{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Examples__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Group Lasso with Interaction Terms__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import adelie as ad\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In regression settings, we typically want to include pairwise interaction terms amongst a subset of features to capture some non-linearity.\n",
    "Moreover, we would like to perform feature selection on the interaction terms as well.\n",
    "However, to achieve an interpretable model, we would like to also impose a hierarchy such that interaction terms are only included in the model if the main effects are included.\n",
    "Michael Lim and Trevor Hastie provide a formalization of this problem using group lasso where the group structure imposes the hierarchy and the group lasso penalty allows for feature selection.\n",
    "For further details, we provide the following reference:\n",
    "\n",
    "- [Learning interactions via hierarchical group-lasso regularization](https://hastie.su.domains/Papers/glinternet_jcgs.pdf) \n",
    "\n",
    "We will work under a simulation setting.\n",
    "We draw $n$ independent samples $Z_i \\in \\mathbb{R}^d$ where the continuous features are sampled from a standard normal and the discrete features are sampled uniformly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1000        # number of samples\n",
    "d_cont = 10     # number of continuous features\n",
    "d_disc = 10     # number of discrete features\n",
    "seed = 1        # random seed\n",
    "\n",
    "np.random.seed(seed)\n",
    "Z_cont = np.random.normal(0, 1, (n, d_cont))\n",
    "levels = np.random.choice(10, d_disc, replace=True) + 1\n",
    "Z_disc = np.array([np.random.choice(lvl, n, replace=True) for lvl in levels]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is customary to first center and scale the continuous features so that they have mean $0$ and standard deviation $1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z_cont_means = np.mean(Z_cont, axis=0)\n",
    "Z_cont_stds = np.std(Z_cont, axis=0, ddof=0)\n",
    "Z_cont = (Z_cont - Z_cont_means) / Z_cont_stds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us a combined data matrix $Z$ with the appropriate levels information.\n",
    "By convention, a $0$-level feature is a continuous feature and otherwise it is a discrete feature with that many levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.asfortranarray(np.concatenate([Z_cont, Z_disc], axis=1))\n",
    "levels = np.concatenate([np.zeros(d_cont), levels])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate the response vector $y$ from a linear model where one continuous and discrete main effects as well as their interaction term are included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z_one_hot_0 = np.zeros((n, int(levels[d_cont])))\n",
    "Z_one_hot_0[np.arange(n), Z_disc[:, 0].astype(int)] = 1\n",
    "Z_cont_0 = Z_cont[:, 0][:, None]\n",
    "Z_sub = np.concatenate([\n",
    "    Z_cont_0,\n",
    "    Z_one_hot_0,\n",
    "    Z_cont_0 * Z_one_hot_0,\n",
    "], axis=1)\n",
    "beta = np.random.normal(0, 1, Z_sub.shape[1])\n",
    "y = Z_sub @ beta + np.random.normal(0, 1, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now in position to construct the full feature matrix to fit a group lasso model.\n",
    "As a demonstration, suppose we (correctly) believe that there is a true interaction term containing the first continuous feature, but we do not know the other feature.\n",
    "We, therefore, wish to construct an interaction between the first continuous feature against all other features.\n",
    "It is easy to specify this pairing, as shown below via `intr_map`. \n",
    "The following code constructs the interaction matrix `X_intr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "intr_map = {\n",
    "    0: None,\n",
    "}\n",
    "X_intr = ad.matrix.interaction(Z, intr_map, levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To put all groups of features on the same relative \"scale\", we must further center and scale all interaction terms between two continuous features.\n",
    "Then, it can be shown that interactions between two discrete features induce a (Frobenius) norm of $1$,\n",
    "a discrete and continuous feature induce a norm of $\\sqrt{2}$,\n",
    "and two continuous features induce a norm of $\\sqrt{3}$.\n",
    "These values will be used as penalty factors later when we call the group lasso solver.\n",
    "We first compute the necessary centers and scales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = X_intr._pairs\n",
    "pair_levels = levels[pairs]\n",
    "is_cont_cont = np.prod(pair_levels == 0, axis=1).astype(bool)\n",
    "cont_cont_pairs = pairs[is_cont_cont]\n",
    "cont_cont = Z[:, cont_cont_pairs[:, 0]] * Z[:, cont_cont_pairs[:, 1]]\n",
    "centers = np.zeros(X_intr.shape[1])\n",
    "scales = np.ones(X_intr.shape[1])\n",
    "cont_cont_indices = X_intr.groups[is_cont_cont] + 2\n",
    "centers[cont_cont_indices] = np.mean(cont_cont, axis=0)\n",
    "scales[cont_cont_indices] = np.std(cont_cont, axis=0, ddof=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we construct the full feature matrix $X$ including the one-hot encoded main effects as well as the standardized version of the interaction terms using the centers and scales defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_one_hot = ad.matrix.one_hot(Z, levels)\n",
    "X = ad.matrix.concatenate([\n",
    "    X_one_hot,\n",
    "    ad.matrix.standardize(\n",
    "        X_intr,\n",
    "        centers=centers, \n",
    "        scales=scales,\n",
    "    ),\n",
    "], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before calling the group lasso solver, we must prepare the grouping and penalty factor information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = np.concatenate([\n",
