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zdv`2U?Oxg-e&hJU@u;)oSP+W7lLw&{Fu)O<&llEv4rCm1>z^-$!0L_$41#i*`N7C| z68Q7161IZ*^Z8_GP%jk>6<~fq$`xK!%V|))yWDsz_mqV=m1E;Uy|6BF1CE_0RQBZ; zEblyGv7Yb{kPzU18-n@~kRVS+cf#g_l0S&Scv;-WRx*R~70>}s%QrMQZ#i#{*z`3c zk@2YI37g@@1ML{6#s?hpm3uqgk1burh5$uXZQ?0oB;z^~l905V;M1S; s<0TdwgA{6~PHppq#P^uvgy(w&%$VWczdzx7&&NDE!X7_bzVvth*?6PrTL1t6 literal 0 HcmV?d00001 diff --git a/notebooks/source/bayesian_hierarchical_stacking.ipynb b/notebooks/source/bayesian_hierarchical_stacking.ipynb index efc631e23..22ad357da 100644 --- a/notebooks/source/bayesian_hierarchical_stacking.ipynb +++ b/notebooks/source/bayesian_hierarchical_stacking.ipynb @@ -17,7 +17,7 @@ "# Bayesian Hierarchical Stacking: Well Switching Case Study\n", "\n", "

\n", - " \n", + " \n", "
Photo by Belinda Fewings, https://unsplash.com/photos/6p-KtXCBGNw.
\n", "
\n", "\n", @@ -81,12 +81,15 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import numpyro\n", - "import jax\n", "import scipy\n", + "from scipy.interpolate import BSpline\n", "import scipy.stats as stats\n", "import seaborn as sns\n", - "from scipy.interpolate import BSpline\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "\n", + "import numpyro\n", "import numpyro.distributions as dist\n", "\n", "plt.style.use(\"seaborn\")\n", @@ -94,6 +97,7 @@ " set_matplotlib_formats(\"svg\")\n", "\n", "numpyro.set_host_device_count(4)\n", + "rng_key = jax.random.PRNGKey(0)\n", "assert numpyro.__version__.startswith(\"0.7.2\")" ] }, @@ -571,7 +575,7 @@ "source": [ "def logistic(x, y=None):\n", " beta = numpyro.sample(\"beta\", dist.Normal(0, 3), sample_shape=(x.shape[1],))\n", - " logits = numpyro.deterministic(\"logits\", jax.numpy.matmul(x, beta))\n", + " logits = numpyro.deterministic(\"logits\", jnp.matmul(x, beta))\n", "\n", " numpyro.sample(\n", " \"obs\",\n", @@ -594,354 +598,16 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "93ab89e59b5c40a4bdb6a660de1b874e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/2000 [00:00\n", "\n", - "What we seek to find is a matrix of weights $W$ with which to multiply the models' predictions. Let's define a matrix $Pred$ such that $Pred_{i, k}$ represents the prediction made for point $i$ by model $k$. Then the final prediction for point $i$ will then be:\n", + "What we seek to find is a matrix of weights $W$ with which to multiply the models' predictions. Let's define a matrix $Pred$ such that $Pred_{i,k}$ represents the prediction made for point $i$ by model $k$. Then the final prediction for point $i$ will then