diff --git a/notebooks/BLB.ipynb b/notebooks/BLB.ipynb new file mode 100644 index 000000000..4a28ec0b8 --- /dev/null +++ b/notebooks/BLB.ipynb @@ -0,0 +1,1228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9e264225", + "metadata": {}, + "source": [ + "# Bag of Little Bootstraps analysis\n", + "\n", + "This notebook inspects our Bag of Little Bootstraps implementation to see how it is doing." + ] + }, + { + "cell_type": "markdown", + "id": "c3210118", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Load libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "115b4f9e", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from scipy.stats import bootstrap" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5de05976", + "metadata": {}, + "outputs": [], + "source": [ + "from lenskit.stats._blb import _BLBConfig, _BLBootstrapper, blb_summary" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fada2c12", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(20250602)" + ] + }, + { + "cell_type": "markdown", + "id": "8f0f4d2c", + "metadata": {}, + "source": [ + "## Initial Test — N=10,000" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "85677bc9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2508 (0.2470, 0.2546)\n" + ] + } + ], + "source": [ + "N = 10_000\n", + "TRUE_MEAN = 0.25\n", + "TRUE_SD = np.sqrt(3 / ((1 + 3) ** 2 * (1 + 3 + 1)))\n", + "data = rng.beta(1, 3, N)\n", + "mean = np.mean(data)\n", + "std = np.std(data)\n", + "ste = std / np.sqrt(N)\n", + "print(\"{:.4f} ({:.4f}, {:.4f})\".format(mean, mean - 1.96 * ste, mean + 1.96 * ste))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4bb810c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ConfidenceInterval(low=np.float64(0.24723537123505493), high=np.float64(0.2546752145819843))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "boot_res = bootstrap([data], np.mean)\n", + "boot_res.confidence_interval" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d136fce6", + "metadata": {}, + "outputs": [], + "source": [ + "config = _BLBConfig(np.average, 0.95, 0.01, 3, 200, 0.7)\n", + "blb = _BLBootstrapper(config, rng)\n", + "blb_df = blb.run_bootstraps(data).samples\n", + "_gstat = blb_df.groupby([\"subset\"])[\"statistic\"]\n", + "blb_df[\"cum_mean\"] = _gstat.cumsum() / (_gstat.cumcount() + 1)\n", + "blb_df = blb_df.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2e37fc8d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hlines([TRUE_MEAN], xmin=0, xmax=blb_df[\"iter\"].max(), label=\"True mean\", color=\"black\")\n", + "plt.hlines([mean], xmin=0, xmax=blb_df[\"iter\"].max(), label=\"Data mean\", color=\"magenta\", ls=\"--\")\n", + "plt.hlines(\n", + " [blb_df[\"statistic\"].mean()],\n", + " xmin=0,\n", + " xmax=blb_df[\"iter\"].max(),\n", + " color=\"red\",\n", + " label=\"BLB mean\",\n", + " ls=\"-.\",\n", + ")\n", + "for snum, sdf in blb_df.groupby(\"subset\"):\n", + " plt.plot(sdf[\"iter\"], sdf[\"cum_mean\"], color=\"steelblue\", alpha=0.5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8bda69d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meanstderrci_lowerci_upper
subset
00.2461180.0019760.2423400.250043
10.2484540.0020310.2445470.252199
20.2538110.0018470.2503330.257304
30.2390530.0019600.2353470.242675
40.2449400.0019080.2412830.248641
50.2590340.0019630.2550480.262777
\n", + "
" + ], + "text/plain": [ + " mean stderr ci_lower ci_upper\n", + "subset \n", + "0 0.246118 0.001976 0.242340 0.250043\n", + "1 0.248454 0.002031 0.244547 0.252199\n", + "2 0.253811 0.001847 0.250333 0.257304\n", + "3 0.239053 0.001960 0.235347 0.242675\n", + "4 0.244940 0.001908 0.241283 0.248641\n", + "5 0.259034 0.001963 0.255048 0.262777" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blb_sdf = (\n", + " blb_df.groupby(\"subset\")[\"statistic\"]\n", + " .apply(\n", + " lambda x: pd.Series(\n", + " {\n", + " \"mean\": x.mean(),\n", + " \"stderr\": x.std(),\n", + " \"ci_lower\": np.quantile(x, 0.025),\n", + " \"ci_upper\": np.quantile(x, 0.975),\n", + " }\n", + " )\n", + " )\n", + " .unstack()\n", + ")\n", + "blb_sdf" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6ecbf7d7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hlines([TRUE_MEAN], xmin=0, xmax=blb_sdf.index.max(), label=\"True mean\", color=\"black\", ls=\"--\")\n", + "plt.hlines([mean], xmin=0, xmax=blb_sdf.index.max(), label=\"Data mean\", color=\"magenta\", ls=\"-.\")\n", + "plt.plot(\n", + " blb_sdf.index,\n", + " blb_sdf[\"mean\"].cumsum() / (blb_sdf.index.values + 1),\n", + " color=\"steelblue\",\n", + " label=\"BLB mean\",\n", + ")\n", + "plt.xlabel(\"Subset\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ca8a9542", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hlines(\n", + " [TRUE_SD / np.sqrt(N)],\n", + " xmin=0,\n", + " xmax=blb_sdf.index.max(),\n", + " label=\"True stderr\",\n", + " color=\"black\",\n", + " ls=\"--\",\n", + ")\n", + "plt.hlines([ste], xmin=0, xmax=blb_sdf.index.max(), label=\"Data stderr\", color=\"magenta\", ls=\"-.\")\n", + "plt.hlines(\n", + " [boot_res.standard_error],\n", + " xmin=0,\n", + " xmax=blb_sdf.index.max(),\n", + " label=\"Bootstrap stderr\",\n", + " color=\"green\",\n", + " ls=\":\",\n", + ")\n", + "plt.plot(\n", + " blb_sdf.index,\n", + " blb_sdf[\"stderr\"].cumsum() / (blb_sdf.index.values + 1),\n", + " color=\"steelblue\",\n", + " label=\"BLB stderr\",\n", + ")\n", + "plt.xlabel(\"Subset\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fa251a70", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hlines([TRUE_MEAN], xmin=0, xmax=blb_sdf.index.max(), label=\"True mean\", color=\"black\")\n", + "plt.hlines(\n", + " [TRUE_MEAN - 1.96 * TRUE_SD / np.sqrt(N), TRUE_MEAN + 1.96 * TRUE_SD / np.sqrt(N)],\n", + " xmin=0,\n", + " xmax=blb_sdf.index.max(),\n", + " label=\"True CI\",\n", + " color=\"black\",\n", + " ls=\"--\",\n", + ")\n", + "plt.hlines(\n", + " [mean - 1.96 * ste, mean + 1.96 * ste],\n", + " xmin=0,\n", + " xmax=blb_sdf.index.max(),\n", + " label=\"Data CI\",\n", + " color=\"magenta\",\n", + " ls=\"-.\",\n", + ")\n", + "plt.hlines(\n", + " [boot_res.confidence_interval.low, boot_res.confidence_interval.high],\n", + " xmin=0,\n", + " xmax=blb_sdf.index.max(),\n", + " label=\"Bootstrap CI\",\n", + " color=\"green\",\n", + " ls=\":\",\n", + ")\n", + "plt.plot(\n", + " blb_sdf.index,\n", + " blb_sdf[\"ci_lower\"].cumsum() / (blb_sdf.index.values + 1),\n", + " color=\"steelblue\",\n", + " label=\"BLB CI\",\n", + ")\n", + "plt.plot(\n", + " blb_sdf.index,\n", + " blb_sdf[\"ci_upper\"].cumsum() / (blb_sdf.index.values + 1),\n", + " color=\"steelblue\",\n", + ")\n", + "plt.xlabel(\"Subset\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8ed0e077", + "metadata": {}, + "source": [ + "## Randomized Testing\n", + "\n", + "Now let's test a bunch of possible values." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "509893b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 10000)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = 100\n", + "N = 10_000\n", + "means = rng.beta(1, 3, size=M)\n", + "stds = rng.standard_exponential(size=M) + 0.1\n", + "\n", + "data = rng.normal(\n", + " np.broadcast_to(means.reshape((M, 1)), (M, N)), np.broadcast_to(stds.reshape((M, 1)), (M, N))\n", + ")\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "1a38c5ce", + "metadata": {}, + "outputs": [], + "source": [ + "data_means = np.mean(data, axis=1)\n", + "data_stds = np.std(data, axis=1)\n", + "param_stats = pd.DataFrame(\n", + " {\n", + " \"rep_mean\": data_means,\n", + " \"rep_var\": (data_stds * data_stds) / N,\n", + " \"ci_lower\": data_means - 1.96 * (data_stds / np.sqrt(N)),\n", + " \"ci_upper\": data_means - 1.96 * (data_stds / np.sqrt(N)),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "3a64a3c6", + "metadata": {}, + "outputs": [], + "source": [ + "# boots = [bootstrap([data[i, :]], np.mean, n_resamples=5000) for i in range(M)]\n", + "# boot_stats = pd.DataFrame.from_records(\n", + "# {\n", + "# \"mean\": np.mean(data[i, :]),\n", + "# \"ci_lower\": boot.confidence_interval.low,\n", + "# \"ci_upper\": boot.confidence_interval.high,\n", + "# }\n", + "# for i, boot in enumerate(boots)\n", + "# )\n", + "# boot_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c59e3e12", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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estimaterep_meanrep_varci_lowerci_upper
00.1555560.1602160.0000010.1576330.162040
10.1037240.0979680.0005610.0710800.148033
20.3435960.3260110.0003130.2902500.354611
30.0751310.0697430.0000100.0624660.073803
40.0726330.0731200.0000020.0706710.074739
..................
950.0646430.0784660.0000340.0685940.090591
960.4491190.4361310.0000410.4282120.448650
970.1568580.1597170.0003380.1268300.194622
980.1272990.1210390.0000470.1129420.136177
990.5343620.5319470.0000070.5284200.537567
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " estimate rep_mean rep_var ci_lower ci_upper\n", + "0 0.155556 0.160216 0.000001 0.157633 0.162040\n", + "1 0.103724 0.097968 0.000561 0.071080 0.148033\n", + "2 0.343596 0.326011 0.000313 0.290250 0.354611\n", + "3 0.075131 0.069743 0.000010 0.062466 0.073803\n", + "4 0.072633 0.073120 0.000002 0.070671 0.074739\n", + ".. ... ... ... ... ...\n", + "95 0.064643 0.078466 0.000034 0.068594 0.090591\n", + "96 0.449119 0.436131 0.000041 0.428212 0.448650\n", + "97 0.156858 0.159717 0.000338 0.126830 0.194622\n", + "98 0.127299 0.121039 0.000047 0.112942 0.136177\n", + "99 0.534362 0.531947 0.000007 0.528420 0.537567\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blbs = [blb_summary(data[i, :], \"mean\", rel_tol=0.05) for i in range(M)]\n", + "blb_stats = pd.DataFrame.from_records(blbs)\n", + "blb_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "b229ea22", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ParametricBLBErrorRelError
quantitysamp
rep_mean00.1555560.1602160.0046600.029956
10.1037240.097968-0.0057570.055498
20.3435960.326011-0.0175840.051178
30.0751310.069743-0.0053880.071712
40.0726330.0731200.0004870.006699
..................
ci_upper950.0525510.0905910.0380410.723889
960.4355980.4486500.0130520.029963
970.1199230.1946220.0746990.622887
980.1138200.1361770.0223570.196422
990.5294350.5375670.0081310.015359
\n", + "

400 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Parametric BLB Error RelError\n", + "quantity samp \n", + "rep_mean 0 0.155556 0.160216 0.004660 0.029956\n", + " 1 0.103724 0.097968 -0.005757 0.055498\n", + " 2 0.343596 0.326011 -0.017584 0.051178\n", + " 3 0.075131 0.069743 -0.005388 0.071712\n", + " 4 0.072633 0.073120 0.000487 0.006699\n", + "... ... ... ... ...\n", + "ci_upper 95 0.052551 0.090591 0.038041 0.723889\n", + " 96 0.435598 0.448650 0.013052 0.029963\n", + " 97 0.119923 0.194622 0.074699 0.622887\n", + " 98 0.113820 0.136177 0.022357 0.196422\n", + " 99 0.529435 0.537567 0.008131 0.015359\n", + "\n", + "[400 rows x 4 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comb_stats = pd.DataFrame(\n", + " {\n", + " \"Parametric\": param_stats.unstack(),\n", + " # \"Bootstrap\": boot_stats.unstack(),\n", + " \"BLB\": blb_stats.drop(columns=[\"estimate\"]).unstack(),\n", + " }\n", + ")\n", + "comb_stats.index.rename([\"quantity\", \"samp\"], inplace=True)\n", + "comb_stats[\"Error\"] = comb_stats[\"BLB\"] - comb_stats[\"Parametric\"]\n", + "comb_stats[\"RelError\"] = (\n", + " np.abs(comb_stats[\"BLB\"] - comb_stats[\"Parametric\"]) / comb_stats[\"Parametric\"].abs()\n", + ")\n", + "comb_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "41e29fb8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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quantitysampParametricBLBErrorRelErrorRealMeanRealSTDAbsMean
0rep_mean00.1555560.1602160.0046600.0299560.1549390.1236400.154939
1rep_mean10.1037240.097968-0.0057570.0554980.0836912.4123700.083691
2rep_mean20.3435960.326011-0.0175840.0511780.3811521.8416010.381152
3rep_mean30.0751310.069743-0.0053880.0717120.0810310.3374560.081031
4rep_mean40.0726330.0731200.0004870.0066990.0722870.1217470.072287
..............................