    "    X_one_hot.groups,\n",
    "    X_one_hot.shape[1] + X_intr.groups,\n",
    "])\n",
    "\n",
    "is_cont_disc = np.logical_xor(pair_levels[:, 0], pair_levels[:, 1])\n",
    "penalty = np.ones(X_intr.groups.shape[0])\n",
    "penalty[is_cont_cont] = np.sqrt(3)\n",
    "penalty[is_cont_disc] = np.sqrt(2)\n",
    "penalty = np.concatenate([\n",
    "    np.ones(X_one_hot.groups.shape[0]),\n",
    "    penalty,\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we call the group lasso solver with our prepared inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:00<00:00:00, 4657.95it/s] [dev:71.1%]\n"
     ]
    }
   ],
   "source": [
    "state = ad.grpnet(\n",
    "    X=X, \n",
    "    glm=ad.glm.gaussian(y), \n",
    "    groups=groups, \n",
    "    penalty=penalty,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, observe that the first two groups of features that enter the model are precisely the first continuous and discrete main effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10, 11, 12, 13, 14], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state.betas[13, :X_one_hot.shape[1]].indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we see that the first interaction terms to be included corresponds to the interaction between the first continuous and discrete features in `Z`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10], dtype=int32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_intr_index = X_one_hot.shape[1] + state.betas[16, X_one_hot.shape[1]:].indices[0]\n",
    "relative_index = np.argmax(groups == first_intr_index) - X_one_hot.groups.shape[0]\n",
    "pairs[relative_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We conclude that the group lasso correctly finds the causal effects of $y$ early in the path."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Model-X Knockoffs with Lasso Feature Importance Scores__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import adelie as ad\n",
    "import knockpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we cover a neat application of Lasso in a popular statistical method called *Knockoffs*,\n",
    "pioneered by Rina Barber and Emmanuel Candès.\n",
    "We briefly describe the Knockoff framework.\n",
    "Knockoffs are fictitious features generated\n",
    "by the user that look similar to an already given set of features.\n",
    "As soon as these knockoffs enjoy certain statistical\n",
    "properties relating their distributions to the original features, we may use them to determine which of the original features are important for predicting the response while controlling for the *false discovery rate* (FDR).\n",
    "For an in-depth treatment of Knockoffs,\n",
    "we provide the following references:\n",
    "\n",
    "- [Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection](https://academic.oup.com/jrsssb/article/80/3/551/7048447)\n",
    "- [Papers about Knockoffs](https://web.stanford.edu/group/candes/knockoffs/papers.html)\n",
    "- [Knockoffs on Wikipedia](https://en.wikipedia.org/wiki/Knockoffs_(statistics))\n",
    "\n",
    "We will work under the Model-X framework.\n",
    "Let's begin with some data.\n",
    "We assume $n$ independent samples $X_i \\sim N(0, \\Sigma)$\n",
    "where $\\Sigma \\in \\mathbb{R}^{p \\times p}$ is the equi-correlation matrix with correlation $\\rho$.\n",
    "Our response $y$ is generated from a linear model\n",
    "$y = X \\beta + \\sigma \\varepsilon$\n",
    "where the first half of the coefficients $\\beta$ are generated from $N(0,1)$ (and the rest set to zero),\n",
    "$\\varepsilon \\sim N(0, I_n)$,\n",
    "and $\\sigma$ such that the signal-to-noise ratio is $1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10000       # number of samples\n",
    "p = 100         # number of features\n",
    "n_h1 = p // 2   # number of features with signal\n",
    "rho = 0.3       # equi-correlation\n",
    "seed = 0        # random seed\n",
    "\n",
    "np.random.seed(seed)\n",
    "W = np.random.normal(0, 1, n)\n",
    "Z = np.random.normal(0, 1, (n, p))\n",
    "X = np.sqrt(rho) * W[:, None] + np.sqrt(1-rho) * Z\n",
    "y = X[:, :n_h1] @ np.random.normal(0, 1, n_h1) + np.sqrt(n_h1) * np.random.normal(0, 1, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we generate our Model-X knockoffs.\n",
    "The definition of Model-X knockoffs states that each knockoff $\\tilde{X}_i \\in \\mathbb{R}^{p}$\n",
    "must satisfy the exchangeability condition that swapping any subset of the features with\n",
    "the respective positions in $X_i$ preserves the joint distribution of $(X_i, \\tilde{X}_i)$,\n",
    "and $\\tilde{X}_i$ must be conditionally independent of the response $y_i$ given $X_i$.\n",
    "Under the Gaussian assumption of $X_i$, \n",
    "it is possible to show that we may then sample $\\tilde{X}_i$ from $N(\\mu_i, V_i)$ where\n",
    "$$\n",
    "\\begin{align*}\n",
    "    \\mu_i &= X_i - \\mathrm{diag}(s) \\Sigma^{-1} X_i \\\\\n",
    "    V_i &= 2 \\mathrm{diag}(s) - \\mathrm{diag}(s) \\Sigma^{-1} \\mathrm{diag}(s)\n",
    "\\end{align*}\n",
    "$$\n",
    "and $\\mathrm{diag}(s)$ is any non-negative diagonal matrix such that\n",
    "$$\n",
    "\\begin{align*}\n",
    "    \\begin{bmatrix}\n",
    "        \\Sigma & \\Sigma - \\mathrm{diag}(s) \\\\\n",
    "        \\Sigma - \\mathrm{diag}(s) & \\Sigma \\\\\n",
    "    \\end{bmatrix}\n",
    "\\end{align*}\n",
    "$$\n",
    "is positive semi-definite.\n",
    "\n",
    "Although there are many possible choices of $s$ that satisfy the conditions above,\n",
    "we use the MVR method in the `knockpy` package.\n",
    "To make the situation more realistic, we first estimate $\\Sigma$ using the empirical covariance matrix from $X$,\n",
