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Last active May 23, 2022 20:10
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misc
dask-worker-space
.local

Active Learning Hacking

Repository for active learning hacks

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Active Learning Notebook\n",
"\n",
"Use the large data set with active learning."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notes 04-27-2022\n",
"\n",
"Did some debugging. The linear model works right now. The GP model is not working currently so have asked Berkay if there are parameters that give some good values. Random runs throught the `multiple_rounds` function, but the accuracy is extremely high at the very start 0.94. This is with 30 training samples. I'm wondering if the data is somehow sorted so that the first 1000 samples that I'm currently using for testing are similar and hence the high accuracy even with only 30 sample for training. Next step is to pre-shuffle the data and see if this makes the early stages of the random have a lower accuracy. \n",
"\n",
"TODO:\n",
"\n",
" - [x] pre-shuffle the data (as you recently did in the pace visualization repository).\n",
" - [ ] try and get the GPR model working\n",
" - [ ] examine efficiency\n",
" - [ ] batch jobs. don't relearn so frequently\n",
" - [ ] parallel"
]
},
{
"cell_type": "code",
"execution_count": 276,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import dask.array as da\n",
"from dask_ml.model_selection import train_test_split\n",
"from sklearn.pipeline import Pipeline\n",
"from dask_ml.decomposition import IncrementalPCA\n",
"from dask_ml.preprocessing import PolynomialFeatures\n",
"from sklearn.linear_model import LinearRegression\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.gaussian_process import GaussianProcessRegressor\n",
"from sklearn.gaussian_process.kernels import RBF, WhiteKernel, Matern\n",
"from sklearn.metrics import mean_squared_error as mse\n",
"from sklearn.metrics import r2_score\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"from pymks import (\n",
" generate_multiphase,\n",
" plot_microstructures,\n",
" PrimitiveTransformer,\n",
" TwoPointCorrelation,\n",
" GenericTransformer,\n",
" solve_fe\n",
")\n",
"\n",
"from toolz.curried import curry, pipe, valmap, itemmap, iterate, do, merge_with\n",
"from toolz.curried import map as map_\n",
"from modAL.models import ActiveLearner, CommitteeRegressor, BayesianOptimizer\n",
"from modAL.disagreement import max_std_sampling\n",
"from modAL.models import BayesianOptimizer\n",
"from modAL.acquisition import max_EI\n",
"import tqdm\n",
"import types\n",
"from pymks.fmks.func import sequence\n",
"from active import gsx_query, gsy_query, igs_query, multiple_rounds, three_way_split, flatten\n",
"\n",
"import dask.array as da"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the data"
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {},
"outputs": [],
"source": [
"data = np.load('data_shuffled.npz')"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {},
"outputs": [],
"source": [
"x_data = data['x_data']\n",
"y_data = data['y_data'].reshape(-1)"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8900, 132651)\n",
"(8900,)\n"
]
}
],
"source": [
"print(x_data.shape)\n",
"print(y_data.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions to generate models\n",
"\n",
"Here we use the GPR model as it returns a probability that's required by the `ActiveLearner` class."
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {},
"outputs": [],
"source": [
"def pca_steps():\n",
" return (\n",
" (\"reshape\", GenericTransformer(\n",
" lambda x: x.reshape(x.shape[0], 51, 51,51)\n",
" )), \n",
" (\"discritize\",PrimitiveTransformer(n_state=2, min_=0.0, max_=1.0)),\n",
" (\"correlations\",TwoPointCorrelation(periodic_boundary=True, cutoff=10, correlations=[(0, 0)])),\n",
" ('flatten', GenericTransformer(lambda x: x.reshape(x.shape[0], -1))),\n",
" ('pca', IncrementalPCA(n_components=3))\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {},
"outputs": [],
"source": [
"def make_gp_model():\n",
" kernel = 1 * RBF(length_scale=1.0, length_scale_bounds=(1e-5, 1e5)) + WhiteKernel()\n",
" #kernel = RBF() + RBF(length_scale=np.ones(56)) + WhiteKernel()\n",
