samples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll import all the necessary libraries and functions that we have implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from preprocess import preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate how our preprocessing works, let's import the dataset that we are using in our experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "      <th>Manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I10</td>\n",
       "      <td>2017</td>\n",
       "      <td>7495</td>\n",
       "      <td>Manual</td>\n",
       "      <td>11630</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>60.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>hyundi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Polo</td>\n",
       "      <td>2017</td>\n",
       "      <td>10989</td>\n",
       "      <td>Manual</td>\n",
       "      <td>9200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>58.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 Series</td>\n",
       "      <td>2019</td>\n",
       "      <td>27990</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>1614</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>49.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yeti Outdoor</td>\n",
       "      <td>2017</td>\n",
       "      <td>12495</td>\n",
       "      <td>Manual</td>\n",
       "      <td>30960</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>150</td>\n",
       "      <td>62.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>skoda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fiesta</td>\n",
       "      <td>2017</td>\n",
       "      <td>7999</td>\n",
       "      <td>Manual</td>\n",
       "      <td>19353</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>125</td>\n",
       "      <td>54.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>ford</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "0            I10  2017   7495       Manual    11630   Petrol  145  60.1   \n",
       "1           Polo  2017  10989       Manual     9200   Petrol  145  58.9   \n",
       "2       2 Series  2019  27990    Semi-Auto     1614   Diesel  145  49.6   \n",
       "3   Yeti Outdoor  2017  12495       Manual    30960   Diesel  150  62.8   \n",
       "4         Fiesta  2017   7999       Manual    19353   Petrol  125  54.3   \n",
       "\n",
       "   engineSize Manufacturer  \n",
       "0         1.0       hyundi  \n",
       "1         1.0   volkswagen  \n",
       "2         2.0          BMW  \n",
       "3         2.0        skoda  \n",
       "4         1.2         ford  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"CarsData.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see what is the effect of our function `preprocess`, used for transforming the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.099154</td>\n",
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       "      <td>-0.185822</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.202668</td>\n",
       "      <td>0.032486</td>\n",
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       "      <td>0.036848</td>\n",
       "      <td>0.035288</td>\n",
       "      <td>0.152585</td>\n",
       "      <td>0.031328</td>\n",
       "      <td>-0.032995</td>\n",
       "      <td>0.013367</td>\n",
       "      <td>-0.010180</td>\n",
       "      <td>0.013057</td>\n",
       "      <td>-0.023202</td>\n",
       "      <td>12495</td>\n",
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       "      <th>4</th>\n",
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       "\n",
       "   componenet21  componenet22  componenet23  componenet24  componenet25  \\\n",
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       "\n",
       "   componenet26  componenet27  componenet28  componenet29  price  \n",
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       "4     -0.001399     -0.006205     -0.004326      0.010764   7999  \n",
       "\n",
       "[5 rows x 31 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess(df, target=\"price\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try to tweak a few parameters. For example, instead of using the default feature extraction method, which is PCA (Principal Component Analysis) we can use LDA (Linear Discriminant Analysis):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>27990</td>\n",
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       "      <td>-0.284876</td>\n",
       "      <td>7999</td>\n",
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       "\n",
       "   componenet26  componenet27  componenet28  componenet29  price  \n",
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       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess(df, target=\"price\", feature_extraction_method=\"LDA\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us increase the value of `feature_selection_treshold`. This will significantly change the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess(df, target=\"price\", feature_selection_treshold=1000.0).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Increasing the value of parameter `variance_treshold` will have a similar effect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "3     0.241407     0.655107     0.514542     0.616875    -0.677277   \n",
       "4     0.009866    -0.853828    -0.820067     0.166179     0.041509   \n",
       "\n",
       "   componenet5  componenet6  componenet7  componenet8  componenet9  ...  \\\n",
       "0     0.227581     0.160091    -0.066428    -0.097149    -0.212016  ...   \n",
       "1     0.209221     0.392469     0.921462     0.185419     0.035166  ...   \n",
       "2    -0.153771     0.439086    -0.120747    -0.755351     0.459142  ...   \n",
       "3     0.443237     0.018986    -0.017203     0.015192    -0.129056  ...   \n",
       "4     0.108878    -0.087663    -0.066869    -0.052325    -0.295070  ...   \n",
       "\n",
       "   componenet21  componenet22  componenet23  componenet24  componenet25  \\\n",
       "0      0.031296     -0.045051     -0.126061     -0.023619      0.000647   \n",
       "1     -0.325824     -0.168750      0.063896      0.018472      0.020985   \n",
       "2     -0.073567     -0.040086      0.025919      0.047930      0.006452   \n",
       "3      0.013639      0.004926      0.162672      0.032276      0.014184   \n",
       "4     -0.048290     -0.027451     -0.044159      0.006841      0.004902   \n",
       "\n",
       "   componenet26  componenet27  componenet28  componenet29  price  \n",
       "0     -0.012043     -0.001093     -0.022724     -0.012200   7495  \n",
       "1      0.018411      0.009309     -0.105912      0.070864  10989  \n",
       "2     -0.010052     -0.274613     -0.069985     -0.027925  27990  \n",
       "3     -0.034926     -0.024312      0.121076      0.073346  12495  \n",
       "4      0.019265      0.012734     -0.029552     -0.018492   7999  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess(df, target=\"price\", variance_treshold=0.015).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To modify the number of components, we can change the parameter `n_components`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>0.475072</td>\n",
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       "      <td>-0.551741</td>\n",
       "      <td>0.395775</td>\n",
       "      <td>0.607504</td>\n",
       "      <td>-0.581399</td>\n",
       "      <td>0.443354</td>\n",
       "      <td>0.092376</td>\n",
       "      <td>0.097957</td>\n",
       "      <td>0.202666</td>\n",
       "      <td>0.032487</td>\n",
       "      <td>0.108166</td>\n",
       "      <td>-0.152247</td>\n",
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       "      <td>0.169809</td>\n",
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       "   componenet0  componenet1  componenet2  componenet3  componenet4  \\\n",
       "0    -0.061325     1.086454    -0.837091     0.603295     0.262565   \n",
       "1    -0.150470     1.128804    -0.862481     0.475072     0.295706   \n",
       "2    -1.581833    -0.123695     0.960080    -0.099154     0.239616   \n",
       "3     0.168580    -0.551741     0.395775     0.607504    -0.581399   \n",
       "4     0.092386     0.741502    -0.837758     0.169809    -0.049627   \n",
       "\n",
       "   componenet5  componenet6  componenet7  componenet8  componenet9  \\\n",
       "0     0.117377     0.128906    -0.016715     0.109266     0.067968   \n",
       "1     0.027867     0.665889     0.829599    -0.006950    -0.097600   \n",
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       "4     0.276358    -0.579975     0.089769    -0.276827    -0.127185   \n",
       "\n",
       "   componenet10  componenet11  componenet12  componenet13  componenet14  price  \n",
       "0      0.151676     -0.467263      0.321976      0.198460      1.046320   7495  \n",
       "1      0.006466     -0.081819      0.261713     -0.130927     -0.153120  10989  \n",
       "2     -0.431352     -0.325708      0.070697      0.121014     -0.130621  27990  \n",
       "3      0.108166     -0.152247      0.018935      0.217054      0.032130  12495  \n",
       "4     -0.093093      0.125193      0.073583     -0.009118     -0.024887   7999  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess(df, target=\"price\", n_components=15).head()"
   ]
  }
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