    "since, in practice, we often do not know the true covariance matrix $\\Sigma$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sigma = np.cov(X.T, ddof=0)\n",
    "S = knockpy.smatrix.compute_smatrix(Sigma, method=\"mvr\", choldate_warning=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we sample the knockoffs using the above formula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sigma_inv = np.linalg.inv(Sigma)\n",
    "mu = X - X @ Sigma_inv @ S\n",
    "V = 2 * S - S @ Sigma_inv @ S\n",
    "\n",
    "X_knock = mu + np.random.multivariate_normal(np.zeros(p), V, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before using `adelie`, we convert `X` and `X_knock` into column-major matrices\n",
    "since this storage order is more favorable for our solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.asfortranarray(X)\n",
    "X_knock = np.asfortranarray(X_knock)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the knockoffs have been constructed, we must construct some *feature importance* statistic\n",
    "that places an importance score for each original and knockoff feature.\n",
    "This is where the lasso comes in!\n",
    "A common method is solve the lasso problem with the combined feature matrix $(X, \\tilde{X})$\n",
    "and the response $y$.\n",
    "Then, we choose our feature importance statistic to be the magnitude of the lasso coefficient,\n",
    "that is, scores $Z_j = |\\hat{\\beta}_j(\\lambda)|$ and $\\tilde{Z}_j = |\\hat{\\beta}_{j+p}(\\lambda)|$\n",
    "for the original and knockoff features, respectively.\n",
    "Furthermore, the theory also allows the user to run cross-validation to pick the best $\\lambda$\n",
    "and construct feature importance statistics based on the solution at that $\\lambda$.\n",
    "\n",
    "\n",
    "To fit lasso, we first need to concatenate $X$ and $\\tilde{X}$.\n",
    "Although one could concatenate the two matrices into one large dense matrix,\n",
    "we take this opportunity to demonstrate the usage of `adelie.matrix.concatenate`,\n",
    "which effectively represents the same matrix but does not require any allocation of a new matrix.\n",
    "In practice, we may work in a high-dimensional setting where the number of features is large,\n",
    "in which case, it may be computationally burdensome to construct a concatenated dense matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xc = ad.matrix.concatenate([X, X_knock], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run a 5-fold cross-validation (CV) lasso under the Gaussian loss using the combined feature matrix and our response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:00<00:00:00, 397.03it/s] [dev:44.7%]\n",
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:00<00:00:00, 415.55it/s] [dev:45.2%]\n",
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 101/101 [00:00:00<00:00:00, 443.93it/s] [dev:44.9%]\n",
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      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 101/101 [00:00:00<00:00:00, 421.39it/s] [dev:45.8%]\n"
     ]
    }
   ],
   "source": [
    "cv_res = ad.cv_grpnet(\n",
    "    X=Xc, \n",
    "    glm=ad.glm.gaussian(y), \n",
    "    min_ratio=1e-3, \n",
    "    seed=0,\n",
    "    intercept=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the average CV loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_res.plot_loss()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot suggests that the best model is somewhere in the middle of the path as the CV loss\n",
    "dips then flattens as we move down the regularization path.\n",
    "We now refit lasso along a regularization path which stops at the best chosen $\\lambda$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:00<00:00:00, 765.17it/s] [dev:44.4%]\n"
     ]
    }
   ],
   "source": [
    "state = cv_res.fit(\n",
    "    X=Xc, \n",
    "    glm=ad.glm.gaussian(y),\n",
    "    intercept=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We construct the feature importance statistics by taking the last coefficient vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = state.betas[-1].toarray()[0]\n",
    "z = np.abs(beta[:p])\n",
    "z_knock = np.abs(beta[p:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we construct the *lasso coefficient difference* statistics $W_j = Z_j - \\tilde{Z}_j$.\n",
    "A large positive $W_j$ suggests that the $j$ th (original) feature is important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = z - z_knock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, to control FDR, we apply the *Knockoff-filter*.\n",
    "For a given level $q \\in [0,1]$, the Knockoff-filter constructs\n",
    "$$\n",
    "\\begin{align*}\n",
    "    \\tau_+ = \\min\\{t > 0 : \\frac{1 + |\\{j : W_j \\leq -t\\}|}{|\\{j : W_j \\geq t\\}|} \\leq q\\}\n",
    "\\end{align*}\n",
    "$$\n",
    "and selects features in $\\hat{S} = \\{j : W_j \\geq \\tau_+\\}$.\n",
    "This selection procedure controls the FDR so that\n",
    "$$\n",
    "\\begin{align*}\n",
    "    \\mathbb{E}\\left[\\frac{|\\{j \\in \\hat{S} \\cap \\mathcal{H}_0\\}|}{|\\hat{S}| \\vee 1}\\right] \\leq q\n",
    "\\end{align*}\n",
    "$$\n",
    "where $\\mathcal{H}_0$ is the true set of null features (in this example, $\\mathcal{H}_0 = \\{p / 2 + 1,\\ldots, p\\}$ since the first half of the features is used to construct $y$).\n",
    "\n",
    "We let $q = 0.05$ in this example and construct $\\tau_+$ using `knockpy`.\n",
    "We then print the *false discovery proportion* (FDP), which is the term inside the expectation above,\n",
    "and the power estimate, which is the proportion of correctly selected non-nulls over the total number of non-nulls, or\n",
    "$$\n",
    "\\begin{align*}\n",