" #kernel = RBF(length_scale=20.2) + WhiteKernel(noise_level=0.00472)\n",
" #kernel = RBF(length_scale=1.) + WhiteKernel()\n",
" #kernel = RBF() + WhiteKernel()\n",
" regressor = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10)\n",
" #kernel = RBF(length_scale=4.38) + WhiteKernel(noise_level=0.00593)\n",
" #kernel = Matern(length_scale=1)\n",
" #regressor = GaussianProcessRegressor(kernel=kernel)#, alpha=1e-6)#, normalize_y=True)\n",
" #regressor = GaussianProcessRegressor(kernel=kernel)\n",
" return Pipeline(steps=pca_steps() + (\n",
"# ('poly', PolynomialFeatures(degree=3)),\n",
" ('regressor', regressor),\n",
" ))\n",
"\n",
"def make_linear_model():\n",
" return Pipeline(steps=pca_steps() + (\n",
" ('poly', PolynomialFeatures(degree=3)),\n",
" ('regressor', LinearRegression()),\n",
" ))\n",
"\n",
"def pca_model():\n",
" return Pipeline(steps=pca_steps())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Oracle\n",
"\n",
"The oracle function is an FE simulation on the 2D grid."
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {},
"outputs": [],
"source": [
"@curry \n",
"def oracle_func(x_data, y_data, query_instance):\n",
" idx, query_value = query_instance\n",
" return query_value.reshape(1, -1), np.array([y_data[idx]]).reshape(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Helper Functions"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {},
"outputs": [],
"source": [
"def plot_parity(y_test, y_predict, label='Testing Data'):\n",
" pred_data = np.array([y_test, y_predict])\n",
" line = np.min(pred_data), np.max(pred_data)\n",
" plt.plot(pred_data[0], pred_data[1], 'o', label=label)\n",
" plt.plot(line, line, '-', linewidth=3, color='k')\n",
" plt.title('Goodness of Fit', fontsize=20)\n",
" plt.xlabel('Actual', fontsize=18)\n",
" plt.ylabel('Predicted', fontsize=18)\n",
" plt.legend(loc=2, fontsize=15)\n",
" return plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up the active learners\n",
"\n",
"One is a GPR using the maximum std and the other is random"
]
},
{
"cell_type": "code",
"execution_count": 340,
"metadata": {},
"outputs": [],
"source": [
"from active import make_igs, make_gsx, make_gsy, make_bayes, make_uncertainty, make_ensemble, make_random\n",
"\n",
"distance_transformer = lambda x: pca_model().fit_transform(x)\n",
"\n",
"\n",
"def make_learners(x_train, y_train):\n",
" return dict(\n",
"# uncertainty=make_uncertainty(make_gp_model, x_train, y_train),\n",
" random=make_random(make_gp_model, x_train, y_train),\n",
"# ensemble=make_ensemble(x_train, y_train),\n",
"# bayes=make_bayes(make_gp_model, x_train, y_train),\n",
"# gsx=make_gsx(distance_transformer)(make_linear_model, x_train, y_train),\n",
"# gsy=make_gsy(make_linear_model, x_train, y_train),\n",
"# igs=make_igs(distance_transformer)(make_linear_model, x_train, y_train)\n",
" )\n",
"\n",
"random_learner = make_random(make_gp_model, x_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check the data"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2880x288 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_microstructures(*x_data[:10].reshape(10, 51, 51, 51)[:, :, :, 0], cmap='gray', colorbar=False);"
]
},
{
"cell_type": "code",
"execution_count": 328,
"metadata": {},
"outputs": [],
"source": [
"n_use = 1000\n",
"props = (0.8, 0.18)\n",
"\n",
"x_pool, x_test, x_train, y_pool, y_test, y_train = three_way_split(x_data[:n_use], y_data[:n_use], props, None)"
]
},
{
"cell_type": "code",
"execution_count": 329,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(800, 132651)\n",
"(180, 132651)\n",
"(20, 132651)\n"
]
}
],
"source": [
"print(x_pool.shape)\n",
"print(x_test.shape)\n",
"print(x_train.shape)"
]
},
{
"cell_type": "code",
"execution_count": 330,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(800,)\n",
"(180,)\n",
"(20,)\n"
]
}
],
"source": [
"print(y_pool.shape)\n",
"print(y_test.shape)\n",
"print(y_train.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test the model"
]
},
{
"cell_type": "code",
"execution_count": 331,
"metadata": {},
"outputs": [],
"source": [
"model = make_gp_model()\n",
"#model = make_linear_model()"
]
},
{
"cell_type": "code",
"execution_count": 332,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('reshape',\n",
" GenericTransformer(func=<function pca_steps.<locals>.<lambda> at 0x7f92c756e670>)),\n",