be:\n", "\n", - "$$ \\sum_k W_{i, k}Pred_{i, k} $$\n", + "$$ \\sum_k W_{i, k}Pred_{i,k} $$\n", "\n", "Such a matrix $W$ would be required to have each column sum to $1$. Hence, we calculate each row $W_i$ of $W$ as:\n", "\n", @@ -1137,30 +803,22 @@ " # effect of continuous features on stacking weights\n", " beta_con = numpyro.sample(\n", " \"beta_con\",\n", - " dist.Normal(0, 1),\n", - " sample_shape=(K - 1, d - d_discrete),\n", + " dist.Normal(0, 1).expand([K - 1, d - d_discrete]),\n", " )\n", " # effect of discrete features on stacking weights\n", - " tau = numpyro.sample(\"tau\", dist.Normal(0, 1), sample_shape=(K - 1, d_discrete))\n", - " # mean effect of discrete features on stacking weights\n", - " mu = numpyro.sample(\n", - " \"mu\",\n", - " dist.Normal(0, tau_mu),\n", - " sample_shape=(K - 1,),\n", - " )\n", - " # standard deviation effect of discrete features on stacking weights\n", - " sigma = numpyro.sample(\n", - " \"sigma\",\n", - " dist.HalfNormal(scale=tau_sigma),\n", - " sample_shape=(K - 1,),\n", - " )\n", + " tau = numpyro.sample(\"tau\", dist.Normal(0, 1).expand([K - 1, d_discrete]))\n", + " with numpyro.plate(\"Candidate models\", K - 1):\n", + " # mean effect of discrete features on stacking weights\n", + " mu = numpyro.sample(\"mu\", dist.Normal(0, tau_mu))\n", + " # standard deviation effect of discrete features on stacking weights\n", + " sigma = numpyro.sample(\"sigma\", dist.HalfNormal(scale=tau_sigma))\n", "\n", " # effects of features on stacking weights\n", " beta = numpyro.deterministic(\n", " \"beta\",\n", - " jax.numpy.vstack(\n", + " jnp.vstack(\n", " [\n", - " jax.numpy.hstack([mu[k] + sigma[k] * tau[k], beta_con[k]])\n", + " jnp.hstack([mu[k] + sigma[k] * tau[k], beta_con[k]])\n", " for k in range(K - 1)\n", " ],\n", " ),\n", @@ -1168,37 +826,21 @@ " assert beta.shape == (K - 1, d)\n", "\n", " # stacking weights (in unconstrained space)\n", - " f = jax.numpy.hstack(\n", - " [\n", - " jax.numpy.vstack(\n", - " [jax.numpy.matmul(X, beta[k]) for k in range(K - 1)],\n", - " ).T,\n", - " jax.numpy.zeros((N, 1)),\n", - " ]\n", - " )\n", + " f = jnp.hstack([X @ beta.T, jnp.zeros((N, 1))])\n", " assert f.shape == (N, K)\n", - " # stacking weights (constrained to sum to 1)\n", - " w = numpyro.deterministic(\"w\", jax.nn.softmax(f, axis=1))\n", "\n", " # log probability of LOO training scores weighted by stacking weights.