diff --git a/notebooks/cornerplot.ipynb b/notebooks/cornerplot.ipynb deleted file mode 100644 index fd675d4..0000000 --- a/notebooks/cornerplot.ipynb +++ /dev/null @@ -1,356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cb44b7e3-e1d9-40c1-92c3-8e312ffd6ecc", - "metadata": {}, - "source": [ - "# Pretty cornerplots\n", - "Uses multiple plotting utilities to demo all of the options. Here I will display two posteriors, both trained using the same priors; one trained using the generative option for SBI, one trained using the pre-generated training set." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f89eab45-407d-42c0-812d-3a0800370ab3", - "metadata": {}, - "outputs": [], - "source": [ - "from scripts import evaluate, io, plot\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "# remove top and right axis from plots\n", - "matplotlib.rcParams[\"axes.spines.right\"] = False\n", - "matplotlib.rcParams[\"axes.spines.top\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9f8923fc-3abd-464e-9b19-fc26ed90437a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../savedmodels/sbi/\n" - ] - } - ], - "source": [ - "# load up the generative model\n", - "modelloader = io.ModelLoader()\n", - "path = \"../savedmodels/sbi/\"\n", - "model_name = \"sbi_linear_generative\"\n", - "posterior_generative = modelloader.load_model_pkl(path, model_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ebbf1d55-24d4-487b-af49-da544cb3f668", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../savedmodels/sbi/\n" - ] - } - ], - "source": [ - "# load up the generative model\n", - "modelloader = io.ModelLoader()\n", - "path = \"../savedmodels/sbi/\"\n", - "model_name = \"sbi_linear_from_data\"\n", - "posterior_static = modelloader.load_model_pkl(path, model_name)" - ] - }, - { - "cell_type": "markdown", - "id": "4f5f4249-7a82-408f-a2bd-165aeeb8d8bc", - "metadata": {}, - "source": [ - "In order to evaluate these, we need a validation set, which we'll load below." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "abaae20b-8028-43c1-aafb-8f4f2dfb4443", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../saveddata/\n" - ] - } - ], - "source": [ - "dataloader = io.DataLoader()\n", - "path = \"../saveddata/\"\n", - "data_name = \"data_validation\"\n", - "validation = dataloader.load_data_h5(data_name, path)\n", - "theta_true = validation['thetas'][0]\n", - "y_true = validation['xs'][0]" - ] - }, - { - "cell_type": "markdown", - "id": "f7ed9d9d-2b36-47b2-a098-84cf4a796f99", - "metadata": {}, - "source": [ - "Visualize the validation data." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "025b6d3a-0405-45f7-a885-a65c5e9942a7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 100, 101)\n", - "plt.clf()\n", - "plt.scatter(x, y_true, color = 'black')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2dc3ee21-3d8a-4c21-8cc2-488f1149ca13", - "metadata": {}, - "source": [ - "Let's draw from the posterior and display the results in a pairplot from mackelab. First for the static results." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "63b30f5a-c2e0-4804-85a4-ee244899da11", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "76668b74f16b496596f5d65b8e66c539", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Drawing 10000 posterior samples: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display = plot.Display()\n", - "posterior_samples_static = posterior_static.sample((10000,), x = y_true)\n", - "display.mackelab_corner_plot(posterior_samples_static,\n", - " labels_list = ['$m$','$b$'],\n", - " truth_list = theta_true,\n", - " truth_color = 'orange',\n", - " plot = True,\n", - " save = True,\n", - " path = '../plots/static/')" - ] - }, - { - "cell_type": "markdown", - "id": "470ec484-1c12-4049-836b-392c5af8bae9", - "metadata": {}, - "source": [ - "Now for the generative model." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0ce170e0-2290-4029-a66e-4706793e0ca9", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c41fd287a97248a4b4361e880b46301a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Drawing 10000 posterior samples: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display = plot.Display()\n", - "posterior_samples_generative = posterior_generative.sample((10000,), x = y_true)\n", - "display.mackelab_corner_plot(posterior_samples_generative,\n", - " labels_list = ['$m$','$b$'],\n", - " truth_list = theta_true,\n", - " truth_color = 'orange',\n", - " plot=True,\n", - " save=True,\n", - " path = '../plots/generative/')" - ] - }, - { - "cell_type": "markdown", - "id": "5eba2920-aae9-4ab1-8a93-67451fbae59d", - "metadata": {}, - "source": [ - "Now let's use the getdist utility. First for the static results" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0bb4540b-a208-47a4-b004-e4a1922f3e2c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removed no burn in\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display.getdist_corner_plot(posterior_samples_static.numpy(),\n", - " labels_list = ['m','b'],\n", - " truth_list = theta_true,\n", - " truth_color = 'orange',\n", - " plot = True,\n", - " save = True,\n", - " path = '../plots/static/')" - ] - }, - { - "cell_type": "markdown", - "id": "be8aac83-6165-45a7-8456-af21fba61f29", - "metadata": {}, - "source": [ - "Now use getdist to plot both posteriors at the same time." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9f9ebbf3-76ca-4a86-9293-b351066da24c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removed no burn in\n", - "Removed no burn in\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display.getdist_corner_plot([posterior_samples_static.numpy(),\n", - " posterior_samples_generative.numpy()],\n", - " labels_list = ['m','b'],\n", - " #limit_list = [],\n", - " truth_list = theta_true,\n", - " truth_color = 'orange',\n", - " plot = True,\n", - " save = True,\n", - " path = '../plots/static/')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47f7f91e-b159-45f3-a46d-33fd58f5ae76", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/evaluate_SBI_generative.ipynb b/notebooks/evaluate_SBI_generative.ipynb deleted file mode 100644 index e5fe131..0000000 --- a/notebooks/evaluate_SBI_generative.ipynb +++ /dev/null @@ -1,548 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a17f8eeb-e16e-49ed-878f-c1419f7da98f", - "metadata": {}, - "source": [ - "# Diagnosing the quality of the posterior generated from SBI\n", - "This notebook demonstrates the approach for running this code in the case of SBI being run on the fly, where the {$\\theta$,$x$} pairs are generated during the training and again to create the validation set.\n", - "\n", - "This is the use case of SBI presented in mackelab's documentation, where the simulator and prior are passed to the SBI training loop. \n", - "\n", - "The other option (that our group uses most of the time) is to train and diagnose SBI using a pre-saved training and test dataset of the {$\\theta$,$x$} pairs. The notebook example for this is `evaluate_SBI_static.ipynb`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "886c1b21-89a8-4d9d-9055-89780006281d", - "metadata": {}, - "outputs": [], - "source": [ - "import sbi\n", - "import torch\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "# remove top and right axis from plots\n", - "matplotlib.rcParams[\"axes.spines.right\"] = False\n", - "matplotlib.rcParams[\"axes.spines.top\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "09aea2db-d63b-461e-a03e-0e02106fd2ce", - "metadata": {}, - "outputs": [], - "source": [ - "from scripts import evaluate, io, plot" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "edb1bcaa-0929-47a3-9798-adb19a3fa843", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../savedmodels/sbi/\n" - ] - } - ], - "source": [ - "modelloader = io.ModelLoader()\n", - "path = \"../savedmodels/sbi/\"\n", - "model_name = \"sbi_linear_generative\"\n", - "posterior = modelloader.load_model_pkl(path, model_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "396f95c0-9e4a-4d90-8eca-56ae1f9fe42e", - "metadata": {}, - "outputs": [], - "source": [ - "def simulator(thetas):#, percent_errors):\n", - " # convert to numpy array (if tensor):\n", - " thetas = np.atleast_2d(thetas)\n", - " # Check if the input has the correct shape\n", - " if thetas.shape[1] != 2:\n", - " raise ValueError(\"Input tensor must have shape (n, 2) where n is the number of parameter sets.