Kognitywistyka

dane_standaryzacja.csv

Kognitywistyka.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zadanie - treść\n",
    "\n",
    "1. ocena - > shift + 1\n",
    "2. standaryzacja: dla każdej sceny (wartość - średnia (po wszytkich userach))/odchylenie standardowe (all users)\n",
    "3. klasteryzacja\n",
    "4. anova\n",
    "5. ranking istotności"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         user            obraz  ocena           system_czas\n",
      "0    Wojciech    BlueBlack.png      1  10.05.2020, 12:34:12\n",
      "1    Wojciech    BlueWhite.png      5  10.05.2020, 12:38:51\n",
      "2    Wojciech   WhiteGreen.png      2  10.05.2020, 12:38:54\n",
      "3    Wojciech  OrangeWhite.png      4  10.05.2020, 12:38:59\n",
      "4    Wojciech    BlueGreen.png      3  10.05.2020, 12:39:02\n",
      "..        ...              ...    ...                   ...\n",
      "354     Karol    OrangeRed.png      1  11.05.2020, 03:49:13\n",
      "355     Karol  YellowBlack.png      1  11.05.2020, 03:49:16\n",
      "356     Karol  YellowWhite.png      1  11.05.2020, 03:49:25\n",
      "357     Karol  BlackYellow.png      4  11.05.2020, 03:49:28\n",
      "358     Karol   BlueOrange.png      3  11.05.2020, 03:49:32\n",
      "\n",
      "[359 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('dane_standaryzacja.csv')\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df.groupby('obraz').mean(), df.groupby('obraz').std(), on='obraz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocena_x</th>\n",
       "      <th>ocena_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obraz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BlackBlue.png</th>\n",
       "      <td>4.6</td>\n",
       "      <td>0.894427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackGray.png</th>\n",
       "      <td>4.2</td>\n",
       "      <td>0.836660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackGreen.png</th>\n",
       "      <td>3.2</td>\n",
       "      <td>0.836660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackOrange.png</th>\n",
       "      <td>3.6</td>\n",
       "      <td>1.516575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackRed.png</th>\n",
       "      <td>2.2</td>\n",
       "      <td>1.095445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowGreen.png</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.894427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowOrange.png</th>\n",
       "      <td>2.6</td>\n",
       "      <td>1.140175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowRed.png</th>\n",
       "      <td>2.6</td>\n",
       "      <td>1.516575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowViolet.png</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.516575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowWhite.png</th>\n",
       "      <td>1.6</td>\n",
       "      <td>1.341641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ocena_x   ocena_y\n",
       "obraz                              \n",
       "BlackBlue.png         4.6  0.894427\n",
       "BlackGray.png         4.2  0.836660\n",
       "BlackGreen.png        3.2  0.836660\n",
       "BlackOrange.png       3.6  1.516575\n",
       "BlackRed.png          2.2  1.095445\n",
       "...                   ...       ...\n",
       "YellowGreen.png       1.4  0.894427\n",
       "YellowOrange.png      2.6  1.140175\n",
       "YellowRed.png         2.6  1.516575\n",
       "YellowViolet.png      2.4  1.516575\n",
       "YellowWhite.png       1.6  1.341641\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"obraz\": \"obraz\", \"ocena_x\": \"mean\", \"ocena_y\": \"std\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obraz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BlackBlue.png</th>\n",
       "      <td>4.6</td>\n",
       "      <td>0.894427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackGray.png</th>\n",
       "      <td>4.2</td>\n",
       "      <td>0.836660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackGreen.png</th>\n",
       "      <td>3.2</td>\n",
       "      <td>0.836660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackOrange.png</th>\n",
       "      <td>3.6</td>\n",
       "      <td>1.516575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackRed.png</th>\n",
       "      <td>2.2</td>\n",
       "      <td>1.095445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowGreen.png</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.894427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowOrange.png</th>\n",
       "      <td>2.6</td>\n",
       "      <td>1.140175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowRed.png</th>\n",
       "      <td>2.6</td>\n",
       "      <td>1.516575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowViolet.png</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.516575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YellowWhite.png</th>\n",
       "      <td>1.6</td>\n",
       "      <td>1.341641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean       std\n",
       "obraz                           \n",
       "BlackBlue.png      4.6  0.894427\n",
       "BlackGray.png      4.2  0.836660\n",
       "BlackGreen.png     3.2  0.836660\n",
       "BlackOrange.png    3.6  1.516575\n",
       "BlackRed.png       2.2  1.095445\n",
       "...                ...       ...\n",
       "YellowGreen.png    1.4  0.894427\n",
       "YellowOrange.png   2.6  1.140175\n",
       "YellowRed.png      2.6  1.516575\n",
       "YellowViolet.png   2.4  1.516575\n",
       "YellowWhite.png    1.6  1.341641\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('odchylenia.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To jeszcze trzeba poddać standaryzacji, czyli dla każdej sceny (wartość - średnia (po wszytkich userach))/odchylenie standardowe (all users).\n",
    "\n",
    "## Standaryzacja\n",
    "\n",
    "Wykorzystano wczesniej obliczane średnie i odchylenia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('dane_standaryzacja.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\dev\\jupyter\\.venv\\lib\\site-packages\\ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "obrazki = df2.iloc[:, 1:3]\n",
    "\n",
    "def standarize(name, value):\n",
    "    mean = df.loc[name, 'mean']\n",
    "    std = df.loc[name, 'std']\n",
    "    standard = (value-mean)/std\n",
    "    return [name, standard]\n",
    "\n",
    "obrazki.apply(lambda x: standarize(x['obraz'], x['ocena']), axis=1, result_type='expand')\n",