\n", - " logp = jax.numpy.log((exp_lpd_point * w).sum(axis=1))\n", - " numpyro.factor(\"logp\", jax.numpy.sum(logp))\n", + " log_w = jax.nn.log_softmax(f, axis=1)\n", + " # stacking weights (constrained to sum to 1)\n", + " w = numpyro.deterministic(\"w\", jnp.exp(log_w))\n", + " logp = jax.nn.logsumexp(lpd_point + log_w, axis=1)\n", + " numpyro.factor(\"logp\", jnp.sum(logp))\n", "\n", " # test set stacking weights (in unconstrained space)\n", - " f_test = jax.numpy.hstack(\n", - " [\n", - " jax.numpy.vstack(\n", - " [jax.numpy.matmul(X_test, beta[k]) for k in range(K - 1)],\n", - " ).T,\n", - " jax.numpy.zeros((N_test, 1)),\n", - " ]\n", - " )\n", + " f_test = jnp.hstack([X_test @ beta.T, jnp.zeros((N_test, 1))])\n", " # test set stacking weights (constrained to sum to 1)\n", " w_test = numpyro.deterministic(\"w_test\", jax.nn.softmax(f_test, axis=1))\n", - " numpyro.deterministic(\n", - " \"w_test\",\n", - " w_test,\n", - " )" + " numpyro.deterministic(\"w_test\", w_test)" ] }, { @@ -1215,69 +857,15 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "78c66fd181f14c419f64bf3eb041e4d2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/2000 [00:00" ] @@ -1403,7 +991,7 @@ " x=train_x_list[k],\n", " )[\"logits\"]\n", " train_preds.append(train_pred.mean(axis=0))\n", - "# reshape, so we have (N_test, K)\n", + "# reshape, so we have (N, K)\n", "train_preds = np.vstack(train_preds).T" ] }, @@ -1514,7 +1102,7 @@ "outputs": [ { "data": { - "image/png": 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9FngjbcN56rCjuygT0+fpOO5hUe4IRESsERFntjfga1lacw+6zR+Zk3LltbOcB9TX0NsQrOFwDWUY2jR3vIqINSPilIhYovtmL5uL8g3w7WFnbcpk6tati/tyM2Vs/sZt289BmZR+YS/bDFTrJgo7diy/GmjNWZqLcjvkx9qOvwKvTICekfdqHcrwpEO6PRnlLoFL8Mrt1lvajzHdOR7lRgb7dKzb22s7nTLnqP0CZquR//8ovXnfi3JjkMH4C+XWwxt3LN+BgYf6OZm+F3ALSli7Z6Cf0RmRmQ8AdwPvjLa7+9XH25dV8oHeth+Mmfxb0m6ZiHhr2/7GUELSH2t4uZ1y8WH36LjjW0QcFxG71MdbRMSp7evUz+8VHeXoYdb9rRpQ2YZwe2ka9vBIGi5vjogF6uM5KVdgW7cebjX27qaMFf9oRNxFuQK8D+W7eB4C1ouIDYGbMnNqZt5bJ/XuRrlq237HLyhzgS4FrolyV6vnKcPQDqLcghfKXIadgNdFxMn1OEvU5x/vsk8y88mI+C2wWz3+Q5SG6Zsot0VeJSI2p9xae7idRhlidU5EHEppYK5EuZ3vPyiNjL5MAg6KiE9ThhG+Dfgo5U5XHwY+GBE/6n1zTqXcKe6cOjzuMcr7vjzl+10G40LK+3RyREylDBN7D6UXpnWnp0mUuTUnRrnRxRspt/o9lTJ85v0RceYAj/cjyh0CPxcRb6Tcrexhyi2M16TcQrr15aMtDwMb1wbbX2p5DgJOinIjiqUpV//PpNwCepeIuL/eHOHHwOFR7ox2B+V7kN5HmeT/fHTcBKTezGMPynn3bcotk2dKZr5Qe1A/GhHHU77D5U2U9/4uyvcx9ecpyp0Al6B8tnek3LDgy20BeiCf0Rn1GcoNUc6PiDMof2/2ptwgodsNI2aVGf5b0uFeSlj9IuWzuQflLnIfhZfnCB1BmZtzSUR8nVde23spfwehfKY/BLw6Ir5L+cwtR+lp+jOv3EDhYWDDiPgI5e6KnTccGbAZKNuQbC91sodH0nC5iDKP4peUq9enUL7bYtPMnABQ78b0Hsp/0N+n3Jjg75QG8xcpVyYvYNqLN61b+57XOWQryy1ZWz0bF1NuJ7whsFdmHl/XeZAy6fwpSiPxZ5RbXj8KbJCZT9DdeylX8r9D+TLCeSiNna9Q5rhcQJmYPazqkLH1KQ3AYyhzS75AKfPmA5gEfATl+1MOozR6t6Dcte2rlAb81+jjDkqZ+WQ9/k2U3omfUBpxW9W7Rs20WvbNKefKVyjzp7agDBdr9fZ9k9KIei+l52cvSkPyS5TvIfkUA2wE1/NrV0oD9NWUu/TdTGmw70w5f1bLzPZ5J4dTwsFZwMaZeSmlTjeg1MWh9d/RlLkLe9X9Q5n7cXJ9/npK+G998WdvZbyd0rDdpU5cH4xDgf+jBNMfUt7391B6aAYyj+c5yut5P2UO2Psp88FeHq46kM/ojMrMyyghd3nKe3MhZTjb9rX+h8Qg/pa03Ev5HqRDKXOytqF86ebLd2jMzG9QQu9ylM/jDymv8x1Zv/C4ngNbUW6DfRYlZH+F8rd3k7aweXw95tcpF0UGZSBlG8rtpXZjenoGM29WkkaWOin9Esr3vPT1RYuSZoGI+CPwYma+pY91JgFvysy+5jNJ0pBwSJukxqjzRz4DXGPYkWatiDgIWAt4f+uGE/UGIm/klS/3laQRx8AjadSLiPGU8fgHUu7qttrwlkhqpGcow//GRMT/Ub435wuU2wOfNHzFkqS+OYdHUhNsSRmXvjqwbWb+YXiLIzVPZp5Bmb/zZsqcinMoE+K3yMxfDWfZJKkvzuGRJEmS1Fj28EiSJElqLOfwaEi9+OJLPU8++e/hLsaotsgi82MdDo51OHjW4eBZh4NnHQ6O9Td41uHgDVUdjh8/bkxvz9nDoyE111wj6QvmRyfrcPCsw8GzDgfPOhw863BwrL/Bsw4Hbzjq0MAjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbHmGu4CqNl2/+S5w10ESZKk/0onH/bO4S7CiGAPjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvDMgIhYISJ6ImKdjuW/jIgJA9zHAhFxXx/PbxwRFw2ijBMiYvsZ3OYtEfHG+viCiJhvZo8vSZIkjSRzDXcBRqF7gPcCPweIiNcDiwxriQZvR+B24E+ZudtwF0aSJKnJpr70AvS8NOTHmTLl6SE/RruxY8cy77wj77q5gWfG/RzYIiLmzMyXgN2AicD8UHpogC8CLwAPAB8A5gEuBuYFbmrtKCI2aFv3fmDfbgeMiIWAH9T9zAN8LDN/FRHHAhsAcwLfyMzz27aZEzgdeB0wFjgiM6+PiNWAbwFTgVuAs4H9gMkR8Wg9zsrAwsAZwNx13Q8CPcBZwF+BVYFfZ+aHZqIOJUmS/is9/fdbeXbyXbPlWPvue95sOU7LmDFzsMUWW/OBD3x4th63Pw5pm3EvALcBm9Tf3wVc2fb8acCumbkR8CSwO/A+4PeZuQFwZ9u6pwDvysxNgX8AO/dyzM2ABzJzY2APYIkalpbPzA2BTYHPdQxF2x14ODM3Ad4NnNR2zI9k5nrAksDTwNXAZzLzF23bHw18tx7zW8BRdfkawOHAmsC2EbFwL2WWJElSh9kVdoZDT89UJk68sv8VZzMDz8y5EHhvRKwMPAg8AxARiwI9mXl/Xe+nwGrASpTeFIBJdd0lgTcAl0TEJEqAWqaX490KrBsRpwGvz8yrgbcD69Rtf0J5L5dq2+btwLvr8xcB80XE3EBk5m8BMnPPzPxbL8d8W6usba8D4C+Z+UhmTgUeAhbqZXtJkiR1mG/8isCY4S7GkBgzZg623HLb4S7GdBzSNnOuBb4BPEwJEy09THsGt4aDzVl/wish83ngwdqD8rI6JG4amflwRKxKCUX715smTKH0wHypY/vWw+eBY9uHudXnpzIw7a+l9ToAXuxYr5mfWEmSpCGw4GvWZYFl3jZb5vB88YCth/wY7ZzD0yCZ+XxE/Iwyr+VN1N6PzHyy3sXtNZn5d2AjypydRSk9JhdTh8LVdYmIlTLzjxFxIHBDt+NFxObA2My8KiL+SBlidhxwfER8mRJIvpqZB7ZtdhtluN35EbEEcHBmHg78MSLWzszbIuK7wPGUMNN5LvyylvX8+jpuH0SVSZIkqZpjzrGUKdZDa9y4BYf8GKOBQ9pm3oXArzLznx3L9wXOq0PJxgIXUG4MsE5EXAcEpfcESmA6MyJuBNYHspdj/QX4bN3n2ZRwcwtlqNmtwM+AOzq2+QHwTETcAlwO3FiXfxw4ISJuAp7MzLvqc6dExGZt2x8B7BkR1wN7A0f2WyOSJEnSCDOmp6en/7WkmbT7J8/1BJMkSRoGJx/2zuEuwnTGjx/H5MlThmK/vU6zsIdHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmON6enpGe4yqNl6Jk+eMtxlGNXGjx+HdTg41uHgWYeDZx0OnnU4ONbf4FmHgzdUdTh+/LgxvT1nD48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxjLwSJIkSWosA48kSZKkxppruAugZtv7zI8PdxEkSZKG3Ve3P2a4i/Bfyx4eSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY1l4JEkSZLUWAYeSZIkSY016gJPRKwQEVMiYlLbv5Mi4q0R8YVB7HeBiLhvgOvuNBP7nxQRK3csOykiXjuj+xqMiLggIubr4/nHuixbMCK2HNqSSZIkSbPeXMNdgJmUmblxl+V3zqbjfxq4aLA7ycyDB1+UGT7mbjOx2erAlsDEWVwcSZIkaUiN1sAznYjYGDgA+AxwDrAu8Frg+/XxO4BDgBeB2zPzkIhYELgYmBe4qcs+x9Z9LQXMAxwJrAKsGhGXALsAZwHLAq8CjsrMKyJiNeBbwFTglsw8rG2fCwLXAB8AvlnLvBOwEBDA/wAHZ+ZVEfEp4L3APcBY4ITMnFT3sznwnszcPyJ2Bz6TmatExFLAecA7gTOBRSjv84GZ+dvai7Uy8Lpa9qeA24Hxmbl33ffRlIDzeK23bwILRsSfgPuAY4BngX8Ae2TmCwN4iyRJkkakqS+8RM/UniE9xpQpTw/p/gHGjh3LvPP2OpDnv1ZjAk9LZv45Iq6iBIqtgI9TwsrngHUz87mI+EFErAesCvw+Mz8REbtSwkW7VYDFM3PDiFgY2DYzvxoRn8rMHSNiCWBiZp4VEa8DLgSuAE4BPlIDxtkRsXzd3xhKyDgqM/8QEe3HWi4zt42IrYH9IuI2Shh6I7Ag8GfghLb1bwGOrY/XAx6NiIXq458CBwNXZ+Z3ImIl4GRgi7btjwSOzsxLI+IHwL/r8kWBizLziIi4FXgL8FVg5cw8PSJ+BBySmTdGxI7AYsAj/bwtkiRJI9KTv3iIZ/70xJAfZ98L9xzyY4wZMwdbbLE1H/jAh4f8WKPJaA08ERGT2n6/Bri57fcv1d9/k5k3R8TawGuAn9SQsRCwPLAScEPdpn1/LXcD4yLie8ClwAUdzz8JrBkRH6b05izWKl9m/hYgM/esBYYSMu7PzKu6HKvVw/RALd/rgd9l5rPAsxHxi/aVM/PfEfFcRMxfX8ulwNqUwHMppadrfES8r24yf8fxVuSVOvsRsHl9/HSr7MCDtSztLgROi4hzgfMz07AjSZJGrdkRdmaXnp6pTJx4pYGnw2gNPNPN4alD2lrmp/SmLFl/fx64IzO36thmPUpQgS43cKihYh3g7cDewPaUnqOW3Sk9IhvUn7fX5VPp7klgi4hYLDMf73juxbbHY+q/9v1062e9CdgMmAL8HNiOMt/mU5TXfGBm3tpLWdr3377vF7us97LM/F5E/AR4N3B5ROyUmXf3cgxJkqQRbYE3Lsozf36ie0trlGn18GhaozXw9OdLlN6UbepQtcuBFSNiicx8tN7N7XQggbdR5vFs0rmTiFgdWCkzz6lDzG6sT7XC0eLAvZk5tQ7vmrsu/2NErJ2Zt0XEd4Hj6/KTKUPRTgH26Oc13AesXOcRLVzL2ekG4DDgx8BvgKOBf2fm87W87wZurUPats7ME9u2/Wvd59XANkwfdNpNpZ4rEfF54Bt1eNsSlF4yA48kSRqVFllraRZabckhn8PzhS0PH9L9g3N4etO4wFOHry1fbx5wKyUUXEWZ03JlRDwH/Bp4CDgbuDQirqP0lnSe6fcCX4yIjwAvUeayAPy6DjHbBfhR7QU6A3ggIo6gzBs6tQ5j+3lm3tWar5OZZ0bELhHxzr5eR2b+IyLOA34B3FV/vtSx2s2UMPe5zHwhIhbglTupfR2YEBE3AnMCB3VsewzwnYj4BPAHph+61u5XwJcj4gHg78C1EfEkpcfqxD62kyRJGvHmGDvnkB9j3LgFh/wY6m5MT08D+u8aKiL2ptxx7UXgd8BWmfnALNr3OpTeoN9GxGeAMZn5xVmx73Z7n/lxTzBJkvRf76vbHzPcRRgRxo8fx+TJU4Ziv2N6e65xPTwN82rgNuA54NxZFXaq54DvRsSzlDu07T4L9y1JkiSNCAaeESwzjwOOG6J9/xpYcyj2LUmSJI0U092ZTJIkSZKawsAjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbHG9PT0DHcZ1Gw9kydPGe4yjGrjx4/DOhwc63DwrMPBsw4HzzocHOtv8KzDwRuqOhw/ftyY3p6zh0eSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSYxl4JEmSJDWWgUeSJElSY8013AVQs1255z7DXQRJkqSZtuYJpwx3ETRI9vBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaiwDjyRJkqTGMvBIkiRJaqy5+noyIlYAfgfcAfQA8wKHZeZNQ1GYiNgb+GdmXjqT228MHJCZO7UtmwBcBDwA7JCZRw6+pAM/fl1+EnByZt47A/t6LDMX71i2CnAyMCewAHAt8OnM7ImI92TmxTNY3m7H+GFmvmtG9iNJkiSNVH0Gniozc2OAiNgQ+Dyw1VAUJjMnDMV+677vBO4cqv33c+yDZ9GuTgE+mZm/jIg5gEuB1SPiceC9wAwFnm4MO5IkaTR5fupUXurpGbL9T5ny9MuP5557KlOmPDPL9j127FjmnXe+WbY/dTeQwNNuSeBBgIhYFfgm8AIwFdgZ+BTwp8z8bl3nj8AGwG7A7nW9yzLzhIhYDfgW8Fz9tytwMPAYcBpwFrAs8CrgqMy8IiImAdcAmwKLA+/IzL8PpODtvS8RsSNwCPAicHtmHlJ7l7YBlq7lPQRYi9KrdVpmfqf2Fj0PLFbXOQtYHvgPsGc91AIRcQ6wKnBhZh5dy30ApZfpXGBB4J91HwsD36vbjgX2ysy/9vIyFgYWAsjMqcC76mv7MbBWRBwBnNFtfxHxfuAgyntwYmZ+v61u3kp5L7YE7svMxbvVNfAwcE59zbcAu2Tmsr3XuiRJ0tC57qknuPNfsy6AdLXvnv2vM5PGjJmDLbbYmg984MNDdgwNbA5PRMSkiPg5cCJwfF2+BHBgZm4C3AzsQWlo71o3Wgm4h9K43wlYH9gQeE9EvAbYB/hW7T36MvDqtmMuCkzMzI2AXYAvtD33dGZuBlwF7NilvBvV8k6qjfatO17MAsDngE3r/peLiPXq06+pZXyc0vBfnxLYjm7bxROZ+R5gL+CRzFwP+D/gnfX5lYAPA+sCB3aU7VDgJ5m5AXAdsDmwFHB0rcczgI92eU0tRwEXRsTEiDg0Ipaqy78K3JCZR3fbX0SMA46or20rSvhs1cfilIC5W2Z2/sXorOutgXkzcx3geko4lCRJGhZDHnaGWE/PVCZOvHK4i9F4Mzqk7U2UBvdqwD+AL0fE/JSG77mZ+fuIWDgixlN6H86l9JK8Afhp3d84YAXgh8CpEfFG4PuZeXdEtI75JLBmRHyY0iOxWFt5bqw/H+hY3nJDlzk87d5MCTY/qcdbiNJjAfDLzOwB/hMRi0bELZQenfFt2/+i/lydElrIzAvqsTYGfpWZ/66/j+k49uqUIYFk5tfqOssBp0TEF4BFKPOlusrMH0bEaymhZXvg8HrMdo902d+KwN2Z+SzwLLVniBJ4vw98pZeess66XpESbgGupPSQSZIkDYu3vmoBfvOvZxi6AW1Dq9XDo6E1Q0Paaih5FliOMnn+y5l5dUQcSplED3AepTdgM0qvx1bAjzPzI537i4g1KQ33s+o+Wnan9PJsUH/e3vZceyO7M1AMxPPAHZk5zTykOqTt+fp4I8pQro0y84WIeKZje4CX6N5D1lcI6LbN0ZRen9MiYidKfXQVEfNl5lOUkPL9iDgS2AG4oZ/99VbWBYHfAvsBl/TzWsbUfy/V33vqP0mSpGGx2cKLssGCCw/pHJ7V/vdLLz9ebLEFePxx5/CMNjMUeCJiUcqQqQcp8zr+GhHzANsCP6+rnU/pvflzZv47Iu7glZ6gZ4GTgE8DH6QEoXNrT8hqbYdaHLg3M6fW+TZzz+wL7CKBFSNiicx8tPaEnN6xzuLA/TXsvBOYMyI6y/BLSii6MCK2B95CmdfSl9Y2v4yIj1Dm/rTqcQyl52XObhtGxILA7yJincx8uC5eFvgZpRes9V5229/dZRexACXEXE6Zr/NUZn4iIs6KiH0z8//6Kf9fKcMTqdvP6BwwSZKkWWruOYb2W1bGjVvw5ccLLTSO55/3W11GmxmZwzOJMozpgMx8Hvg6cBlwYX28V0Ssmpn/AJ6h9PRQh0qdRGmY/5wy7+VZ4C+UsHAdpUfn3LZjXgy8oz73L+CBOiF/0Opws4OBKyPiZspQrYc6VrsWeENE3AD8D3AFcGrHOhcAr6rrHEy5gUF/TgbeXutye0qvyrcp9XdV3edGEbFll3I/DewPXFzfj5uAKZR6u4tyt7avddsfsB5lDs+1wCTgO3XoXsvBwCF1eF1frgAWrMfegDLXSZIkSRqxxvTM4i7AOgn+amCteicxNUTt4dskMy+OiGWA6zLzTX1tc+We+zjsTZIkjVprnnDKy4/Hjx/H5MlThrE0o99Q1eH48eN6neoyS/vkIuLdlIn8nzLsNNIUYJd6x75LgU8Mc3kkSZKkPs3SORiZeRllmJsaKDNfoN52XJIkSRoNnHUlSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIay8AjSZIkqbEMPJIkSZIaa67hLoCabduzz2Ty5CnDXYxRbfz4cdbhIFmHg2cdDp51OHjW4eBYf/pvZQ+PJEmSpMYy8EiSJElqLAOPJEmSpMYy8EiSJElqLAOPJEmSpMYy8EiSJElqLAOPJEmSpMYy8EiSJElqLAOPJEmSpMYy8EiSJElqrDE9PT3DXQZJkiRJGhL28EiSJElqLAOPJEmSpMYy8EiSJElqLAOPJEmSpMYy8EiSJElqLAOPJEmSpMaaa7gLoGaIiK8B6wA9wMcz85dtz20OfBF4CbgyM/93eEo5svVTh/MC3wbenJlvG6Yijnj91OEmwJco52ECH8rMqcNS0BGsnzrcF/ggpQ5/A3wsM/1ugw591WHbOl8C1s3MjWdz8UaFfs7D+4D7KechwB6Z+eBsL+QI108dLgecD8wN/Coz9xueUo5svdVhRCwDnNu26uuAT2fmebO/lCNbP+fhx4D3UT7Lt2fmwUNVDnt4NGgRsRHwhsxcl9IYOqVjlVOA9wDrAVtGxEqzuYgj3gDq8KvAnbO7XKPJAOrwdGCnzFwPGAdsPZuLOOL1VYcRMT+wG7BBrcM3AesOS0FHsAGch9S/gRvO7rKNFgOpQ2CbzNy4/jPsdBhAHZ4AnJCZawEvRcRrZncZR7q+6jAzH2ydf8DmwN+BHw1LQUewfv5PWRA4jPJ/yvrAShGxzlCVxcCjWWEz4DKAzLwLWKSeyETE64AnMvP+ejX9yrq+ptVrHVaHA5cOQ7lGk/7qcI3MfKA+ngwsNnuLNyr0WoeZ+e/M3CwzX6jhZyHgkWEr6cjV33kIpbH52dlcrtFkIHWovvX1//IcwAbUBnpmfiwz/z5M5RzJBnoe7g1cnJnPzL6ijRp91eHz9d8CETEXMD/wxFAVxMCjWeHVlAZky+S6rNtzjwJLzaZyjSZ91SGZOWW2l2j06a8OnwaIiKWALSnhW9Pqsw4BIuLTwF+BH2TmPbOxbKNFn3UYEXsDNwD3zdZSjS79nofAaRFxU0QcFxFjZl/RRo2+6nA8MAX4Wq3DL83uwo0SAzkPAT4EfHe2lGj06bUOM/M/wBeAe4C/Abdl5p+GqiAGHg2Fvv7z8T+mgbGeBm+6OoyIJYDLgY9m5uOzv0ijznR1mJnHUcarbx0R683+Io06L9dhRCwK7EPp4dHAdZ6HRwD/D9gYWJkyZFp9G9PxeBngZGAjYLWI2G5YSjW6dPs/ZV3g7tYFNfWr/e/hgpTRK28EXgusHRGrDtWBDTyaFR5i2qseSwMP9/LcMnWZptVXHWpg+qzD+sf1KuBzmTlxNpdttOi1DiNi0YjYECAzn6XUpYFnen2dh5tSrq7fSBmiunqd0Ktp9flZzsyzM/PRzHyR0lO7ymwu32jQVx0+BvwtM/+amS8B1wFvns3lGw0G8v/y9sC1s61Eo09fdbgicE9mPpaZz1P+Lq4xVAUx8GhWmAjsBBARqwMPtYZgZeZ9wIIRsUIdo7l9XV/T6rUONWD91eEJwNcy8+rhKNwo0VcdjgUmRMQC9fe1KHe707T6+nt4UWaulJnrADtQ7o71ieEr6ojVax1GxEIR8ZOImLuuuxHw++Ep5ojW13n4InBPRLyhrrsGfpa7Gcj/y2tS7lip7vqqw/uAFSNivvr724A/D1VBxvT0eEdRDV5EHEe569BU4GPAasA/M/PSelX4y3XVizPz+GEq5ojWTx1eCCxHuQp3B3C6t7+cXm91CPwEeBK4tW318zLz9NleyBGun/Nw77rsRcp/8vt7W+rp9VWHbeusAEzwttTd9XMefhzYC3gW+DVwoOfh9Pqpw9cDEygXvn9H+Sx7m/4O/X2WI+J3wOaZ+Y/hK+XI1s95+BHKMN8XgVsy85NDVQ4DjyRJkqTGckibJEmSpMYy8EiSJElqLAOPJEmSpMYy8EiSJElqLAOPJEmSpMYy8EjSCFK/s6onIvboWH7fEBzr7RHxuvr4pIgYsi99G2q1zuYa7nK0i4hjIuKo+nhSRMzZx7rbRsSi9fEFEbHMLCzH62f1+dNexoh43wxuu1lEXBIRr42IB2ZluQZ4/E9HxHYDXHdCRHxoBvf/vm6PB7jt1yLigzOyjaT+jaj/HCRJAPwJODIifjTEX0C7D/B9yrddHzyEx/mvN4Dv2/kEsD/wRGbuNvQlGpxWGWvo2Q84ZyDb1S+uPQ1YBxg3ZAXsQ2YeN1T7bq+PGnCPYIB1U30K+G1EXJOZfx+KMkr/jQw8kjTyPEz5stTPA9N9EVtEfBFYD5gPuKFtnW9QGpKPAPcDj2Xm5yJif2BP4HngP8CuwCbAzsBaEfEJSsPsGOBLwMcz85Z6rGuBE4A/AN8C5gcWAA7PzGs7yjUBeAxYkfIluZ8G3gG8BbgpM/fvo/xjKA3hNwHzALdl5kH1Czp/VOtjbUojebvMfKhbxUXEq4DTKV/UOxY4OzNPjYh5gbOAFYAHKF90d01mfqdt2xWA64CrgFXr4t0y88GIeBr4LjBnLdeBwC6U/0fvBj6amc9GxLHA9rX+/wXcVffdU8szFjgTeE3d/2eAlYANgHMjYh/gSmAr4KfAmpn5YN3Hn4F31n2c0La/AzLz1x318PZan5MpX1bcWr5IXT4eWAg4ITPPqz1RiwHLAm8AfpqZB0bEyrU+n6O890dn5o9rj9HmtU5WiYizgf8BPpuZk+qxrgK+nplXthVtX+DqzHw8Il4OPBGxZN3XApT3/yvAnZQvq149IsZQzutPZuZZEbEbsD7w/4BvAq+nnBvnZ+YJ9UtytwcWAU7MzB+3HWsCcBNwAXBeXWcscHlmHsv01oqInWrdnFn3P3e349b9teqjB1g+IiZm5pYRsQtwIOVcnwx8qNZD57l1Wn1dB3cpi6SZ4JA2SRqZTgS2i4hoXxgROwPLZOZGmbkWpcG1PbAZsFb9t0v9vWU+YMvM3Ai4D3hf/abwO4FDMvP6tnXPBXaqx1qCEl4mAqdSGsebUhrd3+llCNmSmbkdcBSlQfixWqa9I2LhPsq/CPDbzNwwM9cGtqyNbSiBYEJmbljLvGsf9XYQ8FRdd1PgU3XY3vuAsXXfHwO27GX711EatRsAk4BD6vIFgCtrg3