lectures/lecture5/lecture5.ipynb

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    "# The Lasso\n",
    "\n",
    "## StatML: Lecture 5\n",
    "\n",
    "### Prof. James Sharpnack\n",
    "\n",
    "- Some content and images are from \"The Elements of Statistical Learning\" by Hastie, Tibshirani, Friedman\n",
    "- Reading ESL Chapter 3"
   ]
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    "### Recall Convex Optimization\n",
    "\n",
    "**Def** A function $f : \\mathbb R^p \\to \\mathbb R$ is convex if for any $0 \\le \\alpha \\le 1$, $x_0, x_1 \\in \\mathbb R^p$,\n",
    "$$\n",
    "f(\\alpha x_0 + (1 - \\alpha) x_1) \\le \\alpha f(x_0) + (1 - \\alpha) f(x_1).\n",
    "$$\n",
    "\n",
    "> For convex functions, local minima are global minima"
   ]
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    "Recall **1st Order Condition**. If f is differentiable then it is convex if \n",
    "$$\n",
    "f(x) \\ge f(x_0) + \\nabla f(x_0)^\\top (x - x_0), \\forall x,x_0\n",
    "$$\n",
    "and when $\\nabla f(x_0) = 0$ then \n",
    "$$\n",
    "f(x) \\ge f(x_0), \\forall x\n",
    "$$\n",
    "so any fixed point of gradient descent is a global min (for convex, differentiable f)"
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   "source": [
    "### Subdifferential\n",
    "\n",
    "**Def.** $g(x_0) \\in \\mathbb R^p$ is a *subgradient* of $f$ at $x_0$ if  \n",
    "$$\n",
    "f(x) \\ge f(x_0) + g(x_0)^\\top (x - x_0), \\forall x.\n",
    "$$\n",
    "The set of all subgradients at $x_0$ is call the *subdifferential*, denoted $\\partial f(x_0)$.\n",
    "\n",
    "> For any global optima, $0 \\in \\partial f(x_0)$."
   ]
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   "source": [
    "### Wavelet denoising\n",
    "\n",
    "Soft thresholding is commonly used for orthonormal bases.  \n",
    "- Suppose that we have a vector $y_1,\\ldots, y_T$ (like a time series).\n",
    "- And we want to reconstruct $y$ with $W \\beta$ where $\\beta$ has a small sum of absolute values $\\sum_i |\\beta_i|$ \n",
    "- $W$ is $T \\times T$ and $W W^\\top = W^\\top W = I$ (orthonormal full rank design)\n",
    "\n",
    "Want to minimize \n",
    "$$\n",
    "\\frac 12 \\sum_{i=1}^T (y - W \\beta)_i^2 + \\lambda \\sum_{i=1}^T |\\beta_i|.\n",
    "$$"
   ]
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "## Explore Turkish stock exchange dataset\n",
    "\n",
    "tse = pd.read_excel('../../data/data_akbilgic.xlsx',skiprows=1)\n",
    "\n",
    "tse = tse.rename(columns={'ISE':'TLISE','ISE.1':'USDISE'})"
   ]
  },
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   "execution_count": 9,
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   "source": [
    "def const_wave(T,a,b):\n",
    "    wave = np.zeros(T)\n",
    "    s1 = (b-a) // 2\n",
    "    s2 = (b-a) - s1\n",
    "    norm_C = (s1*s2 / (s1+s2))**0.5\n",
    "    wave[a:a+s1] = norm_C / s1\n",
    "    wave[a+s1:b] = -norm_C / s2\n",
    "    return wave"
   ]
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   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "def _const_wave_basis(T,a,b):\n",
    "    if b-a < 2:\n",
    "        return []\n",
    "    wave_basis = []\n",
    "    wave_basis.append(const_wave(T,a,b))\n",
    "    mid_pt = a + (b-a)//2\n",
    "    wave_basis += _const_wave_basis(T,a,mid_pt)\n",
    "    wave_basis += _const_wave_basis(T,mid_pt,b)\n",
    "    return wave_basis"
   ]
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   "source": [
    "def const_wave_basis(T,a,b):\n",
    "    father = np.ones(T) / T**0.5\n",
    "    return [father] + _const_wave_basis(T,a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   "source": [
    "# Construct discrete Haar wavelet basis\n",
    "T,p = tse.shape\n",
    "wave_basis = const_wave_basis(T,0,T)\n",