    "obrazki.to_csv('po_standaryzacji.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anova i analiza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.read_csv('po_standaryzacji.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>obraz</th>\n",
       "      <th>ocena</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>BlueBlack.png</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BlueWhite.png</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>WhiteGreen.png</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>OrangeWhite.png</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>BlueGreen.png</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>354</td>\n",
       "      <td>OrangeRed.png</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>355</td>\n",
       "      <td>YellowBlack.png</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>356</td>\n",
       "      <td>YellowWhite.png</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>357</td>\n",
       "      <td>BlackYellow.png</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>358</td>\n",
       "      <td>BlueOrange.png</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>359 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0            obraz  ocena\n",
       "0             0    BlueBlack.png      1\n",
       "1             1    BlueWhite.png      5\n",
       "2             2   WhiteGreen.png      2\n",
       "3             3  OrangeWhite.png      4\n",
       "4             4    BlueGreen.png      3\n",
       "..          ...              ...    ...\n",
       "354         354    OrangeRed.png      1\n",
       "355         355  YellowBlack.png      1\n",
       "356         356  YellowWhite.png      1\n",
       "357         357  BlackYellow.png      4\n",
       "358         358   BlueOrange.png      3\n",
       "\n",
       "[359 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean    4.600000\n",
       "std     0.894427\n",
       "Name: BlackBlue.png, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['BlackBlue.png']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop=False, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BlackGray.png</td>\n",
       "      <td>4.2</td>\n",
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       "      <th>2</th>\n",
       "      <td>BlackGreen.png</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.836660</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BlackOrange.png</td>\n",
       "      <td>3.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BlackRed.png</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.095445</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>YellowGreen.png</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.894427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>YellowOrange.png</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.140175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>YellowRed.png</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.516575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>YellowViolet.png</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.516575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>YellowWhite.png</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.341641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               obraz  mean       std\n",
       "0      BlackBlue.png   4.6  0.894427\n",
       "1      BlackGray.png   4.2  0.836660\n",
       "2     BlackGreen.png   3.2  0.836660\n",
       "3    BlackOrange.png   3.6  1.516575\n",
       "4       BlackRed.png   2.2  1.095445\n",
       "..               ...   ...       ...\n",
       "67   YellowGreen.png   1.4  0.894427\n",
       "68  YellowOrange.png   2.6  1.140175\n",
       "69     YellowRed.png   2.6  1.516575\n",
       "70  YellowViolet.png   2.4  1.516575\n",
       "71   YellowWhite.png   1.6  1.341641\n",
       "\n",
       "[72 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['obraz'] == 'BlackBlue.png'].index.values[0]\n",
    "y = list(df3['ocena'].values)\n",
    "x_obraz = list(df3['obraz'].values)\n",
    "x = [df[df['obraz'] == x].index.values[0] for x in x_obraz]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=444.47047091225863, pvalue=1.1521538948319171e-98)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.kruskal(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test Kruskala wyszedł **pvalue=1.1521538948319171e-98**, czyli bardzo bliskie zera. Z dokumnetacji: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kruskal.html można przeczytać, \n",
    "że \n",
    "\n",
    "> The Kruskal-Wallis H-test tests the null hypothesis that the population median of all of the groups are equal. \n",
    "> Note that rejecting the null hypothesis does not indicate which of the groups differs. \n",
    "\n",
    "Czyli mając bardzo małe *p* odrzucamy hipozę zerową i stwierdzamy że grupy (oceny obrazków) są statystycznie różne."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wykres pudełkowy\n",
    "\n",
    "Należy przekształcić dane sprzed standaryzacji, tak aby w kolumnach były wartości ocen. To znaczy jak niżej:\n",
    "\n",
    "| user | BlackBlue.png | YellowGreen.png | ... | YellowViolet.png |\n",
    "|------|---------------|-----------------|-----|------------------|\n",
    "|Karol | 1             | 2               | 1   |  5               |\n",
    "|Wojciech| 2           | 2               | 1   | 4                |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ta komórka w zasadzie jest niepotrzebna, ale zostawiam, żeby mieć w przyszłości referencje\n",
    "\n",
    "# stworzyć listę ze wszystkimi obrazkami\n",
    "header = ['user'] + df['obraz'].tolist()\n",
    "\n",
    "# stworzenie DataFrame\n",
    "transponowane = pd.DataFrame(columns=header)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('dane_standaryzacja.csv')\n",
    "\n",
    "# Zgrupowanie\n",
    "df2 = df2.groupby('user')\n",
    "df2 = df2.apply(lambda x : x[['obraz', 'ocena']].set_index('obraz').T)\n",
    "df2.reset_index(drop=False, inplace=True)\n",
    "\n",
    "# Usuniecie niepotrzebnej kolumny w indeksie\n",
    "df2 = df2.drop(columns=['level_1'])\n",
    "df2 = df2.set_index('user')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2947e825348>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "# Zmiana nazwy kolumn, żeby lepiej w boxplot wyświetlało się\n",
    "df2.columns = [i+1 for i, _ in enumerate(df2.columns.to_list())]\n",
    "\n",
    "df2.boxplot(figsize=(15, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

odchylenia.csv

po_standaryzacji.csv