\")\n", - "\n", - " # Unpack the parameters\n", - " if thetas.shape[0] == 1:\n", - " # If there's only one set of parameters, extract them directly\n", - " m, b = thetas[0, 0], thetas[0, 1]\n", - " else:\n", - " # If there are multiple sets of parameters, extract them for each row\n", - " m, b = thetas[:, 0], thetas[:, 1]\n", - " x = np.linspace(0, 100, 101)\n", - " rs = np.random.RandomState()#2147483648)# \n", - " # I'm thinking sigma could actually be a function of x\n", - " # if we want to get fancy down the road\n", - " # Generate random noise (epsilon) based on a normal distribution with mean 0 and standard deviation sigma\n", - " sigma = 1\n", - " ε = rs.normal(loc=0, scale=sigma, size=(len(x), thetas.shape[0]))\n", - " \n", - " # Initialize an empty array to store the results for each set of parameters\n", - " y = np.zeros((len(x), thetas.shape[0]))\n", - " for i in range(thetas.shape[0]):\n", - " m, b = thetas[i, 0], thetas[i, 1]\n", - " y[:, i] = m * x + b + ε[:, i]\n", - " return torch.Tensor(y.T)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "89f17859-6f33-4555-86fd-29824f0a4afc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generate a true dataset\n", - "theta_true = [1, 5]\n", - "y_true = simulator(theta_true)\n", - "\n", - "# and visualize it\n", - "plt.clf()\n", - "plt.scatter(np.linspace(0, 100, 101),\n", - " np.array(y_true), color = 'black')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ede9ef23-5039-4664-9a00-48b05b5b334e", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f7d03ef81b894bf58717ae027fde6023", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Drawing 10000 posterior samples: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# sample from the posterior\n", - "posterior_samples_1 = posterior.sample((10000,), x = y_true)\n", - "# that last little part is conditioning on a data value\n", - "# plot posterior samples\n", - "display = plot.Display()\n", - "display.mackelab_corner_plot(posterior_samples_1,\n", - " labels_list = ['$m$','$b$'],\n", - " truth_list = theta_true,\n", - " truth_color = 'orange',\n", - " plot = True,\n", - " save = True,\n", - " path = '../plots/generative/')" - ] - }, - { - "cell_type": "markdown", - "id": "a0b86875-3b61-4a59-89d4-2062ab58366c", - "metadata": {}, - "source": [ - "# Evaluate posterior by running all-in-one helper function\n", - "`run_all_sbc` from the `Diagnose_generative` class." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "435d60e6-6c05-45a3-8728-dd6d68079f1d", - "metadata": {}, - "outputs": [], - "source": [ - "diagnose_model = evaluate.Diagnose_generative()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "39b69de7-52a4-4b04-a143-57c7342e4764", - "metadata": {}, - "outputs": [], - "source": [ - "low_bounds = torch.tensor([0, -10])\n", - "high_bounds = torch.tensor([10, 10])\n", - "\n", - "prior = sbi.utils.BoxUniform(low = low_bounds, high = high_bounds)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "26f6478a-adc7-4d3a-9bf1-44524068d50a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ba357684d1d242899ef47f6c9bc10877", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Running 1000 sbc samples.: 0%| | 0/1000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function sbc_rank_plot in module sbi.analysis.plot:\n", - "\n", - "sbc_rank_plot(ranks: Union[torch.Tensor, numpy.ndarray, List[torch.Tensor], List[numpy.ndarray]], num_posterior_samples: int, num_bins: Optional[int] = None, plot_type: str = 'cdf', parameter_labels: Optional[List[str]] = None, ranks_labels: Optional[List[str]] = None, colors: Optional[List[str]] = None, fig: Optional[matplotlib.figure.Figure] = None, ax: Optional[matplotlib.axes._axes.Axes] = None, figsize: Optional[tuple] = None, kwargs: Dict = {}) -> Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]\n", - " Plot simulation-based calibration ranks as empirical CDFs or histograms.\n", - " \n", - " Additional options can be passed via the kwargs argument, see _sbc_rank_plot.\n", - " \n", - " Args:\n", - " ranks: Tensor of ranks to be plotted shape (num_sbc_runs, num_parameters), or\n", - " list of Tensors when comparing several sets of ranks, e.g., set of ranks\n", - " obtained from different methods.\n", - " num_bins: number of bins used for binning the ranks, default is\n", - " num_sbc_runs / 20.\n", - " plot_type: type of SBC plot, histograms (\"hist\") or empirical cdfs (\"cdf\").\n", - " parameter_labels: list of labels for each parameter dimension.\n", - " ranks_labels: list of labels for each set of ranks.\n", - " colors: list of colors for each parameter dimension, or each set of ranks.\n", - " \n", - " Returns:\n", - " fig, ax: figure and axis objects.\n", - "\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling from the posterior for each obs: 100%|█| 1000/1000 [00:11<00:00, 89.34o\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "labels_list = ['$m$','$b$']\n", - "colorlist = ['#9C92A3','#0F5257']\n", - "diagnose_model.run_all_sbc(prior,\n", - " posterior,\n", - " simulator,\n", - " labels_list,\n", - " colorlist,\n", - " num_sbc_runs=1_000,\n", - " num_posterior_samples=1_000,\n", - " samples_per_inference=1_000,\n", - " plot=True,\n", - " save=False,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "adc29f5c-4fe4-44a3-8776-de58a8323caa", - "metadata": {}, - "source": [ - "# Save to disk" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8d70f7c2-9d56-4b70-8865-c68d7a6664a9", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1d9b27925000464683ede89f5e312322", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Running 1000 sbc samples.: 0%| | 0/1000 [00:00 Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]\n", - " Plot simulation-based calibration ranks as empirical CDFs or histograms.\n", - " \n", - " Additional options can be passed via the kwargs argument, see _sbc_rank_plot.\n", - " \n", - " Args:\n", - " ranks: Tensor of ranks to be plotted shape (num_sbc_runs, num_parameters), or\n", - " list of Tensors when comparing several sets of ranks, e.g., set of ranks\n", - " obtained from different methods.\n", - " num_bins: number of bins used for binning the ranks, default is\n", - " num_sbc_runs / 20.\n", - " plot_type: type of SBC plot, histograms (\"hist\") or empirical cdfs (\"cdf\").\n", - " parameter_labels: list of labels for each parameter dimension.\n", - " ranks_labels: list of labels for each set of ranks.\n", - " colors: list of colors for each parameter dimension, or each set of ranks.\n", - " \n", - " Returns:\n", - " fig, ax: figure and axis objects.\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling from the posterior for each obs: 100%|█| 1000/1000 [00:11<00:00, 85.74o\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqYAAAHACAYAAABwPqpFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABbU0lEQVR4nO3deXQc1Zk28Ke6epfUrd2WbSHLu8ErNjbEBENwwjIsyZAJYQgEhoEwmB3yQSYLkAlhDgkBQjKQ5AvwZTsDCZmEISQhELMZYxYjY2O8L8KrrF3d6u7qqrrfH93Vtth0q61qVXc/v3M4YKmq65WwW4/v8l5FCCFARERERDTKPKNdABERERERwGBKRERERC7BYEpERERErsBgSkRERESuwGBKRERERK7AYEpERERErsBgSkRERESuwGBKRERERK7gHe0CLKlUCqlUKvdrIQQ0TUN9fT0URRnFyoiIiIioEFwTTO+66y7ccccdH/h4X18fIpHIKFRERERE5B6pZAr793ZA9tDO3u4+7Nr+nvTrCyHw7Wefhm6aaK6pgVdVpe67+7IvYfr06dLP+TiKW44kff+IaX9/P5qbmxlMiYiIiAC8vvIt7N29L697/X7/sNck9TS+/cxT8Hg8ePL2f8f4pqa8nnUkXDNiGggEEAgERrsMIiIiIleKDcQAALX1NVJBM5ZM4uevvAjT64HqHT7yGaaJykglhGEgHAwecb35cE0wJSIiIioXQghsXL8Ffb390vfEBuIAgDnHHoNozfCzyX9vW4s9A33QDUP6GYZhoLWhHqrkNP5IYzAlIiIiKrDYQBybN2y1fZ/H40GoQm40M5ZMwTAMTIhU4ez5c6XuMQwDk5vGwisxwuoEBlMiIiKiI6TrBvbvPQA9LTc6aY1+BkNBzJg1Vfo5keoqqWl8ABhMJgEAtZWVOG7mDKl70uk0EokEPJ7R6Sha1MFUCAFd12HYGKIebaqqwuv1sgUWERFRCdm+eQfeXbfZ9n2RaBVaJjVLXWsYJjbt2YP3+nqlrt/VcRACQNDnk65HCAFFUUYtpxRtMNU0Dfv27cPg4OBol2JbOBxGU1OT9N94iIiIqLB6u/tw8ECn9PV739sPIBM0QxUhqXs8Hg+mTG+VfsZ9f/gj/nf169LXAwCEQNBnL+6N5uBZUQZT0zSxY8cOqKqKcePGwe/3F8UIpHVowMGDB7Fjxw5MnTp11IbKiYiI6KO9+tIbSCVTw1/4PjNnT8fY8Y0OVJQZAQWAuqoqhAJyg1seU2B+y1HSz7C6iHLE1AZN02CaJpqbmxEOh0e7HFtCoRB8Ph927doFTdMQHKV2DERERPThdN3IhdLmlvFQPHIhLRgKorGp3rG6Uuk00pqGLyxagPmtE6Xu0TQNAwMDtp/FYJqHYh1tLNa6iYiIipGmaXjrtXVIJTWp64VpAsj8vJ6/eI5rZmXTug7dMNDb04Pemmrp+yoqKqSv5RpTIiIiIhtM00Rvdx9kz648sK8D+/ccsP2cSLTKNaEUADRdBwBUR6oQjUal7hFCwDAMpNNpqevT6fSo9TAFGEyJiIioyLS9vg7v7dxj+76mCWPR3DJO7mJFQW1dte1nOElL64AQ8Kny8S0ej9sOmz6fj+2iiIiIiGT092bWTAaDQaheucDl9amYfswURKuHPzHJrVLpNASAgI32T6ZporKyEpMnT5a+R1VVBlMiIiIiGda09HFL5qO2vmaUq8nfc2+txZa9e6Wvj2cb5vskwziQmcr3+/0IheRaWI02BlMiIiIaVd2dPRjoj0lfn0plgqnPLz9y6Dadff34j9/8N8zsRisZQggoACoCAVvPctM62eGUTDC1Fvfapaqqrf9hO3fuRGtrK373u9/h/vvvx+uvv45Zs2bhiSeewK5du/DVr34Vb7/9Nk444QQ88cQTqK6utl0TERFRuUgmknjpuVcBSO5kOoy/iINpXzwO0zRh6jo+MVV+mn1CTTWiNnfZF1M3oJIJpoZh4E9PPGP7vn847zPweuW/DWvXrgUAPPjgg/jud7+LiooKnHvuufjSl76Eqqoq/OhHP4JhGPiHf/gHPPLII7jhhhts10RERFQuBgeTAARUVUV9Y530fbX1NQgE7Y0cuklK1yGQGf388ilLpQfJFEWxFTQZTEtcW1sbamtr8dhjj6GuLvMHaOnSpXj55Zfxzjvv5Br+H3fccdi/f/9olkpEROR6ena9aEVlGMeftHCUqymclKZld9h7bM/e2sWp/FGgqir+4bzP5HWfHWvXrsXnPve5XCgFgPb2dpx//vlDTqFqb2/Hueeea7seIiKiYpcYTEovr4v1xwEAXhs7zUtBKtuT1OdwKOWI6ShRFMXWlHy+2tra8LWvfW3Ix9auXTtkyj6ZTGLTpk2YO3eu4/UQERG5ya7t76Ht9XW27/P5SyaSSElpmVOo/A5nFwbTEtbf34+dO3di/vz5uY/t2LEDfX19Qz62bt06CCEwe/bs0SiTiIho1PR29wHIHOfpUeUCkcfjwYSjJBvfu9Trm7fgtl/+BklN7thTUwgIAH6bM7eJRAKa5DMAIJVKMZiWqrVr10JVVcyaNSv3MWvNaUtLy5CPTZ48GZWVlaNRJhER0agxjEz7oxmzpmLqTPnd5sVu9cbNGIjHbXUIMk0TkxobbD0nHo8jGo3CJ7n0IRqNFk0PU4DB1Ja1a9di+vTpCAaDQz52+Gip9TFO4xMRUTkys8FMdrS0VKR1HYZpYvHEo3DGvDlS96geD2qqqmw9R1EUjBs3DrW1tfmU6XoMpjZcffXVuPrqq4d87Pbbb//AdT/60Y8KVBEREZG7WCOmqsfeFHWx0/TMOfa1VVVoGefssoRi2mVvV3n9dYaIiIgcZZ1kVG4jplr2HHufzTWj+WAwJSIiIpJgrbG0246x2FkjpnbOsbdLiMzpWKUcTDmVT0RERB+pt7sPnQe7pa8fjCcAlN+IqXWSk091LloJIaAoCoMpERERladVL74BLZWyfZ/PV/wRQ9O03NKE4SSTSQghHO9LymBKREREZckwjFwoHdfcBI9HLhCFwiHU1tc4WZrjfvTHp/DHlStzm7mGM5hKwTRN+B0M5JzKJyIiorKl64d6ci44fm5RNWo/Uk+uXo2++CAAQJH5urMnUE5qGiv9DMMw0NfXJ329rusIBAIMpkRERFR+9HTmPHdVVcsqlAohoGW/9pvOOgO1kYjUfdFwCNFwWPo5mqYhEAigtrZWerOYx+NBIBCQfkaxYTAlIiIqE8lEEm+ufhuphNyaUWt9pbcE1ovaYZhm7sjQiQ0NqK5y7iRHRVHQ3Nxcdl0MPkp5/U4jIiIqYwf2HUTngU7b91VFyuuIbU3XAWTWc/oc3Mxk7bIvp9Ho4TCY5uHWW2/F+vXr8dRTT412KURERNKsEdDa+lrMmDVF8i4FNbVR54pyoUxPUmT6kjo4kimEYCh9HwbTPLS1tWHBggWjXQYREZEtZnaHeSgcRMOY+lGuprBM00QikZC6tq+/H4ZpwuNRoDrYj7Uc+pLaxWCah7a2Nlx66aWjXQYREZEtueNCy3CUrqenBzt27MidTPVxDg4MQEul4CvApq9y/H/xcRhMbdq/fz8OHDgAwzBw0kkn4Y033sAxxxyDhx9+GLNnzx7t8oiIiD5SuQZT0zTxwvp38O6WraioHH697EAiAVVVEQoGHR3N5FT+B5VMMBVCIKmlbd8X9Pts/aZra2sDANx333249957UVNTg+XLl+OCCy7A+vXrbT+fiIioUKxm8R61vKaOX9+8Bff84X+hpVJQZTczKQrCfr+jdVlT+XRIyQTTpJbGGd+83fZ9f/6P2xEKyP/Ga2trQzAYxB/+8AeMGzcOAHDnnXdiyZIl6OzsRH19ea3ZISKi4mGamZ3m5TZK1z0QAwBEQkHMntgidY8CBYunTrb1nFgshlQqJR02NU1DRLJHarkomWBaKG1tbfjCF76QC6UAUFOTOXZN9jxdIiKi0SDKdCo/bRiAEGipq8e/fWaZY89JpVKoq6vL5QIZwWDQsXqKUckE06Dfhz//x+153WdHW1sbvvKVrwz52Kuvvorx48ejsbHR9vOJiIjyJYRAf+9A7gz14SQSSQDlF0x1wzrByvmvOxKJMA8cgZIJpoqi2JqSz8fg4CC2bNkyZEefaZq4//77cckllzj6bCIiovd7a/XbeG/XHtv3eQoQ0JyWTCahaZrUtX0DAzAMA16P86crcc3okSmZYFoIb7/9NlRVxSOPPIKlS5ciEong61//OhKJBG655ZbRLo+IiMpMX98AAMAf8EuPgvr9PowdV/wjejt37kRPT4/Utbva34NhGI6e4mRhMD0yDKY2tLW1Ydq0afjWt76Fz33uc+jt7cXZZ5+NV155BVVVVaNdHhERlRmrYf5xn5iP+sa6Ua6msHRdh8/nQ0VFxbDXBoJBqKqKyoqw43UxmB4ZBlMbrrzySlx55ZUAgC984QujXA0REZU7a2mZx8FjM93KNE14PB6pkWLDNAFFgdfhtbU8xenIFf8iEyIiojJl9SUtxKYeNxFC2OqEo2cDvLcAAZ7B9MhwxJSIiKhImdnApZbZiOnB3j48v+FdmALwSzTB33GwEwBsj5gmk0lbAdgwjLLreDDSGEyJiIiKlGGW54jpfX94Es++uQaAvdZXQRsnORmGgXg8jlAoJH1PJBKBz2evDSUNxWBKRETkAqZp4tUX38BAf0z6nlzD/BIYMRVCSPdj7ervB4TA1KaxqIvIbT6uCASwZPpUW/V4vV5MnToV4bD8pimOmB4ZBlMiIiIX6O8bwMEDnbbvC4aC8PmK+8d5IpHA1q1boeu61PWdXd0QQuCsY+fh2MmTHKnJOsdeVVWGzQIq6t/Jsn+zcptirZuIiJxjtX4KhoJYfOIC6fsqqiqKPjil02nE43H4/X6pzUO6aUDxeGxNzdtl/azmZqbCKspgaq3fGBwctLX2wy0GBwcBgOtQiIgox9pk4/V5UV0bHeVqCsvaZR8Oh6WCoIACj8fjeMN8tn8qvKIMpqqqorq6Gh0dHQAg/Rt5tAkhMDg4iI6ODlRXV5fdLkoiIvpoppkZoSv20c982B2d1E3n2z9ZU/nFkC9KSVEGUwAYO3YsAOTCaTGprq7O1U9ERAQcGjH1lGEQstOSCQD07LIHr4PdCKxgWo5/URhNRRtMFUVBU1MTGhsbkU6nR7scaT6fjyOlRET0AdYaU0+ZtX4C7O+9sBrm+zhiWnKKNphaVFVl0CMioqJnimwwLYEROiFE7rhUGZqm2Xp9K5iqNr5XiUQC8XhcOmgahoFIJMJgWmBFH0yJiIhKQW7EtASC6b59+7B//36pa00h8MuXXsGug50IBgNS92h5jJhqmoZoNGprKZ3X6y2J/x/FhMGUiIjIBXJrTEsgCKXTaQwMDKC6unrYaw/09ODVrdsARYEyID86Gfb7URGQC7KWQCCA+vp6W/dQYTGYEhEROaCzowtr33wHhi43pW01ly+FYGoYBgKBAILB4PAXe1QoHg8ioSAuP/UU6WeMr6211S5KCFES39tSx2BKRETkgN279iFm43hRS1W00oFqCss0TfnWT9lp+bA/gDktRzlWE4NpcWAwJSIicoA1Nd86pQXNreOl7lE9atkF07ThfE9SgMG0WDCYEhEROUBkd9mHK8Ooqa0e3WIKzDAM28HU53W+ww6Dqfvx/xAREZEDTCN7klMZthuyeoDKKERPUguDqftxxJSIiMgBpdKX1DRN7N69O7c5S4amadLBVNPzC6apVMpWTclkkj1JiwCDKRERkQOEmT3/3VPcYUjXdRw8eBDxeBwBifZM+3v78FTbWgjFIxXKu2OZDWJ2p/L7+/sRiUSkr6+rq5Oqn0YXgykREZEDSqUvqRACQgjU1NTA7/cPe/3T6zdg3Z59tp9TW2lv05fH40FzczNqampsP4vci8GUiIjIAVYwLfYRU9M0ba0Z1bLT6wsmTcSxra1S93hVD+bm0SqKU/Olh8GUiIjIAUJYm59KY8RUfjNTtk1WYyNOnDHNsZoABtNSVNx/WoiIiFzKNEpjxNRuMDWyI8Wqg0sYrHoYTEsPgykREZEDTGvEtETWmMqygqnX4a9bUZSi/97SB3Eqn4iISMKa1Wuxb88B6ev1dGatpcdlI6bpdBr9/f3S1w8MDACQnzbniCkdCQZTIiKiYQgh8N7OPbbvU1UVlRF3HTHa29uLrVu3Sl9vdz2nblpHjMoHUyEEUqmU9MisrusMpiWKwZSIiGgY1g57AFj6mSXweuV+fAYCfvj8PqfKyosV/mprax15fcOwRkzl+5KmUikkEgnpPqOqqiIYDEr/f6Diwf+jREREw7DCFgBURSqhFuD4TKccHrKdkM8aU9M0EQgEMHv2bOlRUK4xLU0MpkRERMMQh4W5Yg9Ddjcz2aXnscZUCAFVVaGqKqfny1xx/+kiIiIqgFyzfMVT9MGpYCOmNteYcs0oARwxJSIiGlbueFEbYcut7AbTp9e04Y3tO6Svf6+rG4D9XfnFPhJNI4PBlIiIaBjWGlM3hifDMKBnjwGVkUqlbI1M/v61N5A2DNt11VdVSV9rp4E/lTYGUyIiomEI02qW777wtG3btlyvURmmaUoHbNM0c6H0imWnIOz3S91XV1WJcbU10jUJIVwZ+qnwGEyJiKjsCCHQ3dkLLZWSuj42MAgAUF04lZ9MJqFpGioqKqTv8fnkWlgZh037H9s6ESHJYGoXgylZGEyJiKjsHNh3EKtfesP2fR6XtYmydtgHAgHpHqB2HB5M7bR/GhgYgKZp0tfruo6aGvkRVipdDKZERFR2BuMJAIDP70dlldxIo6IAEycf5WRZtlnB1Kn1mYevLbWzmUnTNNTV1dlq4h8KhWzVRqWJwZSIiMqO1Ze0cWw9Fp4wb3SLOQJOB1PdOsUpj2b20WgUDQ0NTpRFJYwLOoiIqOyYLt7MZIfjwTR77n0+J11xlz3lg8GUiIjKjhDubf9kh2maBRkx9TGYUoFwKp+IiMqONWKquCyYplIp7N69G4Zk31Bd12EYBvwO7ZbPjZja/D7xFCfKF4MpERGVndxJTi4LT6lUCh0dHfB45I8+VRRFuv1TfyKBR1a8iP5EQq6edBqAveNFLcU+Gk2jg8GUiIjKTm6Nqcv6klpT89XV1Y68ftvOXVizY6ft+xpsnOIE8CQnyh+DKRERlR1rV77bwpMQwtHXT2qZEdBpTWNx+rw50vc1hoLo6uqy9Sy3fW+pODCYEhFR2clN5btsutnpYKrpOgBgTDSKBZNape7RdR3xeBwNDQ22eo3aOYmKyMJgSkREZccU7mwXZZqmoyONVjAN+OR//FvT8mPHjmUTfHIcgykRERW9ZCKFjv0HpUccY/0xAICiuG/E1MlR01Q2mPq99oOp20aXqTQxmBIRUdFbs3otDh7otH2f6rXfn9OOwcFBxONx6eu7u7ttvf62/Qfw0sZNQ860/zhb9x8AkF8w5ZpRKgQGUyIiKnrJRBIAUFNXLd3T0+/3YXzzWCfLwsGDB7Fr1y54bQRB2dZPAPDYqtXYtHef7bqqw2Hpa60RXAZTKgQGUyIiKnpG9oSiWfNmora+ZpSrOcRqfl9T40xNiZQGAPjkjOkYUx2VuqciEMAnpk+19RyOmFKhMJgSEVHRc+sue6c3M1knMy2ZMQ0zx49z5BmcyqdCYjAlIqKiZ2ZHTN3YMN/RYJr9ur02AnkqlUIsFrN1fU1NjetCP5UmBlMiIip6hktHTA3DcDSYpo3MiKlXld/ElUwmEY1GEYlEpO/x+/0cMaWCYDAlIqKiV65T+VYgt3OWvRACgUAAzc3NTpVFlDcGUyIiKmpCiNwRo6rDU/mapsHIjlLKcHrEVLdGTD3yI6ZCCKg2RliJConBlIiIXKd9x27EBuT6fx7ekN7JEdN0Oo2NGzcilUpJ36PrOoLBoGM15YKpzUDOaXlyKwZTIiJylf6+Abz12tu27/N4PI6OBJqmCU3ToKqqdK9UwF5fUrt0ayqfI6ZUIhhMiYjIVbRsb06f34fmieOl76tvrHN0V75pmhBCwO/3OxI2hRB4Zu067Ovtlb7ezI4W211j6ra1uEQWBlMiInIVayNTKBTE7PlHj3I1h1jn2Ds1Db6vtxe/WbnK9n0+VUXQZlDmVD65FYMpERG5ipHrSequ6Wang2k8mVm7WhkI4LR5s6XvGxeJIG6jL6mmaQym5FoMpkRE5Cq5Zvkum262pvKdCnWargMAqisqcM7CBdL3dXZ2oqqqytYmq4qKCtv1ERUCgykREbmKWaDWT3ZZI6ZOsYJpwGfvR7OiKBg3bhyqq6sdqIqosBhMiYjIVYwCHS/a39+PPXv2SF9vGIajDfNT2WDq98r/aLaCMqfmqVQwmBIRkeMyoU5utFFPpwE4P5WfSCTQ0dGBcDgsfU84HLYVAjVdz53ONBxrjanPxtpaa2kBgymVCgZTIiJyVPuO3XjrtXUA7E2DOz2VL4SAz+ezdWa8Has2b8HPnl0Bw+b0fz5T+QymVCrctYCHiIhKTueBLtgNpYriQcOYemcKynJyvSgAvLt7r+1QqioKZh8lf4a9NWLqto1iRPniiCkRETnK2sx0zLyZaJ1ylNQ9hQhbTgdT3cwcF/r54xfh9Lly7Z8URYGXU/lUxhhMiYjIUVYw9aqqq47CNLLnzDtFz27i8nu98EluaDJNE/39/fLP0HV4vV4GUyoZDKZEROQoa9OT4nFXeHKyJymA3KYnr42R32QyCY/Hg4qKCqnarHWyThyRSjQaGEyJiMhR1oip01Pzuq5Dz7ZckpFMJh0NptZUvs9rb2re6/Vi+vTpHAWlssRgSkREjipUw/ytW7diYGBA+nohhKNh2ZrKV208g2tGqdwxmBIRkaOsYKo4PGKaSqVgmqat4za9NprZ26Vn17B6PfbW1XKHPZUzBlMiInJUIabyreNC/X6/a9Zb6rlNX/ZGTBlMqZwxmBIRkS1CCFs72k3D+WBqmqbjm5nsMg7rRiDLbV8DUaExmBIRkTQhBFauWI2ug9227y3EiKmTz3jp3U14u71d+voDvX0A7O3KF0K4qqUWUaExmBIRkTTTNPMKpcFQEFXRSgcqyrCCqVOjjaZp4tHnX8xNz0vXZZrwQSAWi0ldH4/HUVtbm0+JRCWBwZSIiKQJ89BpSWd8dhk8kqN7qupxdIratBkY7dJNMxdKzz9hsXTD/IAQaGlslP7a6+rqEA6H866TqNgxmBIRkTRTHAqAXp/XsanzZDKJ9vZ26bWshmFA13X4/X5H6kkfVsdn5s6WXjfa1dWFxsZGjB071pG6iEoNgykREUk7fMTUyRHQVCqFzs5OWzvsVVV1bEe+1fpJgb2+pIqicJc9kQ0MpkREJC13vKhSmKn5SCTi2DPs0LInSvlU1fbXzV32RPL41zgiIpJ2qCeps2FLCDH8RQVkTeXLri21sP0TkT0MpkREJM0KjE6HLbcFU2sq35dHKycGUyJ5nMonIiJphTpe1Olgmkyn8Ze2tRhIJKWu7x9MAGAwJXIagykREUmzNj8VYirfyUD35vYd+J/X3rR9X8jvw+DgoK17GEyJ5DGYEhGRNKtdlN2wZbVzkpVIJBwdNY1lR0on1NZgwaRWqXuEEJhaX2frOZWVlY51CiAqRQymREQk7dCIqb2p/D179qCjo8OJkvKiZdeMtjY24h8XHyd1TzqdRiKRwPTp0xEMBqWfxXZRRPIYTImISFpujanNEdN0Oo1kMoloNCp9T0VFha1n2KonO3ob8Mn/GLSWF6iqyrBJ5BAGUyKiMtbfN4BXX3wDWkqTut6aXre7xtQ0Tfj9fsdOZrLLav8ke4ITcCiYcs0okXMYTImIytjB/Z1IZHec21FTV23retM0XRXorIb5/jyCKUdLiZzDYEpEVMask5yaxo/BMfNmSt2jKEAoHLL5HGeDqabr2HagQ3rDVEdfPwB7DfM5YkrkPAZTIqIyZgU5n9+HisqwY88xslPnTnnob8/hze07bd8XsBlMAbZ/InISgykRURk7tJnJ2elpp0dM9/f0AQAaqqqkNzSFfD5Mqq1Gd3e31PXJZBK1tbUMpkQOYjAlIipj+WxmMgwDO3fuRDqdlg5pdq7Nh25mRmSv+PSnMK1prNQ9vb29qKioQFVVlfRz/H4/gymRgxhMiYjKmNWXVLERTNPpNLq7u5FOp201j3ey/ZOmZ3fZ29yYFA6H0dLS4kRJRJQHBlMiojJmneRkZ6e5NcoaiURcc6qRkR0x9Xnt7bLnDnsid+GfSCKiMpYbMbUxPS2EcPwse7vS2RFTn832TwymRO7CP5FERGXMzGONqWmargumenYTl9djL5iqNoIsETmPwZSIqIyJPHblu23EVAiRO8nJzlQ+wNZPRG7DNaZERGUsn135hQim77y3GweyTfCHY7W8AuwdMQowmBK5jXQw/da3voVbb70V4XCmAXNPTw9qamocK4yIiJxnmgJCALphIJVKSd2TTCYdDaYHevtw95N/sn2fME0k4nHoyaTU9U63sCIi+6SD6Z133omrr746F0xbWlrQ1taGSZMmOVYcERHZM9Afw77dBwDIHc3Z19sPXdexZ89uxJNyI5TA0FHKkdYTjwMAgj4fjmkeL31fcySC6kgEfr9f6vqamprczzQicgfpYPr+84dlzyMmIqLCeeu1dejp6rF5l4CmaQgEAtJ3hEIhm8+QZ60XbYxEcO0Zp0ndI4RAd3c3JkyYgGg06lhtROQsrjElIiohWkoDAIwZ14hAUC5oJpMJGNCkRxqdpuk6APs9SRVF4dQ8UZGTDqaKomBgYADBYDD3BhCLxdDfP3TqJxKJjHiRREQkR2Qb5k+bORm19XL7APbu3YudO3c6WJU91oip32tv7ITBlKj42ZrKnzZt2pBfz58/f8ivFUWBkX1DISKiwjNNa5e9fPsnt71v50ZMbTbLVxSFDfOJipx0MF2xYoWTdRAR0Qiwgqlis/2T0yON8VQKumQAHkhkdtXbGTHlVD5RaZD+U7906VIn6yAiohFgTeV7bDTMNwzD0UD3/Dvv4tHnX5TsE3AI15gSlR/bm5/6+vrwt7/9DTt37oSiKGhtbcWyZcu4tpSIyAVMU8AwDPT29ULT5fqSJhIJRwPdpr37cqFU9ilejweTamrQ1dUldb2u66isrGQwJSpytoLpr371K1x99dUf2PAUjUbx0EMP4fzzzx/R4oiIyB5hmkilNGzbtlV6V75pmo62f7LWjF580ok4dfYxUvfE43Goqopx48ZJP8fj8dhqeUVE7iMdTNesWYNLL70UF154IW644QbMmDEDQghs2LAB9913Hy666CLMmDEDc+fOdbJeIiL6GJk1pgLhigpEo+6YyTqS9k8NDQ1OlUVELiQdTB944AF89rOfxaOPPjrk48ceeyx+8YtfYHBwEPfffz8efvjhka6RiIgkWGfYA/bWmDrNCqZ2NzNxhz1R+ZH+U79y5Up85Stf+cjPX3nllXj55ZdHpCgiIrJPZEdLhQA8NnblO83qSxqw2ZeUwZSo/Ei/S+zdu3dIH9P3mzZtGvbs2TMiRRERkX2mOHR+veJgqEvrOg72D0hfH09mNmH5bI6Yqjb6mBJRaZB+lxgcHEQwGPzIzwcCASSTyREpioiIMg4e6EI8Nih1rWkYyM7kw6nN6UIIfPOxJ7Cvt9f2vf48GuYTUXmxNa/y17/+FdFo9EM/15vHmxQREX20gf4YXnl+da5pvoxcH1OHRkzThpELpRU2dsCPrY6ipaFe+nquMSUqT7aC6Ze//OWP/Tz/dktENHKSiRQMw0A6nUao4qNnrIZSUROJODYNnj7s9KYfXnoRvJLPGRgYQKy/HzHJ52iaxmBKVIakg6lpmsNfREREIyazyx7wqAomTpsgfZ/H44HX5kYjWYcfK6raCI7pdBp1dXUfOev2YcLhsK3aiKj4OfPORURER0yYJgABKIprGscb2UEK1eOxPUtWU1ODxsZGJ8oiohIh/dfdN998E6eccsoHTn0CMseUnnLKKVi7du2IFkdEVM7M7E4mN62S0o1MMPXanGbnZiYikiH9znLPPffgU5/6FCKRD54kEo1G8elPfxrf+973RrQ4IqJyJkwBAXet39fNzFS+7NrSw7np6yAid5IOpqtXr8a55577kZ8/++yz8corr4xIUURElO1LKtwV6KwRUzvrS4HM1+Cmr4OI3En6nWXPnj2oqqr6yM9XVlZi3759I1IUEREdOsnJTazNT17V/lQ+d9kT0XCkNz81NDRg06ZNaG1t/dDPb9y4EfX18j3qiIjKUSKRQCKRkLq2v78fhmHA43PPPlVr85PXo9ru1sIRUyIajvS73bJly3DnnXfi9NNP/8DnhBC48847sWzZshEtjoio1Ozbtw979+6VGj3s74lD13UEPc7tyO+OxfCLF15GTPLkvoSmAULA0NPo6+uTfk4gEOCIKRENSzqYfuMb38CCBQuwePFi3HTTTZg+fTqAzEjpPffcg82bN+PRRx91qk4iopJgGAb8fr9UP0+RVtDnHUAg6FwwXbN9J97aucvWPUIINEQimDJlysceVf1+FRUVdssjojIjHUwnT56MZ599Fpdccgm++MUv5qZkhBA4+uij8be//Q1TpkxxrFAiolJgmqb0lLbItYtybgpc03UAwIxxTfj0nFlS9+i6jtb6OkQiEfj9fsdqI6LyY2vh0sKFC7F+/Xq0tbVhy5YtEEJg2rRpmDdvnkPlERGVFjvrMgsRTPVsPWOiUSycPEnqnmQyCV3XuWaUiEZcXivq582bxzBKRGRTYjCBfe91IJVKIdY7/Aao+EDmGifzX7677D15nPxERDQc92z1JCIqcZve2YYDezohhICqxqTvU/NoZi8rt8vexjMKMZJLROWJwZSIqEDSWhoAEK4MIRKtlLrH4/GgbkyNYzVZU/l2GuZbx4tylz0RjTQGUyKiPBmGgb1798LITocPp7e3B4BApKYS448a