395ci_upper950.0525510.0905910.0380410.7238890.0714230.6220470.071423
396ci_upper960.4355980.4486500.0130520.0299630.4543150.6939250.454315
397ci_upper970.1199230.1946220.0746990.6228870.1710091.9003730.171009
398ci_upper980.1138200.1361770.0223570.1964220.1282950.6873640.128295
399ci_upper990.5294350.5375670.0081310.0153590.5333730.2531680.533373
\n", + "

400 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " quantity samp Parametric BLB Error RelError RealMean \\\n", + "0 rep_mean 0 0.155556 0.160216 0.004660 0.029956 0.154939 \n", + "1 rep_mean 1 0.103724 0.097968 -0.005757 0.055498 0.083691 \n", + "2 rep_mean 2 0.343596 0.326011 -0.017584 0.051178 0.381152 \n", + "3 rep_mean 3 0.075131 0.069743 -0.005388 0.071712 0.081031 \n", + "4 rep_mean 4 0.072633 0.073120 0.000487 0.006699 0.072287 \n", + ".. ... ... ... ... ... ... ... \n", + "395 ci_upper 95 0.052551 0.090591 0.038041 0.723889 0.071423 \n", + "396 ci_upper 96 0.435598 0.448650 0.013052 0.029963 0.454315 \n", + "397 ci_upper 97 0.119923 0.194622 0.074699 0.622887 0.171009 \n", + "398 ci_upper 98 0.113820 0.136177 0.022357 0.196422 0.128295 \n", + "399 ci_upper 99 0.529435 0.537567 0.008131 0.015359 0.533373 \n", + "\n", + " RealSTD AbsMean \n", + "0 0.123640 0.154939 \n", + "1 2.412370 0.083691 \n", + "2 1.841601 0.381152 \n", + "3 0.337456 0.081031 \n", + "4 0.121747 0.072287 \n", + ".. ... ... \n", + "395 0.622047 0.071423 \n", + "396 0.693925 0.454315 \n", + "397 1.900373 0.171009 \n", + "398 0.687364 0.128295 \n", + "399 0.253168 0.533373 \n", + "\n", + "[400 rows x 9 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comb_stats = comb_stats.join(\n", + " pd.Series(means, name=\"RealMean\", index=pd.Index(np.arange(M), name=\"samp\"))\n", + ")\n", + "comb_stats = comb_stats.join(\n", + " pd.Series(stds, name=\"RealSTD\", index=pd.Index(np.arange(M), name=\"samp\"))\n", + ")\n", + "comb_stats[\"AbsMean\"] = comb_stats[\"RealMean\"].abs()\n", + "comb_stats.reset_index(inplace=True)\n", + "comb_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "34c4fd67", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dSqlEd0zMR+K9+D3x2GJxAOh/LI+keLTw/MnCp9JbL5mfjv7KHenNX7w1nXz1giyBDrXOjnGAGllR39MK+gmjrdoDAACgenuDdyef6I5keuEu8HKJ7rh3jsXh8T2i1HrsKo8EuvtiAOh/LI/WnVfe/mi6fdHykoveIvaKtdQyiXGAKltR393gQqk4AAAAqklfe4OX059EdxwzOQ8AAxfLZ08d36l9Z38XvUG1khgHqJEV9f1ZQQ8AAACDpa+9wUstHI974JiUz/cv3WPy2AE+SwCgt7F84+atA7boDaqRxDhABVfhRc+WKE8TK/Fi0NHaPDxtzeW6nSBQKg4AAIBqsS2VzYpbjMX98ZmH75VeM218Wte+peM+2P0uAAxdLI/56W1Z9AbVTmIcoEKr8OKm/0vzZmc9WwrL0xz6qsnprMP3Sp+64b5ue5CbGAAAAKDS+lvZrLjFWOH98enX31vyPhgAGPxYnsvl0sEzJqbbinqMh3h+bKu0IrWtKRf/lTeQ1atXp3HjxqVVq1altra2Sp8O0IBiAuDkqxekmVPHpwVLVqTbiwYZJ71pRsnn85MC3fUgh1ojJgNAdRCTgW2Vr3jW28pmi5euSW+++NaOx+6DQTwGqiOWt2/Zkp5cuSFbrFYYl+fMmJhOmDM97T5pTNp9B21PqF2WdgBUaBXeY8vWdtopnhdl1Us931MPcgAAAKiEuEfty31qYYux8JppE9IVtz2aJcgLW43ds2RF9rz7YAAYmlgeC9U+evWCrP3niXOmZzF55IhhacETK7Pnr3r/gZU+VdgmEuMAFRBl4J5Ztb7ksRhslBMr8AEAAKDWW4zltQxvKtlqLHanxfPugwFg6GL0uvYt3W7c0mOcWjes0icA0KjGjSq92j1W4JVj8AEAAEAti1LrUSI9eovHLvFYPF5csjXE43h+3Cj3wQAwlDG6lHg+jkMtkxgHqLJBRpSlOXjGxJLvMfgAAACg1kWp1ouOmZmuOP61WcnWDZu2lOwvHuL5niqrAQAD2wa0eN46Hkfs1tqEWqeUOkCFBxmnXbcw6x2e99DTq9P5R++Tzrjhvk7PG3wAAABQL2K3+GW3LMoS36s3bC772jUbyx8HAAZOVHK5dN7stGxNe9bOJCqYxmYt89LUA4lxgCodZBh8AAAAUK/ifnf+ohcXg48Y1lT2tUqpA8DQinloc9HUI4lxgCodZBh8AAAAUK9Wb9jU8ffbFi1Lc2dM6kiUF5q756Q0ebuRQ3x2AADUIz3GAQAAAIAh1db60i7wb/zqkXTCwbuluTMmdnpNtBT7nJZiAAAMEDvGAQAAAIAhFe3CIvH9q4eXpXXtW9JJVy1IJx48PR0/Z3p2fNr2o7Od4pLiANSyVevas/YhUSmlbVRzmjRGlVCoJIlxAAAAAGBIRVLgwmNmptOuW9iRHP/yLYuyZPlFx8xMO40fVelTBIBt8tTK9enU6xam+Q+/1Cok4lzEv53FOagIiXEAAAAAYMhFUuDSebOznXQvbNiUtmttznaS20kHQD3sFC9OiodYDBaLwiL+iXfQoD3GL7vssrTbbrul1tbWdOCBB6a77rqrV++75pprUlNTUzrqqKMG/RyB+hycLF66Ji1YsiItfm5N9hgAAAAYOpEU2GPy2DRr2oTsT0kCAOpBLPoqTooXJsfjONCAO8a///3vp1NOOSV97Wtfy5Lil1xySXrrW9+aHnrooTR58uRu3/fYY4+lj3/842nu3LlDer5AfVDGBgAAAACAwRA9xcuJSilAA+4Yv/jii9MHPvCBdMIJJ6S99torS5CPHj06XXHFFd2+Z8uWLem9731v+sxnPpN23333IT1foP7L2Ng5DgAAAABAf7W1Npc9Hu1DgAbbMd7e3p7uvvvudPrpp3c8N2zYsHTooYemO++8s9v3nXPOOdlu8ve9731p/vz5Zb/Hxo0bs6+81atXD9DZA/VcxkbpNhh4YjIAVAcxGQAqTzyG+jZpbEtWoTTmm4vF83F8IMQmr5jPjh3qbaOa06QxLea2oVp3jC9btizb/T1lypROz8fjZ555puR7brvttvStb30rXX755b36HhdccEEaN25cx9fUqVMH5NyBxi1jozc59I+YDADVQUwGgMoTj6G+RXI62nZGErxQPL7omJkDkryOdqEnXb0gvfniW9PRX7kjvfmLt6aTr16QPQ+U1pTL5XKpQp566qm0yy67pDvuuCO9/vWv73j+E5/4RLr11lvTb37zm06vf+GFF9LMmTPTV77ylfT2t789e+74449PK1euTDfccEOvV97FIGPVqlWpra1t0K4NqF6R1I7BQnduPuWQtMfksSWP6U0O/ScmA0B1EJMBoPLEY2gM+R3dsRkryqfHTvGBSIrH50ZSvFRl1JivvnTebDvHodpKqU+aNCkNHz48Pfvss52ej8c77rhjl9cvXrw4PfbYY+mII47oeG7r1q3ZnyNGjEgPPfRQ2mOPPTq9Z+TIkdkXwLaWsempN7nBBpQnJgNAdRCTAaDyxGNoDDFfPBhzxtqFQg2WUm9paUn77bdfuvnmmzsluuNx4Q7yvFe+8pXp3nvvTb///e87vt75znemv/iLv8j+rtwMMJhlbHoz2AAAAIB6p8UYANR2u1BoVBXdMR5OOeWUdNxxx6X9998/HXDAAemSSy5Ja9euTSeccEJ2/Nhjj83KrUfPldbW1rT33nt3ev/48eOzP4ufBygnyp7HDu++lLEx2AAAAKDRaTEGAJXX1tpc9njMdwNVmBh/97vfnZ577rl01llnpWeeeSbNmjUr/exnP0tTpkzJji9ZsiQNG1bRje1AneprGRuDDQAAABqZFmMAUNvtQqHRVTwxHk466aTsq5Rf/vKXZd/77W9/e5DOCqAzgw0AAAAamX6mAFRiUVbEl6jm2TaqOU0aMzg9u2u1XWgsTCucr+6pXSg0uqpIjAPUAoMNAAAAGpkWYwAMJe07Br5dKDQ6iXGAPjDYAAAAoFFpMQbAUO3+1r5jcNqFQqOTGAfoI4MNAAAAGpEWYwAM1e5v7TuAwTBsUD4VAAAAAKiI2GW3eOmatGDJirT4uTXZ44FsMRaJjEJajAFQTk+7v0vFKe07gMFgxzgAAAAA1InB7seqxRgAfdWf3d/adwCDwY5xAAAAAGjQHXn9EcmLPSaPTbOmTcj+lBQHoJz+7P7Ot+8oRfsOoL8kxgEAAACgQXbkAcBQ68/ub+07gMGglDoAAAAA1AH9WAGoRvnd37FIqy+7v7XvACq+Y/wXv/hF+uIXv5huv/327PHXv/71NG3atLTDDjukD3zgA2n9+vUDfpIAAAAAQHn6sQJQjbZl9/dQtu+IliOLl65JC5asSIufWzNgLUiAGt0xfvnll6cPfehDafr06elTn/pUOvvss9NnP/vZ9Hd/93dp2LBh6T/+4z/SxIkT04UXXjh4ZwwAAAAADNiOPAAYbNW++/uplevTqdct7NSSJGJnJPTj3IEG3DH+b//2b+lf//Vf08MPP5xuuOGGdNZZZ6XLLrssffWrX83+/OY3v5muvfbawTtbAAAAAKAk/VgBqGZDufu7L2JneHFSPMRCs9OuW2jnODTqjvFHHnkkvfOd78z+/ra3vS01NTWlAw44oOP4gQcemJ544omBP0sAAAAAoOZ35AFAtYmYWZwUL0yOx3FxFBowMb5hw4Y0atRLJSNGjhyZfRU+3rx588CeIQAAAADQazF5bwIfgEYVO7wjmb16w6bUNqo5TRpTPi7G68qJhWZAAybGY4f4Cy+8kFpbW1Mul8ser1mzJq1evTo7nv8TAAAAAAAAqr1XeFtrc9nPjOorQAP2GI9k+Mtf/vI0YcKEtP3222dJ8dmzZ2eP4+sVr3jF4J0pAAAAAAAADGCv8Gg5EsnzUuL5OA404I7xX/ziF4N3JgAAAAAAADCEvcLjudhRHsnzeF1hUvyiY2ZqTwKNmhg/5JBDyh5ft25d+v3vf7+t5wQAAAAAAAC9ti29wqPM+qXzZmfJ83hdlE+PneKS4tDAifGePPzww2nu3Llpy5YtA/mxAAAAAAAANIAoeR4J6kh0t41qTpPG9C5Bva29wuN7SIRDfRvQxDjQOPo7OAEAAAAAgFKeWrm+S5/wKGkepc5jV3c5+V7hheXQCz9Dr3BAYhwYssHJYCTTJegBAAAAAGpfzPUWzzuHSHRH/+8odV5u7ndbe4Wba4b6JzEODMng5MkV69Ljy9elles3pdbm4enmB5emh55enc45cu80umV4vwYc27J6EAAAAACA6hFzxMXzzoXzz3G8p3nj/vYKN9cMjaFPifEf/ehHZY8/+uij23o+QB0OTv70/Lp06vUL0+2Llnc8N2fGxPS+g6enJ1asS5fevCjNX9T33efbsnoQAAAAAIDqERunyolEd2/0tVe4uWZoHH1KjB911FE9vqapqWlbzgeos8FJDCpOL0qKh3g8LKX09n126pQU7+2AYyBWDwIAAAAAUB3aWpvLHo/d34PBXDM0jshL9drWrVt7/NqyZcvgnS1Qc4OTbFBRlBTPi+entLWWHXAM9upBAAAAAAAqL0qeRzXRUuL5ON6T2Ki1eOmatGDJirT4uTXZ456Ya4bG0a8e48uXL08TJ07M/v7EE0+kyy+/PG3YsCEdccQRae7cuQN9jkAVDk4icd2bwUlPg4qNm7f2a8BRqdWDAAAAAAAMvNiVHS02o5po4fxzzDtfdMzMQesTbq4ZGkefEuP33ntvlvyOZPiee+6ZrrnmmvS2t70trV27Ng0bNixdfPHF6dprr+1VyXWgMQYnPQ0qRo4Y1q8BR18T9AAAADCYYkdaVD6LBeJto5rTpDG962/a3/cBQD2KBHa02IzYGBunYo445np7io3l+oTH82cevlcaPqypZJwd2zoiXfX+A9PK9ZtSa/PwdM+SFemK2x5N69q3mGuGRk6Mf+ITn0j77LNP+t73vpe++93vpsMPPzwddthh2Y7xcPLJJ6cLL7xQYhzqXF8GJ+US2HNnTExLX9hY8nv0NODY1tWDAAAAMFD6u0Otv+8DgHpedBXn2tfzLdcnPJ5/4vl16X3f+V2XOFsqFs+ZMTF9ad7s9P27lqRzjty7pn52QHlNuVwul3pp0qRJ6ZZbbkkzZ85Ma9asSW1tbem3v/1t2m+//bLjDz74YHrd616XVq5cmarV6tWr07hx49KqVauy8wcGf/A1pmVEunvJinTuT/6QrbILc/eclC44ep9sld6p3SS3d+rFJED++/Rl9SBQHcRkAKgOYjJsm7gvPenqBSUn4+P+NhaWl7pP7e/7gPokHjNQGnXRVfQUP/ord3R7/CvvfU368Pfu6RRnQ3exOOavv/CufdOUttZBPGugqneMP//882nHHXfM/j527Ng0ZsyYNGHChI7j8fcXXnhh4M8SqKlVh90Nvm766Ny0en17GjPyxQR2iM/4x0P3TJ887FVpeFNTliif2IcVjP1ZPQgAAAADpdwOtVgEHsdL3bf2930A0J9y4lF5s54XXfWlpWc+zoZyu8zXbNicplinAo2bGA9NTU1lHwONvepwTMvwbgdfZ914X8fgq9xn1OvgDAAAgPoTC8bLiQpnA/k+AOhOvSy66k8p+HItPaM0+oInVnaJsz2VUxaLof70OTF+/PHHp5EjR2Z/37BhQ/rgBz+Y7RwPGzeW7hUMNM6qwzMP36vs4OvJleuzvuK/e3xFuvvxFQ23chEAAIDG2qEWbb8G8n0A0J16WHTVUyn47pLm8RWvifnlwuR4JMVPmDM9ffTqBX2Os2IxNHhi/Ljjjuv0+G//9m+7vObYY4/d9rMCanbV4cr15QdXjy1fl/VyiQHJl+bNzgYk+b7jtbZyEQAAAMrtUIvn863Eio1tHZGuev+B2X10a/PwdM+SFemK2x7N7pHLvQ8AulPri67Kbco6+8b70tlHvDqd/sN7u02ax1dsuor55VXrN6UNm7akOx5Z3mUOujDO9ieGAw2SGL/yyisH70yAqhcDk+fXvdh7pTtRSr03vVxuX7Q8+/PEg6enL9+yqOZWLgIAAECIhd0X/NU+6fHl6zoluR96enU658i9Sy78LrUbLr+A/Pt3Len2fQAwGIu1amFT1it2akunX78wzf/zvHJ3VUjzX0+vXJ8ef35dWrBkRaek+Nw9J6WLCtp5ltpl/oai1wANXEodaEz5m/bjD9qt7OvGtIzodS+XSI6fOGd6za1cBAAAoLb0p1dpX+6XT7u+8+61mHS/4Oh90k7jR/V6N1zcIw9rakpfeNe+aUpb64CcGwCNpbty4oOd6B2oOFuuFPzsqeO7bLDqrgppnM8nrluYtfKMjVkxB71x89Zs01a0+RxdsLmrcJd5bNiKuelYQCApDvVJYhzoUeFN+75Tx2cJ7vyO70IxwBo/urlPvVxiQFJrKxcBAACoHT31Kt0W3SW543GUej3z8L3S8GFNnRIE5XbDxfOr12+SGAeg34Y60TuQcbZcKfjieeRihVVIC2NtqWT6Abtt3+nnkd9lDtQ/iXGgR4UDieh3FqXdQmFyvPOqw/Z07pF7p7Xtm9Pa9i3ZTX3sFC/u5VJYWr3rZwAAAMDg9SotLLvaXz0luZ94fl1633d+1ylBUG43XFjy/Lo0ZuSIbU7aA9C4hirRO9Bxtlwp+PGjet8/vadYq5UnNC6JcaBHhQOJSGxHgruwBM1uE0enXcaPygY5xSsET3rTjPT7JSvSbd3sMJ+xw9h0w4cPUqIGAACAAVcucV1cdrU/JWF7mnjP724rTBCU2w2XNxBJewCopjjbm9harhT8rhNH97p/ek+xVitPaFwS40CPigcSkRwvLEFz8ymHZIOWUisEC3eYFybH87vDo9/armnMkFwHAAAAjaU/O8b6UhK2p4n3wipp+QRBud1w0YYsKq6VS9oDQK3F2b7E1nKl4HvbP71crNXKExqbxDjQo94OJEqtECzcYX7GYXulDZu22B0OAADAkOjrjrFSC75HtwxPM6eOT48tW5ueWbU+u5fN73LrTZK7UEzw7zF5bDaxX/x94vUnzJme3UPnXwsAtR5n+1NuvbAUfH6n+SPL1qZxo5rT59+1b1qzYXPZ/ul9SaIDjUViHOhRbwcS3a0QzO8wP/SVk9OsaROG7LwBAABobH3ZMRYT70+v2pDmHTAtS1Dfs2RFuuauJdn98JW3P9qpclrhLrdS98vFSe686B0e4n3nHbl3WvTcmqzceuwsjyR6vD7uoYMyrwDUQ5wtV279d4+vSCvXbeq2xHq5neax0KyccjvPgcYlMQ4Norf90bZlIKF3CwAAALW40LvUxHskt7953GvTl27+Y7q9oDVY8S63wvvlFevas0T3nY8s75Tkzn9ey/CXSquPH92cvnPHY8q8AlDXcTZ2epcSFVmiBecZN9yb5he14IzPHNMyvM87zUudn0Q4UEhiHBpAX3q49GUgEcn2xUvXdCTbx7aO0LsFAACAqtLTQu/uSrxGMnxYeijtO218uuXB57p8bmEf8PzX/z2xIq1cvyUtWLKiS1I8dpCvWt8e+8az55R5BaAR4mx3m6mi9WZUZOlu8dm5R+7d7U7zeM3yte0d7T37uxkMaDwS41DneurhEgOM59e1dztw6G6nealk+1teNTmdd9Te6Ywb7nNTDwAAQNUot2OsXInX+YuWpePn7Nbt5xb3AR87sjnNu/w32WT/iXOmdymT/uOTDu70emVeAaj3ONtdufXZU8d3alNSKF67tn1zt98vdpvnUkonXb1gmzeDAY1FYhzqXLkb/BhgRD+z933ndyUHDt3tNL/gr/ZJp11/b5fP/fkDS7M/P/+ufdOaDZvd1AMAAFD1YiF4OZHc7m3LsLj/3X/XCSUn+rurpKbMKwD1rLsKKT0prLxSLBagffrG+zqVYO9rmXWgMb3U2AhIjX6Dnx84xC7xcjvNH1++rttkeyTHIym+x+Sxada0CdmfBiEAAABUq+5KvOaNH1X6eKlEd37yP44Vv1YlNQAaVb5Cyi3/fEj673+cm3588py0XeuIdMXxr00nvWlGtgO82LhRzV3iad5Bu0/skhQvbnUCUIod41Bjuitt3t8b/Cjp1t3Aobvk9wsbN2cDlih3E4n11ubh6Z4lK9IVtz2areQrLiUHAAAA1aq7Eq8hnt914ugux8slupVHB4CuIg6ubd+Szi7a6T1nxsT0pXmzs5Yj+V3iEWcnbzey5E7zOFY8p13M/DTQHYlxqCHdlTYv1zel3A1+DDqiz1mpgUP0aCklVu9Nnzgm/fudj3UqDVc4gCkuJQcAAADVvPD8jMP2SncvWZHO/ckfOibl5+45KZ39zlen9s1b+9wyTHl0AOiso0Jp0U7v2//8OMqjx3zz3ILFZ+NGp5KLzXraEW5+GuiOxDjUiHKlzcv1Temuh0sksk+YMz1LZPdl4BADlHN+cn/HgCUv//jMw/cq2TMNAAAAqn3h+U0fnZtWrNuY1rdvTXc8sjwdceltWaI8vyg92oUBAH0XyezuKpTG3PKpb3tlViI9Yu2UttYeF5uVq/ZifhrojsQ41MHAIV/+vLvV6MVl3MaMHJGWr21PG9q3pC+8a99OpdD323VCGjGsKS1fuzFd/YED0+2Ll3eUSA9RPr1wp3jxAOasw/eyKh4AAICaXHh+1o33pbfvs1M6/fp7+7QoHQAoLyq0lPPcCxvTXju1dUqKd6e7zWDlWp0ABIlxqJOBQ099UwpX1sXK+HN+/Ic0f1HnHeRXHPfa1NSU0tu/NL8jEX5wiR4v5azvxWsAAACgWheeH3fQbt0ei0Xm+c+I+/S2Uc1p0hhl0wGgJ209lDeftv3otFM37UJ7sxmsN61OACTGoU4GDr3tm/JSL5dlXXZ7D0sp7bfb9p0S4LctWp6amprSjR+Zk4Y1NaWtudyAnAcAAABU48LzjZu3lnx+dMvwFHfEJ129oEsJ9ti1FhP0AEBpkbQuV/588nYj+/yZ3ZVZB+hO5MGAGho4lNKXvinlVsbPX7Q87bPLuK7PP7wsS4pHf5cYoAzEeQAAAEA1LjwfOaL0dNmJB09Pn77xvpIl2KOUayxEB6D2xb/ni5euSQuWrEiLn1vj3/cBki9/Xjy3rPw5MJTsGIcaMVB9U/q7Mj5fql3/FgAAAOp1x9rcPSelHbYbma44/rXpniUr0hW3PdpRVe2g3SemL9+yqORnxmfFQnT3xAC1LVpQZtU2VQYZFMqfA5UmMQ4NNnDo78r4whLpBjAAAABUWuzg62uv73hP9Ak/+52vTp/+0f2dEh8Hz5iY9Rf/m2/8OkuGz5kxMX1p3uz00asXpP13ndDt/XLxgnIAalNHC8puKoPEfGg9zH/2J34O9PeLyqQAlSAxDjVmW/umlFsZH5MAC55Y2asS6fq3AAAAUEs7+grfE/3CozT6hw7ZI0t4R/W0Ox5ZniXB8zvEb1+0PGsr9l8fnZvGj27OJvXLKVxQDkDtKdeCsl4qgwzEjvi+JNbtwAeqjR7j0GC66+USK+HPOuLV6YGnVnV6Xol0AAAAamlHX6lesM+u3pAeW7Y2zTtgWlYmPZLiUSb9Pd/8TZYUjz+jTHo+KZ4X32Pz1lx2T5xfaF5KqQXlANSWnlpQ1nplkP7Ez1KJ7pOuXpDefPGt6eiv3JHe/MVb08lXL8ieH4zvBzDQ7BiHBlRYCn3V+k3ZSvnhw5rSiGFN6fy/mpnWbNisRDoAAAB1saMv26127f+l+YuWdzxXWCZ95freJULyC81jMr+wCpsF5QD1oacWlLVeGWRbd8T3tdR84ffLV2qZPXV8tiCttXl4Wrluk9gJDDmJcahDvSlnU64U+pS2ITpRAAAAGMQdfR2T+AVJ8XyZ9BCT9D31Di9MhBQuNLegHKC+lGtBWQ+VQbZ1R3xfE+v57xdJ8ViMduXtj2bVWfLm/nlhmZLqwFCSGIc6o28LAAAA9awvO/qWvrCx20n8SI6fOGd6WvDEymxyfn4vEyHlFpoDULvqvTLItu6I72tiPRLi+UVokRTPL0rLm9/NTnOAwSQxDnWkXDmbeP7Mw/fKSqaX2kEOAAAA9bSjLxaOL3l+XdnPinKuDz29Ol1w9D7pkz+8ty4TIQD0Xj1XBtnWHfHlEuuRBJ8wuiUtXromrVrfnkaPHJHNQ8+dMSkrn164U7yvJdwBBpLEONRR2fRRLcO7XQkfzz/x/Lr0vu/8zg5yAAAA6npHX37h+PEH7Vb2s8aPak7nHLl32qmOEyEAtdwOshLqtTLItu6I7y6xHknxK45/bTrjhvvS/EUvHXvTK3dIZxy+V/rTinXbVMIdYCBJjEMdlU3/yntf0+NK+BCDF2VqAAAAqNcdffk+qPtOHZ/mzJjYpXxriPLpe0wem6a0tdZ1IgSgGmkHWV3xM8Ru73KLFLpLrEeV0stuWdQpKR5uefC57M9/esvLt6mEO8BAkhiHGl0pWaps+sgRw8p+duFxZWoAAACoZYWJ7Py99CPL1qaxI0ekVetf3H12xW2Ppi/Nm539vTA5Hknxzx0zsyMpDkB1tIOshc081brTvbeKF4L1ZZFCqcT61lwunX79vSW/VyTH//HQl3e7SK03JdwBBpLEOFSRvgxC8qvfo1TNiQdPz3q1bD+mJevbUrw6L8TgY8ETKzs9p0wNAAAA9XYvHffJV73/ddnf17VvSR+9ekF233zinOlZJbVYNL7HDmOy8ukADL38vGYp1b6Zp952uvdnkUJxYn3BkhVlv8fTqzakE+ZMT7Fla35Bcry3JdwBBpLEOFTJAGTluk3pjBvu7TQ4KDcIiRWJcbMfK9+vvP3R9OVbFnU8zqVcuq3gcyIpHoOPmAwopEwNAAAAtax4Qj9/X/yHp1d17E6L5HjcM+fF8+cftU8FzxqgscW8ZjnVupmn1ne6D9YihbYe5phHDGtKJ1+9IF3z969LH9qwOY1sHpbGj2rp1AIFYKhIjEOVrDI8/qDduiTFyw1CYsARK94jKZ4vQ1O4Ev7Db5yRhg1rSlu25tKdjyzPno/jecrUAAAAUOuKJ/Tz98kLlqwsXUJ9xsR03JzpadX69pTSmIqcM0Cj6ymRWq2beWp5p/tgLlKIOeaYay7sO15cxXS/XSekca3NadftR9fczwioLxLjUCWrDOcdMK1Pg5AYcBy0+8ROq95DfiV8fH37+NemzblcVs6mOCmuTA0AAAC13nN1Sy6XvnXc/lmJ9Nbm4amtdUTHfXKpEuo7bDcy/c03fp1+fNLBg3peAPQvkVrNm3lqdaf7QCxSKBfj488oJR+75gt/p/kqpt+/a0n63DEztTABqoLEOFTJKsO4Qe/LSskYcDT38J51m7akj//g/7KJgI+8cUY2STAuBi7K1AAAAFDjPVfje5774/s7VV+LJHlecQn18JX3vibtv+uEqk26ADSC7hKp1b6Zp1Z3um/rIoXexPj4M0rJx3z3qvUvtgAdPqwp+/rCu/at2t8p0HgkxqFKVhlGSZl8/7Nic/eclMa2dv2/63YlnisUyfaYCFj4xMr03gOmWZUHAABATfZcLd6pNnbkiBe/ZzctybozflRzVSddABpFYSI1dlpHUrnaN/MMxU73oa6+0tMihdDbGB9/VvPvD6BqEuOXXXZZ+vznP5+eeeaZtO+++6ZLL700HXDAASVfe/nll6d///d/T/fdd1/2eL/99kvnn39+t6+Hala4yvCK2x4t2f8skuXHHbRbOuOH96bPHLl3p5X2E0a3pINnTEy3lUqmz5iUpk4YnW4+5ZCqH1QCAABQu7al52pvEgCldqpd9f4DS37Pnhad7zF5bJrS1tqPqwRgoNVaInWwd7pXovpKT4sUFi9dU3d91YHGVvHE+Pe///10yimnpK997WvpwAMPTJdcckl661vfmh566KE0efLkLq//5S9/mebNm5cOOuig1Nrami666KL0l3/5l+n+++9Pu+yyS0WuAQZilWHs7I7+ZzEI+fAbZ2QlZ2LHd9zUx/NxfOPmzqvw4mb+/KP3SZ/84b2dkuORLP/s0XunaRPHVPDqAAAAaAT97bnamwTAs6s3pMeWrU3zDpiW9Sm9Z8mKbGH5yvWlPzO/6HxYU1OXz42khaQ4ANW4030oqq/0Z5FCPfZVBxpbxRPjF198cfrABz6QTjjhhOxxJMh/+tOfpiuuuCKddtppXV7/ve99r9Pjb37zm+m6665LN998czr22GOH7LxhMFYZRvI7vPebv+n1KrxIfn/xr2elFWtjhf3m1NY6Ik0Y0+JmHwAAgKrtudqbBMDa9i3p1Gv/r1O59NgNHonvEU1NJb9XftH5f310btq8NVcz5XkBaOyd7ttSfWUw1WNfdaCxVTQx3t7enu6+++50+umndzw3bNiwdOihh6Y777yzV5+xbt26tGnTprT99tsP4pnC0K0y3LQ11+dVeJEElwgHAACgVnqu9pQAWLluUzrjxvu69BDPl0g/cc70bkum77/rhDR+dLNEOAA1IRaLbdy8JX3lva9Jrc3DO6qj5DdR9WZn9mD1Jh+KvuoADZMYX7ZsWdqyZUuaMmVKp+fj8YMPPtirzzj11FPTzjvvnCXTS9m4cWP2lbd69eptPGsY3FWG0belHKvwgFolJgNAdRCTqUTP1eIJ+y25XBrdMrzTpH+hte2bu02cRzL8fQfvnpVW765kuqQ4UO3EY7prK5KvjpJvr9nTnPBg9iYf7L7qAA1