thesis/binary_features.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "from collections import Counter\n",
    "from scipy.stats import chi2_contingency\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from stats_tests import matthews_significance_test\n",
    "from utils import path_join, load_train_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_filename = \"application_train_binary.csv\"\n",
    "path_to_data = \"../data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load train binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_application_train_binary = load_train_test(\n",
    "    filename=train_filename,\n",
    "    path_to_train_folder=path_to_data,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### contingency matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "text": [
      "p-value: 0.376\n",
      "\n"
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   ],
   "source": [
    "# chi-square test of independence of variables in a contingency matrix\n",
    "for column in df_application_train_binary.columns[1:]:  # exclude target\n",
    "    \n",
    "    feature = df_application_train_binary[column]\n",
    "    target = df_application_train_binary['TARGET']\n",
    "    \n",
    "    target_crosstab = pd.crosstab(\n",
    "        index=feature,\n",
    "        columns=target,\n",
    "        margins=True,\n",
    "    )\n",
    "    display(target_crosstab)\n",
    "    \n",
    "    _, p_value, _, _ = chi2_contingency(target_crosstab)\n",
    "    print(f\"p-value: {round(p_value, 3)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME_CONTRACT_TYPE p-value: 0.0\n",
      "FLAG_OWN_CAR p-value: 0.0\n",
      "FLAG_OWN_REALTY p-value: 0.001\n",
      "FLAG_MOBIL p-value: 1.534\n",
      "FLAG_EMP_PHONE p-value: 0.0\n",
      "FLAG_WORK_PHONE p-value: 0.0\n",
      "FLAG_CONT_MOBILE p-value: 1.675\n",
      "FLAG_PHONE p-value: 0.0\n",
      "FLAG_EMAIL p-value: 0.659\n",
      "REG_REGION_NOT_LIVE_REGION p-value: 0.004\n",
      "REG_REGION_NOT_WORK_REGION p-value: 0.0\n",
      "LIVE_REGION_NOT_WORK_REGION p-value: 0.236\n",
      "REG_CITY_NOT_LIVE_CITY p-value: 0.0\n",
      "REG_CITY_NOT_WORK_CITY p-value: 0.0\n",
      "LIVE_CITY_NOT_WORK_CITY p-value: 0.0\n",
      "EMERGENCYSTATE_MODE p-value: 0.0\n",
      "FLAG_DOCUMENT_2 p-value: 0.005\n",
      "FLAG_DOCUMENT_3 p-value: 0.0\n",
      "FLAG_DOCUMENT_4 p-value: 0.277\n",
      "FLAG_DOCUMENT_5 p-value: 1.722\n",
      "FLAG_DOCUMENT_6 p-value: 0.0\n",
      "FLAG_DOCUMENT_7 p-value: 0.799\n",
      "FLAG_DOCUMENT_8 p-value: 0.0\n",
      "FLAG_DOCUMENT_9 p-value: 0.032\n",
      "FLAG_DOCUMENT_10 p-value: 0.866\n",
      "FLAG_DOCUMENT_11 p-value: 0.038\n",
      "FLAG_DOCUMENT_12 p-value: 1.35\n",
      "FLAG_DOCUMENT_13 p-value: 0.0\n",
      "FLAG_DOCUMENT_14 p-value: 0.0\n",
      "FLAG_DOCUMENT_15 p-value: 0.001\n",
      "FLAG_DOCUMENT_16 p-value: 0.0\n",
      "FLAG_DOCUMENT_17 p-value: 0.122\n",
      "FLAG_DOCUMENT_18 p-value: 0.0\n",
      "FLAG_DOCUMENT_19 p-value: 0.903\n",
      "FLAG_DOCUMENT_20 p-value: 1.81\n",
      "FLAG_DOCUMENT_21 p-value: 0.079\n"
     ]
    }
   ],
   "source": [
    "# chi-square test significance matthews_corrcoef\n",
    "for column in df_application_train_binary.columns[1:]:  # exclude target\n",
    "    \n",
    "    n_samples = sum(~df_application_train_binary[column].isna())\n",
    "    target = df_application_train_binary['TARGET']\n",
    "    \n",
    "    if pd.api.types.is_string_dtype(df_application_train_binary[column]):\n",
    "        feature = LabelEncoder().fit_transform(df_application_train_binary[column])\n",
    "    else:\n",
    "        feature = df_application_train_binary[column]\n",
    "    \n",
    "    matthews_coef = matthews_corrcoef(\n",
    "        y_pred=feature,\n",
    "        y_true=target,\n",
    "    )\n",
    "    \n",
    "    _, p_value = matthews_significance_test(\n",
    "        matthews_coef=matthews_coef,\n",
    "        n_samples=n_samples,\n",
    "        alternative='two-sided',\n",
    "    )\n",
    "    print(f\"{column} p-value: {round(p_value, 3)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender = pd.read_csv(\n",
    "    path_join(\"application_train.csv\"),\n",
    "    index_col=\"SK_ID_CURR\",\n",
    "    usecols=[\"SK_ID_CURR\", \"CODE_GENDER\"],\n",
    ").squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'M': 105059, 'F': 202448, 'XNA': 4})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter XNA\n",
    "gender_filter = (gender != 'XNA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>188278</td>\n",
       "      <td>14170</td>\n",
       "      <td>202448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>94404</td>\n",
       "      <td>10655</td>\n",
       "      <td>105059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>282682</td>\n",
       "      <td>24825</td>\n",
       "      <td>307507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TARGET            0      1     All\n",
       "CODE_GENDER                       \n",
       "F            188278  14170  202448\n",
       "M             94404  10655  105059\n",
       "All          282682  24825  307507"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_crosstab = pd.crosstab(\n",
    "    index=gender[gender_filter],\n",
    "    columns=df_application_train_binary['TARGET'][gender_filter],\n",
    "    margins=True,\n",
    ")\n",
    "target_crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_, p_value, _, _ = chi2_contingency(target_crosstab)\n",
    "print(f\"p-value: {round(p_value, 3)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05471012101772606"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matthews_coef = matthews_corrcoef(\n",
    "    y_pred=LabelEncoder().fit_transform(gender[gender_filter]),\n",
    "    y_true=df_application_train_binary['TARGET'][gender_filter],\n",
    ")\n",
    "matthews_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender p-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "_, p_value = matthews_significance_test(\n",
    "    matthews_coef=matthews_coef,\n",
    "    n_samples=sum(~gender[gender_filter].isna()),\n",
    "    alternative='two-sided',\n",
    ")\n",
    "print(f\"gender p-value: {round(p_value, 3)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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