62xxknTDyLZ/0hGPx6XuGRwcRFVVFUdMiWjEMZgSEeVJ0zTs378fyWQSPp9v2Otj8ThMU8DvH/7aQjFME4ZhwKeq0iOglZWVbP1ERI6QCqZvv/229AvOmTMn72KIiIqJEAJCCNTW1sLrHf7ttPtAP7RE2tE1o3bpRmYqv6qyEnPnzh3laoio3EkF03nz5kFRlNyC9/ezPqcoivSUFhFRsbOCqXRfUtN9m4Z0M/Oe7fd5XVUXEZUnqWC6Y8cOp+sgIio6VjCVvz7zbyfz3+9efQ1Pv7VWui5TCAgAXpUru4ho9Em9E7W0tDhdBxFR0bGa5ds9ycnJZPr6tu25FlCyfB4PpjS5YzMWEZW3vP+KvGHDBrS3t0PTtCEfP+ecc464KCKiYiCEcN0Ro1bD/BvPOgMt9XVS98QHBjBjwnjHaiIikmU7mG7fvh2f+9znsG7duiHrTq03Wq4xJaJiZuc9zLpWOmgWIJimszXVVFSgWnLnvJFMcn0pEbmC7WB63XXXobW1Fc899xxaW1vx2muvoaurCzfddBO+//3vO1EjEVFBdHZ2Yvfu3bbWjZo2ps0P/UXedmnS0rqRGcnVdSSTSbl70mkGUyJyBdvBdNWqVfj73/+O+vp6eDweeDwenHjiibjrrrtw7bXX4q233nKiTiIix6XTafT29qK6ulr6nmg0Kn3toSWmzk7lG7oOD+RHfyORiFS7KyIip9l+JzIMA1VVVQCA+vp67N27F9OnT0dLSws2bdo04gUSERWKEAJ+vx+hUEj6etMwYehyAdDpNaZCCKQNA4qioHViCyaOl183ymBKRG5g+51o1qxZWLt2LVpbW7F48WLcfffd8Pv9+OlPf4pJkyY5USMRUUHYa/0ksOWdHRiMJWw/x6lgaphm7muoCIUZNomo6Nh+1/rGN76RO0/529/+Ns466yx88pOfRF1dHR577LERL5CIqFDs7LA3DDOvUOoP+BEI+aWuFULgpY2b0NHXL1fTYetd/T6GUiIqPrbfuU477bTcf0+ZMgUbN25Ed3c3ampquHieiIqakZ0Gl3H46OqcRTOln6EoivQz3uvqws///oL0awOAAODzqvBztJSIipDtd66+vj4YhoHa2trcx2pra9Hd3Q2v14tIJDKiBRIRFYqtnqSHHS/q8XgcqWcgkdlVXxEI4IRpU6TuMU0Tk+vroaqqIzURETnJdjD94he/iLPPPhtXXXXVkI8//vjjePLJJ/H000/nVUgqlUIqlcr9ur9fbuqKiOijDA4OQtd16etl2ysBgBD2Tn3KhzU13xCpwkUnnSh1TzqdRiKRcCwsExE5yXYwXb16NX7wgx984OMnn3wyvv71r+ddyF133YU77rgj7/uJiA4nhMC2bdsQi8Vs3RMIBOSutUZMHQyAaSPbk9Qw0NvbK3WPpmkIhUJcWkVERcl2ME2lUh86AmH9LT1fX/va13DjjTfmft3f34/m5ua8X4+IypsQArquIxAIIBwOS98nG+jM7BpTj8fJnqQmhGnC5/WioaFB+j6v1wufz+dYXURETrEdTBctWoSf/vSneOCBB4Z8/KGHHsKCBQvyLiQQCEiPVBARDUfkgqPHkdHDw9eYOkU3M/1R/V4v2/ERUVmwHUy/853vYNmyZVi7di1OPfVUAMBzzz2H119/Hc8888yIF0hElA8z29PTqbWWuWb5Do+YAuAOeyIqG7bf7ZYsWYJVq1bhe9/7Hh5//HGEQiHMmTMHP//5zzF16lQnaiQisk0IASGE9IhmKqnhve17pU9xMrKh0ePw8aICgJc77ImoTOT11/B58+bh17/+9UjXQkQ0YuwG077ufsT647af4w/KNcvPh57dlc9gSkTlQiqY9vf35/qTDtfGiX1MicgN7AZTazNTVXUlGsfWyT1EASqq5DdW9cTieHnTZqQlW1ht3X8AEIKnOBFR2ZB6t6upqcG+ffvQ2NiI6urqD32jt34AGIbcNBgRkR2apqGrq0v6PHurJ6n05qTs6/oDflRVV+ZV43D++MabWPHOu7bvqwwGHaiGiMh9pILp3//+99xJTytWrHC0ICKiDxOPx7Fjxw5bR4Z6PB7pE5DMAuyy74nHYRgGpo5pxBjJ2SWvR8FZC/PveEJEVEykgunSpUsBALqu44UXXsC//Mu/YMKECY4WRkR0OGuk9PDjkJ14fSf70idSGoQQOHPBsTh98XHS99npw0pEVMxsLVzyer343ve+h4svvtipeoiIPpSd9aL5vj4AeBT59lJ9g4N4r6tb+vqe+CAAoLG+DvX19fYKJCIqA7ZX1H/qU5/CCy+8gIkTJzpQDhHRh5NdW5r/62f+LZtLDdPEN/77d+jP48S7ylDI9j1EROXAdjA944wzcOutt2LdunVYsGABKioqhnz+nHPOGbHiiIgsZrZ1klOsk5xk5/ITmpYLpc11tVAgd19NKIApTWPzqpGIqNTZDqZXXXUVAOAHP/jBBz7HXflE5BSr/ZOTrw/Ib37Ssi2fVEXBd774T9LP6erqgo8nORERfSjb745Oj1oQEX0Y56fyrTWmcsE0nT0hKp+Q6eRaWSKiYsa/thPRqBgYGEDCxvrMvr4+W69vGAYGY/Kvn9bSAGyMmBqZEVOfqiKdTkvdY3dUloio3EgF0x/+8Ie44oorEAwG8cMf/vBjr7322mtHpDAiKm379+/H3r174ffLHekphJC+FgB2bHovryNGFY/8iKkQAjBNWwE7HA5L91YlIio3UsH03nvvxYUXXohgMIh77733I69TFIXBlIikmKaJUCiEaDTqyOtrqcwopj/gh0eV22rv9aqISJ76pOk6IASCfh+mT58uHZoVRbEVsImIyolUMN2xY8eH/jcRUb5M0yxIX9KJUycgXDl8e6b2zk7c+6e/IP5SSur1zezr+1QvQqEQfD5f/sUSERGAI1xjyvVSRJSvQgVT2Wesb9+N7pj9qf/Wxnq+BxIRjZC8gunPf/5z3HvvvdiyZQsAYOrUqbj++uvxr//6ryNaHBGVLueDaebfso/Qsx1HFk+ZjH86YZHUPalUCjXhMIMpEdEIsR1Mv/Wtb+EHP/gBrrnmGpxwwgkAgFWrVuGGG25Ae3s7vv3tb494kURUego1YiqbTI1sMK0IBtAQiUjdMzg4CEVR4PHIH2NKREQfzXYwffDBB/Gzn/0MF1xwQe5j55xzDubMmYNrrrmGwZSIhmU1y3d0pNHmVL4VTGX7mGYeIeDxeDhiSkQ0QmwH03Q6jYULF37g4wsWLICePQmFiMqLruvYtWsX0um0VEgzTRO6rsPr4AlIdteYGqYJIQR0LY3+/n6pewYHB1FTU5N3jURENJTtnwoXXXQRHnzwwQ8cSfrTn/4UF1544YgVRkTFQ9M0dHV1wTAMW2EzEAg4VpPdNaaGacI0DAT8PlRWyrWMqqioQFVVVZ4VEhHR++W9+emZZ57B8ccfDwBYvXo12tvbcfHFF+PGG2/MXff+8EpEpcmamo9EIo6OgtqpB8hvKr+yshIzZ850qjQiIvoYtn+CrF+/HsceeywAYNu2bQCA+vp61NfXY/369bnruOaKqHwUZM2oHeKw/7YzlQ/Ay1OZiIhGje1gumLFCifqIKIiZmZHG90STMVhyVS6XZSR+Rp8DKZERKOGPU6I6IgJIRxv/2RHrlUU7E/l8xx7IqLRY3vENJlM4oEHHsCKFSvQ0dGRGymxrFmzZsSKI6Li4LZT4A7LpbaDKafyiYhGj+1getlll+GZZ57B5z//eSxatMg1P4iIaOQIIaBpmvT1yWTSwWoyUkkNaS0tda2uG7Zfn8GUiGj02Q6mTz31FJ5++mksWbLEiXqIyAU6Ojqwe/fuIVPiw7FzrV2pZArvtm21dc9ru3fh9T3tCL/zhtT1/ckEIASDKRHRKLIdTMePH8++fUQlzjAMxGIx1NbWSt8TDocdqyeVzIyUKoqCQNAvdc+a/XswoGtIJxV4JMKmonoRUL2YflTzEdVKRET5sx1M77nnHtxyyy146KGH0NLS4kRNRDTKhBDw+Xzw++VCoNOs0dhQOIhpsydJ3RNYswpeQ8Mt/3QepkqGzerKCoyvq8u7TiIiOjK2g+nChQuRTCYxadIkhMNh+Hy+IZ/v7u4eseKIaHS4aYc9AAgzu7nKI19T2sgckTx1/Dgc03KUI3UREdHIsh1ML7jgAuzZswff/e53MWbMGFf98CKikeG6YJrHrn/dMAFFQcDvG/5iIiJyBdvB9JVXXsGqVaswd+5cJ+ohIhcwDPu72p2UTzBNZ78GvwuOSCUiIjm2G+zPmDEDiUTCiVqIyCWKfcRUCAHDMKAA8DGYEhEVDdvv2P/5n/+Jm266CXfeeSdmz579gTWmkUhkxIojopFhmuYHDsP4OLquuy6YCgGk02kMDAwMe33aMGCYJjyKgoBLNnAREdHwbAfT008/HQBw6qmnDvm4EAKKorhuCpCo3AkhsHnzZlszHYZhuC6YmqYJxaNItaVKaBpUVYVHURAOBAtQIRERjQTbwXTFihVO1EFEDjFNE4lEAqlUCqFQSOoej8fjmlZRAGCYAi+3b0dqt8CUdGzY69O6nvta/T5O5RMRFQvb79hLly51og4ickhmGlwgFAohGCzO0cMdHQfxcvt2eL1ebIv1Sd8XDYfh8dheSk9ERKMkr6GEl156CT/5yU+wfft2/Pa3v8X48ePxy1/+Eq2trTjxxBNHukYiOgJWMHXT1Lxpmoj1x2GacseY9g5kRkmrw2Fcfubp0s+ZP1muGT8REbmD7WD6xBNP4KKLLsKFF16INWvWIJVKAQD6+vrw3e9+F08//fSIF0lE+XPyDPt87WvvwMH9XdLX9/ZmNjzVVVTigpNPcqosIiIaZbbnuL7zne/goYcews9+9rMhO/KXLFmCNWvWjGhxRHTkTNN03YipltIAAP6AH+HK8LD/qH4vPB4PIlUVo1w5ERE5yfaI6aZNm3DSSR8csYhGo+jt7R2JmohoBLlyKj87ijtmfD3qGmuGvX4PEvBt9CIcLs41skREJMd2MB07diy2bt2KiRMnDvn4yy+/jEmTuJ6LyGmGYSAWG35numVwcNB1wRQCMAwz83Wow/dX7e7thSkEd9gTEZU42+/yl19+Oa677jo8/PDDUBQFe/fuxapVq3DzzTfjm9/8phM1EtFh+vv7sXXrVqTTael7TNNEVVWVg1XZk0rr2NZ9ELGwgrGVw4+CxgQQDAZRXcUDPIiISpntYHrrrbfCNE2ceuqpGBwcxEknnYRAIICbb74Z11xzjRM1EtFhTNOEruuoq6sb7VLy9symDVi5bSvCu8IIhuSm530+HyKVXGNKRFTKbAdTRVHw9a9/HV/96lexdetWxGIxHH300aisrHSiPiJ6H2vNaDHriscBAGOiUUxoGiN1T9Dnw5nHLXSyLCIiGmV5L9jy+/04+uijR7IWIpJQ7KEUAFJ6ZhnCFz5xPC4887RRroaIiNyCR6IQFZnSCKY6ACDsD4xyJURE5Cbc4kpUZFy3wx7Awf5+7O7qlr4+rmX6mFZIri8lIqLywGBKVGSshvlukdZ1fOuxJzCYPQVORiKZubYiwGBKRESHMJgSjTJN06BlRxBlxLMbh9wilkyhbyAOXdcxIVotdY9R6UdDuAITxzQ6WxwRERUVBlOiUfbee+/h4MGDtu7xeNyzPDypaUindfg8HvzLghOgKHK1KYqCuvpah6sjIqJiwmBKNMrS6TQURbHVAN9NwTSlZXbYez0eLFqyAKqqSt1XGamQ7mFKRETlgcGUaJSZpglVVaUDnduk0pkd9l5VxbjmsUX7dRAR0ehzz7ALUZkyTdN1u+zt0LKtn3yqWtRfBxERjT4GU6JRZprmaJdwRPRsMPV6VFctMSAiouLDqXyiUWQdL+qmkcYt+/bj3j/9BQnJTgGGYQAAfCpDKRERHRkGU6JRJIRw3VT+ht17MJBISI/kCjMTrluqucOeiIiODIMp0QgyTROdnZ3Soc66zk3BVDcMCNPEKbOOxqWfXjbs9QN9Mbz79haMqa0pQHVERFTKGEyJRlAymcSuXbuQSqWkd6cLIRCNRh2uTJ5umhAAaqoiOHrqlGGvP7i/E/u37UUgwHPviYjoyDCYEo0g0zRhmiZqa2uLtm2Snl0z6vfJvT2Y2eNRPap7Rn2JiKg4MZgSjSA3bmayazCRgpZMY9eW3fjjY3+WuCMbTLkjn4iIjhCDKdEIsoJpMRscTAJCQPUosEKnjFoeL0pEREeIwZRoBJXCiKk1lV9XG8Vp53xK6h5FURAIco0pEREdGQZTohFkjZa6LZjaGcnNrTH1+3mWPRERFRSDKdEIcuM0vhACnZ2d8Hrl/rgnNQ1QAC8b5hMRUYExmBJ9jGQyiVgsJn19T0+Pg9Xk5zcvv4Jn2t6Gz++X6hQwGE9AUTyoDFcUoDoiIqJDGEyJPkZ3dze2b99uqyep29pErd6yDYZpwq8ogMQSA0VR4FNVtDY2FqA6IiKiQxhMiT6GEAIejwe1tcW741zTdUBRcPdll2Dy+HHDXv9O20Z07D6Ao+rrC1AdERHRIQymRB/DbefY50PTdQBAU00NGiROmIqGQujzetmXlIiICo4/eYg+hmEYRR1MTdOEbhhQAAT8Pql7hOnOzgJERFT6GEyJPoZpmqNdwhHRsq2fACAkeZZ97ohRD4MpEREVFqfyiT6GG6fyE8kkunt7pa6Np1LQDQOqx4Ogz+aIKafyiYiowBhMiT6G24JpdyyGf//1YxjUNOm+pMFAAD6vF36/X+p6ITKjxB4Xfd1ERFQeGEyprAwMDEDPbgaSoWmaq4Lpjo6DiKVSUD0e+CSDJoTA8dOnYzCekLo8rWem/xVO5RMRUYExmFLZMAwDO3bswMDAgHTYNE0TlZWVDlcmL5lOAwBmThiPh268Tuqe11euQdeBLjz7p+dtPctNgZyIiMoDgymVDSEEdF1HZWUlgsHiPANeS2dGe8OBAAKSa0bjfXEAgMfjkQ6bgWAAdQ3F27uViIiKE4MplQ0hBIQQRT0SmEynASEQDkhO4+NQZ4Gln1mCSLTKqdKIiIiOGIMplQ2RbYPkJjs7DuKva9dBN43hLwawt7sHAKRHS4FDwZQN84mIyO0YTKlsmKbpuhHTp9a8hde3brd1jwCkTnCysC8pEREVCwZTKhtunMpPpXXohoFFk1rRUi+3ptPv9eLMBfOln2EamRFT9iUlIiK3YzClsuHGYKpnT2Y6cdbROOsTJ0jfFwqFpK7LLF+wRkwZTImIyN0YTKloGYaBwcFB6bWj8XjcfcE0u/6zqqoKkUhkxF//8CNV2TCfiIjcjsGUilZfXx+2bdsGw5DbOAQAuq67Kpga2eDoU1VHXt86XhQAPCpHTImIyN0YTKlomaYJTdNQWyvfb1NRFFcFU2sq3+eV32Vvx5ARU07lExGRyzGYUtGypuWLOXAdCqbOjJgeHkzdFMiJiIg+DIMpFS039iW1SzdNKAB8Xrk/iqmUhpefW4XBwaTcAwQ3PhERUfFgMKWi5cZgahgG+vr6pGtLJDMB0y8ZTLs7exAbiNuuq6au2vY9REREhcZgSkXLjcE0nU7D7/ejoaEBXomw6Q8E4U/rqAiHpV5fZKfmq2uiWPgJ+V6m4Qq59lJERESjicGUipYbg+nb7e9hR8dBjB8/HqrE9LkuBHx+P4J+v9TrG9lm+V6/DxWVcmGWiIioWDCYkmuk02mk02np6wcHBx2sxr5YMokfP/McTCEQ3rgZsLHZKBwMSF1nbWaSCb1ERETFhsGUXGPXrl3o7u62dY+bNvX0JxIwTRM+rxdnH79Iehf8lHHj0FhdLXWtdbwoe5ISEVEpYjAl19A0DaZpoqqqSvoe1aHG9PlIpDQAQDQcwk3nfc6RZ1gjpm4K5ERERCOFwZRcQwgBr9crtWmoEIQQ2NFxEEnJ5QU7Ow4CAEKS60XzYTCYEhFRCXNHAiBCptWSm5rAv7xxM3723IrcTngZpmmiIiC3XtSSSqZgmnIbubTsqKzKqXwiIipBDKbkGtZJTm6xv7cXpmGgKhRCtEJuB7yqKDhr0ULpZ2zesA3vrttkuzaOmBIRUSliMCVXEEK4LpjqpgkB4LQF83HLP3/RkWd0d/Zk/0uR3sTv9XrR2NTgSD1ERESjicGUXMGVwTR7jr3X49wGK2sz07GL56B54njHnkNERFQMGEzJMZqmSTfBN7Ih0E2sYOrzOffHhLvsiYiIDmEwJUckEgls2bIFmqZJ36NpGiorKx2syh492zM04GCXgFww5WYmIiIiBlNyhmEYGBwcRCgUkh4NDAaD8Pl8DlcmL20YUAB4HeyVyhFTIiKiQxhMyRHWFL7f73dV6DJNU3p5gW7oEELA5+SIqZGpxeOitbVERESjhcGUHOHGzUymaaKrq0t6VDaeSMLj8SDgd24Ul1P5REREhzCYkiOskUk3BdO3duzEyxs2orq6Gj6JsNmjaQgGg6iORByriVP5REREhzCYkiPcFkoB4FcvvYKD/f0IBYPwSK4b9agq6hhMiYiICoLBlBxhTeW7SSLbIeALJ52IhupqqXvqIxEcO2Wy9DM2vbP1sKb5w9NSaQCA4nFXiCciIhoNDKYkRQhhq9doOp12sJr8pLP1n7VoISY2NY3466eSKWxcv9n2fYqiIBAMjHg9RERExYbBlKR0dHRg7969tkZB3TZiqmfbP/m9zmxm0vVM8PV4PJi7cJb0fVXRSgQCfkdqIiIiKiYMpiRF0zT09fWhpqZG+p5oNOpgRfYc3ibKqV321oiy6vXiqNYJjjyDiIiolDGYkhQhBAKBAILB4GiXkpNKpRCLxaSu1XQdumFAVVXH+pKa2ZOiVLZ+IiIiyguDKUkxTdN1u+w1TUMkEkF9ff2w18aSSQSDQSgAwg6Fa+6wJyIiOjIMpiTFjcFUCAGfz4exY8cOe23PQCzXWF91KDjmpvI5YkpERJQXBlOSYo0GuolVk0xtKT3TJcDvVR0L2NZUPk9xIiIiyg+DKUlx24hpdyyGb/z29xhM6wgE5FstqR65xvoAkEyksOXdbdKtrxKDSQCAx8YziIiI6BAGU5LitpOcth/oQP9gAl7Jc+8tdprlt+/Yje1bdtqsDAgG2fqJiIgoHwymZUgIgc7OTlsN8zVNc1Uw1bPT5jMnjMe9V10pfV8kHJJ/RloHANTW12LsuEapezweBeOah1/zSkRERB/EYFqGDMPA7t270dfXB79ffnSvsrLSwaqAZDIpPW0+EIvBNE2E/H5EK8KO1GOtXa2tr8bUmZMceQYREREdwmBahoQQME0TtbW1ttZnOi0WiyESiUBVh1+jqfp88Pp8CNgI1nax/RMREVFhMZiWIbcdFWrxeDwYN24c6urqhr12U08fQqEQqiorHKuHwZSIiKiw+BO3DAkhXLeZCcjUJRsCdTOzPtanOvd3KwZTIiKiwuJP3DJknRvvtmAKQLqmtJ4Jpl6vc62ZTDMzsuzxuO/7REREVIoYTMuQG0dMreUFsjXphjVi6mQw5YgpERFRIfEnbhlyazD1eDw2RkwzrZwKMZWvMJgSEREVBDc/lQBN09DX1yd9fTweL8gGKF3XpZ+z6+BB/G3tOtRt3SHVwmrT7t0A7E3l67qBvp5+6eu1pAaAU/lERESFwmBaAgYGBrBlyxbb9zk5Ra1pGgYGBuCTPJnp96vfwPrdexDYvddWXdUV8rvyX3n+NfR09Uhfb/GoHDElIiIqBAbTEmCdY19bWzvapeQYhoFQKIQpU6bA6x3+t5l/1WsIBIM4bcGxmDhG7pSlcCCAzyyYL13TQP8AACAUDkmH32AogIbG4dtXERER0ZFjMC0B1ppRN7HqqaiokAqBacOAx+PBsvlzsXjGdEfq0dOZDVOfXHYCQqHgiD+DiIiIjgznKEuA20IpgNzmKtnNTMnsUaRBh05yymxkynyfZEZwiYiIqPAYTEuAW4OpnV32SS2z0SjkUDDVs31PAWd7nxIREVH+OHRUAtwWTIUQeOSFl7G7pweRvz4rdU/3QAyA/IippqXx+itvITmYlLo+15NUVV3VJouIiIgOYTAtAW4Lpp0DA3ht23Z4PB70pjTp+0J+P+qjEblndHSh80Cn7dqqquR38RMREVFhMZi6kBAiN8InI51dn+mkWCwGTZMLmQd7+2CaJiqCQdx75eXSzziqoR7hQEDqWtPIfH+iNVHMmjdD+hnVNVHpa4mIiKiwGExdaMeOHbYa5gPOH5upaRpqa2tRU1Mz7LXK/gMIBoOIVlVi3qRWR+oxskeSBoJ+1LOdExERUUlgMHWhRCKBeDyOyspK6XsiEbkp8HwJIRCJRNDYOHyP0Y7BBLw+H0JB51oyWSPKqsqNTERERKWCwdSFhBAIhUIIhUKjXcoQds+xD3jlTn3KhzWV7/RIMRERERUOf6q7kJ31pYVipyeplg2mfp9zf+8xciOm/C1MRERUKvhT3WWsU5zc1tLI6ksqI5XOBFOfg43sOWJKRERUejiV7zJuDKZ7e3rw6IoXEVz9BoIS60Z7YpmepAEbwVTTNHTs65QeLe7tyWwOYzAlIiIqHQymLlOIYCqEQFdXFwC5daPPvLUWG/bsRSAYtHWc5xiJHfyW9WvexXu79khfb1F5ihMREVHJYDB1GSuYOjkSaJomvF4vmpqaUFExfMP5v2/fiUAwiFPnzcWn5s2Reoaqqjh2ymTpmhLJFAAgEq1CMCS3m9/r9aJl0gTpZxAREZG7MZg6zDAMdHV1SZ/OlE6nHT/JyRqRra+vRzgcHv4GVYXX68XRLUdh6ZzZjtRkrRmdfswUjGtucuQZRERE5G4Mpg5LJBLYuXMn0um01CioFUqdbBVlBVPZ5QKJ7LGiIclTmfKrKRNMFa4ZJSIiKlsMpg6zjhetra11zUadFe+8i1Wbt6B29RtSDeq37z8AAAgH/I7VZG16csv3iIiIiAqPwdRhpmm6bpf97197A/FkEqG+flsjlOPqnDv60zQzI8UeF32fiIiIqLAYTB3mxvZPqXQaAHDdZ89BbVWV1D310QhmNju30UiY1lS+e75PREREVFgMpg5zeiMTkNkwFY/Hpa41hUAqnYaqqlg6exbqohGHq5OTGzHlVD4REVHZYjB1WCGCaSKRQCgUQmVl5bDXJtNp+P1+KIqCoINrRu3iGlMiIiJiMHVYIYKpEALBYBCTJk0a9tq++CAC2d31QZ+bgmnm+8SpfCIiovLFYGqTEAK6rktfr2mag9Vk2GnIr+mZ9aWqR4Gqumd00moXxRFTIiKi8sVgatP+/fuxd+/e0S4jxzRNvLljJ7BnL9r2HRj2+r7sWlS/1+dYTUIItL2+Dj3dfdL3pLVMYPZwxJSIiKhsMZjalE6nEYvFUGPjHPhgUO6IzXy8u2cvHnlxJXw+H/w2GuBXOljTYDyB9h27bd+nqmpumQERERGVHwZTm0zTRCAQcE2A6h0chABQXVGBE445WuoeRVFw8lxnjhYFMsewApmz7BctOVb6vspIJbw+/pYkIiIqV0wBNlm7x500MDCAdLbX6HB6evsgTBNTxzXhmxd+0eHK5Fjn3nt9XjSMrR/laoiIiKhYMJjaZJqm483yU6kU6urqUFFRMey1kYNdCASDCIecm5q3yxoxlTnulIiIiMjCYGpTIU5xUhQFY8aMkVrHGt2xC36/HxXhsKM12WGNmHpctOufiIiI3I/JwaZCjJgCkH6Gdbyo3+uev2PoHDElIiKiPLgnzRQJp9eYWiOyssHUCoFOBlNNS2PX9vdgSPZv7e+LAYCr+qQSERGR+5V9ME0mk7k1kTJ0XXd0xNQKpvIN8zNh0as6979y59Z2vLtuk+37fH7neqUSERFR6SnrYGoYBjZv3oxEImHrnlAo5FhN7Z2deLbtbby46z2pllTrduwEAPi9zk2bp1IpAEC0JoLaOrn+rYpHQcukZsdqIiIiotJT9sE0nU7D7/fD75c7N15RFFtrJ3VdR39/v/SmqV89/xI27j+A0M52eGw8Jyqxgz9f1mamsePGYMasqY49h4iIiMpbWQdTIQSEEPB6vfA6tEYznU4jGAyioaFBanpe9Qfg9/vxqXlz0TKmUeoZFcEgzjxu4ZGW+pGMbDDlmlEiIiJyEoOpEI4/Q1EUjB8/XmrE1OPzwufz4ezjF2HB1CmO1ibLzK7BtTOCS0RERGRXWQ+BWaG0EJuZZJ+R1DLtnwI+92wcyo2YSm7IIiIiIspHWScNa8TU6WBqZ02qpmeCadBFO9qtFlmqgxusiIiIiDiV7/BUvmGa0E0z1wh/OAlNAwD4vc4F060bt2P3rr3S18digwAg3cKKiIiIKB8MpjZHTE3TlO57mtYN3P7EH9AdH0TQZospJ0dMN72zFbpks/zDVVS659hTIiIiKj0lFUx1XUcsFpO+fmBgAIC9NaZ9fX3SU/N7enpwcCBme8f/pLFjUFcVsXWPLCFELpQuOGEefJJrWUPhICLRKkdqIiIiIgJKLJj29vZi27Zttk5yymfEdOzYsRg7duyw1yo7dyEUCmF8XR1+fuO10s8I+nyOTZsb+qHvzdhxjY61ySIiIiKyq6RSiRAChmGgrq7O0ecEg0EEg8Fhr0sLAY/Hg0hFGGGJU5wKQT8smNrZlEVERETktJILpk7usG/v7MTPn10Bf/AVqWDaP5g56rRS4tp8aZqGNa++jWQyKXW9dYqT6vU6+r0iIiIisqvkgqmTu+xf3rgZWw90IBgIwGujz+hRjQ2O1dSxvxMH9nXYvq+qyrkjTImIiIjyUXLB1ElJLQ0FwClzZuGsE46Xuserqpg9caJjNenpzEammrpqTD9G/hz7mrqoUyURERER5aWsg6kQAv39/dKbpXoHBiCEwKSxY7F4xvR8Shxx1mamcEUYY5qcG5klIiIiclpZB1PDMCCEQG1trdSaUV8wAL/fj2hlZb4ljjir9RN31xMREVGxK6k0k8+IqSkEmsaNQ0iiAb4vEIQ/EECk0rn1malkCvv2HJD+WroO9gAAVC9PZSIiIqLiVlLB1DRNWzvNf/zXZ/Hm9h0IhUJQbPQNDfr9+ZQnZe2b72Df7v227/M7eFIUERERUSG4Ppju2LEDVVVyJw4lEgnp1xVCoG1Xu+16qkIhTJ8w3vZ9sgaz59LX1tcgEJTrferzeXFUa7NjNREREREVguuDaXt7OxobG6Wvr6iQm2bXDQOmmenp+etbbkJ1pVz4Dfp98DrYmF7T0gCAY+bNRG1dtWPPISIiInIb1wfTqqoqVFdXS19vGIbULvvBVCqzjlNR0FhdDb+NvqSyTNPEyhWvoa+3X/oeI7uZiVPzREREVG5cH0ztiMVi0DRN6pz57lgcQggEfD74HNrRHhuIo7uz2/Z9wVAQobBzp0URERERuVHJBNNkOo3frX4dCd1AtHr45vGJVAqhUAiRirBjR3OmtczoZygcwidOXiR9Xygc5Dn2REREVHZKJpi27dyFZ9dvgNfnQyAgt2nIo6oYU13jWE16OrNe1B/woZJHgBIRERF9rJIJpgOJJABgYkMDPvvJJVL3KIqCRdOnyT+jP4Z3123OnbY0nFQyBQDwObB+lYiIiKjUlEwwTWgpCABTmsbi/KWflLrHMAzEY4Po7xuQun7zhm159RgNV4Zt30NERERUblwfTF/Y8C5q9h0Y9roN7+2GME1UhuQ3Db303Cr09cjvmLdMnNKCmtrh17ECgMfjQSPPsCciIiIaluuD6ZNtb0P1S6wZFQLCBAZ74ljz6trhLwdyodQfkD/JKRQKYsasqQjYuIeIiIiIhuf6YDq/pRX+4PDn2Pf3DcArFEyprMF7u/ZIv35FZQWW/cPSIymRiIiIiEaA64PpqeMmIRyWWKM5IfMvr8+LaUdPkXptBeA0OxEREZFLuCaYplIppFKp3K/7+voAADX1UeljRr0+L6ZMb5U+Yz5DoL/f/jpTIiIiIsqoqqoakb7wihBCjEA9R+z222/HHXfcMdplEBEREZFNHR0daGg48llo1wTT94+Y9vb2oqWlBe3t7YhG5XbAExG5WX9/P5qbm/Hee+8hEomMdjlEREfMel/r7e0dkbzmmqn8QCDwoSc2RaNRvoETUUmJRCJ8XyOikjJSx7t7RuRViIiIiIiOEIMpEREREbmCa4NpIBDAbbfd9qHT+0RExYjva0RUakb6fc01m5+IiIiIqLy5dsSUiIiIiMoLgykRERERuQKDKRERERG5AoMpEREREbmCa4Ppj3/8Y0ycOBHBYBCLFy/Ga6+9NtolERFJueuuu3DcccehqqoKjY2N+OxnP4tNmzYNuebkk0+GoihD/rnyyitHqWIioo93++23f+A9a8aMGbnPJ5NJLF++HHV1daisrMR5552HAwcO2H6OK4PpY489hhtvvBG33XYb1qxZg7lz5+K0005DR0fHaJdGRDSsF154AcuXL8err76Kv/3tb0in0/jMZz6DeDw+5LrLL78c+/bty/1z9913j1LFRETDO+aYY4a8Z7388su5z91www343//9X/z2t7/FCy+8gL179+If//EfbT/Dle2iFi9ejOOOOw4/+tGPAACmaaK5uRnXXHMNbr311lGujojInoMHD6KxsREvvPACTjrpJACZEdN58+bhvvvuG93iiIgk3H777fjDH/6Atra2D3yur68PDQ0N+M1vfoPPf/7zAICNGzdi5syZWLVqFY4//njp57huxFTTNLz55ptYtmxZ7mMejwfLli3DqlWrRrEyIqL89PX1AQBqa2uHfPzXv/416uvrMWvWLHzta1/D4ODgaJRHRCRly5YtGDduHCZNmoQLL7wQ7e3tAIA333wT6XR6SHabMWMGjjrqKNvZzTuiFY+Azs5OGIaBMWPGDPn4mDFjsHHjxlGqiogoP6Zp4vrrr8eSJUswa9as3Mf/+Z//GS0tLRg3bhzefvtt3HLLLdi0aRN+//vfj2K1REQfbvHixXj00Ucxffp07Nu3D3fccQc++clPYv369di/fz/8fj+qq6uH3DNmzBjs37/f1nNcF0yJiErJ8uXLsX79+iFrsQDgiiuuyP337Nmz0dTUhFNPPRXbtm3D5MmTC10mEdHHOuOMM3L/PWfOHCxevBgtLS14/PHHEQqFRuw5rpvKr6+vh6qqH9jJdeDAAYwdO3aUqiIisu/qq6/GU089hRUrVmDChAkfe+3ixYsBAFu3bi1EaURER6S6uhrTpk3D1q1bMXbsWGiaht7e3iHX5JPdXBdM/X4/FixYgOeeey73MdM08dxzz+GEE04YxcqIiOQIIXD11Vfjf/7nf/D3v/8dra2tw95jbShoampyuDoioiMXi8Wwbds2NDU1YcGCBfD5fEOy26ZNm9De3m47u7lyKv/GG2/El7/8ZSxcuBCLFi3Cfffdh3g8jksvvXS0SyMiGtby5cvxm9/8Bn/84x9RVVWVW2MVjUYRCoWwbds2/OY3v8GZZ56Juro6vP3227jhhhtw0kknYc6cOaNcPRHRB9188804++yz0dLSgr179+K2226Dqqq44IILEI1Gcdlll+HGG29EbW0tIpEIrrnmGpxwwgm2duQDLg2m559/Pg4ePIhvfetb2L9/P+bNm4e//OUvH9gQRUTkRg8++CCATEuowz3yyCO45JJL4Pf78eyzz+b+0t3c3IzzzjsP3/jGN0ahWiKi4e3evRsXXHABurq60NDQgBNPPBGvvvoqGhoaAAD33nsvPB4PzjvvPKRSKZx22mn4r//6L9vPcWUfUyIiIiIqP65bY0pERERE5YnBlIiIiIhcgcGUiIiIiFyBwZSIiIiIXIHBlIiIiIhcgcGUiIiIiFyBwZSIiIiIXIHBlIjIQbfffjvmzZs32mXYNnHiRNx3332jXQYRlRkGUyKiwzz//PNQFAW9vb0j8no333zzkPOjiYjoozGYEhE5QAgBXddRWVmJurq6I3qtdDo9otcREbkVgykRFZWTTz4ZV199Na6++mpEo1HU19fjm9/8Jg4/XbmnpwcXX3wxampqEA6HccYZZ2DLli25z+/atQtnn302ampqUFFRgWOOOQZPP/00du7ciVNOOQUAUFNTA0VRcMkllwAATNPEXXfdhdbWVoRCIcydOxe/+93vcq9pjbT++c9/xoIFCxAIBPDyyy9/YCrfNE18+9vfxoQJExAIBDBv3jz85S9/yX1+586dUBQFjz32GJYuXYpgMIhf//rXH/q9UBQFDz74IM455xxUVFTgzjvvhGEYuOyyy3J1Tp8+Hffff/+Q+y655BJ89rOfxfe//300NTWhrq4Oy5cv/9hg+3//7/9FdXU1R3+JyFHe0S6AiMiu//f//h8uu+wyvPbaa3jjjTdwxRVX4KijjsLll18OIBO8tmzZgieffBKRSAS33HILzjzzTGzYsAE+nw/Lly+Hpml48cUXUVFRgQ0bNqCyshLNzc144okncN5552HTpk2IRCIIhUIAgLvuugu/+tWv8NBDD2Hq1Kl48cUX8aUvfQkNDQ1YunRprrZbb70V3//+9zFp0iTU1NTg+eefH1L7/fffj3vuuQc/+clPMH/+fDz88MM455xz8M4772Dq1KlDXueee+7B/PnzEQwGP/J7cfvtt+M///M/cd9998Hr9cI0TUyYMAG//e1vUVdXh1deeQVXXHEFmpqa8IUvfCF334oVK9DU1IQVK1Zg69atOP/88zFv3rzc9/Bwd999N+6++24888wzWLRoUV7/z4iIpAgioiKydOlSMXPmTGGaZu5jt9xyi5g5c6YQQojNmzcLAGLlypW5z3d2dopQKCQef/xxIYQQs2fPFrfffvuHvv6KFSsEANHT05P7WDKZFOFwWLzyyitDrr3sssvEBRdcMOS+P/zhD0Ouue2228TcuXNzvx43bpy48847h1xz3HHHiauuukoIIcSOHTsEAHHfffcN+70AIK6//vphr1u+fLk477zzcr/+8pe/LFpaWoSu67mP/dM//ZM4//zzc79uaWkR9957r/g//+f/iKamJrF+/fphn0NEdKQ4YkpERef444+Hoii5X59wwgm45557YBgG3n33XXi9XixevDj3+bq6OkyfPh3vvvsuAODaa6/Fv/3bv+GZZ57BsmXLcN5552HOnDkf+bytW7dicHAQn/70p4d8XNM0zJ8/f8jHFi5c+JGv09/fj71792LJkiVDPr5kyRKsXbtW+nWGu+7HP/4xHn74YbS3tyORSEDTtA90BjjmmGOgqmru101NTVi3bt2Qa+655x7E43G88cYbmDRpklQ9RERHgmtMiajs/Ou//iu2b9+Oiy66COvWrcPChQvxwAMPfOT1sVgMAPCnP/0JbW1tuX82bNgwZJ0pAFRUVIxIjbKv8/7r/vu//xs333wzLrvsMjzzzDNoa2vDpZdeCk3Thlzn8/mG/FpRFJimOeRjn/zkJ2EYBh5//PE8vgIiIvsYTImo6KxevXrIr1999VVMnToVqqpi5syZ0HV9yDVdXV3YtGkTjj766NzHmpubceWVV+L3v/89brrpJvzsZz8DAPj9fgCAYRi5a48++mgEAgG0t7djypQpQ/5pbm6WrjsSiWDcuHFYuXLlkI+vXLlySG1HYuXKlfjEJz6Bq666CvPnz8eUKVOwbdu2vF5r0aJF+POf/4zvfve7+P73vz8i9RERfRxO5RNR0Wlvb8eNN96Ir3zlK1izZg0eeOAB3HPPPQCAqVOn4txzz8Xll1+On/zkJ6iqqsKtt96K8ePH49xzzwUAXH/99TjjjDMwbdo09PT0YMWKFZg5cyYAoKWlBYqi4KmnnsKZZ56JUCiEqqoq3HzzzbjhhhtgmiZOPPFE9PX1YeXKlYhEIvjyl78sXftXv/pV3HbbbZg8eTLmzZuHRx55BG1tbR+5896uqVOn4he/+AX++te/orW1Fb/85S/x+uuvo7W1Na/X+8QnPoGnn34aZ5xxBrxeL66//voRqZOI6MMwmBJR0bn44ouRSCSwaNEiqKqK6667DldccUXu84888giuu+46nHXWWdA0DSeddBKefvrp3PS1YRhYvnw5du/ejUgkgtNPPx333nsvAGD8+PG44447cOutt+LSSy/FxRdfjEcffRT/8R//gYaGBtx1113Yvn07qqurceyxx+Lf//3fbdV+7bXXoq+vDzfddBM6Ojpw9NFH48knnxyyI/9IfOUrX8Fbb72F888/H4qi4IILLsBVV12FP//5z3m/5oknnog//elPOPPMM6GqKq655poRqZWI6P0UIQ5r/kdE5HInn3wy5s2bx+MyiYhKENeYEhEREZErMJgSERERkStwKp+IiIiIXIEjpkRERETkCgymREREROQKDKZERERE5AoMpkRERETkCgymREREROQKDKZERERE5AoMpkRERETkCgymREREROQKDKZERERE5Ar/H/6fh6Lpt0jTAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "labels_list = ['$m$','$b$']\n", - "colorlist = ['#9C92A3','#0F5257']\n", - "diagnose_model.run_all_sbc(prior,\n", - " posterior,\n", - " simulator,\n", - " labels_list,\n", - " colorlist,\n", - " num_sbc_runs=1_000,\n", - " num_posterior_samples=1_000,\n", - " samples_per_inference=1_000,\n", - " plot=False,\n", - " save=True,\n", - " path='../plots/generative/'\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "fcb02e88-7ec5-4c46-a0d6-f2d83d2efa92", - "metadata": {}, - "source": [ - "# Evaluate posterior by running individual pieces\n", - "Let's say you just want to get the statistics from the rank plots and print them under the rank plots.