    "    \\widehat{\\mathrm{Power}} = \\frac{|\\hat{S} \\cap \\mathcal{H}_1|}{|\\mathcal{H}_1|}\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDP:    0.11363636363636363\n",
      "Power:  0.78\n"
     ]
    }
   ],
   "source": [
    "tau_plus = knockpy.knockoff_stats.data_dependent_threshhold(w, 0.05)\n",
    "S_hat = np.arange(p)[w >= tau_plus]\n",
    "H0 = np.arange(n_h1, p)\n",
    "H1 = np.arange(n_h1)\n",
    "fdp = len(set(S_hat).intersection(set(H0))) / max(S_hat.shape[0], 1)\n",
    "pwr = len(set(S_hat).intersection(set(H1))) / n_h1\n",
    "print(f\"FDP:    {fdp}\")\n",
    "print(f\"Power:  {pwr}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For any one experiment, the FDP may not be under $q$ \n",
    "(though we hope the FDP has a small enough variance that it is not too far from its mean).\n",
    "The guarantee is that if we repeat this entire procedure many times,\n",
    "the average FDP will be under $q$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Lasso on GWAS Datasets__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import adelie as ad\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pgenlib as pg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we show how to use `adelie` to run lasso on GWAS datasets.\n",
    "These datasets often come as \n",
    "[.bed](https://www.cog-genomics.org/plink2/formats#bed) or \n",
    "[.pgen](https://www.cog-genomics.org/plink/2.0/input#pgen) (PLINK) files\n",
    "accompanied by other files containing metadata.\n",
    "The main feature matrix of interest is the *genotype matrix* or a *single-nucleotide polymorphism (SNP) matrix*.\n",
    "This matrix contains values in the range $\\{0,1,2,\\mathrm{NA}\\}$ with both large number of samples\n",
    "(e.g. 0.5 million) and features (e.g. 1.7 million).\n",
    "As a result, special care must be taken to represent such a large matrix to avoid memory issues\n",
    "and optimize for the matrix operations required to solve the lasso.\n",
    "The response vector is typically some measurement of a phenotype such as standing height.\n",
    "The goal is then to learn the association between the genetic information and the phenotype.\n",
    "We will demonstrate that `adelie` can easily handle such a use-case.\n",
    "\n",
    "As a reference, we list a few works in the literature that aim to apply lasso on the UK Biobank,\n",
    "one popular GWAS dataset that has been carefully studied in a wide range of applications.\n",
    "\n",
    "- [A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641476/)\n",
    "- [Fast numerical optimization for genome sequencing data in population biobanks](https://academic.oup.com/bioinformatics/article/37/22/4148/6306404)\n",
    "- [Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007437/)\n",
    "- [UK Biobank Multi-modality Brain Age LASSO regression analysis](https://james-cole.github.io/UKBiobank-Brain-Age/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we only wish to demonstrate the workflow of using `adelie` to solve lasso on GWAS datasets,\n",
    "we work with a small-scale example.\n",
    "The example dataset is provided in the [data](https://github.com/JamesYang007/adelie/tree/main/data) folder,\n",
    "which contains PLINK files for the SNP matrix\n",
    "and a CSV file containing the phenotype and covariates.\n",
    "The PLINK files are taken from [SnpArrays.jl](https://github.com/OpenMendel/SnpArrays.jl/tree/master/data).\n",
    "For this demonstration, we randomly generated the phenotype.\n",
    "The covariates include sex and the 10 largest principle components (PCs) of the SNP matrix.\n",
    "\n",
    "In the following, we outline the steps of applying `adelie`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Load the Dense SNP Matrix__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to load the SNP matrix as a dense matrix.\n",
    "We enforce this for two reasons.\n",
    "First, we do not wish to have `adelie` depend on any particular third-party file formats.\n",
    "This extra dependence complicates the maintenance of the package.\n",
    "Secondly, `adelie` will save the SNP matrix in its own special format (`.snpdat`)\n",
    "that is optimized for both memory and speed in some matrix operations required by our solver.\n",
    "Hence, we need to be able to take any user-specified representation of the SNP matrix\n",
    "and convert it into our format.\n",
    "We perform this conversion by first asking the user to read the SNP matrix as a dense matrix\n",
    "then use `adelie` to convert and save the dense matrix as a `.snpdat` file.\n",
    "\n",
    "__Due to memory issues, the user should not read the full SNP matrix as a dense matrix!__\n",
    "It is recommended to read them in batches of columns instead.\n",
    "Typically, we read per chromosome so that we create 22 `.snpdat` files.\n",
    "\n",
    "We now demonstrate using `pgenlib` how to read the PLINK files as a dense matrix.\n",
    "For more information on how to use `pgenlib`, we direct the readers to the [GitHub page](https://github.com/BertrandServin/pgenlib).\n",
    "\n",
    "We first define the data directory and the relevant PLINK file names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"../../../data\"\n",
    "bedname = os.path.join(data_dir, \"EUR_subset.bed\")\n",
    "bimname = os.path.join(data_dir, \"EUR_subset.bim\")\n",
    "famname = os.path.join(data_dir, \"EUR_subset.fam\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find the number of samples in the dataset by reading the `.fam` file.\n",
    "This information is required by `pgenlib` since we are working with `.bed` files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>FID</th>\n",
       "      <th>IID</th>\n",
       "      <th>Father</th>\n",
