td>758.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>15:34</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.125</td>\n",
       "      <td>22.000</td>\n",
       "      <td>760.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>999.0</td>\n",
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       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>15:36</td>\n",
       "      <td>51.0</td>\n",
       "      <td>22.125</td>\n",
       "      <td>20.625</td>\n",
       "      <td>758.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>999.0</td>\n",
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       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>15:38</td>\n",
       "      <td>62.0</td>\n",
       "      <td>22.000</td>\n",
       "      <td>20.625</td>\n",
       "      <td>755.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>1003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>22-05-2019</td>\n",
       "      <td>15:40</td>\n",
       "      <td>66.0</td>\n",
       "      <td>20.225</td>\n",
       "      <td>23.825</td>\n",
       "      <td>757.0</td>\n",
       "      <td>788.0</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Time  Light  Temp_Air  Temp_Soil  Soil_Moisture_1  \\\n",
       "205  22-05-2019  15:32   69.0    22.000     20.025            758.0   \n",
       "206  22-05-2019  15:34   66.0    22.125     22.000            760.0   \n",
       "207  22-05-2019  15:36   51.0    22.125     20.625            758.0   \n",
       "208  22-05-2019  15:38   62.0    22.000     20.625            755.0   \n",
       "209  22-05-2019  15:40   66.0    20.225     23.825            757.0   \n",
       "\n",
       "     Soil_Moisture_2  Soil_Moisture_3  \n",
       "205            783.0            998.0  \n",
       "206            780.0            999.0  \n",
       "207            783.0            999.0  \n",
       "208            781.0           1003.0  \n",
       "209            788.0            999.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Time', 'Light', 'Temp_Air', 'Temp_Soil', 'Soil_Moisture_1',\n",
       "       'Soil_Moisture_2', 'Soil_Moisture_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 210 entries, 0 to 209\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Date             210 non-null    object \n",
      " 1   Time             210 non-null    object \n",
      " 2   Light            210 non-null    float64\n",
      " 3   Temp_Air         210 non-null    float64\n",
      " 4   Temp_Soil        210 non-null    float64\n",
      " 5   Soil_Moisture_1  210 non-null    float64\n",
      " 6   Soil_Moisture_2  210 non-null    float64\n",
      " 7   Soil_Moisture_3  210 non-null    float64\n",
      "dtypes: float64(6), object(2)\n",
      "memory usage: 13.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check missing values with missing function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Check missing individual column\n",
    "returned = Preprocessing.missing(df.Light)\n",
    "print(returned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing information for whole dataset\n",
      "Missing by row Date               0\n",
      "Time               0\n",
      "Light              0\n",
      "Temp_Air           0\n",
      "Temp_Soil          0\n",
      "Soil_Moisture_1    0\n",
      "Soil_Moisture_2    0\n",
      "Soil_Moisture_3    0\n",
      "dtype: int64\n",
      "Missing by col 0      0\n",
      "1      0\n",
      "2      0\n",
      "3      0\n",
      "4      0\n",
      "      ..\n",
      "205    0\n",
      "206    0\n",
      "207    0\n",
      "208    0\n",
      "209    0\n",
      "Length: 210, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Missing information for whole dataset\")\n",
    "print(\"Missing by row\", df.apply(Preprocessing.missing, axis=0)) # col\n",
    "print(\"Missing by col\", df.apply(Preprocessing.missing, axis=1)) # row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find variation in each column (Inc, Dec, Eql) from its previous values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend information for each column in the dataset\n",
      "Dictionary of each column with variation count\n",
      " Date                 {'Inc': 0, 'Dec': 0, 'Eql': 209}\n",
      "Time                 {'Inc': 209, 'Dec': 0, 'Eql': 0}\n",
      "Light               {'Inc': 74, 'Dec': 78, 'Eql': 57}\n",
      "Temp_Air           {'Inc': 23, 'Dec': 108, 'Eql': 78}\n",
      "Temp_Soil           {'Inc': 63, 'Dec': 59, 'Eql': 87}\n",
      "Soil_Moisture_1     {'Inc': 84, 'Dec': 81, 'Eql': 44}\n",
      "Soil_Moisture_2    {'Inc': 84, 'Dec': 109, 'Eql': 16}\n",
      "Soil_Moisture_3     {'Inc': 87, 'Dec': 67, 'Eql': 55}\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"Trend information for each column in the dataset\")\n",
    "print(\"Dictionary of each column with variation count\\n\", df.apply(FeatureSelection.count_trend, axis=0)) # col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find maximum variation in each column wrt time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary of each column with variation\n",
    "Light_trend = FeatureSelection.count_trend(df.Light)\n",
    "Tempsoil_trend=FeatureSelection.count_trend(df.Temp_Soil)\n",
