9_kod_waznosc_zmiennych.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "845a87ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district</th>\n",
       "      <th>school</th>\n",
       "      <th>county</th>\n",
       "      <th>grades</th>\n",
       "      <th>students</th>\n",
       "      <th>teachers</th>\n",
       "      <th>calworks</th>\n",
       "      <th>lunch</th>\n",
       "      <th>computer</th>\n",
       "      <th>expenditure</th>\n",
       "      <th>income</th>\n",
       "      <th>english</th>\n",
       "      <th>read</th>\n",
       "      <th>math</th>\n",
       "      <th>size</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75119</td>\n",
       "      <td>Sunol Glen Unified</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>195</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>0.510200</td>\n",
       "      <td>2.040800</td>\n",
       "      <td>67</td>\n",
       "      <td>6384.911133</td>\n",
       "      <td>22.690001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>691.599976</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>17.889909</td>\n",
       "      <td>690.799988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61499</td>\n",
       "      <td>Manzanita Elementary</td>\n",
       "      <td>Butte</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>240</td>\n",
       "      <td>11.150000</td>\n",
       "      <td>15.416700</td>\n",
       "      <td>47.916698</td>\n",
       "      <td>101</td>\n",
       "      <td>5099.380859</td>\n",
       "      <td>9.824000</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>660.500000</td>\n",
       "      <td>661.900024</td>\n",
       "      <td>21.524664</td>\n",
       "      <td>661.200012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61549</td>\n",
       "      <td>Thermalito Union Elementary</td>\n",
       "      <td>Butte</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>1550</td>\n",
       "      <td>82.900002</td>\n",
       "      <td>55.032299</td>\n",
       "      <td>76.322601</td>\n",
       "      <td>169</td>\n",
       "      <td>5501.954590</td>\n",
       "      <td>8.978000</td>\n",
       "      <td>30.000002</td>\n",
       "      <td>636.299988</td>\n",
       "      <td>650.900024</td>\n",
       "      <td>18.697225</td>\n",
       "      <td>643.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61457</td>\n",
       "      <td>Golden Feather Union Elementary</td>\n",
       "      <td>Butte</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>243</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>36.475399</td>\n",
       "      <td>77.049202</td>\n",
       "      <td>85</td>\n",
       "      <td>7101.831055</td>\n",
       "      <td>8.978000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>651.900024</td>\n",
       "      <td>643.500000</td>\n",
       "      <td>17.357143</td>\n",
       "      <td>647.700012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61523</td>\n",
       "      <td>Palermo Union Elementary</td>\n",
       "      <td>Butte</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>1335</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>33.108601</td>\n",
       "      <td>78.427002</td>\n",
       "      <td>171</td>\n",
       "      <td>5235.987793</td>\n",
       "      <td>9.080333</td>\n",
       "      <td>13.857677</td>\n",
       "      <td>641.799988</td>\n",
       "      <td>639.900024</td>\n",
       "      <td>18.671329</td>\n",
       "      <td>640.850006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   district                           school   county grades  students  \\\n",
       "0     75119               Sunol Glen Unified  Alameda  KK-08       195   \n",
       "1     61499             Manzanita Elementary    Butte  KK-08       240   \n",
       "2     61549      Thermalito Union Elementary    Butte  KK-08      1550   \n",
       "3     61457  Golden Feather Union Elementary    Butte  KK-08       243   \n",
       "4     61523         Palermo Union Elementary    Butte  KK-08      1335   \n",
       "\n",
       "    teachers   calworks      lunch  computer  expenditure     income  \\\n",
       "0  10.900000   0.510200   2.040800        67  6384.911133  22.690001   \n",
       "1  11.150000  15.416700  47.916698       101  5099.380859   9.824000   \n",
       "2  82.900002  55.032299  76.322601       169  5501.954590   8.978000   \n",
       "3  14.000000  36.475399  77.049202        85  7101.831055   8.978000   \n",
       "4  71.500000  33.108601  78.427002       171  5235.987793   9.080333   \n",
       "\n",
       "     english        read        math       size       score  \n",
       "0   0.000000  691.599976  690.000000  17.889909  690.799988  \n",
       "1   4.583333  660.500000  661.900024  21.524664  661.200012  \n",
       "2  30.000002  636.299988  650.900024  18.697225  643.600006  \n",
       "3   0.000000  651.900024  643.500000  17.357143  647.700012  \n",
       "4  13.857677  641.799988  639.900024  18.671329  640.850006  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv(\"data/CASchools.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26461e9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "579ae8a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf9e3350",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ed838e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = [\"students\", \"teachers\", \"calworks\", \"lunch\", \"computer\", \"expenditure\", \"income\"]\n",
    "categorical_features = [\"county\", \"grades\"]\n",
    "\n",
    "X = data[numeric_features+categorical_features]\n",
    "y = data.read\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f710bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>students</th>\n",
       "      <th>teachers</th>\n",
       "      <th>calworks</th>\n",
       "      <th>lunch</th>\n",
       "      <th>computer</th>\n",
       "      <th>expenditure</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>195</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>0.510200</td>\n",
       "      <td>2.040800</td>\n",
       "      <td>67</td>\n",
       "      <td>6384.911133</td>\n",
       "      <td>22.690001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>240</td>\n",
       "      <td>11.150000</td>\n",
       "      <td>15.416700</td>\n",
       "      <td>47.916698</td>\n",
       "      <td>101</td>\n",
       "      <td>5099.380859</td>\n",
       "      <td>9.824000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1550</td>\n",
       "      <td>82.900002</td>\n",
       "      <td>55.032299</td>\n",
       "      <td>76.322601</td>\n",
       "      <td>169</td>\n",
       "      <td>5501.954590</td>\n",
