{
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple regression analysis in SQL with 2 independent variables\n",
    "![Screenshot 2023-03-27 at 10.42.04 AM.png](attachment:b94da636-eae3-4302-97e0-05cca04da8e5.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from DATA225utils import make_connection, dataframe_query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = make_connection(config_file = 'homes.ini')\n",
    "cursor = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor.execute('DROP TABLE IF EXISTS homes')\n",
    "\n",
    "sql = \"\"\"\n",
    "    CREATE TABLE homes\n",
    "    (\n",
    "        id        INT NOT NULL,\n",
    "        bedrooms  INT NOT NULL,\n",
    "        bathrooms INT NOT NULL,\n",
    "        price     DOUBLE,\n",
    "        PRIMARY KEY(id)\n",
    "    )\n",
    "    \"\"\"\n",
    "\n",
    "cursor.execute(sql)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Home prices with missing prices\n",
    "#### -1 is the placeholder for missing prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedrooms  = [      3,       2,       4,       2,       3,  5,       2,       5,  2,       4]\n",
    "bathrooms = [      2,       1,       3,       1,       2,  3,       2,       3,  1,       2]\n",
    "prices    = [488_000, 443_000, 538_000, 442_000, 497_000, -1, 449_000, 584_000, -1, 529_000]\n",
    "\n",
    "values = list(zip(range(len(prices)), bedrooms, bathrooms, prices))\n",
    "values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the `homes` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = ( \"\"\"\n",
    "        INSERT INTO homes\n",
    "        VALUES (%s, %s, %s, %s)\n",
    "        \"\"\"\n",
    "      )\n",
    "\n",
    "cursor.executemany(sql, values)\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Function to display the `homes` table and return its rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_and_return(cursor):\n",
    "    \"\"\"\n",
    "    Query the weights table, print its rows as a dataframe,\n",
    "    and return the rows as a numpy array.\n",
    "    \"\"\"\n",
    "    _, df = dataframe_query(conn, 'SELECT * FROM homes')\n",
    "    \n",
    "    pd.options.display.float_format = '${:,.2f}'.format\n",
    "    display(df)\n",
    "    \n",
    "    return df.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The dirty `homes` table\n",
    "#### -1 is the placeholder for missing prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = display_and_return(cursor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Function to execute multiple SQL statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def execute_multiple_SQL(cursor, sql, trace=False):\n",
    "    \"\"\"\n",
    "    Use the cursor to execute multiple SQL statements.\n",
    "    Print an execution trace if trace=True.\n",
    "    \"\"\"\n",
    "    for crsr in cursor.execute(sql, multi=True):\n",
    "        if crsr.with_rows:\n",
    "            results = crsr.fetchall()\n",
    "            if trace:\n",
    "                print(crsr.statement)\n",
    "                print('  ==> ', results)\n",
    "        else:\n",
    "            if trace:\n",
    "                print(crsr.statement)\n",
    "                if crsr.rowcount > 0:\n",
    "                    print(f'  ==> {crsr.rowcount} row(s) affected.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SQL to clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = ( \"\"\"\n",
    "    START TRANSACTION;\n",
    "\n",
    "    DROP VIEW IF EXISTS base;\n",
    "    DROP VIEW IF EXISTS univariate_regression;\n",
    "    DROP VIEW IF EXISTS residuals;\n",
    "    DROP VIEW IF EXISTS multiple_regression_2;\n",
    "\n",
    "    CREATE VIEW base AS\n",
    "        SELECT\n",
    "            id,\n",
    "\n",
    "            bedrooms                          AS x1,\n",
    "            bedrooms - AVG(bedrooms) OVER()   AS x1_centered,\n",
    "\n",
    "            bathrooms                         AS x2,\n",
    "            bathrooms - AVG(bathrooms) OVER() AS x2_centered,\n",
    "\n",
    "            price                             AS y,\n",
    "            price - AVG(price) OVER()         AS y_centered\n",
    "\n",
    "        FROM homes\n",
    "        WHERE price > 0;\n",
    "\n",
    "    CREATE VIEW univariate_regression AS\n",
    "        SELECT\n",
    "            AVG(x2) - AVG(x1) * SUM(x2_centered * x1_centered)\n",
    "                      / SUM(x1_centered * x1_centered)         AS x1_const,\n",
    "            SUM(x2_centered * x1_centered)\n",
    "                      / SUM(x1_centered * x1_centered)         AS beta1,\n",
    "            AVG(x1) - AVG(x2) * SUM(x1_centered * x2_centered)\n",