    "W = np.array(wave_basis).T"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.plot(W[:,:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def soft(y,lamb):\n",
    "    pos_part = (y - lamb) * (y > lamb)\n",
    "    neg_part = (y + lamb) * (y < -lamb)\n",
    "    return pos_part + neg_part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "## Volatility seems most interesting\n",
    "## will construct local measure of volatility\n",
    "## remove rolling window estimate (local centering)\n",
    "## square the residuals\n",
    "\n",
    "tse = tse.set_index('date')\n",
    "tse_trem = tse - tse.rolling(\"7D\").mean()\n",
    "tse_vol = tse_trem**2.\n",
    "\n",
    "## Make wavelet transformation and soft threshold\n",
    "\n",
    "tse_wave = W.T @ tse_vol.values\n",
    "lamb = .001\n",
    "tse_soft = soft(tse_wave,lamb)\n",
    "tse_rec = W @ tse_soft\n",
    "tse_den = tse_vol.copy()\n",
    "tse_den.iloc[:,:] = tse_rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = tse_vol.plot(subplots=True,figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = tse_den.plot(subplots=True,figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wavelet reconstruction\n",
    "\n",
    "Can reconstruct the sequence by\n",
    "$$\n",
    "\\hat y = W \\hat \\beta.\n",
    "$$\n",
    "The objective is likelihood term + L1 penalty term,\n",
    "$$\n",
    "\\frac 12 \\sum_{i=1}^T (y - W \\beta)_i^2 + \\lambda \\sum_{i=1}^T |\\beta_i|.\n",
    "$$\n",
    "> The L1 penalty \"forces\" some $\\beta_i = 0$, inducing sparsity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   2  48  63 344 345]\n"
     ]
    },
    {
     "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.plot(tse_soft[:,4])\n",
    "high_idx = np.where(np.abs(tse_soft[:,4]) > .0001)[0]\n",
    "print(high_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f17b67b3090>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(len(high_idx) + 1,1)\n",
    "for i, idx in enumerate(high_idx):\n",
    "    axs[i].plot(W[:,idx])\n",
    "plt.plot(tse_den['FTSE'],c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Non-orthogonal design\n",
    "\n",
    "The objective is likelihood term + L1 penalty term,\n",
    "$$\n",
    "\\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2 + \\lambda \\sum_{i=1}^T |\\beta_i|.\n",
    "$$\n",
    "does not have closed form for $X$ that is non-orthogonal.\n",
    "\n",
    "- it is convex\n",
    "- it is non-smooth (recall $|x|$)\n",
    "- has tuning parameter $\\lambda$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Compare to best subset selection (NP-hard):\n",
    "$$\n",
    "\\min \\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2.\n",
    "$$\n",
    "for\n",
    "$$\n",
    "\\| \\beta \\|_0 = |{\\rm supp}(\\beta)| < s.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Image of Lasso solution\n",
    "\n",
    "<img src=\"lasso_soln.PNG\" width=100%>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Solving the Lasso\n",
    "\n",
    "The lasso can be written in *regularized form*,\n",
    "$$\n",
    "\\min \\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2 + \\lambda \\sum_{i=1}^T |\\beta_i|,\n",
    "$$\n",
    "or in *constrained form*,\n",
    "$$\n",
    "\\min \\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2, \\quad \\textrm{s.t.} \\sum_{i=1}^T |\\beta_i| \\le C,\n",
    "$$\n",
    "\n",
    "- For every $\\lambda$ there is a $C$ such that the regularized form and constrained form have the same argmin\n",
    "- This correspondence is data dependent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Exercise 5.1. Solving the Lasso\n",
    "\n",
    "A quadratic program (QP) is any convex optimization of the form \n",
    "$$\n",
    "\\min \\beta^\\top Q \\beta + \\beta^\\top a \\quad \\textrm{ s.t. } A\\beta \\le c\n",
    "$$\n",
    "where $Q$ is positive semi-definite.\n",
    "\n",