" ('discritize', PrimitiveTransformer()),\n",
" ('correlations',\n",
" TwoPointCorrelation(correlations=[(0, 0)], cutoff=10)),\n",
" ('flatten',\n",
" GenericTransformer(func=<function pca_steps.<locals>.<lambda> at 0x7f92c756e310>)),\n",
" ('pca', IncrementalPCA(n_components=3)),\n",
" ('regressor',\n",
" GaussianProcessRegressor(kernel=1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=1),\n",
" n_restarts_optimizer=10))])"
]
},
"execution_count": 332,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 333,
"metadata": {},
"outputs": [],
"source": [
"y_train_predict = model.predict(x_train)"
]
},
{
"cell_type": "code",
"execution_count": 334,
"metadata": {},
"outputs": [],
"source": [
"y_test_predict = model.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 335,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/nix/store/c8sgkmibi2vyfw75w9vai2917j5smvq7-python3.8-matplotlib-3.3.1/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 335,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RI9C1a9cKf8YFCxagS5cuWLduHRe7ceMG+vfvDysrKzRv3hzdunWDn58fCgoKuBzWrl2L9+/fc59rxIgRAICTJ0/C0dERlpaWXCHavHlzhfMjpCIePnyI7777DsuXL+eKgoKCAmbNmoXw8HCZFAWArhjKTXw1Amx3CJD3/9MspCSD7Q6BGICCjb0sU8PMmTPx4sULuLq6Ql9fH3fu3IGnpydUVVUxfPhwxMTEYOLEiRgzZgzmz5+P1NRUHD16FKdOnYK1tTV++umnYl1UhoaGiI2NLfF48+fPx7Rp0zBq1CicPXsWq1atQocOHbgCs23bNnh7e2PGjBmwtrZGfHw8du7cWenPOWTIECxbtgzJyckwNDREdnY2unbtipkzZ0JLSwuxsbFYu3YtlJSUsHjxYtjb22P48OE4ffo0fvnlFwCArq4ugMJi2rt3b7Ru3RoaGhq4ceMGfHx8oKuri9GjR1c6V0K+JC8vDyEhIQgODkZeXh4Xb926NYKCgtChQwfZJQcqDOXGDu/+WBSK5OUWxmVYGC5duoSrV6/i0qVL0NXVRUFBAb7++mu8fPkSe/bswfDhw3Hp0iUoKirCz8+PG942cuRIpKWlAQC++uorAOXvotq5cyd69OgBoHAs9fHjx3H+/HnY2NggIyMDQqEQc+bMgaenJ/eexMREnDhxolKf1czMDADw+vVrGBoaws7OjpfvN998gydPnuCvv/7C4sWL0aBBA5iZmUFJSanY5xo6dCjvZ0dHR/zzzz84efIkFQZSpe7cuQNXV1fcv3+fiykrK8PFxQXOzs5SndqioqgwlFdKKevDlhavJhcvXkR6enqJk2gV3R9o0aIFcnNzsXDhQowYMQIdOnSApqZmhW9mGRgYcP+voKAAY2NjJCcnAwBu3bqF1NRUfPfddxXa95cUTWZXVNzy8/Oxfft2nDhxAnFxcUhPT0d+fj6aNGlS5r6ysrKwceNGhIeH4/nz58jIyEBBQQG6desm9bwJAQonvRMKhdi8eTPEYjEX79ixI4RCIVq1aiXD7PioMJSXvgGQklxyXIZSUlLQqFEjbNq0CYqKihCJRNxrysrKAArPhlesWIFNmzbhwIEDUFBQwNdff43AwECYmJhUOgdlZWXui56SkgKgcHidtCUkJEAgEHA5L1y4EIcPH8awYcMwYcIEGBoaYv/+/bh582aZ+xo/fjz++ecfjBs3DrNmzYK+vj42bNgg9ZwJAQpHx7m5ueHZs2dcTE1NDR4eHpg6dWqNm5iRCkM5CYY68e8xAICKKgRDnWSXFABtbW28ffsW7dq1g7q6Onfj9XPTpk3DtGnTEB8fj6tXr8LPzw9eXl7YsWOHVJ+e1NfXB1A42Zeenp7U9gsAR48ehbW1NfT19ZGRkYEjR47A3d0dLi4u3DYRERG895T02R4+fIhr167hp59+wrBhw7j4rl278P79e6nmTORbeno6fH19sXv3bl7c1tYWgYGBXPdoTUOjkspJwcYeAidnQN8QgADQN4TAyVnmN567d++OvLw87Nixo9hrz58/BwDExcVxZ/RmZmYYPXo0+vTpgxcvXgAAtLS0AHw826+MFi1aQEFBAVevXuXFKzsr6U8//YTr169zzzGIRCKIxeJi3Uaf/2LX0tJCZmYmb22Gopt9n76XMYYPHz5UKkdCPhUeHo5evXrxioK2tjaCgoKwf//+GlsUALpikIiCjb1MbzSXxMHBAT179sSqVavw4sUL2NjYIDMzE3/99Rdyc3OxZ88e7NixA1euXMG4ceNgamqK58+f4/jx45g8eTIAQEdHB82aNcOmTZugoaGBlJQUDB48uEL5mJiYoF+/flixYgWys7Oho6OD8PBwHDp0qFx9/wDw5MkTREZGIjc3FwkJCTh27BiuX78OLy8v7iayjo4OOnbsiA0bNkBRURGKioo4cOAAzp49i8aNG3P76tChA/Ly8rBs2TLY29sjNzcXAwYMgImJCVatWoVZs2YhJycHO3fuxM2bN+keA6m0lJQUeHt749ChQ7x43759sXr16irpZpU2Kgy1nEAgwPbt2xEcHIywsDDs3LkTenp6sLKywqRJkwAAw4cPx5s3b7Bx40a8ffsWxsbGmDVrFu9p4HXr1sHT0xPz58+HoaEh+vfvX+Gc/P394eHhgRUrVkBVVRWDBg3CqFGj8O+//5br/du3b8f27duhoqKC+vXro1OnTjh06FCx5yBCQ0Ph5uaGhQsXwtDQEEOHDoWxsTGvO+mrr76Cm5sbdu7ciT///BODBw/G0KFDsW3bNixatAjz5s2DiYkJvv/+e+jr6yM9Pb3Cn5vIN8YYjh49iqVLl/KuvuvXrw8fHx98++23tWZ2XgErGupRy306R/mnsrKyoKGhUc3ZyIaSklKp9xhkbeLEiahXrx5CQkKq5XhV1Ra19ftkYGDAm6FT3km7PV6/fo0lS5bg9OnTvPiwYcOwYsUK7t5bTVPalN10xUCkLiwsDM+fP0fTpk2hqKiIy5cv4/z58zh48KCsUyNEqhhj2Lt3L1atWsW72jQ2Noa/vz+++eYbGWZXcVQYiNQVFBTg999/R2JiIgQCAVq3bo0tW7bA2tpa1qkRIjXPnj2Du7t7sZl/J0yYgCVLlnCDOmojKgxE6oYPH47hw4fLOg1CqoRIJMKWLVsQEBDAG23XrFkzBAUFcVPD1GZUGAghpJxiY2Ph5uaG6OhoLqaoqIhZs2ZhwYIFUFdXl2F20lPnC0MdubdOagj6PsmnvLw8/PzzzwgODkZ+fj4Xt7S0hFAoRLt27WSYnfTV+cIAFP5jri3DxEjNRUVBPkVHR8PNzY0327CKigp++OEHzJkzh5t6pi6p84VBTU0NmZmZ0NTUpOJAKowxhszMzDrTVUDKlp2djYCAAGzZsoU36V3nzp0hFAphYWEhw+yqVp0vDIqKilBXV0dWVhaAkufOqStUVVV5Uz/IM2m2RdGVgrq6eo2b7IxUjcuXL8Pd3R3x8fFcTF1dHZ6enpg0aVKd/x7U+cIAFBYHTU1NWadR5eghpo+oLUhFpKWlwcfHB7/99hsvbmdnB39/f5iamsoos+olF4WBEELKcvr0aXh6eiIxMZGL6ejowNvbG6NGjarTvQ2fo8JACJFr7969w7JlyxAWFsaL9+/fH76+vtyCV/KECgMhRC4xxnD48GH8+OOPvOnaDQ0N4evri4EDB8owO9mSaWGIiIjAsWPHUFBQAFNTUzg7OyM/Px/BwcFISkqCkZERXFxcUK9ePVmmSQipYxISEjBz5kycPXuWFx85ciS8vb2lvshUbSOzwpCamoqDBw8iMDAQ6urq2LJlC/766y+8fv0a1tbW6N27N86cOYMDBw5gypQpskqTEFKHiMVi7NmzB6tXr+ZNemdiYoKAgADY29vLLrkaRGYruBUUFCA3N5eba0RXVxdKSkqIiYlB9+7dARQuf/fpo+eEEFJRcXFxGDVqFDw9PbmiIBAIMHnyZJw7d46KwidkdsVgYGCAgQMHYsGCBbCxsUFqaioWLlyIAwcOcENLNTQ0kJGRUeL7w8PDER4eDgDw8/ODgYFBteVeUykpKVE7/D9qCz55bo+CggJs2LABK1eu5E1617JlS2zatAm2trYyzK5mkllhyMrKws2bN+Hj44OEhAT88ccfiImJKTYkrLTFVhwdHeHo6Mj9TGPWaez+p6gt+OS1Pe7duwc3NzfcuXOHiykqKsLV1RUzZ86EmpqaXLZLkRq3UM/du3dhYmKCxo0bo3HjxlBTU8OpU6egqamJnJwcqKmpISsri248E0Iklpubi+DgYPz888+8k8s2bdpg7dq1sLe3l+uCUBaZFQYjIyPExsYiIyMD9erVw5MnT9CoUSNoaWkhKioKDg4OuHz5Mtq2bSurFAkhtdDNmzfh5uaGR48ecTFVVVUsWLAAs2bNqpOT3kmbzApD8+bN0bdvX3h5eUFBQQFmZmaYOXMmN1w1LCwMhoaGmD9/vqxSJITUIllZWfDz88O2bdt4M+F+9dVXCAoKQosWLWSYXe0iYHVkLuFXr17JOgWZk9d+5JJQW/DV9faIjIzEokWLkJCQwMU0NTWxZMkSTJgwAQoK/AGYdb09yqvG3WMghJDK+vDhA1auXIl9+/bx4vb29vD390fjxo1llFntRoWBEFIr/f3331iyZAnevHnDxXR1dbF8+XKMGDFCria9kzYqDISQWiU5ORlLly7F8ePHefGBAwfC19cXhoaGMsqs7qDCQAipFRhjOHjwIJYvX47U1FQubmRkBF9fXwwYMEB2ydUxVBgIITXey5cv4eHhgfPnz/Pio0ePxo8//ghdXV3ZJFZHUWEghNRYYrEYu3btwurVq5GZmcnFmzRpgoCAANjZ2ckwu7qLCgMhpEZ6/Pgx3N3dcf36dS4mEAgwZcoUeHh4yMVyvbJChYEQUqPk5+dj8+bNWLt2LXJzc7m4hYUFgoKC0KVLFxlmJx+oMBBCaoyYmBi4uroiJiaGiykpKcHZ2RkuLi5QVVWVYXbygwoDIUTmcnJysH79eoSGhkIkEnHxdu3aISgoCG3atJFhdvKHCgMhRKZu3LgBV1dXPHnyhIupqanB1dUVM2bMgJIS/ZqqbtTihBCZyMzMhJ+fH7Zv386b9M7GxgYBAQEwNzeXYXbyjQoDIaTaXbhwAYsWLcKLFy+4WL169eDl5YXx48cXm/SOVC8qDISQavP+/XusXLkSBw4c4MUdHBzg5+cHExMTGWVGPkWFgRBSLU6cOAEvLy8kJydzMT09PaxcuRJDhw6lSe9qECoMhJAq9ebNGyxduhQnT57kxQcPHoxVq1bBwMBARpmR0lBhIIRUCcYYDhw4gBUrVuDDhw9cvEGDBlizZg369u0rw+zIl1BhIIRIXUJCAhYtWoTIyEhefNy4cfDy8oKOjo6MMiPlQYWBECI1YrEYO3bswJo1a5CVlcXFzczMEBAQgB49esgwO1JeVBgIIVLx6NEjuLm54ebNm1xMQUEB06ZNg7u7OzQ0NGSYHZEEFQZCSKXk5+cjNDQU69evR15eHhdv2bIlhEIhOnXqJMPsSEVQYSCEVNi///6LhQsX4t69e1xMSUkJ8+fPx9y5c2nSu1qKCgMhRGLZ2dlYt24dNm3axJv0rn379hAKhWjdurUMsyOVRYWBECKRa9euwc3NDXFxcVxMTU0N7u7umDZtGk16VweU+Tc4YcKECj2RuHPnzgolRAipmTIyMrB69epi/7a7deuGwMBANGvWTEaZEWkrszA0b96cVxhSUlLw9u1btGzZsti27969Q1JSEl1GElLHnDt3Dh4eHnj16hUX09LSwtKlS/H999/TpHd1TJmFYfny5byfg4ODYWpqCldX12Lbvn37FgsWLMCgQYOkliAhRHZSUlKwfPly/Pnnn7y4o6Mj1qxZg0aNGskoM1KVJC7z9+7dK3WedAMDA/Ts2RNHjhypbF6EEBlijOHo0aOwt7fnFQV9fX2EhIRgx44dVBTqMInvEqWnp/MW1ficsbExLly4UKmkCCGyk5iYCC8vL/z999+8+NChQ7FixQrUr19fRpmRT4mvRoAd3g2kvAX0DSAY6gQFG3up7FviK4ZGjRrh1q1bpRaHZ8+e0agEQmohxhh+//139OrVi1cUGjZsiB07duDnn3+molBDiK9GgO0OAVKSATAgJRlsdwjEVyOksn+JC0P//v3x6NEjrF27FvHx8SgoKABQ2Bd58OBBXLx4EZ07d5ZKcoSQ6hEfH48xY8bAzc0NaWlpXHz8+PE4f/48evfuLcPsyOfY4d1AXi4/mJdbGJcCiU/tHRwckJiYiLCwMFy/fh0CgQACgQBisRgA0LRpU0yYMEEqyRFCqpZIJMK2bdvg7++P7OxsLt60aVMEBgaie/fuMsyOlCrlrWRxCVWoz+f7779Hjx49cOHCBbx8+RIikQj6+vpo27YtunXrBkVFRakkR+qOquwPJRXz4MEDuLq6Ijo6mospKChgxowZcHNzg7q6ugyzI1+kb/D/3UglxKWgwjcDTE1N4eTkJJUkSN3G9YcWXfoW9YcCVBxkIC8vDyEhIdiwYQPy8/O5eOvWrREUFIQOHTrILjlSLoKhTvx/UwCgogrBUOn8Tq5wYUhPT8fjx4+Rnp4OKysr6OvrIysrC+np6ahfvz7dgCacL/aHUmGoVrdv34abmxvu37/PxZSVleHi4gJnZ2eoqKjIMDtSXgo29hADVXYVXqHf3r/99htOnjzJ3Xj28PCAvr4+UlNT4erqiqlTp8LR0bHM/RQUFGDv3r24ceMGCgoKEBgYCMYYgoODkZSUBCMjI7i4uKBevXoVSZPUFFXcH0rKlpWVBR8fH2zevJm7HwgAHTt2hFAoRKtWrWSYHakIBRv7KjuxknhU0smTJ3H06FHY2tpixowZvNcaNWoES0tLXLt2rVz72rJlC1RVVREcHIzQ0FBoampi9+7dsLa2xoYNG2BtbY0DBw5ImiKpaUrr95RSfyj5sitXrqBLly7YuHEjVxTU1dWxfPlyhIWFUVEgxUhcGMLDw9GxY0fMmTMHHTt2LPa6paUlXrx4UeZ+UlNT8fDhQ4wcOZIb2SQQCBATE8ONhLC1teXdGCO1k2CoE6Dy2bz8UuwPJSVLS0uDh4cHRowYgSdPnnDxHj164OzZs5g+fbpcDhQRX41A8oyhEE0fApHHVKmN/a9LJO5KevPmDXr16lXq62pqarxx0KV5/vw5BAIBVq5ciffv38Pc3BwzZsxAeno6NDU1AQAaGhrIyMgo8f3h4eEIDw8HAPj5+cHAgM4+lZSUamY7DBqBbC0tZPy2CeK3SVAwMEK9cbOg3rNvlR2yxrZFNTl58iTmzp2Lly9fcjEdHR34+/tj0qRJFZoxuS7IvnAKaXtCwHI/GQixJwSaWlpV+n2sbSQuDGpqarzxzp979epVue4JpKWlwdjYGC4uLlBUVMTu3btx8ODBYl/YovsYn3N0dOTdx3j7lvqrDQwMam47tOkMwepfUXR+mgkgswpzrdFtUYXevXsHb29vHD58mBf/9ttvsXz5cjRs2BDv3r2TUXayJ9oVCuR+NhAiNxdpu0KR2Ub+Hswtbb4ribuSWrVqhStXrvBuYBVJSUnBpUuXyjXttqamJlRVVaGsrAwFBQV89dVXePHiBTQ1NZGTkwOg8IYZ3XgmpGyMMYSFhcHe3p5XFOrXr4+NGzfijz/+QMOGDWWYYQ1BAyHKReLCMGLECCQlJcHb25vr/3/16hXCw8Ph6emJ/Px8DB48uMz9tGrVCvfv30dSUhKAwmF0FhYWsLKyQlRUFADg8uXLaNu2raQpElJlxFcjIPKYWqP6p1+/fo3Jkydjzpw5SElJ4eLDhw9HREQEBg8eLLddR8XQQIhyEbAvTZVaitu3b2Pjxo1ITU3lxTU0NDBjxgx069atXPu5e/cudu/ejYKCArRs2RLTpk1DdnY2goODkZycDENDQ8yfPx/a2tpl7uvTBUTklbx2n5SkKtqi2IN6QOFNdCdnmTyoxxjD3r17sWrVKqSnp3PxRo0awd/fHw4ODlyMvhuFatrfoayV1pVUocIAAPn5+fj333/x4sULiMViNGjQAO3bt4eGhkalEq0oKgz0j/9TVdEWIo+ppUxDYAhF/61SPVZZnj59ikWLFnFX10UmTpwIT09PaGlp8eL03fhIfDUCgqO/QZycJPfTs5RWGCS++Xzz5k00bdoUBgYG6NSpEzp16sR7PT4+HowxNG3atEKJElJj1YD+aZFIhF9//RWBgYHcvTgAaNasGYKCgmBjY1NtudRWCjb2MBg0ggrlF0h8jyEwMBC3b98u9fWLFy9CKBRWJidCaiYZ90/HxsZi8ODBWLVqFVcUFBUV4ezsjDNnzlBRIFIj9RW8tbW1eTfACKkrZPWgXl5eHoRCIfr168c7KbO0tMTx48exZMkSmgmVSJVUZ7p79+4dLl++DF1dXWnulpAaoaonLitJdHQ0XF1d8eDBAy6moqKCH374AXPmzIGysnKVHZvIr3IVhsOHD+PIkSPczzt27MDu3fyVgsRiMfLy8gAUDmklpC6qyonLPpWdnY2AgABs2bKF98xQ586dIRQKYWFhUeU5EPlVrsKgp6eH5s2bAwDu3buH+vXrQ19fn7eNgoICdHV10b59e3z99dfSz5QQOXHp0iUsWrQI8fHxXExDQwOenp6YOHGiXM5vRKpXuQqDvb097O3tAQDOzs4YOXIkevToUZV5ESJ3Pnz4AB8fH+zdu5cXt7OzQ0BAAJo0aSKjzIi8kfgew9q1a5GYmIj3799DT0+P91pqaio+fPgAY2NjWvCDEAmcPn0anp6eSExM5GI6Ojrw9vbGqFGj6MllUq0kHpV06tQpeHh4lDgRV3p6OhYvXozTp09LJTlC6rq3b99i9uzZmDx5Mq8o9O/fH+fPn8fo0aOpKJBqJ3FhuHbtGtq1a4cWLVoUe61Jkybo2LFjsacxCSF8jDEcOnQI9vb2OHr0KBc3NDTEL7/8gi1btqBBgwYyzJDIM4kLw6tXr2BlZVXq6//73//w+vXrSiVFSF328uVLTJw4EfPmzcP79++5+MiRI3H+/HkMHDhQhtkRUoF7DJ8OSy1JXl5eqWsoECLPxGIx9uzZA19fX94CVCYmJggICOAGeBAiaxJfMZiamuLWrVslrscgFotx69YtNG7cWCrJEVJXxMXFYeTIkfD09OSKgkAgwOTJk3Hu3DkqCqRGkbgw9OvXD3FxcQgKCkJ8fDxEIhFEIhHi4+Oxdu1axMXF4ZtvvqmKXAmpdQoKChAaGorevXvj6tWrXNzc3ByHDh2Cj48PtxhVTVzrgcgnibuSbG1t8fTpUxw7dgy3bt2CgkJhbSm6gujVqxdvyU1C5NV///0HNzc33L17l4spKipi9uzZWLBgAdTU1Lh4sXUCUpLBdodADMjtlNBEdio0V9L48eNhY2ODS5cucTeajYyM0LVr1y/emCZEHuTm5mLDhg0ICQnh3W+zsrKCUCgs8d8IO7ybv3gMAOTlFsapMJBqVuFJ9Fq0aFHikFVC5NnNmzfh5uaGR48ecTFVVVUsXLgQM2fOLH3Suxqw1gMhRaQ6uyoh8iozMxP+/v7Ytm0bPl0U0draGoGBgWWfROkblLI6HK1FTKpfuQrD3LlzMXbsWNja2mLFihVlbi8QCPDjjz9WOjlCaoPIyEgsWrQICQkJXExTUxNLlizBhAkTuPtwXyIY6lTyWsRVvNYDISUpV2FITk5GdnY2AODx48dlPqJPj/ATeZCamopVq1Zh3759vLi9vT38/f0lGrYti7UeCCmNgH163VuLvXr1StYpyBwt+P5RVbfFX3/9hSVLliApKYmL6erqYvny5RgxYkSNOzmi7wYftUehRo0alRinewyESCA5ORlLly7F8ePHefFBgwbBx8cHhoaGMsqMEOmhwkBIOTDGcPDgQSxfvhypqalc3MjICKtXr0b//v1llxwhUlZmYXBzc5N4pwKBAIGBgRVKiJCa5sWLF1i8eDHOnz/Pi48ZMwbLli2jNc5JnVNmYSi66VwkJycHWVlZxZb2BICsrCxkZ2fDwICG2JHaTywWY9euXVi9ejUyMzO5eJMmTRAQEAA7OzsZZkdI1SmzMISEhPB+/uWXX/Du3Tt4enoW2/bFixdYtGgR5s6dK70MCZGBx48fw93dHdevX+diAoEAU6ZMgYeHBzQ1NWWYHSFVS+JJ9O7cuYNWrVqV+Frjxo3RvXt3/Pnnn5VOjBBZyM/Px88//4w+ffrwioKFhQWOHDmClStXUlEgdZ7EN59TU1OhpFT625o3b479+/dXKilCZCEmJgaurq6IiYnhYkpKSpg7dy7mz58PVVVVGWZHSPWR+IrB0NCQ9w/nc7R6G6ltcnJy4OfnhwEDBvC+2+3atcPJkyfh7u5ORYHIFYkLg4ODA+7cuYMdO3bwVqESiUSIjIzEuXPn0KZNG6kmSUhVuXHjBvr06YOffvoJIpEIAKCmpoalS5fi2LFj9F0mcknirqRBgwbh6dOn+Ouvv/D3339DT08PSkpKSE1NRV5eHvT19TFp0qQqSJUQ6cnIyICfnx927NjBm/TOxsYGAQEBMDc3l2F2hMiWxIVBQUEBLi4usLW1RWRkJF6+fAmRSAQLCwu0bdsWffv2hYaGRlXkSohUREREYNGiRXj58iUXq1evHry8vDB+/PhyTXpHSF1W4Sefu3Tpgi5dukgzF0Kq1Pv377FixQr88ccfvLiDgwP8/PxgYmIio8wIqVkqXBgYY3j58iUyMjLQuHHjj+vWisV0xkVqnBMnTsDLywvJyR/XPNDT08PKlSsxdOjQGjfpHSGyVKHf4KdPn8b06dPh6uoKb29vPHz4EEDhQ0Fjx47FxYsXpZokIRX15s0bTJ8+HTNmzOAVhSFDhiAiIgLDhg2jokDIZyQuDJcuXcLWrVvRpEkTDB8+nPdaixYtYGFhgStXrki0z6NHj8LV1RUAkJ6eDl9fX7i4uMDX15c38omQ8mKMYf/+/ejVqxdOnjzJxRs2bIjt27cjNDSUpm4hpBQSF4Zjx46hVatW8Pb2hqOjY7HX27Vrx1vJqiyxsbG4dOkS9/Pu3bthbW2NDRs2wNraGgcOHJA0RSLnEhISMHDgQCxcuBAfPnzg4uPGjcO5c+fQp08fGWZHSM0ncWF4+fIlOnXqVOrr9erVQ0pKSrn2lZaWhp07d2L69OlcLCYmBt27dwcA2NraIjo6WtIUiZwSiUTYunUrHBwccPbsWS5uZmaG/fv3IyAgADo6OjLMkJDaQeKbz0pKShCLxaW+npSUVK7hqowxhISEYPz48bx/rOnp6dxcNBoaGqV2JYWHhyM8PBwA4OfnR90CKPy7kdd2uH//PmbPns3rxlRQUMC8efPg7e0t9fmNsi+cQsZvmyB+mwQFAyPUGzcL6j37SvUY0iTP342SUHt8mcSFwdzcHDdu3MCwYcOKvZadnY3Lly+jRYsWZe7nxIkTaNWqFdq0acNbHvHzG4EFBQUlvt/R0ZHXlUXL9MnncoX5+fkIDQ3F+vXrkZeXx8UtLS3h7++PTp06ITs7u9j08ZUhvhoBtjsEyMst/Dn5DdJC/ZCenl5j12iWx+/Gl1B7FJLa0p7fffcdfHx8sG7dOvTq1QtA4VOkd+/exa5du5CWloaBAweWuZ+kpCTcuXMHkZGREIlEePfuHX788UdoamoiJycHampqyMrK4obBEvK5f//9FwsXLsS9e/e4mLKyMubNm4cVK1YgLS2tSo7LDu/migInL7cwXkMLAyGSkLgwtG3bFjNnzsS2bdtw9epVAB/XbFBQUICTkxOsrKzK3M+UKVO4/09KSoK/vz9WrlyJkJAQREVFwcHBAZcvX0bbtm0lTZGUQHw1ovAXV8pbQN8AgqFONfbstizZ2dlYt24dNm3axM1vBAAdOnRAUFAQWrduDRUVlapLIKWUM83S4oTUMhV6wM3BwQGdOnXC1atX8fLlS4jFYjRo0ABdu3ZFgwYNKpWQk5MTgoODERYWBkNDQ8yfP79S+yPFuz6Qkgy2OwRioNYVh6tXr8LNzQ1Pnz7lYmpqanB3d8f06dOhqKhY9UnoGwApySXHCakDBOzTGcTKISwsDM2aNUO7du2qKqcKefXqlaxTkLnS+k1FHlNL+UVmCEX/rdWQWeWlp6djzZo12LlzJy/erVs3BAYGolmzZrx4VfYhFyu0AKCiCoGTc40ttNSnzkftUai0ewwSD1f9/fffJXpOgdQAtbzr49y5c3BwcOAVBS0tLQQEBODAgQPFikJVU7Cxh8DJGdA3BCAA9A1rdFEgRFISdyXp6up+cbgqqYFqaddHSkoKli9fXmyp2N69