XSn1bXHjhhemaa67J+o5Hv/FSLrjggvSZz3xmyM8N+ssqPKBeickAUB3EZIa652qpCfu5e07qMulfKMqol7Nh05b0w3v+lL7wrn3Tmg2blUwHao54THdtRTqqoxw8PX35lkVl54SHojf5YPVVB6iEplwuV75u8yCXUh89enS69tpr01FHHdXx/HHHHZdWrlyZbrzxxm7f+4UvfCGdd9556X//93/T/vvv36eVd1OnTk2rVq1KbW1tA3g1NLKBLlUTkwbdrcLbaRtX+QFUipgMANVBTGao75dPunpByd3fB8+YmGZNm5BN+heK+98zD98rveVff9Xt5171/gPT9Elj3CMDNUs8JiqHvvniW7s9/q3j9k/fueOxsnPCPX3GzacckvaYPHZAzhegHlR0x3hLS0vab7/90s0339yRGN+6dWv2+KSTTur2fZ/73OfSZz/72fTf//3fZZPiYeTIkdkXDFZiezBK1ViFB9QjMRkAqoOYzFAq10v8tkXL04ffOKNTYjy/KDzKrHdXTS12m8ck/5S20tUDAWqBeEzMRZczblRzjzu+V61vL/sZq9aX/x4AjabipdRPOeWUbId4JLgPOOCAdMkll6S1a9emE044ITt+7LHHpl122SUrLRMuuuiidNZZZ6Wrrroq7bbbbumZZ57Jnh87dmz2BX21LYntwlI1cdMe5W1mTx2fNm7emh5fvjYNH9bU7xv1wr7jAAAAUI+T/q3Nw7PdbKUWhZfraSopDkCtayvTNzxM6MX88OiW8imemLMGoIoS4+9+97vTc889lyW7I8k9a9as9LOf/SxNmTIlO75kyZI0bNiwjtd/9atfzUqw/7//9/86fc7ZZ5+dPv3pTw/5+VPbtrUHS37lewwwojfalbc/2mml+9w/37D3d+c4AAAA1POkf+yG667Eq2pqANSziGndVUcp11e80LBhTWnOjIkdfckLxfOxcWuo2oMC1IKK9hivhOjVMm7cOL1aGJAeLAuWrEhHf+WOdNKbZmR/LzUAiUFMTwl2gEYkJgNAdRCTGUwx6X7y1Qu6nfR3vwzwIvG4cauZdlcdpbu+4oUx9unVG9LKtZvS1pRLdyxenq647dG0rn1LlhQ/Yc70tPukMWn3HcYOSXtQgFpQ8R3jUM0l3WJFem9Wvkf59MKd4oViULN87Yu9XqzAAwAAoJHEfW+5kujuiwFoZP2tjlIqsT13xqT0ww8flJ5euSH9bsmK9P27lqQvvGvfAa+iClDLJMZpaD2VdIuBSG/K3URP8e5EmfUoy3DS1QuswAMAAKCu9KYMq5LoANC9iId9iYndJbbnL1qWzvnJH9LsaRPSwidWdrsALd8etJRIjsdxMRqoVxLjNLRt7eOSX/n+2LK13b7mxIOnp0/feF+aX1Rm3Qo8AAAAallfyrDmJ/3zifRHlq1NbaPaVVMDoK4NRh/vcontaPV55mF7pQ8cPL3b77OtVVQBapnEOA0zYBiskm5xsz98WFOau+ekkgOSg3afWLbMuhV4AAAA1OJ9+1k33pf2nTo+HX/Qblkltdbm4emeJSvS2Tfel5VuLb7X1c8UgEYyWHGvp8T2hk1bys43b2sVVYBaJjFO1RnqG+WBKOk2pa01XXD0Pun0H97b5bxHjhhW9r1W4AEAAFBrlq9tT39zwLR05e2PdloMPmfGxHTCnOnZ8cL7av1MAWgkgxn3Bqo9aH+rqALUMolxqkqlbpR76uPS0w72SOZ/+sf3d1opP35Uc9p14ui0YVP3/ceDFXgAAADUms1bc1lSPEq2Fso//vQRr+70vH6mADSSwYx75RLbUdV0Sy6XFj+3ptsqrANRRRWgVkmMU1Wq8Ua5eAf76Jbh6czD90qzp41Pq9dvTuNGjUj3PL4y3bF4efrfB5Z2em8MJj7/rn2twAMAAKCubN2a65IUz4vnt2zNdXpOP1MAGslgxr3uEttzZ0zK5q3/676n09dvfSTtv+uEbquwDkQVVYBaJDFOVam2G+XiHeyRFP/SvNnZqvjTr7+3U6m4eP6jVy9I69q3dDwfA5O1GzdbgQcAAEBdWde+uYfjL90bB/1MAWgkgx338ontZ1ZvSH9asT57bsETK9NRl92ebejKz1WXq8LaUxVVgHokMU5VqbYb5eId7CcePL1sqbg4XthbLaxevyntvsNYK/AAAACoG+NGlb+fHTeq8/27fqYANJKhinvn/fSBTpu6Yn569tTx2ePvvu/A9IuHlqbla7UrAciTGKeqVNuNcvEO9hhUFCe+C5PjJ86Z3m0y3wo8AAAA6sXY1hHp4BkT020lyqnH83G8kH6mANSTqDQam6Bi/rhtVHOXft5DEfci4b3v1PHp+IN2S+1btqapE0anhX9amU4uqGoalU6Pnr3LNn8vgHohMU5VqbYb5eId7Bs3by37+sLj+V7kW3O5tGDJipIDJAAAAKhFG9u3pLMOf3U69yf3p/kFyfGYgD9+zvSsrVgx/UwBqAdPrVzfqf1mfv66uJ93b+NeT0n27uSifPqSFZ02chW3/IzNXJ/+0f3py92UUwdoNBLjVJ1qulEu3sE+csSwsq/PH4+k+BXHvzZddsuiTr3ISw2QAAAAoNYSAp+64d5095KVWcnWSISHyW0j080PLM0m4696/4El36uaGgDVpK9J6Xh9cVI8xPxxqX7ePcW9Ukn2uXtOShccvU962fajy57Hp2+8r1ctP+Oz4xrFXwCJcapUtdwoF+9gX/DEymzVXfGAI8ydMSlN2a41feW9r0m7TRydLrjpwTR/Ue8GSAAAAFALOhICf74vLt6lNnvahGyHWr6tGADU+s7vQpFgLk6KF8799iUB3V2SPR6fdv3CrILqLhNGd38eJeaou2v5GRvQAJAYh17vYH961Yb0+PPr0jv33Tmd+5M/dBqwxM3/h/9iRnpuzcb08R/8X/b64qR4fwdIAAAAUC2KEwJRMS12pc2eOj5rLzZt+9Fpl/GjsgpsAFCt+rrzOy92lhcqjoPtm7dkn92bud9ySfbbFi1Pjy9fl8aOHNGr8yhW3BLUgjWAF0mMQy9K5uT/fv5ND6T9dpuQ3r73jun4g3bLBhhRPj12kr/vO79Ns6eNzwZCPfUit0IPAACAWlQ4ER/JgOhjeuXtj3baOR4lYA95+Q5pXPcVYAGgovq787utIMHcXRzsbTvNnpLbK9dv6tV5lFLYEjTOx4I1gBdJjNPw/vT8unT69S+Vgetu8JIvq/7YsrXpPd/8Ta/L1JRihR4AAAC1KCbi87vj3vjyHdKq9ZvS+w7ePSuhfsVtj2Zl1LMSsNqIAVAFPcH7m5TubmNTJJhj7jiS5xELIyle3Hazt+00e5Pc7s15FIvqprGRK8RroiS7eAzwIolxGtqTK9alU69f2OvBSyTKn1m1vuxnxm7xmBjobmBihR4AAAC1Ku5nrzj+tenSWx7u0l88ds199OoFWXJcGzEABlp/eoL3Nynd3cam/OapmDuO8umFsbBQT3EwEvxbc7n0reP2T01NTemeJSs6FpgVJrePnrVLj+fxq6KfxzlH7p1Wr2/P3htxWywGeInEOA0rBh/Rp6U4KV7YF+aPS9ek7ce0dFp5OG5U+YHE+FHN6TVTx2dl40oNTKzQAwAAoFI75AbCZbcs6nIvnX8c99P5JIE2YgAMVGzrb0/w7oxtHZGuev+BWbny1ubhnRLTPW1siiR8fL+YOy6nuzhYKsE/d8akdNUHXpfe/53fplfsuF06Yc709P27lqRJB0/v8TziZxjfK5L5LyXCx/Tq5wDQaCTGaVgxYIiBT6He9IUpV6Ym+qjtMXlsmtLWmj3ufmACAAAAQ79DblsTE1lP1kXLetVeTBsxAAYqtvW3J3hvv3e+8kkko2PHdU+fFce37+E1peJgdwn+iK25lEs/+IfXpx8tfKpP52G+GaD3hvXhtVBX4oY/+rQU6qkvTAxc8mVqYqBWKB5/7piZHUnxEK+NRPmsaROyPw1SAAAA6IuedsjF8f54dvWG9ODTq9NvH3s+/eHp1emHv38yvfebv0lv/uKt6eSrF2RJg/70ZI32YkEbMQAGMrb1tyd4b793zAd/547H0nlH75N26uWis/wGqlK6i4PlEvy3LVqe7Vj/q1m7pC+8a99enwcAvWfHOA0nvxJ+89ZcGj+6JZ1/9N7pvJ8+kA06etsXpnyZGgAAABgYA7lDLm/J8rXp9B/e22lReGGP8HJlaXvqyRoL0LURA2CgY1t/e4L35XvH82s2bE5T2nr1UWX7fHcXB3tK8D/+/Lp09V1L/vz+3p0HAL0nMU5DeXLFuqyveGHvmAefWp2+ddz+6X3f+V3HyvberDxUpgYAAIDBNlA75Ap3ihcnxUv1CO8uMVGuJ2u0F5uxw9g+93kFoLH0J7aVa2/ZlyolAx1X+7qBqjcLzOb3s286AD2TGKdh/On5denU6xdmN/vRSzxu9l+/+8R08B6TUi6ldONJc9LGTVvKfob+aAAAAAylgdohl7diXXuXpHh3PcKLkwO96cmq7CsAgxHb+rM7e6C+d0+VSSPZ3jaqOU2fNKbH84gFZgfPmJiVTS8WMXXBEyu3qSoMAOVJjFPX8oOTLblcOvfH93ckxeOmPXqJF5ZNj5Xt5x25d3rLqyannz+wtMtn6Y8GAADAUBuoHXL5xPZTK0r3Ds9r37K1ZHKgXE/WYU1NWS/UKW2tvT4XABpXf2PbQLS3HKi4WmqxWLw/kvdxnt1Zu3FzOn7O9GyjVnFLkxPmTM9amvR39zoAPRvWi9dATYrByUlXL0hvvvjW9MTz69L8grJwkRQvXiEfg5gzb7wvffqdr84GMYX0RwMAAKAS8jvktvU+NZ/Yjh1t5ewwdmTJ5EBverICwGDHtji2x+Sxada0CdmffZ2vHYi42t1isUi2x472ON7te9dvypLfs6dNSD8+eU76yntfk7X5jMfxfLQmyVO9FGDg2TFOXSoenBT2Dp89dXynneLFg5cNm7Zu88pDAAAAGCgDsUNu+dr2tO/U8Wl0y4hsAr6pqalTf/D8brWWEcNKJgcGuicrAI1tIGJbpb53ucViv3t8RVq5blOnEuuTxrz02VHKPeJufn56wZIVJVucqF4KMDgkxqlLxYOTkSNeKo5QmCQvJQZD/VltCAAAAP1V3Ke0cBI9xN+35T419+fJ98KF4vn+4C/uXBuflXDdtOXFxeLF32uge50DwLbGtkp97+4Wi+VbeJ5xw70d1UuLS6xHsjtaesbcdSxOi9eHwuR4HFe9FGBwSIxTl5MHazZ2HpwseGJlx4CjMEleipt5AAAAhlJ/+5T25b750zfe12VHWtYfPKV0zd+/Lv3PH57NEuQ/PungkhPxA9nrHABqWXeLxbpr4ZkvsZ5feHbukXunT91wb/a6iL3xvg+/cUYaPqwp202+x6QxaacBiP8AdKXHOHXTR/zor9yR3vzFW7PHkSCPFXp5sfru7CNena2GjyR5/FmKm3kAAACG0rb0KS33mYuXrsl2iC9+bk1a+sLGTjvXCsXzz72wMdtJvv+uE7q9Jx6oXucAUOvyi8WKRQvPUmXR83E94nGYMLo5HT5z56y1yRfetW/2vjsfWZ5O/PZv07dvfzSNH23jFsBgsWOcmt0dvv2YlnTGD+9L8xd1njyIyYSzbrgvnXn4Xun06+/NnouVdhs3b06zp01I+0+bkI6YuVM67ycPdHqvEjUAAAAMtXJ9SmMSPY735j41f7+8Yl17Vg799sXLO/qHx8R7OdFybO6Mien8o/cp+70q2Q8WAKpFfrFYLGArVUmlO0ueX5fGjByRxdNDXr5Dl/dbbAYw+CTGqdnScnFjX5wUL1zxftrbX9WpzNszqzam3/+5n1rsJo8SNcfP2S2bABg3qjlLtAMAAMBQ6q5PaV4koPtTir2wf3hPpm4/Kv3L216Z2rdsrep+sABQLUotFtuay/X4vnxJdYvNACpDYpyaLS0XCe1ylqxYl8458tUp7utjcBHJ788evU/61A/vTbctWp4lyMPBMyams454dXrP5b9Oe+3U1tHrBQAAACrVpzQvJsr7U4o9X8o1FoVHS7HYEV6qnHok0P/7/mez++Gdx7X26xoAoJYqkUYbzkljtj0JXbxYLL5H4UatQvkWn4XVYCw2Axh6EuPUbGm5kSOGlX1Py/Bh2fv23237Ts9fcPQ+aeWGTWnNhi1pbOvwtHT1xiwpHq/tS5k6AAAAGKg+paUm0eP57np+96YUeyTHT5wzPZ189YJ0w4fnpM/85P5OvU9jkv6EOdOzXeWxSLynJDwA1KJSlVUixkY59Ni5PZCJ9vjMUlVc8vG2t9VgABgcEuPUbGm5F1e8TypZTj2/Au8de+/Y5diyte3p6K/c0e33MjABAACg0n1Ke+ozmp+QX762veznR7W16DP+X/c/nQ6fuXOWKI/nYrF53DfHJP3saePT0hc2pv13nTDg1wcAldRdZZWIufmy5n3dJNVTov28I/dOi55b0yXeRjwOFqIBVI7EODUhyqCf9KYZafbU8dmAorV5eLr3yZXp4297Rcr9LJeVRi9egXfVbx5Px8zeZcDL1AEAAMBA6muf0cIJ+W8dt3/Zz85XW7v3T6vSp494WTr9h/d22cV28pv2TLttP1r1NADqTqnKKqNbhmetRmKu+Y9L16Ttx7T0urR6bxLt40c3p+/c8Vi/q8EAMHgkxqkJURZ9wZIVHX3B8zfvs142Pp1x2KvS82s3pZXrN3WswIuk+Afm7pG25HIDXqYOAAAABlpv+4wWT8jHPXDcHxeWSC+uphZ/nnHYXull249OX543O9sdvmr9piwxMKZlRDaBLykOQCNUIo3Y96V5s9OVtz/aaa65u9LqxSXTRzQ1pbsfX1Hye+XbdO4xeWy/qsEAMPgkxql6MfiIFe3FN/nxuCmldN5Re6c/rViVJo4dme0mj5V+O49rTRs2bUnDmuIVA1OmDgAAACptxbr2dPxBu6V5B0zrqKb2voOnZ8cK75uj9dgZh78qPb1yQ9pl/Kg0YXRznxLwAFAPiquHxk7xSIoXzzUX7vgO0aoktlx9+sb70vzC+LrnpCyxXlgavVSbzr5WgwFgaEiMU5PlbvKihPozqzamJSvWZ4nxvKdWbUi/eHBp+sK79i35PgMTAAAAas2S5WvTmTd0nqCP3eAzdxmfXrvb9ln/8DEjR6S1GzdnO8WP/sodWd9wi8ABaFTF1UNjU1XhTvFC8ZpnVm9I5/30gbTv1PFZBdPiBHrMU2/N5bIEe6nPKWzTaTEaQPWRGKdqFJelyfd1KS53U6x5RFNa+MTKLqVverrxNzABAACgVjy7ekO31dTC7GkT0vu+87v08396Q9ph7Mg0cUxLOnrWLhaBA9DQiquHRsXRcv60Yn2W/I7qLN0l0CP2xmK0Ytp0AlQ/