    "Show that the lasso in constrained form is a QP. (Hint: write $\\beta = \\beta_+ - \\beta_-$ where $\\beta_{+,j} = \\beta_{j} 1\\{ \\beta_j > 0\\}$ and $\\beta_{-,j} = - \\beta_{j} 1\\{ \\beta_j < 0\\}$). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Solution to 5.1**\n",
    "\n",
    "The objective is certainly quadratic...\n",
    "$$\n",
    "\\frac 12 \\sum_{i=1}^T (y - X \\beta)_i^2 = \\frac 12 \\beta^\\top (X^\\top X) \\beta - \\beta^\\top (X^\\top y) + C\n",
    "$$\n",
    "and we know that $X^\\top X$ is PSD because $a^\\top X^\\top X a = \\| X a\\|^2 \\ge 0$.\n",
    "\n",
    "What about $\\| \\beta \\|_1$?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](lasso_QP.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Solving the lasso\n",
    "\n",
    "For a single $\\lambda$ (or $C$ in constrained form) can solve the lasso with many specialized methods\n",
    "- quadratic program solver\n",
    "- proximal gradient\n",
    "- alternating direction method of multipliers\n",
    "\n",
    "but $\\lambda$ is a tuning parameter.  Options\n",
    "1. Construct a grid of $\\lambda$ and solve each lasso\n",
    "2. Solve for all $\\lambda$ values - path algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Active sets and why lasso works better\n",
    "\n",
    "- Let $\\hat \\beta_\\lambda$ be the $\\hat \\beta$ at tuning parameter $\\lambda$.\n",
    "- Define $\\mathcal A_\\lambda = {\\rm supp}(\\hat \\beta_\\lambda)$ the non-zero elements of $\\hat \\beta_\\lambda$.\n",
    " 1. For large $\\lambda \\rightarrow \\infty$, $|\\mathcal A_\\lambda| = 0$\n",
    " 2. For small $\\lambda = 0$, $|\\mathcal A_\\lambda| = p$ (when OLS solution has full support)\n",
    " \n",
    "Forward greedy selection only adds elements to the active set, does not remove elements.\n",
    "\n",
    "### Exercise 5.2.1\n",
    "Verify 1 and 2 above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](active.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Lasso Path\n",
    "\n",
    "1. Start at $\\lambda = +\\infty, \\hat \\beta = 0$.\n",
    "2. Decrease $\\lambda$ until $\\hat \\beta_{j_1} \\ne 0$, $\\mathcal A \\gets \\{j_1\\}$. (Hitting event)\n",
    "3. Continue decreasing $\\lambda$ updating $\\mathcal A$ with hitting and leaving events\n",
    "\n",
    "\n",
    "- $x_{j_1}$ is the predictor variable most correlated with $y$\n",
    "- Hitting events are when element is added to $\\mathcal A$\n",
    "- Leaving events are when element is removed from $\\mathcal A$\n",
    "- $\\hat \\beta_{\\lambda,j}$ is piecewise linear, continuous, as a function of $\\lambda$\n",
    "- knots are at \"hitting\" and \"leaving\" events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](lasso_path.PNG)\n",
    "from sklearn.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Least Angle Regression (LAR)\n",
    "\n",
    "1. Standardize predictors and start with residual $r = y - \\bar y$, $\\hat \\beta = 0$\n",
    "2. Find $x_j$ most correlated with $r$\n",
    "3. Move $\\beta_j$ in the direction of $x_j^\\top r$ until the residual is more correlated with another $x_k$\n",
    "4. Move $\\beta_j,\\beta_k$ in the direction of their joint OLS coefficients of $r$ on $(x_j,x_k)$ until some other competitor $x_l$ has as much correlation with the current residual\n",
    "5. Continue until all predictors have been entered.\n",
    "\n",
    "### Exercise 5.2.2 \n",
    "How do we know that LAR does not give us the Lasso solution?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Lasso modification\n",
    "\n",
    "4.5 If a non-zero coefficient drops to 0 then remove it from the active set and recompute the restricted OLS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](lars_lasso.png)\n",
    "from ESL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# %load ../standard_import.txt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing, model_selection, linear_model\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 263 entries, -Alan Ashby to -Willie Wilson\n",