       "      <th>Mother</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Phenotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>HG00096</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>HG00097</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>HG00099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>HG00100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>HG00101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>375</td>\n",
       "      <td>NA20816</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>376</td>\n",
       "      <td>NA20818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>377</td>\n",
       "      <td>NA20819</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>378</td>\n",
       "      <td>NA20826</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>379</td>\n",
       "      <td>NA20828</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>379 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FID      IID  Father  Mother  Sex  Phenotype\n",
       "0      1  HG00096       0       0    1          1\n",
       "1      2  HG00097       0       0    2          1\n",
       "2      3  HG00099       0       0    2          1\n",
       "3      4  HG00100       0       0    2          1\n",
       "4      5  HG00101       0       0    1          1\n",
       "..   ...      ...     ...     ...  ...        ...\n",
       "374  375  NA20816       0       0    1          1\n",
       "375  376  NA20818       0       0    2          1\n",
       "376  377  NA20819       0       0    2          1\n",
       "377  378  NA20826       0       0    2          1\n",
       "378  379  NA20828       0       0    2          1\n",
       "\n",
       "[379 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fam = pd.read_csv(\n",
    "    famname, \n",
    "    sep=\" \", \n",
    "    header=None,\n",
    "    names=[\"FID\", \"IID\", \"Father\", \"Mother\", \"Sex\", \"Phenotype\"],\n",
    ")\n",
    "n_samples = df_fam.shape[0]\n",
    "df_fam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read the `.bim` file to retrieve the SNP metadata.\n",
    "We will use this information to read and process the SNP data per chromosome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>chr</th>\n",
       "      <th>variant</th>\n",
       "      <th>pos</th>\n",
       "      <th>base</th>\n",
       "      <th>a1</th>\n",
       "      <th>a2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>rs34151105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1665</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>rs143500173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2748</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>rs113560219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4702</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>rs1882989</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>15222</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>rs8069133</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>32311</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54046</th>\n",
       "      <td>22</td>\n",
       "      <td>rs113391741</td>\n",
       "      <td>0.750923</td>\n",
       "      <td>51187440</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54047</th>\n",
       "      <td>22</td>\n",
       "      <td>rs151247655</td>\n",
       "      <td>0.750938</td>\n",
       "      <td>51189403</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54048</th>\n",
       "      <td>22</td>\n",
       "      <td>rs187225588</td>\n",
       "      <td>0.751001</td>\n",
       "      <td>51197602</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54049</th>\n",
       "      <td>22</td>\n",
       "      <td>rs9616967</td>\n",
       "      <td>0.751090</td>\n",
       "      <td>51209343</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54050</th>\n",
       "      <td>22</td>\n",
       "      <td>rs148755559</td>\n",
       "      <td>0.751156</td>\n",
       "      <td>51218133</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54051 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr      variant       pos      base a1 a2\n",
       "0       17   rs34151105  0.000000      1665  T  C\n",
       "1       17  rs143500173  0.000000      2748  T  A\n",
       "2       17  rs113560219  0.000000      4702  T  C\n",
       "3       17    rs1882989  0.000056     15222  G  A\n",
       "4       17    rs8069133  0.000499     32311  G  A\n",
       "...    ...          ...       ...       ... .. ..\n",
       "54046   22  rs113391741  0.750923  51187440  A  G\n",
       "54047   22  rs151247655  0.750938  51189403  A  G\n",
       "54048   22  rs187225588  0.751001  51197602  T  A\n",
       "54049   22    rs9616967  0.751090  51209343  T  A\n",
       "54050   22  rs148755559  0.751156  51218133  C  T\n",
       "\n",
       "[54051 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bim = pd.read_csv(\n",
    "    bimname,\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"chr\", \"variant\", \"pos\", \"base\", \"a1\", \"a2\"],\n",
    ")\n",
    "n_snps = df_bim.shape[0]\n",
    "df_bim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try reading the SNP matrix for chromosome `17`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create bed reader\n",
    "reader = pg.PgenReader(\n",
    "    str.encode(bedname),\n",
    "    raw_sample_ct=n_samples,\n",
    ")\n",
    "\n",
    "# get 0-indexed indices for current chromosome\n",
    "df_bim_chr = df_bim[df_bim[\"chr\"] == 17]\n",
    "variant_idxs = df_bim_chr.index.to_numpy().astype(np.uint32)\n",
    "\n",
    "# read the SNP matrix\n",
    "geno_out_chr = np.empty((variant_idxs.shape[0], n_samples), dtype=np.int8)\n",
    "reader.read_list(variant_idxs, geno_out_chr)\n",
    "\n",
    "# convert to sample-major\n",