iXGGLMFd7jWtI4als390f/rfB5Z2vOYtr5qcPv3OV6dRLcPTV977mqxE3D1LVqQrbnu0U5ma8aNa7P4GAACgrq1Y216yj3jhBH1MyE/ebqT7YQAoUT00yqO395AYz+spgV5Mm06A2iAxzoB6auX6dOp1CzuVPo9BQazKiwFId685eMbEdPyc6emOxcuzpPfoluHp3QdMS5+4bmGnG/8oEVfYwyW/Cs/ubwAAAOrZ6g2be3yNCXkAKC3i49r2Lek3jz6fzTGXWmwW/cOjFUkYOWJY2c+btv3odPMph9ioBVBjJMYZMLELvDjhHaJETZSqiVV5odRrold4LqWO3izx55W3P9ptibg4HuXT3fQDAADQCNXXtmstP4Wzy4RRaac/L0gHAErPXd/9+Ips41UonHuODVjnHLl3eseX5mebtsLcGZPS/EWd57Hzr1WhBaA2SYwzYOIGvjjhXZgcj+Ohu9fEQOS0t78q+/trpk0o28PlzMP2Sh84eLrBBwAAAAPe/msoz2HcqObUMnxY1j88f78cE/JnHr5Xmj1tfFq3cXO2Ey28+ZU7pJsffK7L50UVtgnujwFosHjcl88onLuOaqSx8SrakETJ9NgdPmOHsWn86OY0Z4+JWSXT7/3m8XTcnN3S1pTrlECPmHv+0fuYlwaoURLjDJgYgJQTZWViV3g5Tzy/Li1YsiIdvMeksq/bsGmLwQcAAAAD3v5rMMUE/jOrN6Q/rVifmpqa0j1LVqTm4U3prkefTwuWrEwnvWlG2n/ahLTT+NZ03k/+kE6//t6O90bZ1/OO2jul9IdOyfGYoP/s0fukKW2tg37+AFAt8bivn1E4dx0tOos3Zd3w4YPSrpPGpLOPeHU69foX23v++pHnOyXQYzFbzEu3b+lb/3EAqofEOAOm7c8r2IvFSvcYQLQ2D09bc+VT4/GaGHR8+I0zyr4uv1oeAAAABqr912AuwM4m8K9d2KkkayS7zz781em7dz6elXWNlmJhwW0rSrYWO+OG+9Kpb3tl+se3vDyt2bAljW0dnpau3pheWB8V2sYM2rkDQDXF4/58xtiR5VMhY/58fE375o4YXCqB/q3j9k+r15ffIAZA9RpW6ROgfkwa25KtyitOisfNfewCf9u/zU9Pr9qQrWYvJZ7P/Tlxfucjy7MeLqXE94jvBQAAAAPd/mswdEzgF/UpjYn3c39yf7romJlZUjwez546vktSvPD1z72wMR1x6e1p3uW/zv5833d+l0a1WDwOQOPE4/58RrQtiQVppcTzcTysXr+57PeOneM2bQHULolxBkyswotSNYXJ8dgpnr+5D8ObmtLxc6Z3GYTE43g+jocrbns0nXXEXl2S6PHZMWGgjDoAAAAD3f5rsJSbwJ+/aHma3Day4745JtzLKT5u8TgAjRaPe/qMVes3pcVL12SbtRY/tyZboLZyfXs6oZt56Xh+VVZ9Jaqilt9ZPj56mYu7ADVLKXUGVPRviVI1cdMfg5gojV5Ybmbdpi3p4z/4v069WUaOGJYWPLEyffTqBekL79r3xde1b0nL1mzMerpsyeXShvYt2Uq8GHRIigMAADBQ7b/yBnP3V0zGl7N245aOv8c9cjmFxy0eB6AR43FPnxF9wP/qq3d0ipefOuxV2fxzd/PSPz7p4Oy1E8a0ZJu1bitRvSWen7b9aHEXoIZJjDPgYmCQHxzEqrxCMdgo1Zul8HiIMuqTthuZ/u3nf0yfPXofgw0AAAAGpP1XlFgtNli7rrMdaus2peFN5ZPd40a9NMEfE/Sxe61UOfU4zxk7jE03fPggi8cBqLiIc7FBKnZwt8VO6jHdx6X8a2MT1Nw9J5WspNLbeFwupkfy+o5HOsfQeN3bl6xM++86oWNeOlqARpI8WpjERq+tuVx2jlPaWtP5R++TPvnDezslx2O++rNH751etv3oXv1sAKhOEuMMquLVe+Vu8OP5OB6DjI+/9RVZUvxTh+3lJh8AAIABa/912nULO02kD9au66dWrk+3L1qWXjZhVHp02dqy98KtI4aluTMmZmXVo7XYl+bNzo4Vvj5/njuNH5V2TWMG9FwBoD9x7tTrFnZKcEesilgbVUW7e20kpCPO5XK5TonnvsTj7mJ6JNyPO2i3bAd4sXN/8od000fnprNuvC/97vEV2TlEC9DCDVz58582cUz64l/PSivWRtJ/c1ZePXaSR9IcgNrWlIsI1EBWr16dxo0bl1atWpXa2toqfTp1Kb8ifm375mx3+KYtW9Ndjz2f4r+0WS8bn3Ya35rO+8kDaf6izoOWT7/z1Wnzlq1pWFNTGj6sKU0ss8IQgNonJgNAdWi0mJzfsRbtvwZr13V8j5OuXpD+5a2vSBf97MG0YMnKjgn4wmR3fgJ/2vaj0pLn13ccz+9iO2j3iWnE8GGpbdSItFNbq3tkgDpWS/E4H+e62/UdO7DzMavUawvjXLTijMop/YnHxTE9dqMfddnt2Zx0KT/6yEFp14ljsrnrM264N1uQ1tP5A1Bf7BhnQD29cn16/Pl16dJbHu642Y+BzreO2z995ReL0iX/+3DHwOdDb9wjjWwelsaPalH+DQAAgCFv/zVYYpI+EgCRGM/fG5fqazp94ph095IVaeYu49Ln73oozZ42odPxKAX70NOr0xfeta97ZgCqRj7OlRI7uON4Pm6Vem2+1WZ83XzKIWmPyWMHJKYvXrqm26R4GDOyOXt9dk4lkuKlzh+A+iIxzoCJFXq//ONz6ScLn+q0Aj5u/L/8i0UdzxUOfKzAAwAAoN5Er9WwbuNLk/P5e+FCX3nva9KP/u+pNGfGpHTOkXtnZWaLS7oORpl3ABiIONed2MHdn9duq3K9xwv7lw/lOQFQXSTGGTCxkm7ydiO79EybPXV8l5v/PCvwAAAAqAX5cq0xmd4WJV/LtP9qa23O/hwxvKnsZ8au8NhFFz1SY9F4fA12mXcA2Fb5ONediGH9ee226q73ePFCs6E8JwCqi8Q4AyYmB6LcW7FSzxWyAg8AAIBq9tTK9dlu7vlFk+wx+b7z+FHd7li7bdGyNHfGpDR/Udeda3NmTEwLnljZadF4lJKVCAeg2vV2Z3ZfXzsQIi73tNBsqM8JgOoxrNInQG2sio/+LAuWrEiLn1uTPS4lVtrFavdipZ4rZAUeAAAA1SrugYuT4iEm02NHWql75PyOtfufXJVOOHi3NHfGxC5J8RPmTE9X3PZox3MWjQNQK/JxLpLIhUq1AOnLa7dlbrr4/GKx2axpE0ouOtvWcwKgdtkxzoCtio+VdHc99nx2g19YTj1WwBc/V/hZVuABAABQrWLHWXFSvFR7sFKl1r/wrn3T8rXt6awjXp02b82lR5etzRaPx33yR69ekPUdz7NoHIBa0pud2f15baEnV6xLjy9fl1au35Ram4enmx9cmh56enX6zJF7l6zYMljnD0D9kBin36viY+BQvPrvjS/fIU2fNCZ7nE+Exwr4K45/bRrW1NQlwW4FHgAAANUsEt3lxGR6uUXlu+8wtuMe+4KbHlC2FYC6EfO6vZ3b7ctrw5+eX5dOvX5hp81W+YorZ994X7b4bFvnlft6TgDUPolxtnlVfKGdxo9Ko1uGp/OP2ietbd+crX4fN6o5Td5uZPqyFXgAAABUWKmd3eXuTaNtWKG45z3x4Olp9tTxaePmram1ZXi69Y/PpbsfX1F2UXm+bGs8V5gct2gcALrG6tOLkuIh/3j2tAkl56YBoCcS42zTqvi+rrQzWAEAAKAW2oXlxaLueE0ksyMp/qV5s9OVtz+avnzLok472OL54vLoxYvKlW0FgF5u2CrRljOfHD9xzvRu56YBoByJcXq9Kr6Y/mcAAADUa7uwvMKd3jOnjs+S4t3tYIud5IUJ81A8ca9sKwD0bcNWcbWWqE7a09w1AJQiMU63EwbDmlKaO2NSmr9I/zMAAAAar11YXn6n99OrNnRJfBfvYCtmUTkA9E1h0ru7ai1z95yULjh6n/Sy7UdX6CwBqEXDKn0CVJ+nV65PN937THp0+br0kb+Yka76wIHppDfNyAYh4eAZE9O5R+1d6dMEAACAQW8XlhdJ8/WbXiqTXkrsYitkUTkA9F2+jUmIneKlqrXEYrfTrl+YnlyxrkJnCUAtsmOcLjvFH39+XfrJvU91GmzECrwbPzInrdm4Kd384HPp0efWpituf7RsHzYAAACo5XZhcY8cO8kjqd42qjltP7olWzRe2Ee80MgRL+0/iAn9i46ZqWw6APRRYRuTKJ/eXbWW2xYtT48vX5fGjhwh3gLQKxLjdLJy3aZ06S0Pl1yB95kf35/OPXLvdMVtj6a93tXWYx82AAAAqKbdZxf81T5ZX9LY2d3aPDzds2RFdo+7/64Tuuzsfmrl+i49yWPR+BXHvzad+O3fdkmORyJ8xg5j0w0fPihLssfnuVcGgP7JtzH549I1ZV+3cv2msu1QAKCQxHiDK179vmnz1i5J8cIVeNFPLcrX5FfB99SHDQAAAKrB2vYt6aaFT6f5i15KdM+ZMTFLdO+2/ehO97Vxr1ycFA/Z41xKZx72qnT6D+/rsjt8p/Gj0q5pzBBdEQDUt4jNUa2lnJin7qkdCgDkSYw3sFKr37913P49rsB7/e4T052PvJQ8N/AAAACgmnUkuguS4iEWhg9vasp2pBW+NhaFFyfF8+IzPnbonumq9x+Y7TofN8rucAAYLBFjo2JLqbgcC9wWPLEyHT1rl4qcGwC156XmVzSU7la/9yRW4A0f1pSVmivXhw0AAACqRVQ66+7+N18JLb+A/KSrF6RHlq0t+3lLX9iY3vPN32RJ8T0mj5UUB4BBEjH2gqP3SQfPmNglKX7CnOnpoadXd2mHAgDdsWO8QXU3KRAr7GJQUaqcen4F3mumTejopRbl4gw8AAAAqGbRPqycqIRWuID8+IN2K/v6fHsxFdQAYPC9bPvRWcuSx5e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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.relplot(comb_stats, x=\"Parametric\", y=\"BLB\", col=\"quantity\", kind=\"scatter\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4ce6bdd3", + "metadata": {}, + "outputs": [], + "source": [ + "# sns.relplot(comb_stats, x=\"Bootstrap\", y=\"BLB\", col=\"quantity\", kind=\"scatter\")\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "d22ac527", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.relplot(comb_stats, x=\"AbsMean\", y=\"RelError\", col=\"quantity\", kind=\"scatter\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "82008957", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.relplot(comb_stats, x=\"RealSTD\", y=\"RelError\", col=\"quantity\", kind=\"scatter\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bcc2d96", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/lenskit/logging/multiprocess/_worker.py b/src/lenskit/logging/multiprocess/_worker.py index 083e00551..6869c84bd 100644 --- a/src/lenskit/logging/multiprocess/_worker.py +++ b/src/lenskit/logging/multiprocess/_worker.py @@ -80,7 +80,7 @@ def current(cls, *, from_monitor: bool = True): if mon.log_address is None: raise RuntimeError("monitor has no log address") cfg = active_logging_config() - level = cfg.effective_level if cfg is not None else logging.INFO + level = cfg.effective_level if cfg is not None else logging.DEBUG return cls( address=mon.log_address, level=level, authkey=bytes(mp.current_process().authkey) ) diff --git a/src/lenskit/parallel/ray.py b/src/lenskit/parallel/ray.py index 60c5b0429..799560f45 100644 --- a/src/lenskit/parallel/ray.py +++ b/src/lenskit/parallel/ray.py @@ -71,7 +71,7 @@ def init_cluster( proc_slots: int | None = None, resources: dict[str, float] | None = None, worker_parallel: ParallelConfig | None = None, - global_logging: bool = False, + global_logging: bool = True, **kwargs, ): """ @@ -130,7 +130,7 @@ def init_cluster( setup = _worker_setup if global_logging else None runtime = ray.runtime_env.RuntimeEnv(env_vars=env, worker_process_setup_hook=setup) - _log.info("starting Ray cluster") + _log.info("starting Ray cluster", logging=global_logging) ray.init(num_cpus=num_cpus, resources=resources, runtime_env=runtime, **kwargs) @@ -422,5 +422,7 @@ def init_worker(*, autostart: bool = True) -> WorkerContext: if autostart: context.start() + _log.debug("worker context