      "Data columns (total 20 columns):\n",
      "AtBat        263 non-null int64\n",
      "Hits         263 non-null int64\n",
      "HmRun        263 non-null int64\n",
      "Runs         263 non-null int64\n",
      "RBI          263 non-null int64\n",
      "Walks        263 non-null int64\n",
      "Years        263 non-null int64\n",
      "CAtBat       263 non-null int64\n",
      "CHits        263 non-null int64\n",
      "CHmRun       263 non-null int64\n",
      "CRuns        263 non-null int64\n",
      "CRBI         263 non-null int64\n",
      "CWalks       263 non-null int64\n",
      "League       263 non-null object\n",
      "Division     263 non-null object\n",
      "PutOuts      263 non-null int64\n",
      "Assists      263 non-null int64\n",
      "Errors       263 non-null int64\n",
      "Salary       263 non-null float64\n",
      "NewLeague    263 non-null object\n",
      "dtypes: float64(1), int64(16), object(3)\n",
      "memory usage: 43.1+ KB\n"
     ]
    }
   ],
   "source": [
    "## Modified from the github repo: https://github.com/JWarmenhoven/ISLR-python \n",
    "## which is based on the book by James et al. Intro to Statistical Learning.\n",
    "\n",
    "df = pd.read_csv('../../data/Hitters.csv', index_col=0).dropna()\n",
    "df.index.name = 'Player'\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "## Simulate a dataset for lasso\n",
    "\n",
    "n=100\n",
    "p=1000\n",
    "X = np.random.randn(n,p)\n",
    "X = preprocessing.scale(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of non-zero's: 20\n"
     ]
    }
   ],
   "source": [
    "## Subselect true active set\n",
    "\n",
    "sprob = 0.02\n",
    "Sbool = np.random.rand(p) < sprob\n",
    "s = np.sum(Sbool)\n",
    "print(\"Number of non-zero's: {}\".format(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "## Construct beta and y\n",
    "\n",
    "mu = 100.\n",
    "beta = np.zeros(p)\n",
    "beta[Sbool] = mu * np.random.randn(s)\n",
    "\n",
    "eps = np.random.randn(n)\n",
    "y = X.dot(beta) + eps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.3\n",
    "\n",
    "- Run the lasso using `linear_model.lars_path` with the lasso modification (see docstring with ?linear_model.lars_path) \n",
    "- Plot the lasso coefficients that are learned as a function of lambda.  You should have a plot with the x-axis being lambda and the y-axis being the coefficient value, with $p=1000$ lines plotted.  Highlight the $s$ coefficients that are truly non-zero by plotting them in red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "?linear_model.lars_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "## Answer to exercise 5.3\n",
    "## Run lars with lasso mod, find active set\n",
    "\n",
    "larper = linear_model.lars_path(X,y,method=\"lasso\")\n",
    "S = set(np.where(Sbool)[0])\n",
    "\n",
    "def plot_it():\n",
    "    for j in S:\n",
    "        _ = plt.plot(larper[0],larper[2][j,:],'r')\n",
    "    for j in set(range(p)) - S:\n",
    "        _ = plt.plot(larper[0],larper[2][j,:],'k',linewidth=.75)\n",
    "    _ = plt.title('Lasso path for simulated data')\n",
    "    _ = plt.xlabel('lambda')\n",
    "    _ = plt.ylabel('Coef')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_it()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 263 entries, -Alan Ashby to -Willie Wilson\n",
      "Data columns (total 20 columns):\n",
      "AtBat        263 non-null int64\n",
      "Hits         263 non-null int64\n",
      "HmRun        263 non-null int64\n",
      "Runs         263 non-null int64\n",
      "RBI          263 non-null int64\n",
      "Walks        263 non-null int64\n",
      "Years        263 non-null int64\n",
      "CAtBat       263 non-null int64\n",
      "CHits        263 non-null int64\n",
      "CHmRun       263 non-null int64\n",
      "CRuns        263 non-null int64\n",