    "geno_out_chr = np.asfortranarray(geno_out_chr.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Convert to snpdat Format__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the dense SNP matrix for chromosome `17`,\n",
    "we write it in `.snpdat` format using `adelie`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define cache directory and snpdat filename\n",
    "cache_dir = \"/tmp\"\n",
    "snpdat_name = os.path.join(cache_dir, \"EUR_subset_chr17.snpdat\")\n",
    "\n",
    "# create handler to convert the SNP matrix to .snpdat\n",
    "handler = ad.io.snp_unphased(snpdat_name)\n",
    "_ = handler.write(geno_out_chr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Process All Chromosomes__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, to mimic the common use-case, \n",
    "we combine all the steps to process all the chromosomes.\n",
    "\n",
    "We first get a list of the chromosomes in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17, 18, 19, 20, 21, 22])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chromosomes = df_bim[\"chr\"].unique()\n",
    "chromosomes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code saves each SNP matrix per chromosome as a separate `.snpdat` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create bed reader\n",
    "reader = pg.PgenReader(\n",
    "    str.encode(bedname),\n",
    "    raw_sample_ct=n_samples,\n",
    ")\n",
    "\n",
    "for chr in chromosomes:\n",
    "    # get 0-indexed indices for current chromosome\n",
    "    df_bim_chr = df_bim[df_bim[\"chr\"] == chr]\n",
    "    variant_idxs = df_bim_chr.index.to_numpy().astype(np.uint32)\n",
    "\n",
    "    # read the SNP matrix\n",
    "    geno_out = np.empty((variant_idxs.shape[0], n_samples), dtype=np.int8)\n",
    "    reader.read_list(variant_idxs, geno_out)\n",
    "\n",
    "    # define snpdat filename\n",
    "    snpdat_name = os.path.join(cache_dir, f\"EUR_subset_chr{chr}.snpdat\")\n",
    "\n",
    "    # create handler to convert the SNP matrix to .snpdat\n",
    "    handler = ad.io.snp_unphased(snpdat_name)\n",
    "    _ = handler.write(geno_out_chr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Load the Feature Matrix__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first load the covariates as a dense matrix and the phenotype as the response vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>sex</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>phenotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.051678</td>\n",
       "      <td>-0.020465</td>\n",
       "      <td>0.046227</td>\n",
       "      <td>-0.014268</td>\n",
       "      <td>0.085271</td>\n",
       "      <td>-0.002017</td>\n",
       "      <td>-0.010386</td>\n",
       "      <td>0.036105</td>\n",
       "      <td>0.010695</td>\n",
       "      <td>-0.082026</td>\n",
       "      <td>4.612085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.050462</td>\n",
       "      <td>0.008577</td>\n",
       "      <td>-0.058172</td>\n",
       "      <td>-0.048560</td>\n",
       "      <td>0.014092</td>\n",
       "      <td>-0.008358</td>\n",
       "      <td>-0.045067</td>\n",
       "      <td>-0.005221</td>\n",
       "      <td>0.098964</td>\n",
       "      <td>0.019094</td>\n",
       "      <td>5.175936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.051158</td>\n",
       "      <td>0.013547</td>\n",
       "      <td>-0.041690</td>\n",
       "      <td>-0.023781</td>\n",
       "      <td>-0.057576</td>\n",
       "      <td>0.088412</td>\n",
       "      <td>0.025772</td>\n",
       "      <td>0.082086</td>\n",
       "      <td>-0.099380</td>\n",
       "      <td>0.016369</td>\n",
       "      <td>13.531050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.051310</td>\n",
       "      <td>-0.021508</td>\n",
       "      <td>0.049910</td>\n",
       "      <td>-0.038484</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>0.001142</td>\n",
       "      <td>0.063346</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.021950</td>\n",
       "      <td>-0.019257</td>\n",
       "      <td>188.524040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.051329</td>\n",
       "      <td>0.014914</td>\n",
       "      <td>-0.067042</td>\n",
       "      <td>-0.069162</td>\n",
       "      <td>-0.023286</td>\n",
       "      <td>0.006299</td>\n",
       "      <td>-0.032645</td>\n",
       "      <td>-0.019187</td>\n",
       "      <td>-0.006523</td>\n",
       "      <td>-0.033779</td>\n",
       "      <td>65.697673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.051168</td>\n",
       "      <td>-0.048192</td>\n",
       "      <td>0.025948</td>\n",
       "      <td>0.044251</td>\n",
       "      <td>-0.025641</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.057611</td>\n",
       "      <td>0.052506</td>\n",
       "      <td>-0.065036</td>\n",
       "      <td>-0.034339</td>\n",
       "      <td>91.516444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.051801</td>\n",
       "      <td>-0.055517</td>\n",
       "      <td>-0.004016</td>\n",
       "      <td>-0.021153</td>\n",
       "      <td>-0.007273</td>\n",
       "      <td>-0.041470</td>\n",
       "      <td>-0.061246</td>\n",
       "      <td>-0.015746</td>\n",
       "      <td>0.020271</td>\n",
       "      <td>0.014094</td>\n",
       "      <td>74.187329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.052525</td>\n",
       "      <td>-0.022246</td>\n",
       "      <td>-0.056309</td>\n",
       "      <td>0.092208</td>\n",
       "      <td>-0.036200</td>\n",
       "      <td>0.070898</td>\n",
       "      <td>-0.010716</td>\n",
       "      <td>0.055041</td>\n",
       "      <td>-0.086128</td>\n",
       "      <td>-0.041673</td>\n",