initialized") + ensure_parallel_init() return context diff --git a/src/lenskit/random.py b/src/lenskit/random.py index 35015e1b1..160ab7fb3 100644 --- a/src/lenskit/random.py +++ b/src/lenskit/random.py @@ -25,6 +25,22 @@ if TYPE_CHECKING: # avoid circular import from lenskit.data import RecQuery +__all__ = [ + "SeedLike", + "RNGLike", + "RNGInput", + "ConfiguredSeed", + "SeedDependency", + "DerivableSeed", + "load_seed", + "set_global_rng", + "init_global_rng", + "random_generator", + "make_seed", + "RNGFactory", + "derivable_rng", +] + SeedLike: TypeAlias = int | Sequence[int] | np.random.SeedSequence """ Type for RNG seeds (see `SPEC 7`_). diff --git a/src/lenskit/stats/__init__.py b/src/lenskit/stats/__init__.py new file mode 100644 index 000000000..1ddfa5e09 --- /dev/null +++ b/src/lenskit/stats/__init__.py @@ -0,0 +1,15 @@ +# This file is part of LensKit. +# Copyright (C) 2018-2023 Boise State University. +# Copyright (C) 2023-2025 Drexel University. +# Licensed under the MIT license, see LICENSE.md for details. +# SPDX-License-Identifier: MIT + +from ._blb import blb_summary +from ._gini import gini +from ._topn import argtopn + +__all__ = [ + "gini", + "argtopn", + "blb_summary", +] diff --git a/src/lenskit/stats/_blb.py b/src/lenskit/stats/_blb.py new file mode 100644 index 000000000..2dee7bdf9 --- /dev/null +++ b/src/lenskit/stats/_blb.py @@ -0,0 +1,343 @@ +# This file is part of LensKit. +# Copyright (C) 2018-2023 Boise State University. +# Copyright (C) 2023-2025 Drexel University. +# Licensed under the MIT license, see LICENSE.md for details. +# SPDX-License-Identifier: MIT + +from __future__ import annotations + +import warnings +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, ClassVar, Literal, Protocol, TypeAlias, TypeVar + +import numpy as np +import pandas as pd +import scipy.stats +from numpy.typing import NDArray + +from lenskit.diagnostics import DataWarning +from lenskit.logging import Tracer, get_logger, get_tracer +from lenskit.random import RNGInput, random_generator + +from ._distributions import ci_quantiles + +F = TypeVar("F", bound=np.floating, covariant=True) + +SummaryStat: TypeAlias = Literal["mean"] + +_log = get_logger(__name__) +STD_NORM = scipy.stats.norm() + +# dummy assignment to typecheck that we have correctly typed weighted average +__dummy_avg: WeightedStatistic = np.average + + +class WeightedStatistic(Protocol): + """ + Callable interface for weighted statistics, required by the Bag of Little Bootstraps. + """ + + def __call__( + self, + a: NDArray[np.floating[Any]], + /, + *, + weights: NDArray[np.floating[Any] | np.integer[Any]] | None = None, + axis: int | None = None, + ) -> np.floating[Any]: ... + + +def blb_summary( + xs: NDArray[F], + stat: SummaryStat, + *, + ci_width: float = 0.95, + b_factor: float = 0.7, + rel_tol: float = 0.02, + s_window: int = 10, + r_window: int = 50, + r_min: int = 0, + rng: RNGInput = None, +) -> dict[str, float]: + r""" + Summarize one or more statistics using the Bag of Little Bootstraps + :cite:p:`blb`. + + This is a direct, sequential implementation of Bag of Little Bootstraps as + described in the original paper :cite:p:`blb`, with automatic + convergence-based termination. + + Args: + xs: + The array of values to summarize. + stat: + The statistic to compute. The Bag of Little Bootstraps requires + statistics to support weighted computation (this is what allows it + to speed up the bootstrap procedure). + ci_width: + The width of the confidence interval to estimate. + b_factor: + The shrinking factor :math:`\gamma` to use to derive subsample + sizes. Each subsample has size :math:`N^{\gamma}`. + rel_tol: + The relative tolerance for detecting convergence. + s_window: + The window length for detecting convergence in the outer subset loop + (and minimum number of subsets). + r_window: + The window length for detecting convergence in the inner replication + loop (and minimum number of replicates per subset). + rng: + The RNG or seed for randomization. + + Returns: + A dictionary of statistical results of the statistic. + """ + if stat != "mean": + raise ValueError(f"unsupported statistic {stat}") + + n = len(xs) + mask = np.isfinite(xs) + nfinite = np.sum(mask) + if nfinite < n: + warnings.warn(f"ignoring {n - nfinite} nonfinite values", DataWarning, stacklevel=2) + + xs = xs[mask] + est = np.average(xs).item() + + rng = random_generator(rng) + config = _BLBConfig( + statistic=np.average, + ci_width=ci_width, + rel_tol=rel_tol, + s_window=s_window, + r_window=r_window, + r_min=r_min, + b_factor=b_factor, + ) + bootstrapper = _BLBootstrapper(config, rng) + + result = bootstrapper.run_bootstraps(xs) + + result = { + "estimate": est, + "rep_mean": result.rep_mean, + "rep_var": result.rep_var, + "ci_lower": result.ci_lower, + "ci_upper": result.ci_upper, + } + + return result + + +@dataclass +class _BootResult: + estimate: float + "Statistic computed on original data." + + rep_mean: float + "Mean of the statistic computed on the replicates." + rep_var: float + "Variance of the statistic computed on the replicates." + ci_lower: float + "CI lower bound." + ci_upper: float + "CI upper bound." + samples: pd.DataFrame | None = None + "Raw sample data." + + +@dataclass +class _BLBConfig: + statistic: WeightedStatistic + ci_width: float + rel_tol: float + s_window: int + r_window: int + r_min: int + b_factor: float + + @property + def ci_margin(self) -> float: + return 0.5 * (1 - self.ci_width) + + +class _BLBootstrapper: + """ + Implementation of BLB computation. + """ + + _tracer: Tracer + config: _BLBConfig + _ci_qmin: float + _ci_qmax: float + + rng: np.random.Generator + + def __init__(self, config, rng: np.random.Generator): + self.config = config + self.rng = rng + self.ss_stats = {} + + self._tracer = get_tracer(_log, stat=config.statistic.__name__) # type: ignore + + def run_bootstraps(self, xs: NDArray[F]) -> _BootResult: + n = len(xs) + self._ci_qmin, self._ci_qmax = ci_quantiles(self.config.ci_width, expand=n) + b = int(n**self.config.b_factor) + + self._tracer.add_bindings(n=n, b=b) + _log.debug("starting bootstrap", stat=self.config.statistic.__name__, n=len(xs)) # type: ignore + ss_frames = {} + + estimate = float(self.config.statistic(xs)) + + means = StatAccum(np.mean) + vars = StatAccum(np.mean) + lbs = StatAccum(np.mean) + ubs = StatAccum(np.mean) + + self._tracer.trace("let's go!") + + for i, ss in enumerate(self.blb_subsets(n, b)): + self._tracer.add_bindings(subset=i) + self._tracer.trace("starting subset") + res = self.measure_subset(xs, ss, estimate) + ss_frames[i] = res.samples + means.record(res.rep_mean) + vars.record(res.rep_var) + lbs.record(res.ci_lower) + ubs.record(res.ci_upper) + if self._check_convergence( + means, vars, lbs, ubs, tol=self.config.rel_tol, w=self.config.s_window + ): + break + + return _BootResult( + estimate, + means.statistic, + vars.statistic, + lbs.statistic, + ubs.statistic, + pd.concat(ss_frames, names=["subset"]), + ) + + def blb_subsets(self, n: int, b: int): + while True: + yield self.rng.choice(n, b, replace=False) + + def measure_subset(self, xs: NDArray[F], ss: NDArray[np.int64], estimate: float) -> _BootResult: + b = len(ss) + n = len(xs) + xss = xs[ss] + + means = StatAccum(np.mean) + vars = StatAccum(np.var) + lbs = StatAccum(None) + ubs = StatAccum(None) + + for i, weights in