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f55dcde4-9593-47ba-a703-802b69cca222", - "metadata": {}, - "outputs": [], - "source": [ - "diagnose_model = evaluate.Diagnose_generative()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "cfa48dad-26fd-4b08-bae9-86e152719370", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c057017f15854b0cb6d25073e50ca2be", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Running 1000 sbc samples.: 0%| | 0/1000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "num_sbc_runs = 1_000\n", - "num_posterior_samples = 1_000\n", - "thetas, ys, ranks, dap_samples = diagnose_model.generate_sbc_samples(prior,\n", - " posterior,\n", - " simulator,\n", - " num_sbc_runs,\n", - " num_posterior_samples)\n", - "\n", - "diagnose_model.plot_1d_ranks(ranks,\n", - " num_posterior_samples,\n", - " labels_list,\n", - " colorlist,\n", - " plot=True,\n", - " save=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "feb1f057-a17a-4baa-ab00-7f375e89acbe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'ks_pvals': tensor([1.4518e-27, 3.6275e-03]), 'c2st_ranks': tensor([0.6585, 0.5805]), 'c2st_dap': tensor([0.4300, 0.4930])}\n" - ] - } - ], - "source": [ - "stats = diagnose_model.sbc_statistics(ranks,\n", - " thetas,\n", - " dap_samples,\n", - " num_posterior_samples)\n", - "print(stats)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc4690c1-db66-4f76-831e-72f92afda14a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/evaluate_SBI_static.ipynb b/notebooks/evaluate_SBI_static.ipynb deleted file mode 100644 index 71aff7b..0000000 --- a/notebooks/evaluate_SBI_static.ipynb +++ /dev/null @@ -1,555 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a17f8eeb-e16e-49ed-878f-c1419f7da98f", - "metadata": {}, - "source": [ - "# Diagnosing the quality of the posterior generated from training SBI\n", - "This notebook demonstrates the approach for running this code in the case of SBI being run from the static pre-generated data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "886c1b21-89a8-4d9d-9055-89780006281d", - "metadata": {}, - "outputs": [], - "source": [ - "import sbi\n", - "import torch\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "# remove top and right axis from plots\n", - "matplotlib.rcParams[\"axes.spines.right\"] = False\n", - "matplotlib.rcParams[\"axes.spines.top\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "09aea2db-d63b-461e-a03e-0e02106fd2ce", - "metadata": {}, - "outputs": [], - "source": [ - "from scripts import evaluate, io, plot" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "edb1bcaa-0929-47a3-9798-adb19a3fa843", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../savedmodels/sbi/\n" - ] - } - ], - "source": [ - "modelloader = io.ModelLoader()\n", - "path = \"../savedmodels/sbi/\"\n", - "model_name = \"sbi_linear_from_data\"\n", - "posterior = modelloader.load_model_pkl(path, model_name)" - ] - }, - { - "cell_type": "markdown", - "id": "8e9f4054-9783-40c0-b57e-a1be0ab52c1a", - "metadata": {}, - "source": [ - "Also load up the validation dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "fb908fa8-78eb-423f-906c-3855ad005831", - "metadata": {}, - "outputs": [], - "source": [ - "dataloader = io.DataLoader()\n", - "path = \"../saveddata/\"\n", - "data_name = \"data_validation\"\n", - "validation = dataloader.load_data_h5(data_name, path)" - ] - }, - { - "cell_type": "markdown", - "id": "d31330be-fe1f-4ed0-be3b-9b22fa1ff9c8", - "metadata": {}, - "source": [ - "Define the validation set." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6f1245ea-376c-4a89-a47b-e49cd8667817", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([1000, 2])\n" - ] - } - ], - "source": [ - "# the length of the validation is the number of posterior\n", - "# samples you're using in SBC\n", - "# here I choose 1000 to match the number used in the\n", - "# generative tutorial\n", - "len_validation = 1_000\n", - "ys = validation['xs'][0:len_validation]\n", - "thetas = validation['thetas'][0:len_validation]\n", - "print(np.shape(thetas))" - ] - }, - { - "cell_type": "markdown", - "id": "dacc8b3d-582e-4a0f-ad09-473b3932d1de", - "metadata": {}, - "source": [ - "Draw one instance from this." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c174f9d6-878b-4d11-aee6-d4960eceee42", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([101])\n" - ] - } - ], - "source": [ - "print(np.shape(validation['xs'][0]))\n", - "theta_true = validation['thetas'][0]\n", - "y_true = validation['xs'][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ede9ef23-5039-4664-9a00-48b05b5b334e", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2c624e1016c2461e96ebdf9ffb9caea2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Drawing 10000 posterior samples: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# sample from the posterior\n", - "posterior_samples_1 = posterior.sample((10000,), x = y_true)\n", - "display = plot.Display()\n", - "display.mackelab_corner_plot(posterior_samples_1,\n", - " labels_list = ['$m$','$b$'],\n", - " truth_list = theta_true,\n", - " truth_color = 'orange',\n", - " plot = True,\n", - " save = True,\n", - " path = '../plots/static/')" - ] - }, - { - "cell_type": "markdown", - "id": "a0b86875-3b61-4a59-89d4-2062ab58366c", - "metadata": {}, - "source": [ - "# Evaluate posterior by running all-in-one helper function\n", - "`run_all_sbc`" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "435d60e6-6c05-45a3-8728-dd6d68079f1d", - "metadata": {}, - "outputs": [], - "source": [ - "diagnose_model = evaluate.Diagnose_static()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "39b69de7-52a4-4b04-a143-57c7342e4764", - "metadata": {}, - "outputs": [], - "source": [ - "low_bounds = torch.tensor([0, -10])\n", - "high_bounds = torch.tensor([10, 10])\n", - "\n", - "prior = sbi.utils.BoxUniform(low = low_bounds, high = high_bounds)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "26f6478a-adc7-4d3a-9bf1-44524068d50a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a84c488d3d9048008a54009cea9b7731", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Running 1000 sbc samples.: 0%| | 0/1000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function sbc_rank_plot in module sbi.analysis.plot:\n", - "\n", - "sbc_rank_plot(ranks: Union[torch.Tensor, numpy.ndarray, List[torch.Tensor], List[numpy.ndarray]], num_posterior_samples: int, num_bins: Optional[int] = None, plot_type: str = 'cdf', parameter_labels: Optional[List[str]] = None, ranks_labels: Optional[List[str]] = None, colors: Optional[List[str]] = None, fig: Optional[matplotlib.figure.Figure] = None, ax: Optional[matplotlib.axes._axes.Axes] = None, figsize: Optional[tuple] = None, kwargs: Dict = {}) -> Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]\n", - " Plot simulation-based calibration ranks as empirical CDFs or histograms.\n", - " \n", - " Additional options can be passed via the kwargs argument, see _sbc_rank_plot.\n", - " \n", - " Args:\n", - " ranks: Tensor of ranks to be plotted shape (num_sbc_runs, num_parameters), or\n", - " list of Tensors when comparing several sets of ranks, e.g., set of ranks\n", - " obtained from different methods.\n", - " num_bins: number of bins used for binning the ranks, default is\n", - " num_sbc_runs / 20.\n", - " plot_type: type of SBC plot, histograms (\"hist\") or empirical cdfs (\"cdf\").\n", - " parameter_labels: list of labels for each parameter dimension.\n", - " ranks_labels: list of labels for each set of ranks.\n", - " colors: list of colors for each parameter dimension, or each set of ranks.\n", - " \n", - " Returns:\n", - " fig, ax: figure and axis objects.\n", - "\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling from the posterior for each obs: 100%|█| 1000/1000 [00:12<00:00, 80.06o\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "labels_list = ['$m$','$b$']\n", - "colorlist = ['#9C92A3','#0F5257']\n", - "diagnose_model.run_all_sbc(\n", - " prior,\n", - " posterior,\n", - " thetas,\n", - " ys,\n", - " labels_list,\n", - " colorlist,\n", - " num_posterior_samples=1_000,\n", - " samples_per_inference=1_000,\n", - " plot=True,\n", - " save=False,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "adc29f5c-4fe4-44a3-8776-de58a8323caa", - "metadata": {}, - "source": [ - "# Save to disk" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8d70f7c2-9d56-4b70-8865-c68d7a6664a9", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "60a4b66adf574291a15f9bbed579fdf7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Running 1000 sbc samples.: 0%| | 0/1000 [00:00 Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]\n", - " Plot simulation-based calibration ranks as empirical CDFs or histograms.\n", - " \n", - " Additional options can be passed via the kwargs argument, see _sbc_rank_plot.