    "                      / SUM(x2_centered * x2_centered)         AS x2_const,\n",
    "            SUM(x1_centered * x2_centered)\n",
    "                      / SUM(x2_centered * x2_centered)         AS beta2\n",
    "        FROM base;\n",
    "\n",
    "    CREATE VIEW residuals AS \n",
    "        SELECT\n",
    "            x1,\n",
    "            x1 - (SELECT beta2    FROM univariate_regression) * x2\n",
    "               - (SELECT x2_const FROM univariate_regression)      AS x1_resid,\n",
    "\n",
    "            x2,\n",
    "            x2 - (SELECT beta1    FROM univariate_regression) * x1\n",
    "               - (SELECT x1_const FROM univariate_regression)      AS x2_resid,\n",
    "\n",
    "            y,\n",
    "            y_centered\n",
    "\n",
    "        FROM base;\n",
    "\n",
    "    CREATE VIEW multiple_regression_2 AS\n",
    "        SELECT\n",
    "            AVG(y) - AVG(x1) * SUM(y_centered * x1_resid)\n",
    "                     / SUM(x1_resid * x1_resid)\n",
    "                   - AVG(x2) * SUM(y_centered * x2_resid)\n",
    "                     / SUM(x2_resid * x2_resid)           AS beta0,\n",
    "            SUM(y_centered * x1_resid)\n",
    "                / SUM(x1_resid * x1_resid)                AS beta1,\n",
    "            SUM(y_centered * x2_resid)\n",
    "                / SUM(x2_resid * x2_resid)                AS beta2\n",
    "\n",
    "        FROM residuals;\n",
    "\n",
    "    -- Replace missing home prices.\n",
    "\n",
    "    SELECT *\n",
    "    FROM multiple_regression_2\n",
    "    INTO @beta_0, @beta_1, @beta_2;\n",
    "\n",
    "    UPDATE homes\n",
    "    SET price := @beta_0 + @beta_1*bedrooms + @beta_2*bathrooms\n",
    "    WHERE price < 0;\n",
    "\n",
    "    COMMIT;\n",
    "    \"\"\"\n",
    "      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "execute_multiple_SQL(cursor, sql)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The cleaned `homes` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = display_and_return(cursor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain the regression coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor.execute('SELECT * FROM multiple_regression_2')\n",
    "result = cursor.fetchone()\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = DataFrame([result])\n",
    "df.columns = ['β0', 'β1', 'β2']\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor.close()\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression coefficients using `numpy` matrix arithmetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = rows[:, 1]  # bedrooms\n",
    "X2 = rows[:, 2]  # bathrooms\n",
    "Y  = rows[:, 3]  # price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones = np.array([1]*len(Y))\n",
    "ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([ones, X1, X2]).T\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATA = A.T@A\n",
    "\n",
    "b = Y\n",
    "ATb = A.T@b\n",
    "\n",
    "ATAinv = np.linalg.inv(ATA)\n",
    "Xhat = ATAinv@ATb\n",
    "\n",
    "Xhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "β0 = Xhat[0]\n",
    "β1 = Xhat[1]\n",
    "β2 = Xhat[2]\n",
    "\n",
    "df = DataFrame([[β0, β1, β2]])\n",
    "df.columns = ['β0', 'β1', 'β2']\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Regression coefficients using `sklearn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "X = np.array([X1, X2]).T\n",
    "\n",
    "linear_regression = LinearRegression()\n",
    "linear_regression.fit(X, Y)\n",
    "\n",
    "β0 = linear_regression.intercept_\n",
    "β1 = linear_regression.coef_[0]\n",
    "β2 = linear_regression.coef_[1]\n",
    "\n",
    "df = DataFrame([[β0, β1, β2]])\n",
    "df.columns = ['β0', 'β1', 'β2']\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Betas = np.array([β1, β2])\n",
    "Yhat  = []\n",
    "\n",
    "for x in X:\n",
    "    Yhat.append(x@Betas + β0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Errors = Y - Yhat\n",
    "table  = []\n",
    "\n",
    "for i in range(len(Y)):\n",
    "    pct = abs(100*Errors[i]/Y[i])\n",
    "    row = np.concatenate([X[i], [Y[i], Yhat[i], Errors[i], pct]])\n",
    "    \n",
    "    table.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = DataFrame(table)\n",
    "df.columns = ['bedrooms', 'bathrooms', 'price', \n",
    "              'est. price', 'error', '% error']\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "display(df)\n",
    "\n",
    "print(f'correlation coefficient = {np.corrcoef(Y, Yhat)[0, 1]:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (c) 2023 by Ronald Mak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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