enumerate(self.miniboot_weights(n, b), start=1): + self._tracer.add_bindings(rep=i) + self._tracer.trace("starting replicate") + assert weights.shape == (b,) + assert np.sum(weights) == n + stat = self.config.statistic(xss, weights=weights) + means.record(stat) + vars.record(stat) + + stats = means.values + # ql, qh = _bca_range(estimate, stats, self.config.ci_margin, accel) + # self._tracer.trace("bias-corrected quantiles: [%.4f, %.4f]", ql, qh, accel=accel) + lb, ub = np.quantile(stats, [self._ci_qmin, self._ci_qmax]) + # lb, ub = np.quantile(stats, [ql, qh]) + self._tracer.trace("CI bounds: %f < s < %f", lb, ub) + lbs.record(stat, lb) + ubs.record(stat, ub) + del stats + + if i >= self.config.r_min and self._check_convergence( + means, vars, lbs, ubs, tol=self.config.rel_tol, w=self.config.r_window + ): + break + + df = pd.DataFrame({"statistic": means.values}) + df.index.name = "iter" + self._tracer.remove_bindings("rep") + return _BootResult( + estimate, means.statistic, vars.statistic, lbs.statistic, ubs.statistic, df + ) + + def miniboot_weights(self, n: int, b: int): + flat = np.full(b, 1.0 / b) + + while True: + yield self.rng.multinomial(n, flat) + + def _check_convergence(self, *arrays: StatAccum, tol: float, w: int) -> bool: + gaps = np.zeros(w) + for arr in arrays: + if len(arr) < w + 1: + return False + + stats = arr.stat_history + cur = arr.statistic + gaps += np.abs(stats[-(w + 1) : -1] - cur) / np.abs(cur) + + gaps /= len(arrays) + self._tracer.trace("max gap: %.3f", np.max(gaps)) + return np.all(gaps < tol).item() + + +class StatAccum: + INIT_SIZE: ClassVar[int] = 100 + + _stat_func: Callable[[NDArray[np.floating[Any]]], np.floating[Any]] + + _len: int = 0 + _values: NDArray[np.float64] + _cum_stat: NDArray[np.float64] + + def __init__(self, stat: Callable[[NDArray[np.floating[Any]]], np.floating[Any]]): + self._stat_func = stat + + self._values = np.zeros(self.INIT_SIZE) + self._cum_stat = np.zeros(self.INIT_SIZE) + + @property + def values(self) -> NDArray[np.float64]: + return self._values[: self._len] + + @property + def statistic(self) -> float: + if self._len: + return self._cum_stat[self._len - 1] + else: + return np.nan + + @property + def stat_history(self) -> NDArray[np.float64]: + return self._cum_stat[: self._len] + + def record( + self, x: float | np.floating[Any], stat: float | np.floating[Any] | None = None + ) -> None: + "Record a new value in the accumulator." + self._expand_if_needed() + i = self._len + self._len += 1 + + # record and update the cumulative mean + self._values[i] = x + if stat is None: + stat = self._stat_func(self.values) + self._cum_stat[i] = stat + + def _expand_if_needed(self): + cap = len(self._values) + if cap == self._len: + self._values.resize(cap * 2) + self._cum_stat.resize(cap * 2) + + def __len__(self): + return self._len diff --git a/src/lenskit/stats/_distributions.py b/src/lenskit/stats/_distributions.py new file mode 100644 index 000000000..f9dab3453 --- /dev/null +++ b/src/lenskit/stats/_distributions.py @@ -0,0 +1,42 @@ +# This file is part of LensKit. +# Copyright (C) 2018-2023 Boise State University. +# Copyright (C) 2023-2025 Drexel University. +# Licensed under the MIT license, see LICENSE.md for details. +# SPDX-License-Identifier: MIT + +""" +Distribution utilities. +""" + +from typing import Annotated + +import numpy as np +from annotated_types import Gt, Lt +from pydantic import validate_call +from scipy import stats + + +@validate_call +def ci_quantiles( + width: Annotated[float, Gt(0), Lt(1)], *, expand: Annotated[int, Gt(1)] | None = None +) -> tuple[float, float]: + r""" + Convert a confidence interval width to CI quantile bounds. + + Args: + width: + The CI interval width. + expand: + If not ``None``, a sample size :math:`n` to use to + expand the CI as in the expanded percentile bootstrap. + """ + + margin = 0.5 * (1 - width) + if expand: + factor = np.sqrt(expand / (expand - 1)) + # get t_(alpha/2),n-1 + t = stats.t.ppf(margin, expand - 1) + # get standard normal CDF + margin = stats.norm.cdf(factor * t) + + return margin, 1 - margin diff --git a/src/lenskit/stats.py b/src/lenskit/stats/_gini.py similarity index 62% rename from src/lenskit/stats.py rename to src/lenskit/stats/_gini.py index 6b66a59cc..b456beb2f 100644 --- a/src/lenskit/stats.py +++ b/src/lenskit/stats/_gini.py @@ -4,10 +4,6 @@ # Licensed under the MIT license, see LICENSE.md for details. # SPDX-License-Identifier: MIT -""" -LensKit statistical computations. -""" - from __future__ import annotations import warnings @@ -15,7 +11,6 @@ import numpy as np from numpy.typing import ArrayLike -from lenskit.data.types import NPVector from lenskit.diagnostics import DataWarning @@ -59,40 +54,4 @@ def gini(xs: ArrayLike) -> float: warnings.warn( "Gini coefficient is not defined for non-positive totals", DataWarning, stacklevel=2 ) - return max(num / denom, 0) - - -def argtopn(xs: ArrayLike, n: int) -> NPVector[np.int64]: - """ - Compute the ordered positions of the top *n* elements. Similar to - :func:`torch.topk`, but works with NumPy arrays and only returns the - indices. - - .. deprecated:: 2025.3.0 - - This was never declared stable, but is now deprecated and will be - removed in 2026.1. - """ - if n == 0: - return np.empty(0, np.int64) - - xs = np.asarray(xs) - - N = len(xs) - invalid = np.isnan(xs) - if np.any(invalid): - mask = ~invalid - vxs = xs[mask] - remap = np.arange(N)[mask] - res = argtopn(vxs, n) - return remap[res] - - if n >= 0 and n < N: - parts = np.argpartition(-xs, n) - top_scores = xs[parts[:n]] - top_sort = np.argsort(-top_scores) - order = parts[top_sort] - else: - order = np.argsort(-xs) - - return order + return max(num / denom, 0.0) diff --git a/src/lenskit/stats/_topn.py b/src/lenskit/stats/_topn.py new file mode 100644 index 000000000..9c76ed552 --- /dev/null +++ b/src/lenskit/stats/_topn.py @@ -0,0 +1,51 @@ +# This file is part of LensKit. +# Copyright (C) 2018-2023 Boise State University. +# Copyright (C) 2023-2025 Drexel University. +# Licensed under the MIT license, see LICENSE.md for details. +# SPDX-License-Identifier: MIT + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +from numpy.typing import ArrayLike + +if TYPE_CHECKING: + from lenskit.data.types import NPVector + + +def argtopn(xs: ArrayLike, n: int) -> NPVector[np.int64]: + """ + Compute the ordered positions of the top *n* elements. Similar to + :func:`torch.topk`, but works with NumPy arrays and only returns the + indices. + + .. deprecated:: 2025.3.0 + + This was never declared stable, but is now deprecated and will be + removed in 2026.1. + """ + if n == 0: + return np.empty(0, np.int64) + + xs = np.asarray(xs) + + N = len(xs) + invalid = np.isnan(xs) + if np.any(invalid): + mask = ~invalid + vxs = xs[mask] + remap = np.arange(N)[mask] + res = argtopn(vxs, n) + return remap[res] # type: ignore + + if n >= 0 and n < N: + parts = np.argpartition(-xs, n) + top_scores = xs[parts[:n]] + top_sort = np.argsort(-top_scores) + order = parts[top_sort] + else: + order = np.argsort(-xs) + + return order # type: ignore diff --git a/tests/math/__init__.py b/tests/stats/__init__.py similarity index 100% rename from tests/math/__init__.py rename to tests/stats/__init__.py diff --git a/tests/math/test_argtopn.py b/tests/stats/test_argtopn.py similarity index 100% rename from tests/math/test_argtopn.py rename to tests/stats/test_argtopn.py diff --git