       "      <td>77.536242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.051577</td>\n",
       "      <td>-0.081801</td>\n",
       "      <td>0.113044</td>\n",
       "      <td>0.024027</td>\n",
       "      <td>0.046088</td>\n",
       "      <td>-0.002275</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.020224</td>\n",
       "      <td>0.038375</td>\n",
       "      <td>-0.036612</td>\n",
       "      <td>264.019774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.051611</td>\n",
       "      <td>-0.084751</td>\n",
       "      <td>0.088275</td>\n",
       "      <td>0.026785</td>\n",
       "      <td>0.026810</td>\n",
       "      <td>-0.091582</td>\n",
       "      <td>0.057924</td>\n",
       "      <td>-0.046984</td>\n",
       "      <td>-0.040138</td>\n",
       "      <td>-0.042171</td>\n",
       "      <td>-51.117530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>379 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sex       PC1       PC2       PC3       PC4       PC5       PC6  \\\n",
       "0      0 -0.051678 -0.020465  0.046227 -0.014268  0.085271 -0.002017   \n",
       "1      1 -0.050462  0.008577 -0.058172 -0.048560  0.014092 -0.008358   \n",
       "2      1 -0.051158  0.013547 -0.041690 -0.023781 -0.057576  0.088412   \n",
       "3      1 -0.051310 -0.021508  0.049910 -0.038484  0.006369  0.001142   \n",
       "4      0 -0.051329  0.014914 -0.067042 -0.069162 -0.023286  0.006299   \n",
       "..   ...       ...       ...       ...       ...       ...       ...   \n",
       "374    0 -0.051168 -0.048192  0.025948  0.044251 -0.025641  0.000769   \n",
       "375    1 -0.051801 -0.055517 -0.004016 -0.021153 -0.007273 -0.041470   \n",
       "376    1 -0.052525 -0.022246 -0.056309  0.092208 -0.036200  0.070898   \n",
       "377    1 -0.051577 -0.081801  0.113044  0.024027  0.046088 -0.002275   \n",
       "378    1 -0.051611 -0.084751  0.088275  0.026785  0.026810 -0.091582   \n",
       "\n",
       "          PC7       PC8       PC9      PC10   phenotype  \n",
       "0   -0.010386  0.036105  0.010695 -0.082026    4.612085  \n",
       "1   -0.045067 -0.005221  0.098964  0.019094    5.175936  \n",
       "2    0.025772  0.082086 -0.099380  0.016369   13.531050  \n",
       "3    0.063346  0.045536  0.021950 -0.019257  188.524040  \n",
       "4   -0.032645 -0.019187 -0.006523 -0.033779   65.697673  \n",
       "..        ...       ...       ...       ...         ...  \n",
       "374  0.057611  0.052506 -0.065036 -0.034339   91.516444  \n",
       "375 -0.061246 -0.015746  0.020271  0.014094   74.187329  \n",
       "376 -0.010716  0.055041 -0.086128 -0.041673   77.536242  \n",
       "377  0.008942  0.020224  0.038375 -0.036612  264.019774  \n",
       "378  0.057924 -0.046984 -0.040138 -0.042171  -51.117530  \n",
       "\n",
       "[379 rows x 12 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(data_dir, \"master_phe.csv\"), sep=\"\\t\", index_col=0)\n",
    "covars_dense = df.iloc[:, :-1].to_numpy()\n",
    "y = df.iloc[:, -1].to_numpy()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the SNP matrices have been processed into `.snpdat` files,\n",
    "we may now load this information as a `adelie.matrix.snp_unphased` object per file.\n",
    "This matrix class implements certain matrix operations highly efficiently for SNP matrices\n",
    "and is recognized by our solver.\n",
    "Finally, we column-wise concatenate all these matrices as well as the dense covariates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(379, 66257)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = ad.matrix.concatenate(\n",
    "    [ad.matrix.dense(covars_dense)]\n",
    "    + [\n",
    "        ad.matrix.snp_unphased(\n",
    "            ad.io.snp_unphased(\n",
    "                os.path.join(cache_dir, f\"EUR_subset_chr{chr}.snpdat\"),\n",
    "            )\n",
    "        )\n",
    "        for chr in chromosomes\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Fit Lasso__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit lasso using `X` and `y` under the Gaussian loss with intercept.\n",
    "Note that we unpenalize the covariates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "penalty = np.concatenate([\n",
    "    np.zeros(covars_dense.shape[-1]),\n",
    "    np.ones(X.shape[-1] - covars_dense.shape[-1]),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m      | 44/100 [00:00:00<00:00:01, 55.52it/s] [dev:90.3%]\n"
     ]
    }
   ],
   "source": [
    "state = ad.grpnet(\n",
    "    X=X,\n",
    "    glm=ad.glm.gaussian(y),\n",
    "    penalty=penalty,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Run Diagnostics (optional)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The user may wish to look at some diagnostic information afterwards.\n",
    "Here is an example of plotting the training $R^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dg = ad.diagnostic.diagnostic(state)\n",
    "dg.plot_devs()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Cross-Validation__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the dataset contains few samples, the user may be interested in using cross-validation\n",
    "to determine the best model.\n",
    "\n",
    "To demonstrate this, we run a 5-fold cross-validation on our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 102/102 [00:00:01<00:00:00, 61.82it/s] [dev:94.4%]\n",
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 106/106 [00:00:01<00:00:00, 62.96it/s] [dev:94.4%]\n",
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 102/102 [00:00:01<00:00:00, 62.49it/s] [dev:94.7%]\n",
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:01<00:00:00, 64.71it/s] [dev:94.1%]\n",
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:01<00:00:00, 55.79it/s] [dev:93.8%]\n"
     ]
    }
   ],
   "source": [