      "CRBI         263 non-null int64\n",
      "CWalks       263 non-null int64\n",
      "League       263 non-null object\n",
      "Division     263 non-null object\n",
      "PutOuts      263 non-null int64\n",
      "Assists      263 non-null int64\n",
      "Errors       263 non-null int64\n",
      "Salary       263 non-null float64\n",
      "NewLeague    263 non-null object\n",
      "dtypes: float64(1), int64(16), object(3)\n",
      "memory usage: 43.1+ KB\n"
     ]
    }
   ],
   "source": [
    "## Hitters dataset\n",
    "\n",
    "df = pd.read_csv('../../data/Hitters.csv', index_col=0).dropna()\n",
    "df.index.name = 'Player'\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>-Alan Ashby</td>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-Alvin Davis</td>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-Andre Dawson</td>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-Andres Galarraga</td>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-Alfredo Griffin</td>\n",
       "      <td>594</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>4408</td>\n",
       "      <td>1133</td>\n",
       "      <td>19</td>\n",
       "      <td>501</td>\n",
       "      <td>336</td>\n",
       "      <td>194</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>282</td>\n",
       "      <td>421</td>\n",
       "      <td>25</td>\n",
       "      <td>750.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  \\\n",
       "Player                                                                          \n",
       "-Alan Ashby          315    81      7    24   38     39     14    3449    835   \n",
       "-Alvin Davis         479   130     18    66   72     76      3    1624    457   \n",
       "-Andre Dawson        496   141     20    65   78     37     11    5628   1575   \n",
       "-Andres Galarraga    321    87     10    39   42     30      2     396    101   \n",
       "-Alfredo Griffin     594   169      4    74   51     35     11    4408   1133   \n",
       "\n",
       "                   CHmRun  CRuns  CRBI  CWalks League Division  PutOuts  \\\n",
       "Player                                                                    \n",
       "-Alan Ashby            69    321   414     375      N        W      632   \n",
       "-Alvin Davis           63    224   266     263      A        W      880   \n",
       "-Andre Dawson         225    828   838     354      N        E      200   \n",
       "-Andres Galarraga      12     48    46      33      N        E      805   \n",
       "-Alfredo Griffin       19    501   336     194      A        W      282   \n",
       "\n",
       "                   Assists  Errors  Salary NewLeague  \n",
       "Player                                                \n",
       "-Alan Ashby             43      10   475.0         N  \n",
       "-Alvin Davis            82      14   480.0         A  \n",
       "-Andre Dawson           11       3   500.0         N  \n",
       "-Andres Galarraga       40       4    91.5         N  \n",
       "-Alfredo Griffin       421      25   750.0         A  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 263 entries, -Alan Ashby to -Willie Wilson\n",
      "Data columns (total 6 columns):\n",
      "League_A       263 non-null uint8\n",
      "League_N       263 non-null uint8\n",
      "Division_E     263 non-null uint8\n",
      "Division_W     263 non-null uint8\n",
      "NewLeague_A    263 non-null uint8\n",
      "NewLeague_N    263 non-null uint8\n",
      "dtypes: uint8(6)\n",
      "memory usage: 3.6+ KB\n",
      "                   League_A  League_N  Division_E  Division_W  NewLeague_A  \\\n",
      "Player                                                                       \n",
      "-Alan Ashby               0         1           0           1            0   \n",