       "      <td>8.978000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>243</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>36.475399</td>\n",
       "      <td>77.049202</td>\n",
       "      <td>85</td>\n",
       "      <td>7101.831055</td>\n",
       "      <td>8.978000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1335</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>33.108601</td>\n",
       "      <td>78.427002</td>\n",
       "      <td>171</td>\n",
       "      <td>5235.987793</td>\n",
       "      <td>9.080333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   students   teachers   calworks      lunch  computer  expenditure     income\n",
       "0       195  10.900000   0.510200   2.040800        67  6384.911133  22.690001\n",
       "1       240  11.150000  15.416700  47.916698       101  5099.380859   9.824000\n",
       "2      1550  82.900002  55.032299  76.322601       169  5501.954590   8.978000\n",
       "3       243  14.000000  36.475399  77.049202        85  7101.831055   8.978000\n",
       "4      1335  71.500000  33.108601  78.427002       171  5235.987793   9.080333"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[numeric_features].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6b7e972",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5924d2ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "686271d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()), ('model', LinearRegression())])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"model\", LinearRegression())])\n",
    "\n",
    "pipeline.fit(X_train[numeric_features], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b634747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8083078681288711"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, pipeline.predict(X_test[numeric_features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10d20838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -5.34922232,   3.40620508,   1.58942431, -16.80398733,\n",
       "         0.57474973,   2.15571779,   2.54716239])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.named_steps[\"model\"].coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2bb66250",
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = np.abs(pipeline.named_steps[\"model\"].coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da813edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = np.argsort(importances)\n",
    "p = len(importances)\n",
    "colors = np.array([\"red\", \"green\"])[(np.sign(pipeline.named_steps[\"model\"].coef_)>0).astype(int)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e2bc83e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Feature Importances')\n",
    "plt.barh(range(p), importances[indices], color=colors[indices])\n",
    "plt.yticks(range(p), np.array(numeric_features)[indices])\n",
    "plt.xlabel('Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04fb1df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9b577d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(min_samples_leaf=5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeRegressor(min_samples_leaf=5)\n",
    "model.fit(X_train[numeric_features], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8cb3e4e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6835538073161102"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, model.predict(X_test[numeric_features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e07c7fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01521429, 0.01339342, 0.01226134, 0.87310021, 0.01115376,\n",
       "       0.02540334, 0.04947364])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8200b866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances= model.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)\n",
    "p = len(importances)\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(p), importances[indices])\n",
    "plt.yticks(range(p), np.array(numeric_features)[indices])\n",
    "plt.xlabel('Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "686f4672",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25aee74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['students',\n",
       " 'teachers',\n",
       " 'calworks',\n",
       " 'lunch',\n",
       " 'computer',\n",
       " 'expenditure',\n",
       " 'income']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c4f1f9b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['county', 'grades']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f2e31c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
       "                                                  ['students', 'teachers',\n",
       "                                                   'calworks', 'lunch',\n",
       "                                                   'computer', 'expenditure',\n",
       "                                                   'income']),\n",
       "                                                 ('cat',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['county', 'grades'])])),\n",
       "                ('model',\n",
       "                 RandomForestRegressor(min_samples_leaf=2, n_estimators=300))])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor = ColumnTransformer([\n",
    "    (\"num\", StandardScaler(), numeric_features),\n",
    "    (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), categorical_features)\n",
    "])\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"model\", RandomForestRegressor(300, min_samples_leaf=2))\n",
    "])\n",
    "\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1ac71167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7781714616226801"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, pipeline.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4dbf0149",
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = pipeline.named_steps[\"model\"].feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "93141bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted_preprocessor = pipeline.steps[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "33889e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = fitted_preprocessor.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "61131ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,20))\n",
    "indices = np.argsort(importances)\n",
    "p = len(importances)\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(p), importances[indices])\n",
    "plt.yticks(range(p), np.array(feature_names)[indices])\n",
    "plt.xlabel('Importance')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Elearn",
   "language": "python",
   "name": "elearn"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}