e2PNmjUwNjaWUWb/3wVHhYDUURJfMVhbW+PmzZtVkQupIoKhToDKZyuQqagWxmsgxhiOHj0Ke3t7XlHQ19dHaGgotm/fLtOiQEhdJ3Fh6NOnD549e4bTp08jIyOj1D+k5qhNXR+JiYmYOnUqZs+ejXfv3nHxoUOH4sKFCxgyZAhNekdIFZP45vPo0aPL3qlAgH379lU4qYqgm8+1+4YaYwz79u3DypUrec8fNGzYEH5+fujdu7dE+6vNbVEVqD34qD0KSe0BNzs7OzpjI1IVHx8Pd3d3XL58mRcfP348vLy8oK2tLaPMCJFPEhWG/Px8DBgwAABgampaPWPGSZ0lEomwbds2+Pv786asaNq0KQIDA7nJFAkh1avcheHcuXPYtWsX9w9YW1sbTk5OsLOzq7LkSN314MEDuLq68mbPVVBQwMyZM+Hq6gp1dXUZZkeIfCtXYbh//z42b94MHR0d2NjYQFVVFbdu3UJISAhEIhE3ZxIhZcnLy0NISAg2bNiA/Px8Lt66dWsIhUK0b99ehtkRQoByFoYTJ05AV1cX/v7+0NXVBVC46Imvry9+//132Nvb030HUqbbt2/Dzc0N9+/f52LKyspwcXGBs7Nz1c5vRAgpt3INV33w4AG+/vprrigAgIqKCoYOHYoPHz7Qk9Dki7Kzs7Fq1Sp8++23vKLQsWNHnDp1CgsWLKCiQEgNUq4rhoyMDBgZGRWLFw11Sk1NhampqXQzI3VCVFQU3N3d8ezZMy6mrq4ODw8PTJkyhQYwEFIDlaswiMViKCgUv7hQUip8e2mL6RD5lZaWBl9fX+zZs4cX79GjBwICAmBmZiajzAghZSn3qKS7d+8iJyeHF8vKygIAXL9+vdgDZoMGDZJCeqQ2Cg8Ph4eHBxITE7mYtrY2vL29MXr0aLofRUgNV+7CcO3aNVy7dq3E186fP18sRoVB/rx79w7e3t44fPgwL963b1+sXr0aDRs2lFFmhBBJlKsweHt7V3UepBZjjCEsLAzLli1DSkoKFzcwMICPjw8GDRpEVwmE1CLlKgyWlpZVnQeppV69egVPT0+Eh4fz4sOHD8fy5cuhr68vo8wIIRVVoaU9CRGLxdi7dy98fHyQnp7OxRs1agR/f384ODjIMDtCSGVQYSASe/r0Kdzd3XHlyhVefOLEifD09ISWlpaMMiOESAMVhgoSX40AO7y7cHlMfQMIhjrVyPUNpEkkEuHXX39FYGAgb4Ras2bNIBQK0bVrVxlmRwiRFioMFVBsMfiUZLDdIRADdbY4xMbGwtXVFbdv3+ZiioqKmD17Nn744Qea9I6QOoQKQwWww7s/FoUiebmF8TpWGHJzc/Hzzz/jp59+4k16Z2lpCaFQiHbt2skwO0JIVaDCUBEppaz8VFq8lvrnn3/g5uaGBw8ecDEVFRUsWLAAs2fPhrKystSOJY9dc4TUVFQYKkLfAEhJLjleB2RlZSEgIABbtmzBpyu/du7cGUKhEBYWFlI9njx2zRFSk5VrdlXCJxjqBKio8oMqqoXxWu7SpUtwdHTEr7/+yhUFDQ0NrFq1CocPH5Z6UQDK6JojhFQ7umKoAAUbe4iBOtX18eHDB/j4+GDv3r28uJ2dHQICAtCkSZOqO7icdM0RUltQYaggBRv7OnOj+fTp0/D09ORNeqejowNvb2+MGjWq6qezqONdc4TUNtSVJMfevn2L2bNnY/LkybyiMGDAAERERFTbTKh1uWuOkNqIrhjkEGMMhw4dwo8//ojU1FQubmhoCF9fXwwcOLBa86mLXXOE1GZUGOTMy5cvsXjxYpw7d44XHzlyJLy9vaGnpyeTvOpS1xwhtR0VBjkhFouxe/durF69GhkZGVy8cePG8Pf3h729veySI4TUKFQY5EBcXBzc3d1x9epVLiYQCDB58mQsXrwYmpqaMsyOEFLTUGGowwoKCvDLL79AKBTyJr0zNzeHUCjEV199JcPsCCE1FRWGWqg800f8999/cHNzw927d7mYoqIi5syZgx9++AFqamrVnDUhpLagwlDLfGn6CAwagdzcXGzYsAEhISEoKCjg3mdlZQWhUAgrK6vqyZFGGBFSa8msMOTl5cHf3x9JSUlQUFBAz549MWzYMKSnpyM4OBhJSUkwMjKCi4sL6tWrJ6s0a5wvTR9x1aAxpk2bhkePHnEvqaqqYuHChZg5c6ZUJ70rDc17REjtJ9MrhiFDhqBdu3bIy8uDl5cXOnXqhJMnT8La2hq9e/fGmTNncODAAUyZMkWWadYsJUwTkVkgQuClf7D9N3vepHfW1tYIDAxEixYtqi09eZqSnJC6SmZPPquoqHBz+auoqKBBgwZITU1FTEwMunfvDgCwtbVFdHS0rFKsmT6bJiIyOQ19Lt7DtmdJXFHQ1NSEr68v/vzzz2otCgBo3iNC6oAacY8hNTUVjx49wuzZs5Gens4Nn9TQ0OCNuf9UeHg4wsPDAQB+fn4wMJCPeXWyJ8xB2kY/pGZkwvf+C+x/8Y73ep8+ffDzzz/DzMxMJvklGxpBnPymWFzB0Kha/46UlJTk5jtRHtQefNQeXybzwpCfn49169Zh7Nix0NTULDY3z6c3UD/l6OgIR0dH7ue3b+XkjLRNZ/xtYQ2v4I1IzvnYZaOrq4u1a9eiT58+EAgEMmsPNngc8Ok9BgBQUQUbPK5aczIwMJCf70Q5UHvwUXsUatSoUYlxmRaGgoICCIVCdOjQgXvyVlNTEzk5OVBTU0NWVhbdeP5EUlISli5dihMnTvDigwYNgo+PD1q3bi3zLzvNe0RI7SezwpCbm4ugoCC0adMG3333HRe3srJCVFQUHBwccPnyZbRt21ZWKdYYjDEcPHgQy5cv5016Z2RkhNWrV6N///6yS64ENO8RIbWbzArD48eP8d9//yE5ORkREREACkfRODk5ITg4GGFhYTA0NMT8+fNllWKpqnOc/osXL+Dh4cG1UZGxY8di6dKl0NXVrZLjEkLkl4B9Or6xFnv16lW1HKfYOH2gcO0AJ2epFgexWIxdu3Zh9erVyMzM5OJNmjRBQEAA7Ozsir2H+k0/orbgo/bgo/YoVCPvMdRG1TFO//Hjx3B3d8f169e5mEAgwNSpU+Hh4QENDQ2pHIcQQkpChUFSVThOPz8/H5s3b8batWuRm/ux+FhYWCAoKAhdunSp9DEIIaQsVBgkVUXrE8fExMDV1RUxMTFcTElJCXPnzsX8+fOhqqr6hXcTQoj00JrPEpL2+sQ5OTlYs2YNBgwYwCsKbfW1cdymJRa+jYVy9JXKpEwIIRKhKwYJSXOc/o0bN+Dq6oonT55wMVUVZbhaNMK0JvWhpCCgSegIIdWOCgMkH35a2XH6GRkZ8PPzw44dO3iT3tnY2MDfSBnNCrL4b6jAzW2a+poQUlFy35XEDT9NSQbAPp6hX42okuNFRETAwcEB27dv54pCvXr1sGbNGvzxxx9oVpBd8hsluLld3Z+JEFK3yP0VQ1UPPy06c09NTMTKuCQcjOM/b+Hg4AA/Pz+YmJgAAERSuLlNU18TQipD7q8YqnL4adGZ+4n/HsIhMoZXFPT09PDTTz9h165dXFEApHRzm6a+JoRUgtxfMVTV8FMASPztV/x45R7+epPKiw82a4hVR0+VOO2vVG5uV+FnIoTUfXJfGARDnUqe4uKTM3RJb+QyxnDgwAEsP3oRaQUiLt5AVRm+Vqbo00APil+YC76yN7fL85kIIaQ0cl8YSjtDBwCRx9TiZ95lDB9NSEjAokWLEBkZyYuPbWIAr/81hrayYpWfudPU14SQypD7wgAUP0MvcaK8T5VwI1ckEmHHjh3w8/NDVtbH4aamGqrwtzKFrYF2YaCaztxp6mtCSEVRYShBiaN6PvfJjdxHjx7B1dUVt27d4mIKCgqYNm0a3OxtoHbyAJ25E0JqDSoMJSnP6B19A+Tn5yM0NBTr169HXl4e91KrVq0QFBSETp06FQZ69q2iRAkhRPqoMJSktFE9RVRU8W+7HnDr3x/379/nwsrKypg3bx7mzZsHFRWVakiUEEKkjwpDCUoc1fP/crT1sT5HDZs9vSESfRxx1KFDBwQFBaF169bVmSohhEgdFYYSlDaq5zrU4ObmhqdPn3LbqqmpYdGiRZg2bRoUFRVllzQhhEgJFYZSfDqqJz09HWvWrMHOnTt523Tv3h2BgYFo2rRptedHCCFVhQpDGc6ePYvFixfz1pTW0tLCsmXL8P3330MgEMgwO0IIkT4qDKVISUmBt7c3Dh06xIv37t0ba9asgbGxsYwyI4SQqkWF4TOiK+dxbEMgfrz2H97lFXBxfX19+Pj4YPDgwXSVQAip06gwfOLVycPwWvYjTiem8OJD7WyxMmQT9PX1ZZQZIYRUHyoMKJz0bt++fVjpuRhp+R+vEozVlLHaygzfmGpBkYoCIUROyH1hiI+Ph7u7Oy5fvsyLjzc1gGerxtBSVizXk9C0lCYhpK6Q28IgEomwdetWBAQEIDv743KaTTVUEdDWDDb1tT5uXMZsqMUm3StjBlZCCKnJ5LYwuLi44PDhw9zPCgoKmPFtfywQvYW6+GN3UnlmQ6WlNAkhdYncLu35/fffc//funVrHD9+HMtCf4HG5PmAviEAAaBvCIGTc9ln/bSUJiGkDpHbK4bu3btjypQp0NfXh7OzMzfpXYXWMaClNAkhdYjcFgYAWLVqlVT2Q0tpEkLqErkuDNJCS2kSQuoSKgxSQktpEkLqCrm9+UwIIaRkVBgIIYTwUGEghBDCQ4WBEEIIDxUGQgghPALGGJN1EoQQQmoOumKoQxYvXizrFGoMags+ag8+ao8vo8JACCGEhwoDIYQQHioMdYijo6OsU6gxqC34qD34qD2+jG4+E0II4aErBkIIITxUGAghhPDQ7Kq1UF5eHvz9/ZGUlAQFBQX07NkTw4YNQ3p6OoKDg5GUlAQjIyO4uLigXr16sk632hw9ehQXLlyAUCiU67YoKCjA3r17cePGDRQUFCAwMBCMMbltj4iICBw7dgwFBQUwNTWFs7Mz8vPz5bY9yoPuMdRCeXl5iI2NRbt27ZCXlwcvLy84Ozvj5MmTsLCwQO/evXHmzBkkJCRgypQpsk63WsTGxmLbtm0QiUQQCoUIDQ2V27bYtGkT9PT0MGrUKC62ceNGuWyP1NRULF26FIGBgVBXV8eWLVtQv359vH79Wi7bo7yoK6kWUlFRQbt27bj/b9CgAVJTUxETE4Pu3bsDAGxtbREdHS3LNKtNWloadu7cienTp3MxeW2L1NRUPHz4ECNHjoRAIOD+yGt7FBQUIDc3Fzk5OQAAXV1dKCkpyW17lBcVhlouNTUVjx49goWFBdLT06GpqQkA0NDQQEZGhoyzq3qMMYSEhGD8+PHQ0dHh4vLYFgDw/PlzCAQCrFy5Ei4uLggODkZOTo7ctoeBgQEGDhyIBQsWYNOmTXj8+DH69u0rt+1RXlQYarH8/HysW7cOY8eOhaamJgQCAe/1goICGWVWfU6cOIFWrVqhTZs2vLg8tgVQePVkbGwMLy8vrFu3Djo6Ojh48KDctkdWVhZu3rwJHx8ftG/fHklJSYiJiZHb9igvuvlcSxUUFEAoFKJDhw6wt7cHAGhqaiInJwdqamrIysqSi5tpSUlJuHPnDiIjIyESifDu3Tv8+OOPctkWQOF3QFVVFcrKygCAr776CkePHpXb9rh79y5MTEzQuHFjNG7cGGpqajh16pTctkd50RVDLZSbmwt/f3/873//w9ChQ7m4lZUVoqKiAACXL19G27ZtZZVitZkyZQo2bNiA9evXY9myZTA2NsbKlSvlsi0AoFWrVrh//z6SkpIAALdv34aFhYXctoeRkRFiY2O5rqInT56gUaNGctse5UWjkmqh//77D76+vjAyMuJi1tbWGDRoEIKDg5GcnAxDQ0PMnz8f2traMsy0eiUlJcHf3x9CoRBpaWly2xZ3797F7t27UVBQgJYtW2LatGnIzs6W2/Y4efIkTp06BQUFBZiZmWHmzJnccFV5bI/yoMJACCGEh7qSCCGE8FBhIIQQwkOFgRBCCA8VBkIIITxUGAghhPBQYSCkFlu+fDmcnJxknQapY6gwELkXHx+PUaNGYdSoUTh48GCF93PixAmcOnVKipkRIhtUGIjcu3DhAgQCAbS1tREZGVnh/Zw8eRKnT5+WYmaEyAYVBiLXxGIxLl26BEtLS/Tq1QuJiYl48OCBrNMiRKZoEj0i1+7cuYPU1FSMGTMGFhYWCAsLw4ULF9CqVSvedmKxGGfOnMG5c+fw8uVLqKmpwcTEBN27d0fv3r0xZswYbttPF8gJCAhA06ZNMWrUKHTq1AmLFy/m7ffo0aPYs2cPvL29uRli4+LisGfPHiQmJuLDhw8ACuf8sbe3R//+/aGiolJVzUEIACoMRM5duHABysrKsLGxgYaGBpo2bYorV65g8uTJ3AylRctiRkVFoU2bNhg+fDjEYjFu376NrVu3okePHpg2bRr27dsHZWVlDB8+nNt//fr1Jc4pPT0dKioq+Prrr6GjowOxWIwnT55g7969SEhIwNy5c6X2+QkpCRUGIreK5urv0qULNDQ0AAB2dnbYtWsXbt26BRsbGwDAlStXEBUVhd69e/NWiRs+fDiio6OhrKyMPn36ICwsDGpqaujTp0+l8mrfvj3at2/P/cwYQ05ODrKzs3H58mXMmDGDrhpIlaLCQOTW1atXkZeXh6+//pqL2draYs+ePYiMjOQKw6VLlyAQCHjdRUU6duxYJbndvHkTZ8+exZMnT/Dhwwd8OtdlWloaDAwMquS4hABUGIgci4yMhJqaGho2bIjExEQu3rx5c0RHRyMtLQ3a2tp4/fo19PT0oKWlVS157d27F0eOHIGuri5sbGxgYmICbW1tXLhwAf/88w/EYnG15EHkFxUGIpeSk5Nx//59MMawcOHCEre5fPky+vfvD6D4UqFVJS0tDceOHYOZmRlWrlwJdXV17rV79+5VSw6EUGEgcikyMhKMMUyaNAn6+vq81xhjCA0NRWRkJPr37w8jIyPcvn0bGRkZX1wCUkFBodSzeWVlZeTm5paZ15s3byASidC1a1deUSCkOlFhIHIpMjISRkZGGDBgQImvX7t2DVFRUXjx4gW6du2K6Oho7N+/H1OnTuVtFxsbi+bNm0NFRQW6urp49uwZ8vLyit0cNjIyKvZaVlYWYmNjedsVFak3b97w4m/fvsXDhw8r9ZkJKS8qDETuPHz4EK9fv+atl/25Hj16ICoqCpGRkRgzZgyuXLmCU6dO4cWLF9z6wDExMYiJicH27duhoqKC9u3b48GDB1izZg06duyId+/eYciQIdDX10evXr2wZ88e+Pr6omvXrkhJScGFCxeQmZnJO279+vXRqVMnREZGQlFREaampkhMTMSFCxeQl5dXpe1CSBF68pnInaJpL2xtbUvdpkOHDtDU1MTFixcBAB4eHhg7dizS0tLw559/4vjx48jPz8e0adO4Lp/Bgwejb9++ePXqFfbv3487d+5w+xs4cCBGjhyJt2/fYs+ePYiOjsaQIUMwcuTIYseeN28e7O3tcePGDezbtw9xcXGYPHkyvvnmG2k2AyGlojWfCSGE8NAVAyGEEB4qDIQQQnioMBBCCOGhwkAIIYSHCgMhhBAeKgyEEEJ4qDAQQgjhocJACCGEhwoDIYQQnv8DkobDsxdDBe0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_parity(y_train, y_train_predict)"
]
},
{
"cell_type": "code",
"execution_count": 336,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/nix/store/c8sgkmibi2vyfw75w9vai2917j5smvq7-python3.8-matplotlib-3.3.1/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 336,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_parity(y_test, y_test_predict)"
]
},
{
"cell_type": "code",
"execution_count": 344,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.6292903991069115\n",
"0.6292903991069115\n"
]
}
],
"source": [
"from sklearn.metrics import r2_score\n",
"\n",
"print(r2_score(y_test, y_test_predict))\n",
"#print(y_test.shape)\n",
"#print(y_test)\n",
"print(model.score(x_test, y_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the learners"
]
},
{
"cell_type": "code",