    "cv_res = ad.cv_grpnet(\n",
    "    X=X,\n",
    "    glm=ad.glm.gaussian(y),\n",
    "    seed=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the average cross-validation loss in the following way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_res.plot_loss()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the best model chosen by 5-fold CV is at index `48` of the $\\lambda$ sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_res.best_idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To refit the model at the best model, run the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:01<00:00:00, 72.60it/s] [dev:51.7%]\n"
     ]
    }
   ],
   "source": [
    "state = cv_res.fit(\n",
    "    X=X,\n",
    "    glm=ad.glm.gaussian(y),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last fitted regularization value is the one corresponding to the best model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Clean-up Files__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we are done analyzing the dataset, we may remove the `.snpdat` files that we created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for chr in chromosomes: \n",
    "    os.remove(os.path.join(cache_dir, f\"EUR_subset_chr{chr}.snpdat\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __SNP Phased Ancestry__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usual phased calldata is a `(n, 2s)` matrix of type `np.int8` since the entries are one of `{0,1}` (assuming no missing values), where `n` is the number of observations and `s` is the number of SNPs.\n",
    "The factor of `2` comes from the fact that there are always 2 haplotypes.\n",
    "The ancestry information is also a `(n, 2s)` matrix of type `np.int8` where each entry\n",
    "takes on a value in `{0,1,..., A-1}` where `A` is the number of ancestry categories.\n",
    "Each ancestry information corresponds to the individual, haplotype, and SNP as in the calldata.\n",
    "We assume that the user has access to a dense matrix of the phased calldata \n",
    "and the corresponding ancestry information prior to using `adelie`.\n",
    "\n",
    "The data matrix we ultimately want to use is the sum of the ancestry-expanded calldata for each haplotype, described next.\n",
    "Fix a haplotype, and consider the corresponding calldata and ancestry matrix.\n",
    "For each `(i,j)` entry of the calldata, suppose it is exanded into a length `A` vector\n",
    "where a mutation is recorded in entry `k` if the ancestry at `(i,j)` is `k` (and zero otherwise).\n",
    "The concatenation of all these expanded vectors results in `(n, As)` matrix.\n",
    "Then, we wish to run group lasso by grouping every `A` columns as a group (i.e. SNP).\n",
    "\n",
    "To fully mimic the real application, we assume that the calldata is split into chromosomes\n",
    "so that we load column-blocks of the calldata, one chromosome at a time.\n",
    "We will generate `n` observations and `ss[i]` SNPs per chromosome for each chromosome `i`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1000                        # number of observations\n",
    "ss = [1000, 2000, 3000]         # number of SNPs per chromosome (3 chromosomes)\n",
    "A = 8                           # number of ancestries\n",
    "n_threads = 1                   # number of threads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the unphased calldata example,\n",
    "we generate our data for each chromosome using a helper function \n",
    "and store a sparse representation in `/tmp/spa_chr_i.snpdat` for each chromosome `i`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = [\n",
    "    f\"/tmp/spa_chr_{i}.snpdat\"\n",
    "    for i in range(len(ss))\n",
    "]\n",
    "for i, s in enumerate(ss):\n",
    "    data = ad.data.snp_phased_ancestry(n, s, A)\n",
    "    handler = ad.io.snp_phased_ancestry(filenames[i])\n",
    "    handler.write(data[\"X\"], data[\"ancestries\"], A, n_threads)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then instantiate our special matrix object for phased ancestry SNP data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ad.matrix.concatenate(\n",
    "    [\n",
    "        ad.matrix.snp_phased_ancestry(\n",
    "            ad.io.snp_phased_ancestry(filename), \n",
    "            n_threads=n_threads,\n",
    "        )\n",
    "        for filename in filenames\n",
    "    ],\n",
    "    axis=1,\n",
    "    n_threads=n_threads,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For demonstration purposes, we generate our response vector `y` from a linear model\n",
    "with only the last three SNPs active in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = int(np.sum(ss))\n",
    "p = A * s\n",
    "\n",
    "np.random.seed(314)\n",
    "beta = np.random.normal(0, 1, 3*A)\n",
    "Xb = np.zeros(n)\n",
    "X.btmul(p-3*A, 3*A, beta, Xb)\n",
    "y = Xb + np.random.normal(0, 1, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we use `adelie` to fit group lasso!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m    | 62/100 [00:00:00<00:00:00, 136.40it/s] [dev:90.5%]\n"
     ]
    }
   ],
   "source": [
    "state = ad.grpnet(\n",
    "    X=X,\n",
    "    glm=ad.glm.gaussian(y=y),\n",
    "    groups=A * np.arange(s),\n",
    "    n_threads=n_threads,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can use our diagnostic object to inspect our solutions.\n",
    "For example, we can plot the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dg = ad.diagnostic.diagnostic(state)\n",
    "dg.plot_coefficients()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean-up generated files!\n",
    "for filename in filenames: os.remove(filename)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "adelie",
   "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}