QtYAdgw8xcF3gK+FBEvBHYo77ed1OCQ6dDgfsz8+3AXpRG76mUxvwemfnHut5LwA+A9wDU4Yb/zMy7KO/RfrXX6KPAd5je8cCnMnOzuu+WYyiBY1PKt58fHRHj63OrUd77NYF9ajjaF/hhZm5CCa+LdRznSOB3mbkn8G1g71reRYEAru5Yf+suywCOBm6or+ldlPPtMeBVEbEgsDLwa2Cjuv4mlBD8ceChWr61gd0i4i11nbcC27aHnQ5bUM6JDYC3A89ERLd20dLANpSAdXh9bb0dt70+jgQm17CzHPBZYPPMXJ9ybh1e9//yuVV/v6bWk6RZxB4eSRqBMvO5iDgMOIVytb9lE2DdiJhUf18IeC0wN3BjZr4E/Csi2huVjwNXRsRUSg/Hw30c+gLgZsoV5p2ACzPzpYjYBBgXEUfW9V4AlgA6e1purj8fAO7KzKcAIuLxWtbeyn8lsFxE3ErpTVgKWBx4htJT9Ye6/t+ARfso/9rABIDa43I7sDql8TupLn8kIm7qZfvHM7PVI3Izr1xlH9P22jamBLWf1jz6qlofqwB3ZOZz9TX/rJfynVrL8Wfg/X28lnMpPTmnUELeOTWEBvDdtiy8YETMkZlT27ZdhdKLAXA9JQhCqf81I2Kv+vsLlPqH0gv3EvBsRDxGqeeLgQkRsTxwBfC9Psr7A+CYOmxtB+DcjjJB6Xm7v8u27fXyaJ3bE7Xs61Pq+2zggLr+epQwuj+wbES0gtC8dV2AX7Xei17cTAl8P6Ccf9/pUl6AazOzB3gqIv5CCbKb9HLcJ3o51rqUc/on9X2bB7i3Ptd+bkE5x1foo9ySZpCBR5JGqMy8MiL2j4gd2hY/B5yemce3rxsRnwTaG2sv1eXLUq72v7k2JKfZrssxH4mIe2ovxq6U4NM67o6Z+Vg/xX6xl8dQGna9lX8PSs/CBpn5Yg0qfe2nNz1d1u2hjGiYrn66aL/C39q25fn68zngR5l5QNtz1GFP7cfodpOCVln6lZm/jIglImIpYEdKI/854LkBzAka01aW9nI8Rxl+116/RMS2dKnnzPxZ7WnbjNJ78z5g917K+5+IuIQSdnai9D4NVG/v20RKT1RQeuZ2iIi1KSH4mYh4jjLM7qKO17M3r7xfXdXPw6qUMPIu4PaIWD0zn+1Ytf09bZWrt+Nu3MvhngN+kZnb9/J8n2WVNDgOaZOkke1gyryaeervNwE7toaTRcQREfEGyjySdSJiTETMzyu9QktQGoeP1qE4W7btaypl7kKnc4EPAou29XbcRBkqR0QsHhEnzeTr6a38SwJZw84alKvl8/Sxn978nPra63yeNShzWO6mDFtqDdVbv5ftF4mI1erj9YHfdlnnZmCb2pNBRHw0ItalzNdZPSLmjoixvDL8qt0t1OFKUe7Id11d3tt7cQFlLtefMvMfmflP4L4aUIiIN0bEEV22+yOlIQ9lrk1L+/s4X0R8q5ehidR1DgSWzczLKefE2h2rdJb7dErQGZOZ9zK9+ym9PJ3a37elKb0hSZnHtB6wVJ23dSPwOcpwts7XM0dEnFjP835FxJaU+WA3Z+YnKb2JS3RZddO6/iKUIY9/6uO47fXR/viXlLlAr67b7BwR7+qlaMtThp5KmkUMPJI0gmXmX4GLgFfXRZdQGty31OFfSwL3UIbk3A/cTgkst1Cu2N8J/DkifkGZU3MkZX7G+pS5At+OiB07DnsJ5Sr++W3LDqJcXb+xHut6Zk5v5b+QMtTtBsq8leMpQ7kWmcH9f50y9O5ntYxHZ+Z9lGFui9djnkRpOHf2aAA8SJlvdD2lof21zhVq78g3gUl1aNzGwG/qsLvLgNvq67mzy/5PoYSqGykT3I+py38CXB7lZgPtzgU+zLR3+toT+Ex9jWdR3sdOnwROiogrgQXblh8FvKGW+2fArzOzWz203A2cHxE/BX5MuRFFuz8AS0bENQB1DtKc1GGFXVzNtEM0W44E1q9DHS8BPpyZz9QhkXMAv6vr3QBsR+n5gfI+PFPf159T5m/1NqysUwKHRMSN9bgTM/NvXdZ7KCIuo5wzR9cy9Xbc9vp4CHgkIu4A/kmZ93NFfd8+WLfrZnO6z3OSNJPG9PR09iJLkkabiFiIMlH+7MzsiYgfUe4cdX7fW/53iHK74Ldn5oV1YvqvgP0z89a2dVagzGNZdpiKOerVOrwSWDUzX+jy/AKUmw+sk5mPz+bijXhR7v72G2DrXsKXpJlgD48kNcMUSo/EHRFxM+VGBRcOb5FGlKcod9L6BXArcFV72NHgRcThwA+BfbuFHYDMfIbyPTX/F+VW05rWl4HjDTvSrGUPjyRJkqTGsodHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ1loFHkiRJUmMZeCRJkiQ11v8HM+JdP/Ul4sUAAAAASUVORK5CYII=\n", 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\n", 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