a/tests/stats/test_blb.py b/tests/stats/test_blb.py new file mode 100644 index 000000000..df9620276 --- /dev/null +++ b/tests/stats/test_blb.py @@ -0,0 +1,206 @@ +# This file is part of LensKit. +# Copyright (C) 2018-2023 Boise State University. +# Copyright (C) 2023-2025 Drexel University. +# Licensed under the MIT license, see LICENSE.md for details. +# SPDX-License-Identifier: MIT + +import os +from math import sqrt +from typing import ClassVar + +import numpy as np +from numpy.typing import NDArray +from scipy.stats import binomtest, describe, ttest_1samp, ttest_rel + +import hypothesis.extra.numpy as nph +import hypothesis.strategies as st +from hypothesis import assume, given +from pytest import approx, mark, warns + +from lenskit.data.types import NPVector +from lenskit.diagnostics import DataWarning +from lenskit.logging import Stopwatch, get_logger +from lenskit.parallel.ray import ensure_cluster, ray_available +from lenskit.random import random_generator +from lenskit.stats import blb_summary + +_log = get_logger(__name__) + + +def test_blb_single_array(rng: np.random.Generator): + "Quick one-array test to fail fast" + xs = rng.standard_normal(40_000) + 1.0 + mean = np.mean(xs) + ste = np.std(xs) / 200 + + summary = blb_summary(xs, "mean", rng=rng) + print(summary) + assert isinstance(summary, dict) + assert summary["estimate"] == approx(mean) + assert summary["rep_mean"] == approx(mean, rel=0.05) + # assert summary["rep_var"] == approx(ste * ste, rel=0.05) + + assert summary["ci_lower"] == approx(mean - 1.96 * ste, rel=0.01) + assert summary["ci_upper"] == approx(mean + 1.96 * ste, rel=0.01) + + +class CITester: + NBATCHES: ClassVar[int] = 20 + PERBATCH: ClassVar[int] = int(os.environ.get("BLB_TRIALS_PER_BATCH", 50)) + + parameter: float + + def generate_sample(self, size: int, rng: np.random.Generator) -> NDArray[np.float64]: ... + def compute_stats( + self, xs: NDArray[np.float64], rng: np.random.Generator + ) -> dict[str, float]: ... + + def expected_width(self, size: int) -> float | None: + return None + + @mark.filterwarnings(r"error:.*ignoring \d+ nonfinite values") + @mark.parametrize("size", [1000]) + def test_compute(self, size: int, rng: np.random.Generator): + import ray + + ensure_cluster() + + results = [] + times = [] + n_trials = self.NBATCHES * self.PERBATCH + + worker = ray.remote(num_cpus=2)(_blb_worker) + rngs = rng.spawn(self.NBATCHES) + tasks = [worker.remote(self.PERBATCH, size, self, t) for t in rngs] + for task in tasks: + bres = ray.get(task) + for mean, summary, time in bres: + assert isinstance(summary, dict) + assert summary["estimate"] == approx(mean) + + results.append(summary) + times.append(time) + + _log.info("completed %d trials (avg %.2fms / trial)", len(results), np.mean(times) * 1000) + n_lb_good = len([r for r in results if r["ci_lower"] <= self.parameter]) + f_lb_good = n_lb_good / n_trials + n_ub_good = len([r for r in results if self.parameter <= r["ci_upper"]]) + f_ub_good = n_ub_good / n_trials + n_good = len([r for r in results if r["ci_lower"] <= self.parameter <= r["ci_upper"]]) + f_good = n_good / n_trials + bt = binomtest(n_good, n_trials, 0.95) + _log.info( + "binomal test for CI hit rate: stat=%.3f, p=%.3g", bt.statistic, bt.pvalue, test=bt + ) + + smeans = np.array([r["estimate"] for r in results]) + smt = ttest_1samp(smeans, self.parameter) + _log.info("sample means: %s", describe(smeans)) + if smt.pvalue >= 0.05: + _log.info( + "t-test for sample means: stat=%.5f, p=%.3g", smt.statistic, smt.pvalue, test=smt + ) + else: + _log.warn( + "t-test for sample means: stat=%.5f, p=%.3g", smt.statistic, smt.pvalue, test=smt + ) + try: + rmeans = np.array([r["rep_mean"] for r in results]) + rmt = ttest_rel(rmeans, smeans) + _log.info("bootstrap means: %s", describe(rmeans)) + if rmt.pvalue >= 0.05: + _log.info( + "t-test for CI centers: stat=%.5f, p=%.3g", rmt.statistic, rmt.pvalue, test=rmt + ) + else: + _log.warn( + "t-test for CI centers: stat=%.5f, p=%.3g", rmt.statistic, rmt.pvalue, test=rmt + ) + except KeyError: + pass + + widths = np.array([r["ci_upper"] - r["ci_lower"] for r in results]) + ew = self.expected_width(size) + _log.info("bootstrap CI widths (expected: {:.4f}): {}".format(ew, describe(widths))) + if ew is not None: + wt = ttest_1samp(widths, ew) + if wt.pvalue >= 0.05: + _log.info( + "t-test for CI width: stat=%.5f, p=%.3g", wt.statistic, wt.pvalue, test=wt + ) + else: + _log.warn( + "t-test for CI width: stat=%.5f, p=%.3g", wt.statistic, wt.pvalue, test=wt + ) + + if bt.pvalue >= 0.05: + _log.info( + "{:.1%} CIs good ({:1%} LB fail, {:.1%} UB fail), p={:.3g}".format( + f_good, 1 - f_lb_good, 1 - f_ub_good, bt.pvalue + ), + ) + else: + _log.error( + "{:.1%} CIs good ({:1%} LB fail, {:.1%} UB fail), p={:.3g}".format( + f_good, 1 - f_lb_good, 1 - f_ub_good, bt.pvalue + ), + ) + + # leave some wiggle room + assert bt.pvalue >= 0.05 + + +def _blb_worker( + nreps: int, size: int, test: CITester, rng: np.random.Generator +) -> list[tuple[float, dict[str, float], float]]: + results = [] + + for _i in range(nreps): + xs = test.generate_sample(size, rng) + mean = np.mean(xs).item() + + timer = Stopwatch() + s = test.compute_stats(xs, rng) + + results.append((mean, s, timer.elapsed())) + + return results + + +class TestParamNormal(CITester): + parameter = 1.0 + true_sd = 1.0 + + def expected_width(self, size: int): + se = self.true_sd / np.sqrt(size) + return 2 * 1.96 * se + + def generate_sample(self, size: int, rng): + return rng.normal(self.parameter, self.true_sd, size=size) + + def compute_stats(self, xs, rng: np.random.Generator): + mean = np.mean(xs) + ssd = np.std(xs, ddof=1) + sse = ssd / np.sqrt(len(xs)) + return { + "estimate": mean, + "ci_lower": mean - 1.96 * sse, + "ci_upper": mean + 1.96 * sse, + } + + +class TestSimpleNormal(CITester): + parameter = 1.0 + true_sd = 1.0 + + def expected_width(self, size: int): + se = self.true_sd / np.sqrt(size) + return 2 * 1.96 * se + + def generate_sample(self, size: int, rng): + return rng.normal(self.parameter, self.true_sd, size=size) + + def compute_stats(self, xs, rng: np.random.Generator): + return blb_summary( + xs, "mean", rng=rng, b_factor=0.8, s_window=20, r_window=25, r_min=100, rel_tol=0.01 + ) diff --git a/tests/stats/test_ci_utils.py b/tests/stats/test_ci_utils.py new file mode 100644 index 000000000..420c86107 --- /dev/null +++ b/tests/stats/test_ci_utils.py @@ -0,0 +1,30 @@ +# This file is part of LensKit. +# Copyright (C) 2018-2023 Boise State University. +# Copyright (C) 2023-2025 Drexel University. +# Licensed under the MIT license, see LICENSE.md for details. +# SPDX-License-Identifier: MIT + +""" +Test confidence interval utilities. +""" + +import hypothesis.strategies as st +from hypothesis import given +from pytest import approx + +from lenskit.stats._distributions import ci_quantiles + + +@given(st.floats(0, 1, exclude_max=True, exclude_min=True)) +def test_ci_bounds(width: float): + qlo, qhi = ci_quantiles(width) + assert qhi - qlo == approx(width) + assert 1 - qhi == approx(qlo) + + +@given(st.floats(0.1, 0.9)) +def test_ci_bounds_expanded(width: float): + oql, oqh = ci_quantiles(width) + qlo, qhi = ci_quantiles(width, expand=500) + assert qlo < oql + assert qhi > oqh diff --git a/tests/math/test_gini.py b/tests/stats/test_gini.py similarity index 100% rename from tests/math/test_gini.py rename to tests/stats/test_gini.py