    "Tempair_trend=FeatureSelection.count_trend(df.Temp_Air)\n",
    "Soil_m1_trend=FeatureSelection.count_trend(df.Soil_Moisture_1)\n",
    "Soil_m2_trend=FeatureSelection.count_trend(df.Soil_Moisture_2)\n",
    "Soil_m3_trend=FeatureSelection.count_trend(df.Soil_Moisture_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Values showing maximum variation in each column wrt Time\n",
    "light = max(Light_trend.items(), key=operator.itemgetter(1))[0]\n",
    "soil_temp= max(Tempsoil_trend.items(),key=operator.itemgetter(1))[0]\n",
    "air_temp= max(Tempair_trend.items(),key=operator.itemgetter(1))[0]\n",
    "soil_m1= max(Soil_m1_trend.items(),key=operator.itemgetter(1))[0]\n",
    "soil_m2= max(Soil_m2_trend.items(),key=operator.itemgetter(1))[0]\n",
    "soil_m3= max(Soil_m3_trend.items(),key=operator.itemgetter(1))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max. Variation Involved in each Sensor Column values are:\n",
      "Note: Inc-Increasing ; Dec-Decreasing ; Eq-Equal \n",
      "For Light Column: Dec\n",
      "For Temp_Air Column: Dec\n",
      "For Temp_Soil Column: Eql\n",
      "For Soil_Moisture_1 Column: Inc\n",
      "For Soil_Moisture_2 Column: Dec\n",
      "For Soil_Moisture_3 Column: Inc\n"
     ]
    }
   ],
   "source": [
    "print('Max. Variation Involved in each Sensor Column values are:')\n",
    "print('Note: Inc-Increasing ; Dec-Decreasing ; Eq-Equal ')\n",
    "print('For Light Column:',light)\n",
    "print('For Temp_Air Column:',air_temp)\n",
    "print('For Temp_Soil Column:',soil_temp)\n",
    "print('For Soil_Moisture_1 Column:',soil_m1)\n",
    "print('For Soil_Moisture_2 Column:',soil_m2)\n",
    "print('For Soil_Moisture_3 Column:',soil_m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# forming variation-based array to define how each column value differs from its previous value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n"
     ]
    }
   ],
   "source": [
    "trend=list()\n",
    "i=0\n",
    "av=df.to_numpy()\n",
    "while i!=len(av)-1:\n",
    "    vg=[]\n",
    "    for k, v in zip(av[i], av[i+1]):\n",
    "        if k==v:\n",
    "            vg.append('Eq')\n",
    "        elif k>v:\n",
    "            vg.append('Dec')\n",
    "        elif k<v:\n",
    "            vg.append('Inc')\n",
    "    i+=1\n",
    "    trend.append(vg)\n",
    "\n",
    "mat = np.array(trend)\n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate ratios for each variation in each column wrt other column values & finding max ratio to establish change of a given column wrt other column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Ratios of Variations Of Values of Each Sensor Column wrt other Sensor Column **\n",
      " Note: Inc-Increasing ; Dec-Decreasing ; Eq-Equal \n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "For Sensor Column:- Light\n",
      "Ratio is: 0.6756756756756757\n",
      "When Sensor Column 'Light' values are Inc , Sensor Column 'Soil_Moisture_1' values are Dec\n",
      "------------------------\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "For Sensor Column:- Temp_Air\n",
      "Ratio is: 1.0\n",
      "When Sensor Column 'Temp_Air' values are Inc , Sensor Column 'Light' values are Dec\n",
      "------------------------\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "For Sensor Column:- Temp_Soil\n",
      "Ratio is: 0.9736842105263158\n",
      "When Sensor Column 'Temp_Soil' values are Eq , Sensor Column 'Light' values are Inc\n",
      "------------------------\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "For Sensor Column:- Soil_Moisture_1\n",
      "Ratio is: 1.0\n",
      "When Sensor Column 'Soil_Moisture_1' values are Dec , Sensor Column 'Temp_Soil' values are Dec\n",
      "------------------------\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "For Sensor Column:- Soil_Moisture_2\n",
      "Ratio is: 1.0\n",
      "When Sensor Column 'Soil_Moisture_2' values are Eq , Sensor Column 'Light' values are Dec\n",
      "------------------------\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "[['Eq' 'Inc' 'Eq' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Eq' 'Dec']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Eq' 'Inc']\n",
      " ...\n",
      " ['Eq' 'Inc' 'Dec' ... 'Dec' 'Inc' 'Eq']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Dec' 'Dec' 'Inc']\n",
      " ['Eq' 'Inc' 'Inc' ... 'Inc' 'Inc' 'Dec']]\n",
      "For Sensor Column:- Soil_Moisture_3\n",
      "Ratio is: 1.0\n",
      "When Sensor Column 'Soil_Moisture_3' values are Dec , Sensor Column 'Soil_Moisture_1' values are Inc\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "# To print Ratios of Variations of Values in each column\n",
    "cols = list(df.columns)\n",
    "cols = cols[2:]\n",
    "print('** Ratios of Variations Of Values of Each Sensor Column wrt other Sensor Column **')\n",
    "print(' Note: Inc-Increasing ; Dec-Decreasing ; Eq-Equal ')\n",
    "for i in cols:\n",
    "    FeatureSelection.ratio(df,i)\n",
    "    print('------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate change & rate of change in average, std. deviation across window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------\n",
      "** Avg. and Standard deviations for each Sensor Column **\n",
      "Enter Time in Minutes for the Window: (Must be a Multiple of 2):10\n",
      "Rate of Change of AVG Across Window For Sensor Column Light: 63.22857142857143\n",
      "Rate of Change of STD Across Window For Sensor Column Light: 4.258943346730935\n",
      "Rate of Change of AVG Across Window For Sensor Column Temp_Air: 22.417083333333334\n",
      "Rate of Change of STD Across Window For Sensor Column Temp_Air: 0.29864460799447445\n",
      "Rate of Change of AVG Across Window For Sensor Column Temp_Soil: 20.939821428571427\n",
      "Rate of Change of STD Across Window For Sensor Column Temp_Soil: 0.5650260796431359\n",
      "Rate of Change of AVG Across Window For Sensor Column Soil_Moisture_1: 755.8285714285714\n",
      "Rate of Change of STD Across Window For Sensor Column Soil_Moisture_1: 5.002229665551033\n",
      "Rate of Change of AVG Across Window For Sensor Column Soil_Moisture_2: 783.4285714285714\n",
      "Rate of Change of STD Across Window For Sensor Column Soil_Moisture_2: 2.1554160501394413\n",
      "Rate of Change of AVG Across Window For Sensor Column Soil_Moisture_3: 1001.9952380952382\n",
      "Rate of Change of STD Across Window For Sensor Column Soil_Moisture_3: 1.7217233719081362\n"
     ]
    }
   ],
   "source": [
    "print('--------------------------------------------------------------------')\n",
    "print('** Avg. and Standard deviations for each Sensor Column **')\n",
    "FeatureSelection.window(df=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the sensors/features to verify slope For Sensors/Columns Showing Max. Variation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FeatureSelection.plot(df, logging_interval=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Derive new features*** \n",
    "Change over time, Rate of change over time, Growth and/or decay, Rate of growth and/or decay, No. of elements above threshold (mean) value for growth and/or decay, etc.\n",
    "'''\n",
    "Feature Engineering \n",
    "    a) Features involving trend of values across various aggregation windows: change and rate of change in average,\n",
    "    std. deviation across window\n",
    "    b) Ratio of changes, growth rate with std. deviation\n",
    "    c) Change over time\n",
    "    d) Rate of change over time\n",
    "    e) Growth and/or decay\n",
    "    f) Rate of growth and/or decay\n",
    "    g) Count of Growth/Decay values above or below a threshold value \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of Growth/Decay value for each Sensor Column Values above or below a threshold value:\n",
      " {'Light': 74, 'Temp_Air': 101, 'Temp_Soil': 63, 'Soil_Moisture_1': 84, 'Soil_Moisture_2': 84, 'Soil_Moisture_3': 142}\n"
     ]
    }
   ],
   "source": [
    "change_ovr_time,rate_of_change_ovr_time,growth_decay,rate_growth_decay,(threshold_growth_decay,count_decay_growth) = FeatureSelection.features(df=df, start_sensor_column_index=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot each sensor column & the derived features with correlation (slope)\n",
    "1) Feature: Change over time\n",
    "2) Feature: Rate of change over time\n",
    "3) Feature: Growth decay\n",
    "4) Feature: Rate of growth decay\n",
    "5) Feature: threshold growth decay, count growth decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FeatureSelection.plot_change_ovr_time(df=df, feature=change_ovr_time, logging_interval=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FeatureSelection.plot_rate_of_change_ovr_time(df=df, feature=rate_of_change_ovr_time, logging_interval=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FeatureSelection.plot_growth_decay(df=df, feature=growth_decay, logging_interval=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FeatureSelection.plot_rate_growth_decay(df=df, feature=rate_growth_decay, logging_interval=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FeatureSelection.Threshold_Counts(df=df,feature=(threshold_growth_decay,count_decay_growth))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CONCLUSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The plots shows each sensor values and features with correlation (slope) is provided.\n",
    "\n",
    "The most appropriate sensors to be selected based on our approach would be then 'Temp_Soil', 'Light' in the \n",
    "order of highest importance.\n",
    "\n",
    "The reasons are as follows:-\n",
    "   * These column values mostly remain constant or increases over time as seen from the slope.\n",
    "   * The rate of change of column values remain constant or increases over time as seen from the slope.\n",
    "   * Maximum variation within the column values shows an increasing slope.\n",
    "   * It has constant decay slope.\n",
    "   * Rate of Decay is positive or constant.\n",
    "   * It has moderate number of values above the threshold value (i.e., mean).\n",
    "\n",
    "From our method of array values evaluation, it can be observed that the various characteristics involved in each sensor column help in selection of most appropriate sensor column by observing the plot of the characteristics, and corresponding slope."
   ]
  }
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