      "-Alvin Davis              1         0           0           1            1   \n",
      "-Andre Dawson             0         1           1           0            0   \n",
      "-Andres Galarraga         0         1           1           0            0   \n",
      "-Alfredo Griffin          1         0           0           1            1   \n",
      "\n",
      "                   NewLeague_N  \n",
      "Player                          \n",
      "-Alan Ashby                  1  \n",
      "-Alvin Davis                 0  \n",
      "-Andre Dawson                1  \n",
      "-Andres Galarraga            1  \n",
      "-Alfredo Griffin             0  \n"
     ]
    }
   ],
   "source": [
    "dummies = pd.get_dummies(df[['League', 'Division', 'NewLeague']])\n",
    "dummies.info()\n",
    "print(dummies.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 263 entries, -Alan Ashby to -Willie Wilson\n",
      "Data columns (total 19 columns):\n",
      "AtBat          263 non-null float64\n",
      "Hits           263 non-null float64\n",
      "HmRun          263 non-null float64\n",
      "Runs           263 non-null float64\n",
      "RBI            263 non-null float64\n",
      "Walks          263 non-null float64\n",
      "Years          263 non-null float64\n",
      "CAtBat         263 non-null float64\n",
      "CHits          263 non-null float64\n",
      "CHmRun         263 non-null float64\n",
      "CRuns          263 non-null float64\n",
      "CRBI           263 non-null float64\n",
      "CWalks         263 non-null float64\n",
      "PutOuts        263 non-null float64\n",
      "Assists        263 non-null float64\n",
      "Errors         263 non-null float64\n",
      "League_N       263 non-null uint8\n",
      "Division_W     263 non-null uint8\n",
      "NewLeague_N    263 non-null uint8\n",
      "dtypes: float64(16), uint8(3)\n",
      "memory usage: 35.7+ KB\n"
     ]
    }
   ],
   "source": [
    "y = df.Salary\n",
    "\n",
    "# Drop the column with the independent variable (Salary), and columns for which we created dummy variables\n",
    "X_ = df.drop(['Salary', 'League', 'Division', 'NewLeague'], axis=1).astype('float64')\n",
    "# Define the feature set X.\n",
    "X = pd.concat([X_, dummies[['League_N', 'Division_W', 'NewLeague_N']]], axis=1)\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>League_N</th>\n",
       "      <th>Division_W</th>\n",
       "      <th>NewLeague_N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>-Alan Ashby</td>\n",
       "      <td>315.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-Alvin Davis</td>\n",
       "      <td>479.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-Andre Dawson</td>\n",
       "      <td>496.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-Andres Galarraga</td>\n",
       "      <td>321.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-Alfredo Griffin</td>\n",
       "      <td>594.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   AtBat   Hits  HmRun  Runs   RBI  Walks  Years  CAtBat  \\\n",
       "Player                                                                     \n",
       "-Alan Ashby        315.0   81.0    7.0  24.0  38.0   39.0   14.0  3449.0   \n",
       "-Alvin Davis       479.0  130.0   18.0  66.0  72.0   76.0    3.0  1624.0   \n",
       "-Andre Dawson      496.0  141.0   20.0  65.0  78.0   37.0   11.0  5628.0   \n",
       "-Andres Galarraga  321.0   87.0   10.0  39.0  42.0   30.0    2.0   396.0   \n",
       "-Alfredo Griffin   594.0  169.0    4.0  74.0  51.0   35.0   11.0  4408.0   \n",
       "\n",
       "                    CHits  CHmRun  CRuns   CRBI  CWalks  PutOuts  Assists  \\\n",
       "Player                                                                      \n",
       "-Alan Ashby         835.0    69.0  321.0  414.0   375.0    632.0     43.0   \n",
       "-Alvin Davis        457.0    63.0  224.0  266.0   263.0    880.0     82.0   \n",