"execution_count": 338,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"evaluating random\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 30/30 [02:30<00:00, 5.00s/it]\n"
]
}
],
"source": [
"scores = multiple_rounds(x_data[:n_use], y_data[:n_use], 1, 30, make_learners, oracle_func, props)"
]
},
{
"cell_type": "code",
"execution_count": 339,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'random': (array([ 0.85138909, -0.08698856, -0.03713712, 0.87531828, 0.88663354,\n",
" 0.89196135, 0.84875733, 0.84487646, 0.87902882, 0.86255135,\n",
" -0.00512397, 0.85110648, 0.8241965 , 0.82709585, 0.61190579,\n",
" -0.00211859, 0.82854455, 0.8599785 , 0.79788255, 0.81335599,\n",
" 0.83125758, 0.89707252, 0.84381052, 0.84837045, 0.86059883,\n",
" 0.82204144, 0.82579239, 0.83051567, 0.8100553 , 0.79624862,\n",
" 0.79536141]),\n",
" array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))}"
]
},
"execution_count": 339,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scores\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## The results"
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 1.0)"
]
},
"execution_count": 320,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use('ggplot')\n",
"plt.figure(figsize=(10, 8))\n",
"ax = plt.gca()\n",
"\n",
"for k, v in scores.items():\n",
" y = v[0]\n",
" e = v[1]\n",
" x = np.arange(len(y))\n",
" ax.plot(x, y, label=k)\n",
" if k in ['std', 'bayes', 'gsx', 'gsy', 'igs']:\n",
" ax.fill_between(x, y - e, y + e, alpha=0.1)\n",
"plt.legend()\n",
"plt.xlabel('iterations')\n",
"plt.ylabel('R^2');\n",
"plt.ylim(0, 1)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.9, 1.0)"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use('ggplot')\n",
"plt.figure(figsize=(10, 8))\n",
"ax = plt.gca()\n",
"\n",
"for k, v in scores.items():\n",
" y = v[0]\n",
" e = v[1]\n",
" x = np.arange(len(y))\n",
" ax.plot(x, y, label=k)\n",
" if k in ['std', 'bayes', 'gsx', 'gsy', 'igs']:\n",
" ax.fill_between(x, y - e, y + e, alpha=0.1)\n",
"plt.legend()\n",
"plt.xlabel('iterations')\n",
"plt.ylabel('R^2');\n",
"plt.ylim(0.9, 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check what the accuracy actually looks like"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"y_pred_std = learner_accuracy['std'][1].predict(x_test)\n",
"y_pred_random = learner_accuracy['random'][1].predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/nix/store/c8sgkmibi2vyfw75w9vai2917j5smvq7-python3.8-matplotlib-3.3.1/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_parity(y_test, y_pred_random, label='random')"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/nix/store/c8sgkmibi2vyfw75w9vai2917j5smvq7-python3.8-matplotlib-3.3.1/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_parity(y_test, y_pred_std, label='std')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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#
# $ nix-shell --pure --arg withBoost false --argstr tag 20.09
#
{
tag ? "20.09",
pymksVersion ? "cf653e004848c9c68ca31a85add0d1ac8611a93f"
}:
let
pkgs = import (builtins.fetchTarball "https://github.com/NixOS/nixpkgs/archive/${tag}.tar.gz") {};
pymkssrc = builtins.fetchTarball "https://github.com/materialsinnovation/pymks/archive/${pymksVersion}.tar.gz";
pymks = pypkgs.callPackage "${pymkssrc}/default.nix" { graspi = null; };
pypkgs = pkgs.python3Packages;
extra = with pypkgs; [ black pylint flake8 ipywidgets zarr pymks h5py ];
in
(pymks.overridePythonAttrs (old: rec {
propagatedBuildInputs = old.propagatedBuildInputs;
nativeBuildInputs = propagatedBuildInputs ++ extra;
postShellHook = ''
export OMPI_MCA_plm_rsh_agent=${pkgs.openssh}/bin/ssh
SOURCE_DATE_EPOCH=$(date +%s)
export PYTHONUSERBASE=$PWD/.local
export USER_SITE=`python -c "import site; print(site.USER_SITE)"`
export PYTHONPATH=$PYTHONPATH:$USER_SITE
export PATH=$PATH:$PYTHONUSERBASE/bin
jupyter nbextension install --py widgetsnbextension --user > /dev/null 2>&1
jupyter nbextension enable widgetsnbextension --user --py > /dev/null 2>&1
pip install jupyter_contrib_nbextensions --user > /dev/null 2>&1
jupyter contrib nbextension install --user > /dev/null 2>&1
jupyter nbextension enable spellchecker/main > /dev/null 2>&1
pip install --user nbqa
pip install --user tqdm
pip install --user modAL
'';
}))
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