\n", - " \n", - " Args:\n", - " ranks: Tensor of ranks to be plotted shape (num_sbc_runs, num_parameters), or\n", - " list of Tensors when comparing several sets of ranks, e.g., set of ranks\n", - " obtained from different methods.\n", - " num_bins: number of bins used for binning the ranks, default is\n", - " num_sbc_runs / 20.\n", - " plot_type: type of SBC plot, histograms (\"hist\") or empirical cdfs (\"cdf\").\n", - " parameter_labels: list of labels for each parameter dimension.\n", - " ranks_labels: list of labels for each set of ranks.\n", - " colors: list of colors for each parameter dimension, or each set of ranks.\n", - " \n", - " Returns:\n", - " fig, ax: figure and axis objects.\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling from the posterior for each obs: 100%|█| 1000/1000 [00:12<00:00, 80.75o\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "labels_list = ['$m$','$b$']\n", - "colorlist = ['#9C92A3','#0F5257']\n", - "diagnose_model.run_all_sbc(prior,\n", - " posterior,\n", - " thetas,\n", - " ys,\n", - " labels_list,\n", - " colorlist,\n", - " num_posterior_samples=1_000,\n", - " samples_per_inference=1_000,\n", - " plot=False,\n", - " save=True,\n", - " path='../plots/static/'\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "fcb02e88-7ec5-4c46-a0d6-f2d83d2efa92", - "metadata": {}, - "source": [ - "# Evaluate posterior by running individual pieces\n", - "Let's say you just want to get the statistics from the rank plots and print them under the rank plots.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f55dcde4-9593-47ba-a703-802b69cca222", - "metadata": {}, - "outputs": [], - "source": [ - "diagnose_model = evaluate.Diagnose_static()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "cfa48dad-26fd-4b08-bae9-86e152719370", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6647c626d67a421f90eb567dffb6857a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Running 1000 sbc samples.: 0%| | 0/1000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "num_posterior_samples = 1_000\n", - "thetas, ys, ranks, dap_samples = diagnose_model.generate_sbc_samples(posterior,\n", - " thetas,\n", - " ys,\n", - " num_posterior_samples)\n", - "\n", - "diagnose_model.plot_1d_ranks(ranks,\n", - " num_posterior_samples,\n", - " labels_list,\n", - " colorlist,\n", - " plot=True,\n", - " save=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "feb1f057-a17a-4baa-ab00-7f375e89acbe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'ks_pvals': tensor([2.2656e-20, 1.6848e-01]), 'c2st_ranks': tensor([0.6015, 0.5760]), 'c2st_dap': tensor([0.4655, 0.4800])}\n" - ] - } - ], - "source": [ - "stats = diagnose_model.sbc_statistics(ranks,\n", - " thetas,\n", - " dap_samples,\n", - " num_posterior_samples)\n", - "print(stats)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc4690c1-db66-4f76-831e-72f92afda14a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/example.ipynb b/notebooks/example.ipynb index 17d3c0f..b27bf1b 100644 --- a/notebooks/example.ipynb +++ b/notebooks/example.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -18,7 +18,7 @@ "from deepdiagnostics import models\n", "from deepdiagnostics import data\n", "from deepdiagnostics.utils.config import Config\n", - "from deepdiagnostics.utils.register import register_simulator\n", + "from deepdiagnostics.utils.simulator_utils import register_simulator\n", "\n", "from deepdiagnostics.plots import CDFRanks, CoverageFraction, Ranks, TARP, LC2ST\n", "\n", @@ -246,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -260,7 +260,7 @@ "source": [ "%%writefile my_simulator.py \n", "\n", - "from deepdiagnostics.utils.register import register_simulator\n", + "from deepdiagnostics.utils.register_simulator import register_simulator\n", "from deepdiagnostics.data.simulator import Simulator\n", "import numpy as np \n", "\n", diff --git a/notebooks/save_and_load_data.ipynb b/notebooks/save_and_load_data.ipynb deleted file mode 100644 index 9dfae5e..0000000 --- a/notebooks/save_and_load_data.ipynb +++ /dev/null @@ -1,405 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7601f39a-b3bf-4f55-82bc-d3ec40e5a38e", - "metadata": {}, - "source": [ - "# Save and load data\n", - "Utilize a prior and a simulator to create said dataset. Save a proportion as a training set, and part as a validation set. Then, run SBI using this static training set. An alternate way to run SBI using a simulator to generate data on the fly is demonstrated in the `train_SBI.ipynb` tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d0030c2f-0f2f-4426-9eb1-86cbe83510d8", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "# remove top and right axis from plots\n", - "matplotlib.rcParams[\"axes.spines.right\"] = False\n", - "matplotlib.rcParams[\"axes.spines.top\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4b02d2c4-e28e-4cbc-8244-d4906cea7079", - "metadata": {}, - "outputs": [], - "source": [ - "import sbi\n", - "from sbi.inference import SNPE\n", - "from sbi.inference.base import infer\n", - "from sbi.analysis import pairplot\n", - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e5980d55-247f-4f78-b3c3-3dde9025d346", - "metadata": {}, - "outputs": [], - "source": [ - "from scripts.io import DataLoader, ModelLoader" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5782898a-caae-4141-a972-070944fb7f93", - "metadata": {}, - "outputs": [], - "source": [ - "def simulator(thetas):#, percent_errors):\n", - " # convert to numpy array (if tensor):\n", - " thetas = np.atleast_2d(thetas)\n", - " # Check if the input has the correct shape\n", - " if thetas.shape[1] != 2:\n", - " raise ValueError(\"Input tensor must have shape (n, 2) where n is the number of parameter sets.\")\n", - "\n", - " # Unpack the parameters\n", - " if thetas.shape[0] == 1:\n", - " # If there's only one set of parameters, extract them directly\n", - " m, b = thetas[0, 0], thetas[0, 1]\n", - " else:\n", - " # If there are multiple sets of parameters, extract them for each row\n", - " m, b = thetas[:, 0], thetas[:, 1]\n", - " x = np.linspace(0, 100, 101)\n", - " rs = np.random.RandomState()#2147483648)# \n", - " # I'm thinking sigma could actually be a function of x\n", - " # if we want to get fancy down the road\n", - " # Generate random noise (epsilon) based on a normal distribution with mean 0 and standard deviation sigma\n", - " sigma = 5\n", - " ε = rs.normal(loc=0, scale=sigma, size=(len(x), thetas.shape[0]))\n", - " \n", - " # Initialize an empty array to store the results for each set of parameters\n", - " y = np.zeros((len(x), thetas.shape[0]))\n", - " for i in range(thetas.shape[0]):\n", - " m, b = thetas[i, 0], thetas[i, 1]\n", - " y[:, i] = m * x + b + ε[:, i]\n", - " return torch.Tensor(y.T)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4d35f73f-01a1-4e30-a717-adb81a21849a", - "metadata": {}, - "outputs": [], - "source": [ - "low_bounds = torch.tensor([0, -10])\n", - "high_bounds = torch.tensor([10, 10])\n", - "\n", - "prior = sbi.utils.BoxUniform(low = low_bounds, high = high_bounds)" - ] - }, - { - "cell_type": "markdown", - "id": "734a48cb-1874-4705-b8f7-66e39888fd72", - "metadata": {}, - "source": [ - "To create the training set, sample from this prior and run it through the simulator." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "561950b0-d037-4d09-910e-7c80331273f8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "$\\theta$s tensor([[ 3.0800, -4.7952],\n", - " [ 1.8481, 6.3294],\n", - " [ 4.0461, 2.8588],\n", - " ...,\n", - " [ 8.1919, -1.6658],\n", - " [ 5.0935, 0.3070],\n", - " [ 4.0631, 9.8509]]) xs tensor([[-8.5266e+00, -1.8273e+00, -1.1451e+00, ..., 2.9787e+02,\n", - " 3.0003e+02, 3.1156e+02],\n", - " [ 1.1926e+01, 6.5993e+00, 5.1448e+00, ..., 1.8203e+02,\n", - " 1.8708e+02, 1.8575e+02],\n", - " [-4.1232e-01, 1.1292e+01, 1.0386e+01, ..., 3.9838e+02,\n", - " 4.0421e+02, 4.0578e+02],\n", - " ...,\n", - " [-1.4934e+00, 1.5889e+01, 1.6708e+01, ..., 8.0203e+02,\n", - " 8.0764e+02, 8.1931e+02],\n", - " [ 1.0923e+01, 6.6249e+00, 1.7376e+01, ..., 5.0127e+02,\n", - " 5.0772e+02, 5.1401e+02],\n", - " [ 9.2832e+00, 1.3263e+01, 2.0634e+01, ..., 4.1520e+02,\n", - " 4.1219e+02, 4.0914e+02]])\n" - ] - } - ], - "source": [ - "params = prior.sample((10000,))\n", - "xs = simulator(params)\n", - "print(r'$\\theta$s', params, 'xs', xs)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5b6fff5d-87a0-4456-a915-2ffec3c0812d", - "metadata": {}, - "outputs": [], - "source": [ - "# Save both params and xs to a .pkl file\n", - "data_to_save = {'thetas': params, 'xs': xs}\n", - "dataloader = DataLoader()\n", - "dataloader.save_data_h5('data_train',\n", - " data_to_save,\n", - " path = '../saveddata/')" - ] - }, - { - "cell_type": "markdown", - "id": "c56d415e-a5e7-4abb-86e3-a69e206a9d2c", - "metadata": {}, - "source": [ - "Redo this with a validation set that is the same size." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8275530a-bc1b-46e1-99aa-5d5ddbba0bfe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "$\\theta$s tensor([[ 8.6844, -7.0421],\n", - " [ 3.0001, -5.9178],\n", - " [ 8.5659, -7.1641],\n", - " ...,\n", - " [ 4.7512, -6.2531],\n", - " [ 8.1654, -1.4571],\n", - " [ 1.2068, 3.5740]]) xs tensor([[-1.6683e+00, 5.4582e+00, 1.9455e+01, ..., 8.4058e+02,\n", - " 8.4980e+02, 8.6225e+02],\n", - " [-2.8954e+00, -1.2797e+01, 1.1486e+00, ..., 2.9071e+02,\n", - " 2.8894e+02, 2.9775e+02],\n", - " [-1.0352e+00, -1.2929e+00, 1.2169e+01, ..., 8.3395e+02,\n", - " 8.3806e+02, 8.4531e+02],\n", - " ...,\n", - " [-3.2291e-01, -1.4686e+01, 9.0462e+00, ..., 4.5691e+02,\n", - " 4.6361e+02, 4.6903e+02],\n", - " [-7.1130e+00, 5.8362e+00, 8.4475e+00, ..., 8.0142e+02,\n", - " 8.1157e+02, 8.0876e+02],\n", - " [-4.1383e+00, -4.4619e+00, 1.4715e+01, ..., 1.2880e+02,\n", - " 1.3755e+02, 1.1952e+02]])\n" - ] - } - ], - "source": [ - "params_valid = prior.sample((10000,))\n", - "xs_valid = simulator(params_valid)\n", - "print(r'$\\theta$s', params_valid, 'xs', xs_valid)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "fc9f3c22-8317-4f76-b860-43cac495d529", - "metadata": {}, - "outputs": [], - "source": [ - "# Save both params and xs to a .pkl file\n", - "data_to_save_valid = {'thetas': params_valid, 'xs': xs_valid}\n", - "\n", - "dataloader.save_data_h5('data_validation',\n", - " data_to_save_valid,\n", - " path = '../saveddata/')" - ] - }, - { - "cell_type": "markdown", - "id": "7844698f-1b62-4113-b5d9-1b6b1c7e258c", - "metadata": {}, - "source": [ - "## Now load up this data and run SBI using it" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7c2dfe9f-d2e2-4a9e-8fd8-ad7aff7bd228", - "metadata": {}, - "outputs": [], - "source": [ - "train_h5 = dataloader.load_data_h5(\n", - " 'data_train',\n", - " '../saveddata/',)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3bcc3421-25f7-4cd6-bf64-d05fa0df1c25", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'thetas': tensor([[ 3.0800, -4.7952],\n", - " [ 1.8481, 6.3294],\n", - " [ 4.0461, 2.8588],\n", - " ...,\n", - " [ 8.1919, -1.6658],\n", - " [ 5.0935, 0.3070],\n", - " [ 4.0631, 9.8509]]), 'xs': tensor([[-8.5266e+00, -1.8273e+00, -1.1451e+00, ..., 2.9787e+02,\n", - " 3.0003e+02, 3.1156e+02],\n", - " [ 1.1926e+01, 6.5993e+00, 5.1448e+00, ..., 1.8203e+02,\n", - " 1.8708e+02, 1.8575e+02],\n", - " [-4.1232e-01, 1.1292e+01, 1.0386e+01, ..., 3.9838e+02,\n", - " 4.0421e+02, 4.0578e+02],\n", - " ...,\n", - " [-1.4934e+00, 1.5889e+01, 1.6708e+01, ..., 8.0203e+02,\n", - " 8.0764e+02, 8.1931e+02],\n", - " [ 1.0923e+01, 6.6249e+00, 1.7376e+01, ..., 5.0127e+02,\n", - " 5.0772e+02, 5.1401e+02],\n", - " [ 9.2832e+00, 1.3263e+01, 2.0634e+01, ..., 4.1520e+02,\n", - " 4.1219e+02, 4.0914e+02]])}\n" - ] - } - ], - "source": [ - "print(train_h5)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "835b93f4-6f7d-4b18-82b2-3d74213fc532", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Neural network successfully converged after 104 epochs." - ] - } - ], - "source": [ - "# instantiate the neural density estimator\n", - "neural_posterior = sbi.utils.posterior_nn(model='maf')\n", - "\n", - "'''\n", - "model,\n", - " embedding_net=embedding_net,\n", - " hidden_features=hidden_features,\n", - " num_transforms=num_transforms)\n", - "'''\n", - "\n", - "#from me:\n", - "#infer(simulator, prior, \"SNPE\", num_simulations=10000)\n", - "\n", - "low_bounds = torch.tensor([0, -10])\n", - "high_bounds = torch.tensor([10, 10])\n", - "\n", - "prior = sbi.utils.BoxUniform(low = low_bounds, high = high_bounds)\n", - "\n", - "# setup the inference procedure with the SNPE-C procedure\n", - "inference = SNPE(prior=prior, density_estimator=neural_posterior, device=\"cpu\")\n", - "\n", - "\n", - "density_estimator = inference.append_simulations(train_h5['thetas'],\n", - " train_h5['xs']).train()\n", - "posterior = inference.build_posterior(density_estimator)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef901ae9-d074-472d-a99f-e2afd8ec1a05", - "metadata": {}, - "outputs": [], - "source": [ - "modelloader = ModelLoader()\n", - "path = \"../savedmodels/sbi/\"\n", - "model_name = \"sbi_linear_from_data\"\n", - "modelloader.save_model_pkl(path, model_name, posterior)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8803927-2642-42ec-abc2-6871869dbeda", - "metadata": {}, - "outputs": [], - "source": [ - "# generate a true dataset\n", - "theta_true = [1, 5]\n", - "y_true = simulator(theta_true)\n", - "\n", - "# and visualize it\n", - "plt.clf()\n", - "plt.scatter(np.linspace(0, 100, 101),\n", - " np.array(y_true), color = 'black')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b86aeb37-d8cf-4354-b441-45b0294268bb", - "metadata": {}, - "outputs": [], - "source": [ - "# sample from the posterior\n", - "posterior_samples_1 = posterior.sample((10000,), x = y_true)\n", - "# that last little part is conditioning on a data value\n", - "# plot posterior samples\n", - "fig, axes = sbi.analysis.pairplot(\n", - " posterior_samples_1, \n", - " labels = ['m', 'b'],\n", - " #limits = [[0,10],[-10,10],[0,10]],\n", - " truths = theta_true,\n", - " figsize=(5, 5)\n", - ")\n", - "axes[0, 1].plot([theta_true[1]], [theta_true[0]], marker=\"o\", color=\"red\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2790962d-e1a8-4f3f-ac2a-cf4c2fa17101", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/train_SBI.ipynb b/notebooks/train_SBI.ipynb deleted file mode 100644 index 7c91717..0000000 --- a/notebooks/train_SBI.ipynb +++ /dev/null @@ -1,297 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d467063e-00c2-48e1-a214-434767a4bc37", - "metadata": {}, - "source": [ - "# Quick train SBI\n", - "Then save the posterior as a pkl using the evaluate module. This notebook demonstrates how to run SBI using the 'on the fly' technique, where SBI uses the prior and simulator to generate data as it trains. An alternate method (that is adventageous for expensive data generation) is demonstrated in the `save_and_load_data.ipynb` tutorial, where the data is pre-generated and used to train SBI." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ebb1cf48-c144-4a56-b46a-edef83f443fa", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "# remove top and right axis from plots\n", - "matplotlib.rcParams[\"axes.spines.right\"] = False\n", - "matplotlib.rcParams[\"axes.spines.top\"] = False" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "486dda47-bf7b-45ea-88fe-55960d81c4bb", - "metadata": {}, - "outputs": [], - "source": [ - "import sbi\n", - "from sbi.inference import SNPE\n", - "from sbi.inference.base import infer\n", - "from sbi.analysis import pairplot\n", - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f64b72b1-3c46-45af-932e-59512b2adbc8", - "metadata": {}, - "outputs": [], - "source": [ - "from scripts import io, plot" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cd5034fb-94da-4b3d-b5ca-89f16ed98ff9", - "metadata": {}, - "outputs": [], - "source": [ - "def simulator(thetas):#, percent_errors):\n", - " # convert to numpy array (if tensor):\n", - " thetas = np.atleast_2d(thetas)\n", - " # Check if the input has the correct shape\n", - " if thetas.shape[1] != 2:\n", - " raise ValueError(\"Input tensor must have shape (n, 2) where n is the number of parameter sets.\")\n", - "\n", - " # Unpack the parameters\n", - " if thetas.shape[0] == 1:\n", - " # If there's only one set of parameters, extract them directly\n", - " m, b = thetas[0, 0], thetas[0, 1]\n", - " else:\n", - " # If there are multiple sets of parameters, extract them for each row\n", - " m, b = thetas[:, 0], thetas[:, 1]\n", - " x = np.linspace(0, 100, 101)\n", - " rs = np.random.RandomState()#2147483648)# \n", - " # I'm thinking sigma could actually be a function of x\n", - " # if we want to get fancy down the road\n", - " # Generate random noise (epsilon) based on a normal distribution with mean 0 and standard deviation sigma\n", - " sigma = 1\n", - " ε = rs.normal(loc=0, scale=sigma, size=(len(x), thetas.shape[0]))\n", - " \n", - " # Initialize an empty array to store the results for each set of parameters\n", - " y = np.zeros((len(x), thetas.shape[0]))\n", - " for i in range(thetas.shape[0]):\n", - " m, b = thetas[i, 0], thetas[i, 1]\n", - " y[:, i] = m * x + b + ε[:, i]\n", - " return torch.Tensor(y.T)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9fe446e0-e80e-4c6a-a67e-8a8bd19d2787", - "metadata": {}, - "outputs": [], - "source": [ - "low_bounds = torch.tensor([0, -10])\n", - "high_bounds = torch.tensor([10, 10])\n", - "\n", - "prior = sbi.utils.BoxUniform(low = low_bounds, high = high_bounds)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b4d1c9af-cedc-483b-92ba-8bb1d195bccf", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7639525299e34b499057a4ba5f2cbf03", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Running 10000 simulations.: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generate a true dataset\n", - "theta_true = [1, 5]\n", - "y_true = simulator(theta_true)\n", - "\n", - "# and visualize it\n", - "plt.clf()\n", - "plt.scatter(np.linspace(0, 100, 101),\n", - " np.array(y_true), color = 'black')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3b68d4b4-91e2-4120-8cc3-8bc1eafbf883", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f2a9dfd8e7bf4074868c37496b49770c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Drawing 10000 posterior samples: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display = plot.Display()\n", - "display.mackelab_corner_plot(posterior_samples_1,\n", - " labels_list = ['$m$','$b$'],\n", - " truth_list = theta_true,\n", - " truth_color = 'orange',\n", - " plot = True,\n", - " save = True,\n", - " path = '../plots/generative/')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b65e0866-a399-44ef-941e-fb5c102bf2a0", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}