       "-Andre Dawson      1575.0   225.0  828.0  838.0   354.0    200.0     11.0   \n",
       "-Andres Galarraga   101.0    12.0   48.0   46.0    33.0    805.0     40.0   \n",
       "-Alfredo Griffin   1133.0    19.0  501.0  336.0   194.0    282.0    421.0   \n",
       "\n",
       "                   Errors  League_N  Division_W  NewLeague_N  \n",
       "Player                                                        \n",
       "-Alan Ashby          10.0         1           1            1  \n",
       "-Alvin Davis         14.0         0           1            0  \n",
       "-Andre Dawson         3.0         1           0            1  \n",
       "-Andres Galarraga     4.0         1           0            1  \n",
       "-Alfredo Griffin     25.0         0           1            0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5.4\n",
    "\n",
    "You should cross-validate to select the lambda just like any other tuning parameter.  Sklearn gives you the option of using their fast cross-validation script via `linear_model.LassoCV`, see the documentation.  You can create a leave-one-out cross validator with `model_selection.LeaveOneOut` then pass this to `LassoCV` with the `cv` argument.  Do this, and see what the returned fit and selected lambda are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=None, copy_X=True,\n",
       "        cv=<generator object BaseCrossValidator.split at 0x7f17afebdcd0>,\n",
       "        eps=0.001, fit_intercept=True, max_iter=2000, n_alphas=100, n_jobs=None,\n",
       "        normalize=False, positive=False, precompute='auto', random_state=None,\n",
       "        selection='cyclic', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Answer to 5.4\n",
    "## Fit the lasso and cross-validate, increased max_iter to achieve convergence\n",
    "loo = model_selection.LeaveOneOut()\n",
    "looiter = loo.split(X)\n",
    "hitlasso = linear_model.LassoCV(cv=looiter,max_iter=2000) \n",
    "hitlasso.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The selected lambda value is 540.66\n"
     ]
    }
   ],
   "source": [
    "print(\"The selected lambda value is {:.2f}\".format(hitlasso.alpha_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.        ,  1.49586273,  0.        ,  0.        ,  0.        ,\n",
       "        1.13979587, -0.        , -0.33734233,  0.82306877,  0.        ,\n",
       "        0.79947238,  0.65458696, -0.03331169,  0.27575248,  0.13166923,\n",
       "       -0.        ,  0.        , -0.        ,  0.        ])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hitlasso.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can also compare this to the selected model from forward stagewise regression:\n",
    "\n",
    "```\n",
    "[-0.21830515,  0.38154135,  0.        ,  0.        ,  0.        ,\n",
    "        0.16139123,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "        0.09994524,  0.56696569, -0.16872682,  0.16924078,  0.        ,\n",
    "        0.        ,  0.        , -0.19429699,  0.        ]\n",
    "```\n",
    "\n",
    "This is not exactly the same model with differences in the inclusion or exclusion of AtBat, HmRun, Runs, RBI, Years, CHmRun, Errors, League_N, Division_W, NewLeague_N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "bforw = [-0.21830515,  0.38154135,  0.        ,  0.        ,  0.        ,\n",
    "        0.16139123,  0.        ,  0.        ,  0.        ,  0.        ,\n",
    "        0.09994524,  0.56696569, -0.16872682,  0.16924078,  0.        ,\n",
    "        0.        ,  0.        , -0.19429699,  0.        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AtBat, HmRun, Runs, RBI, Years, CHmRun, Errors, League_N, Division_W, NewLeague_N\n"
     ]
    }
   ],
   "source": [
    "print(\", \".join(X.columns[(hitlasso.coef_ != 0.) != (bforw != 0.)]))"
   ]
  }
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